# rss_items

title: 'No way to prevent this,' says only package manager where this regularly happens
feed_id: hacker-news
url: https://kevinpatel.xyz/posts/no-way-to-prevent-this/
guid: https://news.ycombinator.com/item?id=48155690
published_at: 2026-05-16T00:36:02
author: alligatorplum
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Article URL: https://kevinpatel.xyz/posts/no-way-to-prevent-this/ Comments URL: https://news.ycombinator.com/item?id=48155690 Points: 218 # Comments: 84


title: I broke AppLovin's mediation cipher protocol
feed_id: hacker-news
url: https://www.buchodi.com/i-broke-applovins-mediation-cipher-protocol/
guid: https://news.ycombinator.com/item?id=48155678
published_at: 2026-05-16T00:35:01
author: lmbbuchodi
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Article URL: https://www.buchodi.com/i-broke-applovins-mediation-cipher-protocol/ Comments URL: https://news.ycombinator.com/item?id=48155678 Points: 17 # Comments: 3


title: Show HN: Epiq – Distributed Git based issue tracker TUI
feed_id: hacker-news
url: https://ljtn.github.io/epiq/
guid: https://news.ycombinator.com/item?id=48155570
published_at: 2026-05-16T00:18:03
author: jolaflow
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Issue trackers typically live outside of your workflow, with poor ergonomics. Epiq aims to solve that, bringing issue tracking into your terminal. Multi-user collaboration is achieved via git using user-scoped immutable event logs that converge in memory. Put my all into it. Let me know what you think. Comments URL: https://news.ycombinator.com/item?id=48155570 Points: 58 # Comments: 20


title: NYT and Vaping: How to Lie by Saying Only True Things
feed_id: hacker-news
url: https://gwern.net/vaping
guid: https://news.ycombinator.com/item?id=48155555
published_at: 2026-05-16T00:15:53
author: Ariarule
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Article URL: https://gwern.net/vaping Comments URL: https://news.ycombinator.com/item?id=48155555 Points: 10 # Comments: 2


title: The main thing about P2P meth is that there's so much of it (2021)
feed_id: hacker-news
url: https://dynomight.net/p2p-meth/
guid: https://news.ycombinator.com/item?id=48155324
published_at: 2026-05-15T23:37:32
author: tomjakubowski
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://dynomight.net/p2p-meth/ Comments URL: https://news.ycombinator.com/item?id=48155324 Points: 119 # Comments: 139


title: Erlang/OTP 29.0
feed_id: hacker-news
url: https://www.erlang.org/news/188
guid: https://news.ycombinator.com/item?id=48155297
published_at: 2026-05-15T23:33:32
author: pyinstallwoes
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://www.erlang.org/news/188 Comments URL: https://news.ycombinator.com/item?id=48155297 Points: 202 # Comments: 36


title: Spectre Programming Language
feed_id: hacker-news
url: https://spectre-docs.pages.dev
guid: https://news.ycombinator.com/item?id=48155159
published_at: 2026-05-15T23:14:51
author: asdkop
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Article URL: https://spectre-docs.pages.dev Comments URL: https://news.ycombinator.com/item?id=48155159 Points: 29 # Comments: 3


title: How to Write to SSDs [pdf]
feed_id: hacker-news
url: https://www.vldb.org/pvldb/vol19/p1469-lee.pdf
guid: https://news.ycombinator.com/item?id=48154711
published_at: 2026-05-15T22:23:38
author: matt_d
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://www.vldb.org/pvldb/vol19/p1469-lee.pdf Comments URL: https://news.ycombinator.com/item?id=48154711 Points: 110 # Comments: 13


title: I believe there are entire companies right now under AI psychosis
feed_id: hacker-news
url: https://twitter.com/mitchellh/status/2055380239711457578
guid: https://news.ycombinator.com/item?id=48153379
published_at: 2026-05-15T20:26:47
author: reasonableklout
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
https://xcancel.com/mitchellh/status/2055380239711457578https://hachyderm.io/@mitchellh/116580433508108130 Comments URL: https://news.ycombinator.com/item?id=48153379 Points: 1336 # Comments: 659


title: Bill to block publishers from killing online games advances in California
feed_id: hacker-news
guid: https://news.ycombinator.com/item?id=48152994
published_at: 2026-05-15T19:48:48
author: Lihh27
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://arstechnica.com/gaming/2026/05/bill-to-keep-online-games-playable-clears-key-hurdle-in-california/

summary:
Article URL: https://arstechnica.com/gaming/2026/05/bill-to-keep-online-games-playable-clears-key-hurdle-in-california/ Comments URL: https://news.ycombinator.com/item?id=48152994 Points: 482 # Comments: 302


title: ABC News has taken all FiveThirtyEight articles offline
feed_id: hacker-news
url: https://twitter.com/baseballot/status/2055309076209492208
guid: https://news.ycombinator.com/item?id=48152553
published_at: 2026-05-15T19:07:08
author: cmsparks
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Article URL: https://twitter.com/baseballot/status/2055309076209492208 Comments URL: https://news.ycombinator.com/item?id=48152553 Points: 273 # Comments: 128


title: The Zulip Foundation
feed_id: hacker-news
url: https://blog.zulip.com/2026/05/15/announcing-zulip-foundation/
guid: https://news.ycombinator.com/item?id=48152168
published_at: 2026-05-15T18:37:01
author: boramalper
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://blog.zulip.com/2026/05/15/announcing-zulip-foundation/ Comments URL: https://news.ycombinator.com/item?id=48152168 Points: 271 # Comments: 70


title: Waymo updates 3,800 robotaxis after they 'drive into standing water'
feed_id: hacker-news
guid: https://news.ycombinator.com/item?id=48151767
published_at: 2026-05-15T18:00:43
author: drob518
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
url:
https://www.cnbc.com/2026/05/12/waymo-recalls-3800-robotaxis-after-able-drive-into-standing-water.html

summary:
Article URL: https://www.cnbc.com/2026/05/12/waymo-recalls-3800-robotaxis-after-able-drive-into-standing-water.html Comments URL: https://news.ycombinator.com/item?id=48151767 Points: 170 # Comments: 162


title: U.S. DOJ demands Apple and Google unmask over 100k users of car-tinkering app
feed_id: hacker-news
guid: https://news.ycombinator.com/item?id=48151383
published_at: 2026-05-15T17:28:24
author: tencentshill
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
url:
https://macdailynews.com/2026/05/15/u-s-doj-demands-apple-and-google-unmask-over-100000-users-of-popular-car-tinkering-app-in-emissions-crackdown/

summary:
Article URL: https://macdailynews.com/2026/05/15/u-s-doj-demands-apple-and-google-unmask-over-100000-users-of-popular-car-tinkering-app-in-emissions-crackdown/ Comments URL: https://news.ycombinator.com/item?id=48151383 Points: 382 # Comments: 250


title: I designed a nibble-oriented CPU in Verilog to build a scientific calculator
feed_id: hacker-news
url: https://github.com/gdevic/FPGA-Calculator
guid: https://news.ycombinator.com/item?id=48151237
published_at: 2026-05-15T17:15:58
author: gdevic
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://github.com/gdevic/FPGA-Calculator Comments URL: https://news.ycombinator.com/item?id=48151237 Points: 103 # Comments: 33


title: Hightouch (YC S19) Is Hiring
feed_id: hacker-news
url: https://hightouch.com/careers
guid: https://news.ycombinator.com/item?id=48151034
published_at: 2026-05-15T17:01:15
author: joshwget
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://hightouch.com/careers Comments URL: https://news.ycombinator.com/item?id=48151034 Points: 0 # Comments: 0


title: Project Gutenberg – keeps getting better
feed_id: hacker-news
url: https://www.gutenberg.org/
guid: https://news.ycombinator.com/item?id=48150431
published_at: 2026-05-15T16:15:36
author: JSeiko
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://www.gutenberg.org/ Comments URL: https://news.ycombinator.com/item?id=48150431 Points: 930 # Comments: 197


title: Image-blaster: Creates 3D environments, SFX, and meshes from a single image
feed_id: hacker-news
url: https://github.com/neilsonnn/image-blaster
guid: https://news.ycombinator.com/item?id=48150069
published_at: 2026-05-15T15:42:37
author: MattRogish
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://github.com/neilsonnn/image-blaster Comments URL: https://news.ycombinator.com/item?id=48150069 Points: 160 # Comments: 31


title: Additive Blending on the Nintendo 64
feed_id: hacker-news
url: https://phoboslab.org/log/2026/05/n64-additive-blending
guid: https://news.ycombinator.com/item?id=48149259
published_at: 2026-05-15T14:39:51
author: ibobev
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://phoboslab.org/log/2026/05/n64-additive-blending Comments URL: https://news.ycombinator.com/item?id=48149259 Points: 108 # Comments: 9


title: ASCII by Jason Scott
feed_id: hacker-news
url: https://ascii.textfiles.com/
guid: https://news.ycombinator.com/item?id=48148726
published_at: 2026-05-15T14:02:36
author: bookofjoe
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
https://web.archive.org/web/20260501220231/https://ascii.tex... Comments URL: https://news.ycombinator.com/item?id=48148726 Points: 192 # Comments: 22


title: RJ Scaringe has raised more than $12B across three startups and investors still want more
feed_id: techcrunch
guid: https://techcrunch.com/?p=3122332
published_at: 2026-05-15T20:30:20
author: Kirsten Korosec
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/rj-scaringe-has-raised-more-than-12-billion-across-three-startups-and-investors-still-want-more/

summary:
Investors can't seem to get enough of RJ Scaringe or his ideas. Storytelling and communication are one of his superpowers, according to Jiten Behl, who joined Rivian when the company had just a handful of employees.


title: General Catalyst posted VC rage bait and it worked, especially on a16z
feed_id: techcrunch
guid: https://techcrunch.com/?p=3123082
published_at: 2026-05-15T20:25:35
summary: Compulsive X user Marc Andreessen himself couldn't resist responding, many, many times.
author: Julie Bort
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/general-catalyst-posted-vc-rage-bait-and-it-worked-especially-on-a16z/


title: A hotel check-in system left a million passports and driver’s licenses open for anyone to see
feed_id: techcrunch
guid: https://techcrunch.com/?p=3123092
published_at: 2026-05-15T18:51:33
author: Zack Whittaker
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/a-hotel-check-in-system-left-a-million-passports-and-drivers-licenses-open-for-anyone-to-see/

summary:
The tech company that maintains the hotel check-in system set its cloud storage to public, allowing anyone to access customers' data without a password.


title: Silicon Valley’s vacationland needs a new energy provider just as AI is driving prices up
feed_id: techcrunch
guid: https://techcrunch.com/?p=3123049
published_at: 2026-05-15T18:14:44
author: Tim De Chant
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/silicon-valleys-vacationland-needs-a-new-energy-provider-just-as-ai-is-driving-prices-up/

summary:
Lake Tahoe, Silicon Valley's favorite vacation spot, is about to get hit with higher energy prices as AI drives demand for electricity.


title: Tesla reveals two Robotaxi crashes involving teleoperators
feed_id: techcrunch
url: https://techcrunch.com/2026/05/15/tesla-reveals-two-robotaxi-crashes-involving-teleoperators/
guid: https://techcrunch.com/?p=3123083
published_at: 2026-05-15T17:13:26
author: Sean O'Kane
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Newly unredacted crash reports reveal some of the troubles Tesla has had as it tries to scale its robotaxis.


title: OpenAI launches ChatGPT for personal finance, will let you connect bank accounts
feed_id: techcrunch
guid: https://techcrunch.com/?p=3122997
published_at: 2026-05-15T16:00:00
author: Ivan Mehta
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/openai-launches-chatgpt-for-personal-finance-will-let-you-connect-bank-accounts/

summary:
Once users connect their accounts, they will see a dashboard of their portfolio performance, spending, subscriptions, and upcoming payments.


title: Power prices are up 76% on America’s biggest grid, and a watchdog is pointing fingers
feed_id: techcrunch
guid: https://techcrunch.com/?p=3123043
published_at: 2026-05-15T15:45:53
author: Tim De Chant
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/power-prices-are-up-76-on-americas-biggest-grid-and-a-watchdog-is-pointing-fingers/

summary:
The price spike is a reminder of a deeper problem: The U.S. power grid was not designed for the electricity demands of an AI-driven economy, and the gap between what the grid can deliver and what the industry needs is widening.


title: US orders travelers on Air Force One to throw away gifts, pins, and burner phones after China trip
feed_id: techcrunch
guid: https://techcrunch.com/?p=3123047
published_at: 2026-05-15T15:42:11
author: Lorenzo Franceschi-Bicchierai
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/us-orders-travelers-on-air-force-one-to-throw-away-gifts-pins-and-burner-phones-after-china-trip/

summary:
While the summit appeared cordial, China remains a key adversary of the United States, given its advanced intelligence and espionage capabilities.


