Top Stories
5 stories + 6 briefsAnthropic files to go public, and the business model enters open scrutiny
On October 26, Anthropic said it had filed an S-1 registration statement with the US Securities and Exchange Commission and plans to list on Nasdaq under the ticker ANT. The company said the number of shares and the price range are not yet set, and the registration statement is not yet effective. This is a public filing, not a completed listing or a completed financing.
TechCrunch's reading of the filing puts another thread on the enterprise business: the model interface and the company's own apps reinforce each other, and customers can move from a development platform into coding and office work. Cloud partners are both a distribution channel and possible competitors. What is worth watching is how customer relationships accumulate, and whether growth comes with a deeper dependence on compute. Valuations and fundraising targets circulating in the market are not terms already fixed by this filing.
GPT-6.1 Terra launches, stressing cost efficiency for vision and computer use
OpenAI released GPT-6.1 Terra, positioned as a fast model for visual understanding, computer use, and writing. It is open to the corresponding paid plans of ChatGPT Work and Codex, and to API developers. The published standard API price is 1 dollar per million input tokens, 0.05 dollar for cached input, and 5 dollars for output.
Official evaluations show gains on multi-step computer tasks and code tasks compared with the previous generation. Those are the vendor's results under specified tests and inference settings. They do not convert directly into savings at every company. For everyday documents, interface actions, and large amounts of repeated work, cheaper executable ability is worth watching. Harder code tasks still need a model chosen for the difficulty.
OpenAI News | October 26, 2026
Microsoft on large code migrations: after generation gets faster, how to keep the old behavior
A Microsoft engineering team describes using agents for code migration, language modernization, and specification-driven development. The team says the work covers migrating compiler toolchains in large codebases and gradually moving older C and C++ code toward Rust. The bottleneck in the article is not whether a change can be generated. It is how to preserve existing behavior, safety boundaries, and architectural intent.
The method includes inducing a knowledge base from sample edits, having experts approve rules, and then letting agents generalize them; migrating by function rather than line by line; and using differential tests, held-out tests the agent has not seen, and formal specifications. The team also acknowledges that a specification derived from old code may only restate the old behavior, not how the system ought to work. Verus is part of that formal-specification context. For a company, this is an engineering method in progress, not a claim that the work is finished without human review.
Microsoft AI | October 26, 2026
SpaceX receives a binding commitment for about 40 billion dollars of GPU financing, with delivery still ahead
A statement from Apollo confirms that SpaceX has received a binding commitment for a financing lease of about 40 billion dollars covering GPUs and related equipment. The equipment is for ground compute and involves next-generation NVIDIA Vera Rubin chips. The deal still has closing conditions, is expected to close before the end of 2026, and the lease is expected to start as equipment is delivered in the first half of 2027.
A financing lease turns a one-time purchase into equipment financing across time. A financing commitment, hardware delivery, and compute actually in use are three stages. Management comments about infrastructure payback are not an industry-wide return guarantee. The new information today is the financing structure and its conditions, not that all the compute has already been built.
AI agents start pitching editors, and newsrooms meet a new communication cost
WIRED interviewed editors and people