title: Runway started by helping filmmakers — now it wants to beat Google at AI
feed_id: techcrunch
guid: https://techcrunch.com/?p=3116150
published_at: 2026-05-15T14:00:00
author: Rebecca Bellan
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/runway-started-by-helping-filmmakers-now-it-wants-to-beat-google-at-ai/

summary:
AI video-generation startup Runway is betting that video generation is the path to world models. And that being an AI outsider is an advantage, not a liability.


title: Even GoPro is pivoting to defense
feed_id: techcrunch
url: https://techcrunch.com/2026/05/15/even-gopro-is-pivoting-to-defense/
guid: https://techcrunch.com/?p=3123023
published_at: 2026-05-15T13:14:13
author: Sean O'Kane
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
The action camera maker, like so many other companies these days, is looking to defense applications as it evaluates a possible sale.


title: Meridian Ventures launched a $35M fund with a focus on MBA-deferred founders
feed_id: techcrunch
guid: https://techcrunch.com/?p=3122581
published_at: 2026-05-15T13:00:00
author: Dominic-Madori Davis
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/15/meridian-ventures-launched-35m-fund-to-back-mba-deferred-founders/

summary:
This new fund will back founders building enterprise technology in the United States. Meridian is agnostic, co-founder Devon Gethers said, noting that the firm has already invested in companies in fintech, logistics, healthcare, and of course, AI.


title: Osaurus brings both local and cloud AI models to your Mac
feed_id: techcrunch
url: https://techcrunch.com/2026/05/15/osaurus-brings-both-local-and-cloud-ai-models-to-your-mac/
guid: https://techcrunch.com/?p=3122568
published_at: 2026-05-15T12:19:48
author: Sarah Perez
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Osaurus￼ combines local and cloud AI models in a Mac app that keeps users’ memory, files, and tools on their own hardware.


title: Indian Uber rival Rapido raises $240M at $3B valuation
feed_id: techcrunch
url: https://techcrunch.com/2026/05/15/indian-uber-rival-rapido-raises-240m-at-3b-valuation/
guid: https://techcrunch.com/?p=3123000
published_at: 2026-05-15T11:24:12
author: Jagmeet Singh
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Rapido has driven its growth by enabling ride-hailing for lower-cost and more flexible modes of transport such as motorbikes and auto-rickshaws.


title: What the jury will actually decide in the case of Elon Musk vs. Sam Altman
feed_id: techcrunch
guid: https://techcrunch.com/?p=3122645
published_at: 2026-05-14T22:47:51
summary: Here's what the biggest tech court case of the year is all about.
author: Tim Fernholz
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/14/what-the-jury-will-actually-decide-in-the-case-of-elon-musk-vs-sam-altman/


title: Elon Musk’s SpaceXAI has been bleeding staff since its merger
feed_id: techcrunch
url: https://techcrunch.com/2026/05/14/elon-musks-spacexai-has-been-bleeding-staff-since-its-merger/
guid: https://techcrunch.com/?p=3122877
published_at: 2026-05-14T21:30:44
author: Rebecca Bellan
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
More than 50 employees have reportedly left Elon Musk’s newly merged SpaceXAI since February, raising questions about burnout, leadership changes, talent poaching, and whether liquidity events weakened retention incentives.


title: Lovable just backed a company that’s looking to bring vibe coding to hardware
feed_id: techcrunch
guid: https://techcrunch.com/?p=3122579
published_at: 2026-05-14T20:43:03
author: Dominic-Madori Davis
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/14/lovable-just-backed-a-company-thats-looking-to-bring-vibe-coding-to-hardware/

summary:
Hardware company Atech raised $800,000 in pre-seed funding, including from a16z’s scout fund, Sequoia Scout Fund, and Nordic Makers.


title: Cerebras IPO makes billions for Benchmark but VC Eric Vishria almost didn’t take the meeting
feed_id: techcrunch
guid: https://techcrunch.com/?p=3122714
published_at: 2026-05-14T20:23:03
author: Julie Bort
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
url:
https://techcrunch.com/2026/05/14/cerebras-ipo-makes-billions-for-benchmark-but-vc-eric-vishria-almost-didnt-take-the-meeting/

summary:
Benchmark almost never backs hardware startups. So Eric Vishria dragged his feet 10 years ago before agreeing to hear Cerebras' pitch.


title: What happens when AI starts building itself?
feed_id: techcrunch
url: https://techcrunch.com/2026/05/14/what-happens-when-ai-starts-building-itself/
guid: https://techcrunch.com/?p=3122712
published_at: 2026-05-14T19:57:20
author: Russell Brandom
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Richard Socher's new $650 million startup wants to build an AI that can research and improve itself indefinitely — and he insists it will actually ship products.


feed_id: techcrunch
guid: https://techcrunch.com/?p=3122744
published_at: 2026-05-14T19:12:03
author: Connie Loizos
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
title:
OpenAI is reportedly preparing legal action against Apple; it wouldn’t be the first partner to feel burned

url:
https://techcrunch.com/2026/05/14/openai-is-reportedly-preparing-legal-action-against-apple-it-wouldnt-be-the-first-partner-to-feel-burned/

summary:
OpenAI is so frustrated with Apple over a ChatGPT integration that failed to deliver the subscribers and prominence it expected that the company is now actively exploring legal action against the iPhone maker.


title: A new personal finance experience in ChatGPT
feed_id: openai-news
url: https://openai.com/index/personal-finance-chatgpt
guid: https://openai.com/index/personal-finance-chatgpt
published_at: 2026-05-15T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Preview a new personal finance experience in ChatGPT for Pro users in the U.S. Securely connect your financial accounts and get AI-powered insights and guidance grounded in your financial context, goals, and priorities.


title: Databricks brings GPT-5.5 to enterprise agent workflows
feed_id: openai-news
url: https://openai.com/index/databricks
guid: https://openai.com/index/databricks
published_at: 2026-05-15T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Databricks uses GPT-5.5 for enterprise agent workflows after the model set a new state of the art on the OfficeQA Pro benchmark.


title: Sea's View on the Future of Agentic Software Development with Codex
feed_id: openai-news
url: https://openai.com/index/sea-david-chen
guid: https://openai.com/index/sea-david-chen
published_at: 2026-05-14T20:30:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Sea Limited's CPO explains why the company is deploying Codex across engineering teams to accelerate AI-native software development in Asia.


title: Work with Codex from anywhere
feed_id: openai-news
url: https://openai.com/index/work-with-codex-from-anywhere
guid: https://openai.com/index/work-with-codex-from-anywhere
published_at: 2026-05-14T13:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Use Codex anywhere with the ChatGPT mobile app. Monitor, steer, and approve coding tasks in real time across devices and remote environments.


title: Helping ChatGPT better recognize context in sensitive conversations
feed_id: openai-news
url: https://openai.com/index/chatgpt-recognize-context-in-sensitive-conversations
guid: https://openai.com/index/chatgpt-recognize-context-in-sensitive-conversations
published_at: 2026-05-14T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Learn how new ChatGPT safety updates improve context awareness in sensitive conversations, helping detect risk over time and respond more safely.


title: Building a safe, effective sandbox to enable Codex on Windows
feed_id: openai-news
url: https://openai.com/index/building-codex-windows-sandbox
guid: https://openai.com/index/building-codex-windows-sandbox
published_at: 2026-05-13T11:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Learn how OpenAI built a secure sandbox for Codex on Windows, enabling safe, efficient coding agents with controlled file access and network restrictions.


title: Our response to the TanStack npm supply chain attack
feed_id: openai-news
url: https://openai.com/index/our-response-to-the-tanstack-npm-supply-chain-attack
guid: https://openai.com/index/our-response-to-the-tanstack-npm-supply-chain-attack
published_at: 2026-05-13T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI details its response to the TanStack “Mini Shai-Hulud” supply chain attack, outlines protections taken to secure systems and signing certificates, and explains why macOS users must update OpenAI apps by June 12, 2026. Learn what happened, what was affected, and how OpenAI is strengthening defenses against evolving software supply chain threats.


title: How finance teams use Codex
feed_id: openai-news
url: https://openai.com/academy/how-finance-teams-use-codex
guid: https://openai.com/academy/how-finance-teams-use-codex
published_at: 2026-05-12T15:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
See how finance teams can use Codex to build MBRs, reporting packs, variance bridges, model checks, and planning scenarios from real work inputs.


title: How NVIDIA engineers and researchers build with Codex
feed_id: openai-news
url: https://openai.com/index/nvidia
guid: https://openai.com/index/nvidia
published_at: 2026-05-12T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Teams use Codex with GPT-5.5 to ship production systems and turn research ideas into runnable experiments.


title: AutoScout24 scales engineering with AI-powered workflows
feed_id: openai-news
url: https://openai.com/index/autoscout24
guid: https://openai.com/index/autoscout24
published_at: 2026-05-12T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Learn how AutoScout24 Group uses Codex and ChatGPT to speed development cycles, improve code quality, and expand AI adoption.


title: What Parameter Golf taught us about AI-assisted research
feed_id: openai-news
url: https://openai.com/index/what-parameter-golf-taught-us
guid: https://openai.com/index/what-parameter-golf-taught-us
published_at: 2026-05-12T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Parameter Golf brought together 1,000+ participants and 2,000+ submissions to explore AI-assisted machine learning research, coding agents, quantization, and novel model design under strict constraints.


title: How ChatGPT adoption broadened in early 2026
feed_id: openai-news
url: https://openai.com/signals/research/2026q1-update
guid: https://openai.com/signals/research/2026q1-update
published_at: 2026-05-11T15:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
ChatGPT adoption surged in Q1 2026, with fastest growth among users over 35 and more balanced gender usage, signaling broader mainstream AI adoption.


title: OpenAI Campus Network: Student club interest form
feed_id: openai-news
url: https://openai.com/index/openai-campus-network-student-club-interest-form
guid: https://openai.com/index/openai-campus-network-student-club-interest-form
published_at: 2026-05-11T10:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Join the OpenAI Campus Network—connect student clubs worldwide, access AI tools, host events, and build an AI-powered campus community.


title: How enterprises are scaling AI
feed_id: openai-news
url: https://openai.com/business/guides-and-resources/how-enterprises-are-scaling-ai
guid: https://openai.com/business/guides-and-resources/how-enterprises-are-scaling-ai
published_at: 2026-05-11T10:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
How enterprises scale AI: from early experiments to compounding impact through trust, governance, workflow design, and quality at scale.


title: OpenAI launches DeployCo to help businesses build around intelligence
feed_id: openai-news
url: https://openai.com/index/openai-launches-the-deployment-company
guid: https://openai.com/index/openai-launches-the-deployment-company
published_at: 2026-05-11T06:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI launches DeployCo, a new enterprise deployment company built to help organizations bring frontier AI into production and turn it into measurable business impact.


title: Running Codex safely at OpenAI
feed_id: openai-news
url: https://openai.com/index/running-codex-safely
guid: https://openai.com/index/running-codex-safely
published_at: 2026-05-08T12:30:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
How OpenAI runs Codex securely with sandboxing, approvals, network policies, and agent-native telemetry to support safe and compliant coding agent adoption.


title: Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber
feed_id: openai-news
url: https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber
guid: https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber
published_at: 2026-05-07T13:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI expands Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber, helping verified defenders accelerate vulnerability research and protect critical infrastructure.


title: Parloa builds service agents customers want to talk to
feed_id: openai-news
url: https://openai.com/index/parloa
guid: https://openai.com/index/parloa
published_at: 2026-05-07T11:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Parloa leverages OpenAI models to power scalable, voice-driven AI customer service agents, enabling enterprises to design, simulate, and deploy reliable, real-time interactions.


title: Advancing voice intelligence with new models in the API
feed_id: openai-news
url: https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api
guid: https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api
published_at: 2026-05-07T10:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.


title: Introducing Trusted Contact in ChatGPT
feed_id: openai-news
url: https://openai.com/index/introducing-trusted-contact-in-chatgpt
guid: https://openai.com/index/introducing-trusted-contact-in-chatgpt
published_at: 2026-05-07T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Introducing Trusted Contact in ChatGPT, an optional safety feature that notifies someone you trust if serious self-harm concerns are detected.


title: Testing ads in ChatGPT
feed_id: openai-news
url: https://openai.com/index/testing-ads-in-chatgpt
guid: https://openai.com/index/testing-ads-in-chatgpt
published_at: 2026-05-07T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control.


title: Simplex rethinks software development with Codex
feed_id: openai-news
url: https://openai.com/index/simplex
guid: https://openai.com/index/simplex
published_at: 2026-05-07T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Simplex boosts software development with ChatGPT Enterprise and Codex, reducing design, build, and testing time while scaling AI-driven workflows.


title: How ChatGPT learns about the world while protecting privacy
feed_id: openai-news
url: https://openai.com/index/how-chatgpt-protects-privacy
guid: https://openai.com/index/how-chatgpt-protects-privacy
published_at: 2026-05-06T08:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Learn how ChatGPT safeguards your privacy, reduces personal data in training, and gives you control over whether your conversations improve AI models.


title: Singular Bank helps bankers move fast with ChatGPT and Codex
feed_id: openai-news
url: https://openai.com/index/singular-bank
guid: https://openai.com/index/singular-bank
published_at: 2026-05-06T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Singular Bank built Singularity, an internal assistant using ChatGPT and Codex to help bankers save 60–90 minutes daily on meeting prep, portfolio analysis, and follow-up.


title: Uber uses OpenAI to help people earn smarter and book faster
feed_id: openai-news
url: https://openai.com/index/uber
guid: https://openai.com/index/uber
published_at: 2026-05-06T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Uber uses OpenAI to power AI assistants and voice features that help drivers earn smarter and riders book faster across a global real-time marketplace.


title: How frontier firms are pulling ahead
feed_id: openai-news
url: https://openai.com/index/introducing-b2b-signals
guid: https://openai.com/index/introducing-b2b-signals
published_at: 2026-05-06T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.


title: Introducing ChatGPT Futures: Class of 2026
feed_id: openai-news
url: https://openai.com/index/introducing-chatgpt-futures-class-of-2026
guid: https://openai.com/index/introducing-chatgpt-futures-class-of-2026
published_at: 2026-05-06T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Meet the ChatGPT Futures Class of 2026—26 student innovators using AI to build, research, and drive real-world impact. Discover how this generation is redefining learning, creativity, and opportunity with ChatGPT.


title: GPT-5.5 Instant: smarter, clearer, and more personalized
feed_id: openai-news
url: https://openai.com/index/gpt-5-5-instant
guid: https://openai.com/index/gpt-5-5-instant
published_at: 2026-05-05T10:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
GPT-5.5 Instant updates ChatGPT’s default model with smarter, more accurate answers, reduced hallucinations, and improved personalization controls.


title: Unlocking large scale AI training networks with MRC (Multipath Reliable Connection)
feed_id: openai-news
url: https://openai.com/index/mrc-supercomputer-networking
guid: https://openai.com/index/mrc-supercomputer-networking
published_at: 2026-05-05T10:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI introduces MRC (Multipath Reliable Connection), a new supercomputer networking protocol released via OCP to improve resilience and performance in large-scale AI training clusters.


title: New ways to buy ChatGPT ads
feed_id: openai-news
url: https://openai.com/index/new-ways-to-buy-chatgpt-ads
guid: https://openai.com/index/new-ways-to-buy-chatgpt-ads
published_at: 2026-05-05T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI expands ChatGPT ads with a beta self-serve Ads Manager, CPC bidding, and enhanced measurement tools—built to protect privacy and keep conversations separate from ads.


title: Advancing youth safety and wellbeing in EMEA
feed_id: openai-news
url: https://openai.com/index/advancing-youth-safety-in-emea
guid: https://openai.com/index/advancing-youth-safety-in-emea
published_at: 2026-05-05T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Explore OpenAI’s European Youth Safety Blueprint and EMEA Youth & Wellbeing Grants, advancing safe, responsible AI for teens, families, and educators.


title: OpenAI and PwC collaborate to reimagine the office of the CFO
feed_id: openai-news
url: https://openai.com/index/openai-pwc-finance-collaboration
guid: https://openai.com/index/openai-pwc-finance-collaboration
published_at: 2026-05-04T21:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI and PwC are partnering to help enterprises use AI agents to automate finance workflows, improve forecasting, strengthen controls, and modernize the CFO function.


title: How OpenAI delivers low-latency voice AI at scale
feed_id: openai-news
url: https://openai.com/index/delivering-low-latency-voice-ai-at-scale
guid: https://openai.com/index/delivering-low-latency-voice-ai-at-scale
published_at: 2026-05-04T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
How OpenAI rebuilt its WebRTC stack to power real-time Voice AI with low latency, global scale, and seamless conversational turn-taking.


title: Introducing Advanced Account Security
feed_id: openai-news
url: https://openai.com/index/advanced-account-security
guid: https://openai.com/index/advanced-account-security
published_at: 2026-04-30T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Introducing Advanced Account Security: phishing-resistant login, stronger recovery, and enhanced protections to safeguard sensitive data and prevent account takeover.


title: Where the goblins came from
feed_id: openai-news
url: https://openai.com/index/where-the-goblins-came-from
guid: https://openai.com/index/where-the-goblins-came-from
published_at: 2026-04-29T20:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
How goblin outputs spread in AI models: timeline, root cause, and fixes behind personality-driven quirks in GPT-5 behavior.


title: Building the compute infrastructure for the Intelligence Age
feed_id: openai-news
url: https://openai.com/index/building-the-compute-infrastructure-for-the-intelligence-age
guid: https://openai.com/index/building-the-compute-infrastructure-for-the-intelligence-age
published_at: 2026-04-29T15:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI scales Stargate to build the compute infrastructure powering AGI, adding new data center capacity to meet growing AI demand.


title: Cybersecurity in the Intelligence Age
feed_id: openai-news
url: https://openai.com/index/cybersecurity-in-the-intelligence-age
guid: https://openai.com/index/cybersecurity-in-the-intelligence-age
published_at: 2026-04-29T04:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI outlines a five-part action plan for strengthening cybersecurity in the Intelligence Age, focused on democratizing AI-powered cyber defense and protecting critical systems.


title: OpenAI models, Codex, and Managed Agents come to AWS
feed_id: openai-news
url: https://openai.com/index/openai-on-aws
guid: https://openai.com/index/openai-on-aws
published_at: 2026-04-28T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI GPT models, Codex, and Managed Agents are now available on AWS, enabling enterprises to build secure AI in their AWS environments.


title: Our commitment to community safety
feed_id: openai-news
url: https://openai.com/index/our-commitment-to-community-safety
guid: https://openai.com/index/our-commitment-to-community-safety
published_at: 2026-04-28T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Learn how OpenAI protects community safety in ChatGPT through model safeguards, misuse detection, policy enforcement, and collaboration with safety experts.


title: OpenAI available at FedRAMP Moderate
feed_id: openai-news
url: https://openai.com/index/openai-available-at-fedramp-moderate
guid: https://openai.com/index/openai-available-at-fedramp-moderate
published_at: 2026-04-27T14:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI is available at FedRAMP Moderate authorization for ChatGPT Enterprise and the OpenAI API, enabling secure AI adoption for U.S. federal agencies.


title: The next phase of the Microsoft OpenAI partnership
feed_id: openai-news
url: https://openai.com/index/next-phase-of-microsoft-partnership
guid: https://openai.com/index/next-phase-of-microsoft-partnership
published_at: 2026-04-27T06:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
OpenAI and Microsoft announce an amended agreement that simplifies the partnership, adds long-term clarity, and supports continued AI innovation at scale.


title: An open-source spec for orchestration: Symphony
feed_id: openai-news
url: https://openai.com/index/open-source-codex-orchestration-symphony
guid: https://openai.com/index/open-source-codex-orchestration-symphony
published_at: 2026-04-27T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Learn how Symphony, an open-source spec for Codex orchestration, turns issue trackers into always-on agent systems—boosting engineering output and reducing context switching.


title: Choco automates food distribution with AI agents
feed_id: openai-news
url: https://openai.com/index/choco
guid: https://openai.com/index/choco
published_at: 2026-04-27T00:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
How Choco used OpenAI APIs to streamline food distribution, boost productivity, and unlock growth—an in-depth customer story on real-world AI impact.


title: Our principles
feed_id: openai-news
url: https://openai.com/index/our-principles
guid: https://openai.com/index/our-principles
published_at: 2026-04-26T16:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Our mission is to ensure that AGI benefits all of humanity. Sam Altman shares five principles that guide our work.


title: Introducing GPT-5.5
feed_id: openai-news
url: https://openai.com/index/introducing-gpt-5-5
guid: https://openai.com/index/introducing-gpt-5-5
published_at: 2026-04-23T11:00:00
first_crawl_time: 11:50
last_crawl_time: 17:53
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 17:55:52
summary:
Introducing GPT-5.5, our smartest model yet—faster, more capable, and built for complex tasks like coding, research, and data analysis across tools.


title: Plugins and skills
feed_id: openai-news
url: https://openai.com/academy/codex-plugins-and-skills
guid: https://openai.com/academy/codex-plugins-and-skills
published_at: 2026-04-23T10:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Learn how to use Codex plugins and skills to connect tools, access data, and follow repeatable workflows to automate tasks and improve results.


title: How to get started with Codex
feed_id: openai-news
url: https://openai.com/academy/codex-how-to-start
guid: https://openai.com/academy/codex-how-to-start
published_at: 2026-04-23T10:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Learn how to get started with Codex by setting up projects, creating threads, and completing your first tasks with step-by-step guidance.


title: Codex settings
feed_id: openai-news
url: https://openai.com/academy/codex-settings
guid: https://openai.com/academy/codex-settings
published_at: 2026-04-23T10:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
Learn how to configure Codex settings, including personalization, detail level, and permissions, to run tasks smoothly and customize your workflow.


title: Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12620
guid: oai:arXiv.org:2605.12620v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12620v1 Announce Type: new Abstract: Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly advanced the reasoning capabilities of such agents through strong vision-language knowledge and chain-of-thought (CoT) reasoning, yet remain brittle when faced with challenging out-of-distribution scenarios. To address this, we propose Verifier-Guided Action Selection (Veg...

author:
Nishad Singhi, Christian Bialas, Snehal Jauhri, Vignesh Prasad, Georgia Chalvatzaki, Marcus Rohrbach, Anna Rohrbach


title: Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12655
guid: oai:arXiv.org:2605.12655v1
published_at: 2026-05-15T04:00:00
author: Wo Wei Lin, Ethan Rathbun, Enrico Marchesini Xiang Zhi Tan
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12655v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives. However, conditioning rewards on instructions introduces a fundamental failure mode as Bellman updates couple value estimates across instruction contexts, leading to inconsistent values when instructions interrupt macro-actions. We propose Macro-Actio...


title: Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12673
guid: oai:arXiv.org:2605.12673v1
published_at: 2026-05-15T04:00:00
author: Hao Wang, Hanchen Li, Qiuyang Mang, Alvin Cheung, Koushik Sen, Dawn Song
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12673v1 Announce Type: new Abstract: Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitting. We argue that benchmarks must be secure by design. From past incidents of reward hacks, we derive a taxonomy of eight recurring flaw patterns and compile them into the...


title: Revealing Interpretable Failure Modes of VLMs
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12674
guid: oai:arXiv.org:2605.12674v1
published_at: 2026-05-15T04:00:00
author: Isha Chaudhary, Vedaant V Jain, Kavya Sachdeva, Sayan Ranu, Gagandeep Singh
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12674v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to generalize with minimal task-specific engineering. Despite these advantages, they can exhibit catastrophic failures in specific real-world situations, constituting failure modes. We introduce REVELIO, a framework for systematically uncovering interpretable failure modes in VLMs. We define a failure mode as...


title: Learning Transferable Latent User Preferences for Human-Aligned Decision Making
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12682
guid: oai:arXiv.org:2605.12682v1
published_at: 2026-05-15T04:00:00
author: Alina Hyk, Sandhya Saisubramanian
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12682v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications. While they are efficient in certain tasks, LLMs often struggle to produce human-aligned solutions. Human-aligned decision making requires accounting for both explicitly stated goals and latent user preferences that shape how ambiguous situations should be resolved. Existing approaches to incorporating such preferences either rely on extensive and repeated ...


title: On the Size Complexity and Decidability of First-Order Progression
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12691
guid: oai:arXiv.org:2605.12691v1
published_at: 2026-05-15T04:00:00
author: Jens Classen, Daxin Liu
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12691v1 Announce Type: new Abstract: Progression, the task of updating a knowledge base to reflect action effects, generally requires second-order logic. Identifying first-order special cases, by restricting either the knowledge base or action effects, has long been a central topic in reasoning about actions. It is known that local-effect, normal, and acyclic actions, three increasingly expressive classes, admit first-order progression. However, a systematic analysis of the size of su...


title: DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Models
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12702
guid: oai:arXiv.org:2605.12702v1
published_at: 2026-05-15T04:00:00
author: Eugenia Kim, Ioana Tanase, Christina Mallon
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12702v1 Announce Type: new Abstract: General-purpose safety benchmarks for large language models do not adequately evaluate disability-related harms. We introduce DisaBench: a taxonomy of twelve disability harm categories co-created with people with disabilities and red teaming experts, a taxonomy-driven evaluation methodology that pairs benign and adversarial prompts across seven life domains, and a dataset of 175 prompts with human-annotated labels on 525 prompt-response pairs. Anno...


title: CHAL: Council of Hierarchical Agentic Language
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12718
guid: oai:arXiv.org:2605.12718v1
published_at: 2026-05-15T04:00:00
author: Tommaso Giovannelli, Griffin D. Kent
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12718v1 Announce Type: new Abstract: Multi-agent debate has emerged as a promising approach for improving LLM reasoning on ground-truth tasks, yet current methodologies face certain structural limitations: debate tends to induce a martingale over belief trajectories, majority voting accounts for most observed gains, and LLMs exhibit confidence escalation rather than calibration across rounds. We argue that the genuine value of debate, and dialectic systems as a whole, lies not in grou...


title: BEHAVE: A Hybrid AI Framework for Real-Time Modeling of Collective Human Dynamics
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12730
guid: oai:arXiv.org:2605.12730v1
published_at: 2026-05-15T04:00:00
author: Helene Malyutina
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12730v1 Announce Type: new Abstract: Existing AI systems for modeling human behavior operate at the level of individuals or detect events after they occur. As a result, they systematically fail to capture the collective dynamics that determine whether a group remains stable or transitions into escalation or breakdown. We propose a different foundation: a group of interacting humans constitutes a complex dynamical system in the precise mathematical sense, exhibiting emergence, nonlinea...


title: State-Centric Decision Process
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12755
guid: oai:arXiv.org:2605.12755v1
published_at: 2026-05-15T04:00:00
author: Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun, Mahdi Imani, Mohsen Imani
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12755v1 Announce Type: new Abstract: Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysis requires. No explicit state space, no observation-to-state mapping, no certified transitions, and no termination criterion. We introduce the State-Centric Decision Process (SDP), a runtime framework that constructs these missing inputs by having the agent build them, pr...


title: PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Models
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12835
guid: oai:arXiv.org:2605.12835v1
published_at: 2026-05-15T04:00:00
author: Sridhar Mahadevan
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12835v1 Announce Type: new Abstract: Large language models can extract local causal claims from text, but those claims become more useful when organized as persistent, navigable world models rather than as flat summaries. We introduce PROMETHEUS, a framework that turns retrieved literature, filings, reviews, reports, agent traces, source data, code, simulations, and scientific models into causal atlases: sheaf-like families of local causal predictive-state models over an explicit cove...


title: Multimodal Hidden Markov Models for Persistent Emotional State Tracking
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12838
guid: oai:arXiv.org:2605.12838v1
published_at: 2026-05-15T04:00:00
author: Anamika Ragu, Aneesh Jonelagadda
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12838v1 Announce Type: new Abstract: Tracking an interpretable emotional arc of a conversation via the sentiment of individual utterances processed as a whole is central to both understanding and guiding communication in applied, especially clinical, conversational contexts. Existing approaches to emotion recognition operate at the utterance level, obscuring the persistent phases that characterize real conversational dynamics. We propose a lightweight framework that models conversatio...


title: Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12856
guid: oai:arXiv.org:2605.12856v2
published_at: 2026-05-15T04:00:00
author: Ali Al-Lawati, Nafis Tripto, Abolfazl Ansari, Jason Lucas, Suhang Wang, Dongwon Lee
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12856v2 Announce Type: new Abstract: The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with malicious intent may contribute harmful content that appears benign to evade content-based moderation, while compromising the system through exploitative and malicious behavior manifested across their overall interaction patterns within the community. To address this, we introduce BOT-MOD (BOT-MODeration), a moderation frame...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12894
guid: oai:arXiv.org:2605.12894v1
published_at: 2026-05-15T04:00:00
author: Harshita Chopra, Kshitish Ghate, Aylin Caliskan, Tadayoshi Kohno, Chirag Shah, Natasha Jaques
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
title:
Beyond Cooperative Simulators: Generating Realistic User Personas for Robust Evaluation of LLM Agents

summary:
arXiv:2605.12894v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly deployed in settings where they interact with a wide variety of people, including users who are unclear, impatient, or reluctant to share information. However, collecting real interaction data at scale remains expensive. The field has turned to LLM-based user simulators as stand-ins, but these simulators inherit the behavior of their underlying models: cooperative and homogeneous. As a result, agen...


title: When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12922
guid: oai:arXiv.org:2605.12922v1
published_at: 2026-05-15T04:00:00
author: Vardhan Dongre, Joseph Hsieh, Viet Dac Lai, Seunghyun Yoon, Trung Bui, Dilek Hakkani-T\"ur
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12922v1 Announce Type: new Abstract: Large language models can follow complex instructions in a single turn, yet over long multi-turn interactions they often lose the thread of instructions, persona, and rules. This degradation has been measured behaviorally but not mechanistically explained. We propose a channel-transition account: goal-defining tokens become less accessible through attention, while goal-related information may persist in residual representations. We introduce the Go...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12963
guid: oai:arXiv.org:2605.12963v1
published_at: 2026-05-15T04:00:00
author: James M. Mazzu
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
title:
Sustaining AI safety: Control-theoretic external impossibility, intrinsic necessity, and structural requirements

summary:
arXiv:2605.12963v1 Announce Type: new Abstract: As AI systems become increasingly capable, safety strategies must be evaluated not only by how much they reduce present risk, but by whether they could sustain safety once external control can no longer reliably constrain system behavior. This paper addresses that problem by using control theory to clarify, at a structural level, whether externally enforced safety-sustaining strategies can succeed and, if not, what any alternative strategy would ha...


title: Position: Agentic AI System Is a Foreseeable Pathway to AGI
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12966
guid: oai:arXiv.org:2605.12966v1
published_at: 2026-05-15T04:00:00
author: Junwei Liao, Shuai Li, Muning Wen, Jun Wang, Weinan Zhang
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12966v1 Announce Type: new Abstract: Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is sufficient to achieve Artificial General Intelligence. Instead, we identify Agentic AI as a necessary paradigm for mastering the complex, heterogeneous distribution of real-world tasks. Through rigorous theoretical derivations, we contrast the optimization constraints of monolithic learners against the efficiency of Agentic systems, pro...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12975
guid: oai:arXiv.org:2605.12975v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
title:
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation

summary:
arXiv:2605.12975v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where solving the task requires chaining multiple retrieval and reasoning steps. Key challenges are that current methods represent reasoning through free-form natural language, where intermediate states are implicit, retrieval queries can drift from intended entities, and errors ...

author:
Jiashuo Sun, Jimeng Shi, Yixuan Xie, Saizhuo Wang, Jash Rajesh Parekh, Pengcheng Jiang, Zhiyi Shi, Jiajun Fan, Qinglong Zheng, Peiran Li, Shaowen Wang, Ge Liu, Jiawei Han


title: Useful Memories Become Faulty When Continuously Updated by LLMs
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12978
guid: oai:arXiv.org:2605.12978v1
published_at: 2026-05-15T04:00:00
author: Dylan Zhang, Yanshan Lin, Zhengkun Wu, Yihang Sun, Bingxuan Li, Dianqi Li, Hao Peng
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled across many episodes into reusable, schema-like lessons. Recent agentic-memory systems pursue the consolidated form: an LLM rewrites past trajectories into a textual memory bank that it continuously updates with new interactions, promising self-improving agents without paramet...


title: Retrieval-Augmented Tutoring for Algorithm Tracing and Problem-Solving in AI Education
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.12988
guid: oai:arXiv.org:2605.12988v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.12988v1 Announce Type: new Abstract: Students learning algorithms often need support as they interpret traces, debug reasoning errors, and apply procedures across unfamiliar problem instances. In this paper, we present KITE (Knowledge-Informed Tutoring Engine), a Retrieval-Augmented Generation (RAG)-based intelligent tutoring system designed to serve as a classroom teaching assistant for algorithmic reasoning and problem-solving tasks. KITE uses an intent-aware Socratic response strat...

author:
Mragisha Jain, Tirth Bhatt, Griffin Pitts, Aum Pandya, Peter Brusilovsky, Narges Norouzi, Arto Hellas, Juho Leinonen, Bita Akram


title: MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13037
guid: oai:arXiv.org:2605.13037v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13037v1 Announce Type: new Abstract: Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforehand. This temporal inversion leads to Delayed Environmental Perception: agents must infer environmental constraints through trial-and-error, resulting in an Epistemic Bottleneck that traps them in inefficient failure cycles. Inspired by human affordance perception and cog...

author:
Yuxin Liu, Ziang Ye, Yueqing Sun, Mingye Zhu, Jinwei Xiao, Zhuowen Han, Qi GU, Xunliang Cai, Lei Zhang


title: An Agentic LLM-Based Framework for Population-Scale Mental Health Screening
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13046
guid: oai:arXiv.org:2605.13046v1
published_at: 2026-05-15T04:00:00
author: Giuliano Lorenzoni, Paulo Alencar, Donald Cowan
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from electronic records, telemedicine platforms, and population-level screening programs. At the same time, the emergence of novel AI-based approaches in healthcare calls for intelligent frameworks capable of processing domain-specific unstructured clinical information while adapting to patient-specific nee...


title: GRACE: Gradient-aligned Reasoning Data Curation for Efficient Post-training
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13130
guid: oai:arXiv.org:2605.13130v1
published_at: 2026-05-15T04:00:00
author: Junjie Li, Ziao Wang, NingXuan Ma, Jianghong Ma, Xiaofeng Zhang
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13130v1 Announce Type: new Abstract: Existing reasoning data curation pipelines score whole samples, treating every intermediate step as equally valuable. In reality, steps within a trace contribute very unevenly, and selecting reasoning data well requires assessing them individually. We present GRACE, a gradient-aligned curation method that views each reasoning trace as a sequence of optimization events and scores every step by two complementary signals: its alignment with the answer...


title: A Constraint Programming Approach for $n$-Day Lookahead Playoff Clinching
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13142
guid: oai:arXiv.org:2605.13142v1
published_at: 2026-05-15T04:00:00
author: Gili Rosenberg, Kyle E. C. Booth, J. Kyle Brubaker, Ruben S. Andrist
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13142v1 Announce Type: new Abstract: In professional sports, a team has clinched the playoffs if they are guaranteed a postseason spot, regardless of the outcomes of any remaining games. As the season progresses, sports fans and other stakeholders are interested in precisely when, and under what conditions, their team will clinch the playoffs. In this paper, we investigate playoff clinching in the context of the National Hockey League (NHL), where it is computationally challenging to ...


title: Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13153
guid: oai:arXiv.org:2605.13153v1
published_at: 2026-05-15T04:00:00
author: Rikui Huang, Shengzhe Zhang, Wei Wei
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13153v1 Announce Type: new Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniformly weights all events, ignoring that most are trivial repetitions, which overestimate the true reasoning ability. Therefore, the rare outstanding events, whose prediction demands deeper reasoning, should be distinguished and emphasized. To this end, we propose a strikingness-aware evaluation framewor...


title: Formal Conjectures: An Open and Evolving Benchmark for Verified Discovery in Mathematics
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13171
guid: oai:arXiv.org:2605.13171v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13171v1 Announce Type: new Abstract: As automated reasoning systems advance rapidly, there is a growing need for research-level formal mathematical problems to accurately evaluate their capabilities. To address this, we present Formal Conjectures, an evolving benchmark of currently 2615 mathematical problem statements formalized in Lean 4. Sourced from areas of active mathematical research, the dataset features 1029 open research conjectures providing a zero-contamination benchmark fo...

author:
Moritz Firsching, Paul Lezeau, Salvatore Mercuri, Mikl\'os Z. Horv\'ath, Ya\"el Dillies, Calle S\"onne, Eric Wieser, Fred Zhang, Thomas Hubert, Blaise Ag\"uera y Arcas, Pushmeet Kohli


title: Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13213
guid: oai:arXiv.org:2605.13213v1
published_at: 2026-05-15T04:00:00
author: Hao Zhou, Tiru Wu, Yan Jiang, Wanqi Zhou, Junxing Hu, Ai Han
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13213v1 Announce Type: new Abstract: Multi-modal multi-agent systems (MM-MAS) have gained increasing attention for their capacity to enable complex reasoning and coordination across diverse modalities. As these systems continue to expand in scale and functionality, investigating their potential vulnerabilities has become increasingly important. However, existing studies on adversarial attacks in multi-agent systems primarily focus on isolated agents or unimodal settings, leaving the v...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13221
guid: oai:arXiv.org:2605.13221v1
published_at: 2026-05-15T04:00:00
author: Hanwen Zhang, Dusit Niyato, Wei Zhang, Xin Lou, Malcolm Yoke Hean Low
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
title:
An Agentic AI Framework with Large Language Models and Chain-of-Thought for UAV-Assisted Logistics Scheduling with Mobile Edge Computing

summary:
arXiv:2605.13221v1 Announce Type: new Abstract: In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC). This joint operation forms a hybrid scheduling problem, where physical logistics decisions are coupled with computational task scheduling. In this paper, UAVs collect finished products from manufacturing stations and transport them back to a central depot. Meanwhile, computational tasks generated by industrial sensor devices ...


title: Improving Code Translation with Syntax-Guided and Semantic-aware Preference Optimization
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13229
guid: oai:arXiv.org:2605.13229v1
published_at: 2026-05-15T04:00:00
author: Yuhan Wu, Huan Zhang, Wei Cheng, Chen Shen, Jingyue Yang, Wei Hu
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13229v1 Announce Type: new Abstract: LLMs have shown immense potential for code translation, yet they often struggle to ensure both syntactic correctness and semantic consistency. While preference-based learning offers a promising alignment strategy, it is hindered by unreliable semantic rewards derived from sparse test cases or restrictive reference translations. We argue that a robust semantic reward for code translation must be derived directly from the source code. In this paper, ...


title: It's not the Language Model, it's the Tool: Deterministic Mediation for Scientific Workflows
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13245
guid: oai:arXiv.org:2605.13245v1
published_at: 2026-05-15T04:00:00
author: Marios Adamidis, Danae Katrisioti, Yannis Tzitzikas, Emmanuel Stratakis
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13245v1 Announce Type: new Abstract: Language models can produce convincing scientific analyses, but repeated generations on the same data do not guarantee the same result. A researcher may regenerate an identical query and receive a different fit, a different peak position or a different analysis procedure, without an obvious way to decide which output to trust. We propose typed mediation, a pattern in which the model orchestrates deterministic tools rather than generating analytical...


title: Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13255
guid: oai:arXiv.org:2605.13255v1
published_at: 2026-05-15T04:00:00
author: Junlong Ke, Zichen Wen, Weijia Li, Conghui He, Linfeng Zhang
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13255v1 Announce Type: new Abstract: On-policy self-distillation trains a reasoning model on its own rollouts while a teacher, often the same model conditioned on privileged context, provides dense token-level supervision. Existing objectives typically weight the teacher's token-level signal uniformly across a chain-of-thought sequence, despite substantial variation in the entropy of the teacher's predictive distribution. We propose EGRSD (Entropy-Guided Reinforced Self-Distillation),...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13276
guid: oai:arXiv.org:2605.13276v2
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
title:
D-VLA: A High-Concurrency Distributed Asynchronous Reinforcement Learning Framework for Vision-Language-Action Models

summary:
arXiv:2605.13276v2 Announce Type: new Abstract: The rapid evolution of Embodied AI has enabled Vision-Language-Action (VLA) models to excel in multimodal perception and task execution. However, applying Reinforcement Learning (RL) to these massive models in large-scale distributed environments faces severe systemic bottlenecks, primarily due to the resource conflict between high-fidelity physical simulation and the intensive VRAM/bandwidth demands of deep learning. This conflict often leaves ove...

author:
Yucheng Guo, Yongjian Guo, Zhong Guan, Wen Huang, Haoran Sun, Haodong Yue, Xiaolong Xiang, Shuai Di, Zhen Sun, Luqiao Wang, Junwu Xiong, Yicheng Gong


title: Differentiable Learning of Lifted Action Schemas for Classical Planning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13282
guid: oai:arXiv.org:2605.13282v1
published_at: 2026-05-15T04:00:00
author: Jonas Reiter, Jakob Elias Gebler, Hector Geffner
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13282v1 Announce Type: new Abstract: Classical planners can effectively solve very large deterministic MDPs represented in STRIPS or PDDL where states are sets of atoms over objects and relations, and lifted action schemas add or delete these atoms. This compact representation yields strong search heuristics and provides an ideal setting for structural generalization, since lifted relations and action schemas give rise to infinitely many domain instances. A central challenge is to lea...


title: What properties of reasoning supervision are associated with improved downstream model quality?
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13290
guid: oai:arXiv.org:2605.13290v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13290v1 Announce Type: new Abstract: Validating training data for reasoning models typically requires expensive trial-and-error fine-tuning cycles. In this work, we investigate whether the utility of a reasoning dataset can be reliably predicted prior to training using intrinsic data metrics. We propose a suite of quantitative measures and evaluate their predictive power by fine-tuning 8B and 11B models on semantically distinct variants of a Polish reasoning dataset. Our analysis reve...

author:
Miko{\l}aj Langner, Dzmitry Pihulski, Jan Eliasz, Micha{\l} Rajkowski, Przemys{\l}aw Kazienko, Maciej Piasecki, Jan Koco\'n, Teddy Ferdinan


title: Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13296
guid: oai:arXiv.org:2605.13296v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13296v1 Announce Type: new Abstract: Multi-Agent Path Finding (MAPF) is a coordination problem that requires computing globally consistent, collision-free trajectories from individual start positions to assigned goal positions under combinatorial planning complexity. In dense environments, suboptimal initial plans induce compound conflicts that hinder feasible repair. For repair-based solvers like LNS2, initial plan quality critically affects downstream repair, yet this factor remains...

author:
Yuanzhe Wang, Tian Zhi, Zihang Wei, Hongguang Wang, Jiaming Guo, Yang Zhao, Zisheng Liu, Shiyu Quan, Xing Hu, Zidong Du, Yunji Chen


title: Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13301
guid: oai:arXiv.org:2605.13301v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13301v1 Announce Type: new Abstract: Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performance on International Mathematical Olympiad (IMO) and International Physics Olympiad (IPhO) problems. In this paper, we introduce a simple and unified recipe for converting a post-trained reasoning backbone into a rigorous olympiad-level solver. The recipe first uses a rev...

author:
Yafu Li, Runzhe Zhan, Haoran Zhang, Shunkai Zhang, Yizhuo Li, Zhilin Wang, Jiacheng Chen, Futing Wang, Xuyang Hu, Yuchen Fan, Bangjie Xu, Yucheng Su, Xinmiao Han, Chenxi Li, Haodi Lei, Yufeng Zhao, Zejin Lin, Qianjia Cheng, Tong Zhu, Xiaoye Qu, Ganqu Cui, Peng Ye, Yun Luo, Zhouchen Lin, Yu Qiao, Bowen Zhou, Ning Ding, Yu Cheng


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13311
guid: oai:arXiv.org:2605.13311v1
published_at: 2026-05-15T04:00:00
author: Joy Bose
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
title:
IdeaForge: A Knowledge Graph-Grounded Multi-Agent Framework for Cross-Methodology Innovation Analysis and Patent Claim Generation

summary:
arXiv:2605.13311v1 Announce Type: new Abstract: Current AI-assisted innovation systems typically apply a single ideation methodology (such as TRIZ or Design Thinking) using sequential prompt-based workflows that do not preserve intermediate reasoning structure. As a result, insights generated across methodologies remain fragmented, limiting traceability, synthesis, and systematic evaluation of novelty. We present IdeaForge, a knowledge graph-grounded multi-agent framework for innovation analysis...


title: VERA-MH: Validation of Ethical and Responsible AI in Mental Health
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13318
guid: oai:arXiv.org:2605.13318v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13318v1 Announce Type: new Abstract: Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Validations of Ethical and Responsible AI in Mental Health (VERA-MH), a novel clinically-validated evaluation for safety of chatbots in the context of mental health support. The first iteration of VERA-MH focuses on Suicidal Ideation (SI) risks, by assessing how well chatbots can responds to users that m...

author:
Luca Belli, Kate H. Bentley, Josh Gieringer, Emily Van Ark, Nilu Zhao, Pradip Thachile, Matt Hawrilenko, Millard Brown, Adam M. Chekroud


title: Diversity of Extensions in Abstract Argumentation
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13332
guid: oai:arXiv.org:2605.13332v1
published_at: 2026-05-15T04:00:00
author: Johannes K. Fichte, Markus Hecher, Yasir Mahmood, Zhengjun Wang
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13332v1 Announce Type: new Abstract: Argumentation is an important topic of AI for modelling and reasoning about arguments. In abstract argumentation, we consider directed graphs, so-called argumentation frameworks (AF), that express conflicts between arguments. The semantics is defined by the notion of extensions, which are sets of arguments that satisfy particular relationship conditions in the AF. Usually, standard reasoning in argumentation do not reveal how far apart extensions a...


title: Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13335
guid: oai:arXiv.org:2605.13335v1
published_at: 2026-05-15T04:00:00
author: Qinchuan Cheng, Zhantao Gong, Pengzhan Sun, Angela Yao, Xulei Yang, Shijie Li
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13335v1 Announce Type: new Abstract: Embodied agents in household environments must plan under partial observation: they need to remember objects, track state changes, and recover when actions fail. Existing benchmarks only partially test this ability. Egocentric video datasets capture realistic human activities but remain passive, while interactive simulators support execution but rely on synthetic scenes and hand-crafted dynamics, introducing a sim-to-real gap and often assuming ful...


title: Multi-Agent Systems in Emergency Departments: Validation Study on a ED Digital Twin
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13345
guid: oai:arXiv.org:2605.13345v1
published_at: 2026-05-15T04:00:00
author: Markus Wenzel, Tobias Strapatsas, Jessika Kress, Dorothea Sauer, Nele Gessler, Horst K. Hahn
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13345v1 Announce Type: new Abstract: Emergency departments (ED) face challenges in patient care and resource management. We propose to explore optimization strategies in a realistic and flexible model and develop a hybrid Discrete Event Simulation (DES) and Agent-Based Model (ABM) simulating highly configurable ED environments. We specifically focus on the validation of the modeling approach. We derive configurations for ED sizes, patient load, and staffing from real-world studies. We...


title: RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13391
guid: oai:arXiv.org:2605.13391v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13391v1 Announce Type: new Abstract: The rise of multi-modal large language models (MLLMs) is shifting remote sensing (RS) intelligence from "see" to "action", as OpenClaw-style frameworks enable agents to autonomously operate massive RS image-processing tools for complex tasks. Existing RS agents adopt a passive selection paradigm for tool invocation, relying on either full tool registration (Flat) or retrieval-augmented generation (RAG). However, in the massive and multi-source hete...

author:
Liangtian Liu, Zeyuan Wang, Ziyu Li, Kai Ouyang, Zichao Tang, Chengfu Liu, Haifeng Li, Hanwen Yu, Wentao Yang, Cheng Yang, Dongyang Hou


title: TRIAGE: Evaluating Prospective Metacognitive Control in LLMs under Resource Constraints
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13414
guid: oai:arXiv.org:2605.13414v1
published_at: 2026-05-15T04:00:00
author: Zabir Al Nazi, Shubhashis Roy Dipta
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13414v1 Announce Type: new Abstract: Deploying language models as autonomous agents requires more than per-task accuracy: when an agent faces a queue of problems under a finite token budget, it must decide which to attempt, in what order, and how much compute to commit to each, all before any execution feedback is available. This is the prospective form of metacognitive control studied for decades in human cognition, yet whether language models possess it remains untested. We introduc...


title: Cognifold: Always-On Proactive Memory via Cognitive Folding
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13438
guid: oai:arXiv.org:2605.13438v1
published_at: 2026-05-15T04:00:00
author: Suli Wang, Yiqun Duan, Yu Deng, Rundong Zhao, Dai Shi, Xinliang Zhou
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13438v1 Announce Type: new Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure. Toward genuinely autonomous agents, we introduce Cognifold, a brain-inspired "always-on" agent memory designed for the next generation of proactive assistants. CogniFold continuously folds fragmented event streams into self-emerging cognitive structures, bootstrapping progressively h...


title: Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13450
guid: oai:arXiv.org:2605.13450v1
published_at: 2026-05-15T04:00:00
author: Samuel Schapiro, Alexi Gladstone, Jonah Black, Heng Ji
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13450v1 Announce Type: new Abstract: Measuring the creativity of large language models (LLMs) is essential for designing methods that can improve creativity and for enhancing our scientific understanding of this ability. To accomplish this, it has become common in recent years to administer tests of human creativity to LLMs. Although these tests provide a convenient and fully automated way to score "creativity," their validity as measures of machine creativity has not been established...


title: MMSkills: Towards Multimodal Skills for General Visual Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13527
guid: oai:arXiv.org:2605.13527v2
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13527v2 Announce Type: new Abstract: Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable code, or learned routines. For visual agents, however, procedural knowledge is inherently multimodal: reuse depends not only on what operation to perform, but also on recognizing the relevant state, interpreting visual evidence of progress or failure, and deciding what to d...

author:
Kangning Zhang, Shuai Shao, Qingyao Li, Jianghao Lin, Lingyue Fu, Shijian Wang, Wenxiang Jiao, Yuan Lu, Weiwen Liu, Weinan Zhang, Yong Yu


title: AI-Generated Slides: Are They Good? Can Students Tell?
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13532
guid: oai:arXiv.org:2605.13532v1
published_at: 2026-05-15T04:00:00
author: Juho Leinonen, Lisa Zhang, Arto Hellas
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13532v1 Announce Type: new Abstract: As generative AI (GenAI) tools become easily accessible, there is promise in using such tools to support instructors. To that end, this paper examines using GenAI to help generate slides from instructor authored course notes, emphasizing instructor and student perceptions. We examine an end-to-end education tool (NotebookLM), two general-purpose LLMs (Claude, M365 Copilot), and two coding assistants (Cursor, Claude Code). We first analyze whether G...


title: Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13534
guid: oai:arXiv.org:2605.13534v1
published_at: 2026-05-15T04:00:00
author: Jiabei Liu, Wenyu Mao, Junfei Tan, Chunxu Shen, Lingling Yi, Jiancan Wu, Xiang Wang
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13534v1 Announce Type: new Abstract: Deep search agents have proven effective in enhancing LLMs by retrieving external knowledge during multi-step reasoning. However, existing methods often generate a single query for retrieval at each reasoning step, limiting information coverage and introducing high noise. This may result in low signal-to-noise ratios (SNR) during search, degrading reasoning accuracy and leading to unnecessary reasoning steps. In this paper, we introduce MultiSearch...


title: RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13542
guid: oai:arXiv.org:2605.13542v1
published_at: 2026-05-15T04:00:00
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13542v1 Announce Type: new Abstract: Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, underscoring a clear need for reliable AI decision support. Existing ICU benchmarks typically treat historical clinician actions as ground truth. However, these actions are made under incomplete information and limited temporal context of the underlying patient state, and may the...

author:
Chengzhi Shen (Cherise), Weixiang Shen (Cherise), Tobias Susetzky (Cherise), Chen (Cherise), Chen (Cherise), Jun Li, Yuyuan Liu, Xuepeng Zhang, Zhenyu Gong, Daniel Rueckert, Jiazhen Pan


title: Learning Local Constraints for Reinforcement-Learned Content Generators
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13570
guid: oai:arXiv.org:2605.13570v1
published_at: 2026-05-15T04:00:00
author: Debosmita Bhaumik, Julian Togelius, Georgios N. Yannakakis, Ahmed Khalifa
first_crawl_time: 11:50
last_crawl_time: 11:50
created_at: 2026-05-16 11:53:09
updated_at: 2026-05-16 11:53:09
summary:
arXiv:2605.13570v1 Announce Type: new Abstract: Constraint-based game content generators that learn local constraints from existing content, such as Wave Function Collapse (WFC), can generate visually satisfying game levels but face challenges in guaranteeing global properties, such as playability. On the other hand, reinforcement-learning trained generators can guarantee global properties -- because such properties can easily be included in reward functions -- but the results can be visually di...


title: Someone Shared a Real Monet Painting as AI and Asked for Critiques
feed_id: hacker-news
guid: https://news.ycombinator.com/item?id=48158110
published_at: 2026-05-16T08:30:51
author: ZeljkoS
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
url:
https://petapixel.com/2026/05/14/someone-shared-a-real-monet-painting-as-ai-and-asked-for-critiques/

summary:
Article URL: https://petapixel.com/2026/05/14/someone-shared-a-real-monet-painting-as-ai-and-asked-for-critiques/ Comments URL: https://news.ycombinator.com/item?id=48158110 Points: 14 # Comments: 7


title: Frontier AI has broken the open CTF format
feed_id: hacker-news
url: https://kabir.au/blog/the-ctf-scene-is-dead
guid: https://news.ycombinator.com/item?id=48157559
published_at: 2026-05-16T07:01:55
author: frays
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://kabir.au/blog/the-ctf-scene-is-dead Comments URL: https://news.ycombinator.com/item?id=48157559 Points: 112 # Comments: 89


title: SQL patterns I use to catch transaction fraud
feed_id: hacker-news
url: https://analytics.fixelsmith.com/posts/sql-fraud-patterns/
guid: https://news.ycombinator.com/item?id=48155212
published_at: 2026-05-15T23:22:30
author: redbell
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://analytics.fixelsmith.com/posts/sql-fraud-patterns/ Comments URL: https://news.ycombinator.com/item?id=48155212 Points: 210 # Comments: 66


title: Orthrus-Qwen3: up to 7.8×tokens/forward on Qwen3, identical output distribution
feed_id: hacker-news
url: https://github.com/chiennv2000/orthrus
guid: https://news.ycombinator.com/item?id=48154865
published_at: 2026-05-15T22:38:58
author: FranckDernoncou
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://github.com/chiennv2000/orthrus Comments URL: https://news.ycombinator.com/item?id=48154865 Points: 86 # Comments: 14


title: Bun Rust rewrite: "codebase fails basic miri checks, allows for UB in safe rust"
feed_id: hacker-news
url: https://github.com/oven-sh/bun/issues/30719
guid: https://news.ycombinator.com/item?id=48150900
published_at: 2026-05-15T16:51:25
author: ndiddy
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://github.com/oven-sh/bun/issues/30719 Comments URL: https://news.ycombinator.com/item?id=48150900 Points: 406 # Comments: 285


title: A 0-click exploit chain for the Pixel 10
feed_id: hacker-news
url: https://projectzero.google/2026/05/pixel-10-exploit.html
guid: https://news.ycombinator.com/item?id=48148460
published_at: 2026-05-15T13:39:54
author: happyhardcore
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://projectzero.google/2026/05/pixel-10-exploit.html Comments URL: https://news.ycombinator.com/item?id=48148460 Points: 376 # Comments: 205


title: SigNoz (YC W21, open source Datadog) Is hiring for growth and engineering roles
feed_id: hacker-news
url: https://signoz.io/careers
guid: https://news.ycombinator.com/item?id=48147533
published_at: 2026-05-15T12:00:31
author: pranay01
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
Article URL: https://signoz.io/careers Comments URL: https://news.ycombinator.com/item?id=48147533 Points: 0 # Comments: 0


title: How sales teams use Codex
feed_id: openai-news
url: https://openai.com/academy/codex-for-work/how-sales-teams-use-codex
guid: https://openai.com/academy/codex-for-work/how-sales-teams-use-codex
published_at: 2026-05-15T00:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
See how sales teams can use Codex to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.


title: How business operations teams use Codex
feed_id: openai-news
url: https://openai.com/academy/codex-for-work/how-business-operations-teams-use-codex
guid: https://openai.com/academy/codex-for-work/how-business-operations-teams-use-codex
published_at: 2026-05-15T00:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
See how business operations teams can use Codex to create initiative briefs, strategy updates, leadership decision packets, progress updates, and more from real work inputs.


title: How data science teams use Codex
feed_id: openai-news
url: https://openai.com/academy/codex-for-work/how-data-science-teams-use-codex
guid: https://openai.com/academy/codex-for-work/how-data-science-teams-use-codex
published_at: 2026-05-15T00:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
See how data science teams can use Codex to build root-cause briefs, impact readouts, KPI memos, scoped analyses, and dashboard specs from real work inputs.


title: GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13848
guid: oai:arXiv.org:2605.13848v1
published_at: 2026-05-16T04:00:00
author: Yeahia Sarker, Md Rahmat Ullah, Musa Molla, Shafiq Joty
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer from hallucinated routing, infinite loops, and non-reproducible execution. We introduce GraphBit, an engine-orchestrated framework that defines workflows explicitly and deterministically as a directed acyclic graph (DAG). Unlike prompted orchestration, agents in GraphBit operate as typed functions, while a Rust-based engi...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13849
guid: oai:arXiv.org:2605.13849v1
published_at: 2026-05-16T04:00:00
author: Francisco Aguilera Moreno
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Mixed Integer Goal Programming for Personalized Meal Optimization with User-Defined Serving Granularity

summary:
arXiv:2605.13849v1 Announce Type: new Abstract: Determining what to eat to satisfy nutritional requirements is one of the oldest optimization problems in operations research, yet existing formulations have two persistent limitations: continuous variables produce impractical fractional servings (1.7 eggs, 0.37 bananas), and hard nutrient constraints cause infeasibility when targets conflict. A systematic review of 56 diet optimization papers found that none combine integer programming with goal p...


title: A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13850
guid: oai:arXiv.org:2605.13850v1
published_at: 2026-05-16T04:00:00
author: Jia Huang, Joey Tianyi Zhou
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.13850v1 Announce Type: new Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys focus on cognitive function -- what the agent does. Neither axis alone disambiguates architecturally distinct systems: the same Orchestrator-Workers topology can implement Plan-and-Execute, Hierarchical Delegation, or Adversar...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13851
guid: oai:arXiv.org:2605.13851v1
published_at: 2026-05-16T04:00:00
author: Hiroki Fukui
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems

summary:
arXiv:2605.13851v1 Announce Type: new Abstract: Multi-agent orchestration -- in which a hidden coordinator manages specialized worker agents -- is becoming the default architecture for enterprise AI deployment, yet the safety implications of orchestrator invisibility have never been empirically tested. We conducted a preregistered 3x2 experiment (365 runs, 5 agents per run) crossing three organizational structures (visible leader, invisible orchestrator, flat) with two alignment conditions (base...


title: PREPING: Building Agent Memory without Tasks
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.13880
guid: oai:arXiv.org:2605.13880v1
published_at: 2026-05-16T04:00:00
author: Yumin Choi, Sangwoo Park, Minki Kang, Jinheon Baek, Sung Ju Hwang
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.13880v1 Announce Type: new Abstract: Agent memory is typically constructed either offline from curated demonstrations or online from post-deployment interactions. However, regardless of how it is built, an agent faces a cold-start gap when first introduced to a new environment without any task-specific experience available. In this paper, we study pre-task memory construction: whether an agent can build procedural memory before observing any target-environment tasks, using only self-g...


title: PolitNuggets: Benchmarking Agentic Discovery of Long-Tail Political Facts
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14002
guid: oai:arXiv.org:2605.14002v1
published_at: 2026-05-16T04:00:00
author: Yifei Zhu
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14002v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long context question answering into open-ended exploration. Yet real world use requires models to discover and synthesize "long-tail" facts from dispersed sources, a capability that remains under-evaluated. We introduce PolitNuggets, a multilingual benchmark for agentic information synthesis via constructing political biographies for 40...


title: Conditional Attribute Estimation with Autoregressive Sequence Models
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14004
guid: oai:arXiv.org:2605.14004v1
published_at: 2026-05-16T04:00:00
author: Erica Stutz, Giacomo Marino, Daniella Meeker, Qiao Liu, Andrew J. Loza
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14004v1 Announce Type: new Abstract: Generative models are often trained with a next-token prediction objective, yet many downstream applications require the ability to estimate or control sequence-level properties. Next-token prediction can lead to overfitting of local patterns during training, underfitting of global structure, and requires significant downstream modifications or expensive sampling to guide or predict the global attributes of generated samples at inference time. Here...


title: Sheaf-Theoretic Transport and Obstruction for Detecting Scientific Theory Shift in AI Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14033
guid: oai:arXiv.org:2605.14033v1
published_at: 2026-05-16T04:00:00
author: David N. Olivieri, Roque J. Hern\'andez
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14033v1 Announce Type: new Abstract: Scientific theory shift in AI agents requires more than fitting equations to data. An artificial scientific agent must detect whether an existing representational framework remains transportable into a new regime, or whether its language has become locally-to-globally obstructed and must be extended. This paper develops a finite sheaf-theoretic framework for detecting theory-shift candidates through transport and obstruction. Contexts are organized...


title: From Descriptive to Prescriptive: Uncover the Social Value Alignment of LLM-based Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14034
guid: oai:arXiv.org:2605.14034v1
published_at: 2026-05-16T04:00:00
author: Jinxian Qu, Qingqing Gu, Teng Chen, Luo Ji
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14034v1 Announce Type: new Abstract: Wide applications of LLM-based agents require strong alignment with human social values. However, current works still exhibit deficiencies in self-cognition and dilemma decision, as well as self-emotions. To remedy this, we propose a novel value-based framework that employs GraphRAG to convert principles into value-based instructions and steer the agent to behave as expected by retrieving the suitable instruction upon a specific conversation contex...


title: Enhanced and Efficient Reasoning in Large Learning Models
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14036
guid: oai:arXiv.org:2605.14036v1
published_at: 2026-05-16T04:00:00
author: Leslie G. Valiant
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14036v1 Announce Type: new Abstract: In current Large Language Models we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, there is no comparably principled basis to justify trust in the content of the text produced. It appears to be conventional wisdom that addressing this issue by adding more principled reasoning is not computationally affordable. Here we propose a principled method of reasoning that is efficient enough t...


title: Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14038
guid: oai:arXiv.org:2605.14038v1
published_at: 2026-05-16T04:00:00
author: Yize Cheng, Chenrui Fan, Mahdi JafariRaviz, Keivan Rezaei, Soheil Feiz
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14038v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior work studying adaptive tool use has largely treated tool necessity as a model-agnostic property, annotated by human or LLM judge, and mostly cover cases where the answer is obvious (e.g., fetching the weather vs. paraphrasing text). However, tool necessity in the wild is more nuanced due to the diverge...


title: Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14048
guid: oai:arXiv.org:2605.14048v1
published_at: 2026-05-16T04:00:00
author: Leo Milecki, Qingyu Hu, Bahram Jafrasteh, Mert R. Sabuncu, Qingyu Zhao
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14048v1 Announce Type: new Abstract: Masked autoencoders (MAEs) have recently shown promise for self-supervised representation learning of resting-state brain functional connectivity (FC). However, a fundamental question remains unresolved: how should FC matrices be tokenized to align with the intrinsic modular organization of large-scale brain networks? Existing approaches typically adopt region-centric or graph-based schemes that treat FC as structurally homogeneous elements and ove...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14049
guid: oai:arXiv.org:2605.14049v1
published_at: 2026-05-16T04:00:00
author: Olivia Peiyu Wang, Leilani H. Gilpin
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning

summary:
arXiv:2605.14049v1 Announce Type: new Abstract: The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit from AI that can reason over contracts, draft documents, and analyze sources at scale, yet the high-stakes nature of legal work demands a level of rigor that current AI systems do not provide. The central problem is not simply that LLMs hallucinate facts and references; it is that they systematically...


title: SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14051
guid: oai:arXiv.org:2605.14051v1
published_at: 2026-05-16T04:00:00
author: Yusuke Ozaki, Dhaval Patel
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14051v1 Announce Type: new Abstract: Industrial LLM agent systems often separate planning from execution, yet LLM planners frequently produce structurally invalid or unnecessarily long workflows, leading to brittle failures and avoidable tool and API cost. We propose \texttt{SPIN}, a planning wrapper that combines validated Directed Acyclic Graph (DAG) planning with prefix based execution control. \texttt{SPIN} enforces a strict DAG contract through \texttt{\_validate\_plan\_text} and...


title: Bad Seeing or Bad Thinking? Rewarding Perception for Vision-Language Reasoning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14054
guid: oai:arXiv.org:2605.14054v1
published_at: 2026-05-16T04:00:00
author: Haozhe Wang, Qixin Xu, Changpeng Wang, Taofeng Xue, Chong Peng, Wenhu Chen, Fangzhen Lin
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14054v1 Announce Type: new Abstract: Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal via architectural designs or agentic workflows. However, these approaches are often limited by static textual reasoning or complicated by the significant compute and engineering burden of external agentic complexity. Worse, this heavy investment does not yield proportional gains, often witnessing a "...


title: MathAtlas: A Benchmark for Autoformalization in the Wild
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14061
guid: oai:arXiv.org:2605.14061v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14061v1 Announce Type: new Abstract: Current autoformalization benchmarks are largely focused on olympiad or undergraduate mathematics, while graduate and research-level mathematics remains underexplored. In this paper, we introduce MathAtlas, the first large-scale autoformalization benchmark of in the wild graduate-level mathematics, containing ~52k theorems, definitions, exercises, examples, and proofs extracted from 103 graduate mathematics textbooks. MathAtlas is enriched with a m...

author:
Nilay Patel, Noah Arias, Davit Babayan, Victoria Cochran, Timothy Libman, Hafsah Mahmood, Liam McCarty, Soli Munoz, Laurel Willey, Jeffrey Flanigan


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14062
guid: oai:arXiv.org:2605.14062v1
published_at: 2026-05-16T04:00:00
author: Anjir Ahmed Chowdhury, Syed Zawad, Feng Yan
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Know When To Fold 'Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection

summary:
arXiv:2605.14062v1 Announce Type: new Abstract: While synthetic data generation with large language models (LLMs) is widely used in post-training pipelines, existing approaches typically generate full outputs before applying quality filters, leading to substantial token waste on samples that are ultimately discarded. To address this, we propose Multi-Stage In-Flight Rejection (MSIFR), a lightweight, training-free framework that detects and terminates low-quality generation trajectories at interm...


title: SkillFlow: Flow-Driven Recursive Skill Evolution for Agentic Orchestration
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14089
guid: oai:arXiv.org:2605.14089v1
published_at: 2026-05-16T04:00:00
author: Mingda Zhang, Tiesunlong Shen, Haoran Luo, Wenjin Liu, Zikai Xiao, Erik Cambria, Xiaoying Tang
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14089v1 Announce Type: new Abstract: In recent years, a variety of powerful LLM-based agentic systems have been applied to automate complex tasks through task orchestration. However, existing orchestration methods still face key challenges, including strategy collapse under reward maximization, high gradient variance with opaque credit assignment, and unguided skill evolution whose decisions are typically made by directly prompting an LLM to judge rather than derived from principled t...


title: ChromaFlow: A Negative Ablation Study of Orchestration Overhead in Tool-Augmented Agent Evaluation
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14102
guid: oai:arXiv.org:2605.14102v1
published_at: 2026-05-16T04:00:00
author: Tarun Mittal
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14102v1 Announce Type: new Abstract: Autonomous language-model agents increasingly combine planning, tool use, document processing, browsing, code execution, and verification loops. These capabilities make agent systems more useful, but they also introduce operational failure modes that are not visible from final accuracy alone. This report presents ChromaFlow, a tool-augmented autonomous reasoning framework built around planner-directed execution, specialized tool use, and telemetry-...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14111
guid: oai:arXiv.org:2605.14111v1
published_at: 2026-05-16T04:00:00
author: Yaniv Eliyahu Amiri, Noah Chicoine, Jacqueline Griffin, Stacy Marsella
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition

summary:
arXiv:2605.14111v1 Announce Type: new Abstract: Hospital pharmacists make high-stakes decisions to mitigate drug shortages under uncertainty, time pressure, and patient risk. Interviews revealed that pharmacists focus attention on a small subset of drugs, limiting cognitive effort to the most urgent cases. Motivated by these findings, we formalize a bounded-rational, attention-guided decision framework that dynamically decomposes drugs into a subset for high-cost reasoning and a complementary su...


title: ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14133
guid: oai:arXiv.org:2605.14133v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14133v1 Announce Type: new Abstract: Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static prompt evaluation misses failures that only appear when agents operate over persistent state. Existing interactive benchmarks have advanced agent evaluation significantly, but most initialize tasks from clean state and do not systematically test how agents handle pre-existi...

author:
Yuxiang Lai, Peng Xia, Haonian Ji, Kaiwen Xiong, Kaide Zeng, Jiaqi Liu, Fang Wu, Jike Zhong, Zeyu Zheng, Cihang Xie, Huaxiu Yao


title: Distribution-Aware Algorithm Design with LLM Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14141
guid: oai:arXiv.org:2605.14141v1
published_at: 2026-05-16T04:00:00
author: Saharsh Koganti, Priyadarsi Mishra, Pierfrancesco Beneventano, Tomer Galanti
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14141v1 Announce Type: new Abstract: We study learning when the learned object is executable solver code rather than a predictor. In this setting, correctness is not enough: two solvers may both return valid solutions on the deployment distribution while differing substantially in runtime. Given samples from an unknown task distribution, the learner returns code evaluated on fresh instances by both solution quality and execution time. Our central abstraction is a \emph{solver hint}: r...


title: Agentic Systems as Boosting Weak Reasoning Models
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14163
guid: oai:arXiv.org:2605.14163v1
published_at: 2026-05-16T04:00:00
author: Varun Sunkaraneni, Pierfrancesco Beneventano, Riccardo Neumarker, Tomaso Poggio, Tomer Galanti
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14163v1 Announce Type: new Abstract: Can a committee of weak reasoning-model calls reach the performance of much stronger models? We study verifier-backed committee search as inference-time boosting for reasoning language models. The mechanism is not simply that ``more agents help'': samples expose latent correct solutions, while critics and comparators must recover them without access to the hidden verifier. We formalize this view by separating proposal coverage, local identifiabilit...


title: Unsteady Metrics and Benchmarking Cultures of AI Model Builders
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14164
guid: oai:arXiv.org:2605.14164v1
published_at: 2026-05-16T04:00:00
author: Stefan Baack, Christo Buschek, Maty Bohacek
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14164v1 Announce Type: new Abstract: The primary way to establish and compare competencies in foundation and generative AI models has shifted from peer-reviewed literature to press releases and company blog posts, where model builders highlight results on selected benchmarks. These artifacts now largely define the state of the art for researchers and the public. Despite their prominence, which benchmarks model builders choose to highlight, and what they communicate through this select...


title: The Evaluation Trap: Benchmark Design as Theoretical Commitment
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14167
guid: oai:arXiv.org:2605.14167v1
published_at: 2026-05-16T04:00:00
author: Theodore J Kalaitzidis
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14167v1 Announce Type: new Abstract: Every AI benchmark operationalizes theoretical assumptions about the capability it claims to assess. When assumptions function as unexamined commitments, benchmarks stabilize the dominant paradigm by narrowing what counts as progress. Over time, narrow evaluation reorganizes capability concepts: architectures and definitions are selected for benchmark legibility until evaluation ceases to track an independent object and instead produces a version o...


title: Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14175
guid: oai:arXiv.org:2605.14175v1
published_at: 2026-05-16T04:00:00
author: Qisong He, Yi Dong, Xiaowei Huang
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14175v1 Announce Type: new Abstract: In long conversations, an LLM can produce a next utterance that sounds plausible but rests on premises the conversation has already abandoned. Context-manipulation attacks against deployed agents now actively exploit this gap. We close it with a runtime verifier that maintains an explicit dependency graph: an LLM classifies each turn into one of 8 update operations drawn from four formalisms (dynamic epistemic logic, abductive reasoning, awareness ...


title: SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14205
guid: oai:arXiv.org:2605.14205v1
published_at: 2026-05-16T04:00:00
author: Zahra Zanjani Foumani, Alberto Castelo, Shuang Xie, Ted Chaiwachirasak, Han Li, Lingyun Wang
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14205v1 Announce Type: new Abstract: LLM-based web agents can navigate live storefronts, yet they often collapse to a single "average buyer" policy, failing to capture the heterogeneous and distributional nature of real buyer populations. Existing personalization methods rely on hand-crafted prompt-based personas that are brittle, difficult to scale, context-inefficient, and unable to faithfully represent population-level behavior. We introduce SimPersona, a novel framework that learn...


title: ASH: Agents that Self-Hone via Embodied Learning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14211
guid: oai:arXiv.org:2605.14211v1
published_at: 2026-05-16T04:00:00
author: Benjamin Schneider, Xavier Schneider, Victor Zhong, Sun Sun
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14211v1 Announce Type: new Abstract: Long-horizon embodied tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrations, neither of which scales. We introduce ASH, an agentic system that learns an embodied policy from unlabeled, noisy internet video, without reward shaping or expert annotation. ASH follows a self-improvement loop; when it gets stuck, ASH learns an Inverse Dynamics Model (IDM) from its own trajectories,...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14212
guid: oai:arXiv.org:2605.14212v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
MetaAgent-X : Breaking the Ceiling of Automatic Multi-Agent Systems via End-to-End Reinforcement Learning

summary:
arXiv:2605.14212v1 Announce Type: new Abstract: Automatic multi-agent systems aim to instantiate agent workflows without relying on manually designed or fixed orchestration. However, existing automatic MAS approaches remain only partially adaptive: they either perform training-free test-time search or optimize the meta-level designer while keeping downstream execution agents frozen, which creating a frozen-executor ceiling and leaving the end-to-end training of self-designing and self-executing ...

author:
Yaolun Zhang, Yujie Zhao, Nan Wang, Yiran Wu, Jiayu Chang, Yizhao Chen, Qingyun Wu, Jishen Zhao, Huazheng Wang


title: GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14215
guid: oai:arXiv.org:2605.14215v1
published_at: 2026-05-16T04:00:00
author: Noah Flynn
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14215v1 Announce Type: new Abstract: Genetic circuit design remains a laborious, expert-driven process despite decades of progress in synthetic biology. We study this problem through code generation: models produce Python code in pysbol3 to construct genetic circuits in the Synthetic Biology Open Language (SBOL), a formal representation that supports automated verification. We introduce GenCircuit-RL, a reinforcement learning framework built around hierarchical verification rewards th...


title: Fusion-fission forecasts when AI will shift to undesirable behavior
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14218
guid: oai:arXiv.org:2605.14218v1
published_at: 2026-05-16T04:00:00
author: Neil F. Johnson, Frank Yingjie Huo
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14218v1 Announce Type: new Abstract: The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes -- and no one can yet predict when. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards. Here we show that a vector generalization of fusion-fis...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14237
guid: oai:arXiv.org:2605.14237v1
published_at: 2026-05-16T04:00:00
author: Xiaohua Wang, Kai Yu, XuXiao Liang, Liang Wang, Chao Han
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay

summary:
arXiv:2605.14237v1 Announce Type: new Abstract: Deploying AI agents for repetitive periodic tasks exposes a critical tension: Large Language Models (LLMs) offer unmatched flexibility in tool orchestration, yet their inherent stochasticity causes unpredictable failures, and repeated invocations incur prohibitive token costs. We present the LOOP SKILL ENGINE, a system that achieves a combined 99% success rate and 99% token reduction for periodic agent tasks through a one-shot recording, determinis...


title: Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14259
guid: oai:arXiv.org:2605.14259v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14259v1 Announce Type: new Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning. Existing paradigms (e.g., GraphRAG, NL2SQL) lack the semantic grounding and auditable execution required for these complex environments. We introduce HEAR, an enterprise agentic reasoner built on a Stratified Hypergraph Ontology. Its base Graph Layer virtualizes provenance-aware data interfaces, while t...

author:
Ling Wang, Songnan Liu, Jianan Wang, Cheng Cheng, Xin Liu, Yihan Zhu, Enyu Li, Yu Xiao, Jiangyong Xie, Duogong Yan, Jiangyi Chen


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14261
guid: oai:arXiv.org:2605.14261v1
published_at: 2026-05-16T04:00:00
author: Juho Kim, Tuomas Sandholm
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Heuristic Pathologies and Further Variance Reduction via Uncertainty Propagation in the AIVAT Family of Techniques

summary:
arXiv:2605.14261v1 Announce Type: new Abstract: How should an agent's performance in a multiagent environment be evaluated when there is a limited sample size or a high cost of running a trial? The AIVAT family of variance reduction techniques was proposed to address this challenge by introducing unbiased low-variance estimators of agents' expected payoffs. An important component of AIVAT is a heuristic value function that discriminates between potentially low- and high-value counterfactual hist...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14266
guid: oai:arXiv.org:2605.14266v1
published_at: 2026-05-16T04:00:00
author: Vidya K Sudarshan, Anushka Sisodia, Reshma A Ramachandra, Sia Batra, Josephine Chong Leng Leng
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence

summary:
arXiv:2605.14266v1 Announce Type: new Abstract: Integration of artificial intelligent (AI) agents in higher education is transforming teaching, learning and administrative processes. Although existing AI agents effectively support individual tasks, their implementation remains fragmented and inefficient for handling the complexity of educational institutions. This highlights a significant research gap: the lack of integrated eco-system-level agentic multi-agent AI platform capable of coordinated...


title: Parallelizing Counterfactual Regret Minimization
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14277
guid: oai:arXiv.org:2605.14277v1
published_at: 2026-05-16T04:00:00
author: Juho Kim, Tuomas Sandholm
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14277v1 Announce Type: new Abstract: Parallelization has played an instrumental role in the field of artificial intelligence (AI), drastically reducing the time taken to train and evaluate large AI models. In contrast to its impact in the broader field of AI, applying parallelization to computational game solving is relatively unexplored, despite its great potential. In this paper, we parallelize the family of counterfactual regret minimization (CFR) algorithms, which were central to ...


title: Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14294
guid: oai:arXiv.org:2605.14294v1
published_at: 2026-05-16T04:00:00
author: Hengjie Liu, Zhenya Zhang, Jianjun Zhao
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14294v1 Announce Type: new Abstract: Formal verification of transformers has become increasingly important due to their widespread deployment in safety-critical applications. Compared to classic neural networks, the inferences of transformers involve highly complex computations, such as dot products in self-attention layers, rendering their verification extremely difficult. Existing approaches explored over-approximation methods by constructing convex constraints to bound the output r...


title: Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14318
guid: oai:arXiv.org:2605.14318v1
published_at: 2026-05-16T04:00:00
author: Emilio Mastriani, Alessandro Costa, Federico Incardona, Kevin Munari, Sebastiano Spinello
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14318v1 Announce Type: new Abstract: Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-relevant information and reduce model interpretability. This work proposes a semantic feature segmentation framework that decomposes the monitored feature space into a canonical component,expected to retain the dominant predictive information, and a residual component containing structurally peripheral si...


title: Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14322
guid: oai:arXiv.org:2605.14322v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14322v1 Announce Type: new Abstract: Language agents are increasingly deployed in complex professional workflows, with tutoring emerging as a particularly high-stakes capability that remains largely unmeasured in existing benchmarks. Effective tutor agents require more than producing correct answers or executing accurate tool calls: a robust tutor must diagnose learner state, adapt support over time, make pedagogically justified decisions grounded in educational evidence, and execute ...

author:
Zixin Chen, Peng Liu, Rui Sheng, Haobo Li, Jianhong Tu, Xiaodong Deng, Kashun Shum, Dayiheng Liu, Huamin Qu


title: CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14344
guid: oai:arXiv.org:2605.14344v1
published_at: 2026-05-16T04:00:00
author: Yuyang Wu, Stefano Falletta, Delia McGrath, Sherry Yang
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14344v1 Announce Type: new Abstract: Generative modeling has emerged as a promising approach for crystal structure discovery. However, existing LLM-based generative models struggle with low-level atomic precision, while diffusion-based methods fall short in integrating high-level scientific knowledge. As a result, generated structures are often invalid, unstable, or do not possess desirable properties. To address this gap, we propose CrystalReasoner (\method), an end-to-end LLM framew...


title: Herculean: An Agentic Benchmark for Financial Intelligence
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14355
guid: oai:arXiv.org:2605.14355v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14355v1 Announce Type: new Abstract: As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional work. Existing financial benchmarks offer only a partial view of this ability, as they primarily evaluate static competencies such as question answering, retrieval, summarization, and classification. We introduce Herculean, the first skilled benchmark for agentic financi...

author:
Xueqing Peng, Zhuohan Xie, Yupeng Cao, Haohang Li, Lingfei Qian, Yan Wang, Vincent Jim Zhang, Huan He, Xuguang Ai, Linhai Ma, Ruoyu Xiang, Yueru He, Yi Han, Shuyao Wang, Yuqing Guo, Mingyang Jiang, Yilun Zhao, Youzhong Dong, Xiaoyu Wang, Yankai Chen, Ye Yuan, Qiyuan Zhang, Fuyuan Lyu, Haolun Wu, Yonghan Yang, Zichen Zhao, Yuyang Dai, Fan Zhang, Rania Elbadry, Ayesha Gull, Muhammad Usman Safder, Nuo Chen, Fengbin Zhu, Tianshi Cai, Zimu Wang, Polydoros Giannouris, Yuechen Jiang, Zhiwei Liu, Mohsinul Kabir, Yuyan Wang, Yixiang Zheng, Yangyang Yu, Weijin Liu, Wenbo Cao, Anke Xu, Peng Lu, Jerry Huang, Fengran Mo, Mingquan Lin, Prayag Tiwari, Yijia Zhao, Victor Gutierrez Basulto, Xiao-Yang Liu, Kaleb E Smith, Jiahuan Pei, Arman Cohan, Jimin Huang, Yuehua Tang, Alejandro Lopez-Lira, Xi Chen, Xue Liu, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou


title: Uncovering the Representation Geometry of Minimal Cores in Overcomplete Reasoning Traces
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14358
guid: oai:arXiv.org:2605.14358v1
published_at: 2026-05-16T04:00:00
author: Sanjoy Chowdhury, Dinesh Manocha
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14358v1 Announce Type: new Abstract: Language models often generate long chain-of-thought traces, but it remains unclear how much of this reasoning is necessary for preserving the final prediction. We study this through the lens of overcomplete reasoning traces: generated traces that contain more intermediate steps than are needed to support the model's answer. We define the minimal core as the smallest subset of steps that preserves either the final answer or predictive distribution,...


title: Nexus : An Agentic Framework for Time Series Forecasting
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14389
guid: oai:arXiv.org:2605.14389v1
published_at: 2026-05-16T04:00:00
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14389v1 Announce Type: new Abstract: Time series forecasting is not just numerical extrapolation, but often requires reasoning with unstructured contextual data such as news or events. While specialized Time Series Foundation Models (TSFMs) excel at forecasting based on numerical patterns, they remain unaware to real-world textual signals. Conversely, while LLMs are emerging as zero-shot forecasters, their performance remains uneven across domains and contextual grounding. To bridge t...

author:
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar, Nanyun Peng, Vishy Tirumalashetty, Chun-Liang Li, Rui Zhang, Jinsung Yoon, Tomas Pfister


title: Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14392
guid: oai:arXiv.org:2605.14392v1
published_at: 2026-05-16T04:00:00
author: Yucheng Shi, Zhenwen Liang, Kishan Panaganti, Dian Yu, Wenhao Yu, Haitao Mi
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14392v1 Announce Type: new Abstract: We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the environments that train it. In zero-data reasoning RL, this reframes self-improvement from a data-generation loop into an environment-construction loop, where each artifact is a reusable executable object that samples instances, computes references, and scores responses. Whether this vision sustains imp...


title: Coding Agent Is Good As World Simulator
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14398
guid: oai:arXiv.org:2605.14398v1
published_at: 2026-05-16T04:00:00
author: Hongyu Wang, Jingquan Wang, Bocheng Zou, Radu Serban, Dan Negrut
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14398v1 Announce Type: new Abstract: World models have emerged as a powerful paradigm for building interactive simulation environments, with recent video-based approaches demonstrating impressive progress in generating visually plausible dynamics. However, because these models typically infer dynamics from video and represent them in latent states, they do not explicitly enforce physical constraints. As a result, the generated video rollouts are not physically plausible, exhibiting un...


title: Metis AI: The Overlooked Middle Zone Between AI-Native and World-Movers
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14407
guid: oai:arXiv.org:2605.14407v1
published_at: 2026-05-16T04:00:00
author: Xiang Li
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14407v1 Announce Type: new Abstract: The dominant discourse on AI limitations frames the boundary of AI capability as a divide between digital tasks (where AI excels) and physical tasks (where embodiment is required). We argue this framing misses the most consequential boundary: the one within digital tasks. We identify a class of tasks we call Metis AI, named for the Greek concept of metis (practical, contextual knowledge), that are performed entirely on computers yet resist reliable...


feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14416
guid: oai:arXiv.org:2605.14416v1
published_at: 2026-05-16T04:00:00
author: Wen Wang, Xiangchen Wu, Liang Wang, Hao Hu, Xianping Tao
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
title:
A Unified Knowledge Embedded Reinforcement Learning-based Framework for Generalized Capacitated Vehicle Routing Problems

summary:
arXiv:2605.14416v1 Announce Type: new Abstract: The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem with broad applications in logistics and transportation. Real-world CVRPs often involve diverse objectives and complex constraints, such as time windows or backhaul requirements, motivating the development of a unified solution framework. Recent reinforcement learning (RL) approaches have shown promise in combinatorial optimization, yet they rely on end-to-end learning ...


title: DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14420
guid: oai:arXiv.org:2605.14420v1
published_at: 2026-05-16T04:00:00
author: Pengyun Zhu, Yuqi Ren, Zhen Wang, Lei Yang, Deyi Xiong
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14420v1 Announce Type: new Abstract: Current Large Language Models (LLMs) typically rely on coarse-grained national labels for pluralistic value alignment. However, such macro-level supervision often obscures intra-country value heterogeneity, yielding a loose alignment. We argue that resolving this limitation requires shifting from national labels to multi-dimensional demographic constraints, which can identify groups with predictable, high-consensus value preference. To this end, we...


title: BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14438
guid: oai:arXiv.org:2605.14438v1
published_at: 2026-05-16T04:00:00
author: Juntong Wu, Jialiang Cheng, Qishen Yin, Yue Dai, Yuliang Yan, Fuyu Lv, Ou Dan, Li Yuan
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14438v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K routing strategy, leading to redundant computation and suboptimal inference latency. Existing acceleration methods either require costly retraining with architectural changes or suffer from severe performance drop at high sparsity due to train-inference mismatch. To addr...


title: Synthesizing POMDP Policies: Sampling Meets Model-checking via Learning
feed_id: arxiv-cs-ai
url: https://arxiv.org/abs/2605.14440
guid: oai:arXiv.org:2605.14440v1
published_at: 2026-05-16T04:00:00
author: Debraj Chakraborty, Anirban Majumdar, Prince Mathew, Sayan Mukherjee, Jean-Fran\c{c}ois Raskin
first_crawl_time: 17:53
last_crawl_time: 17:53
created_at: 2026-05-16 17:55:52
updated_at: 2026-05-16 17:55:52
summary:
arXiv:2605.14440v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods scale well, they lack formal correctness guarantees, making them unsuitable for safety-critical applications. Conversely, formal synthesis techniques provide correctness-by-construction but often struggle with scalability, as general POMDP synthesis is undecidable. To bridge this gap, we propose a sy...


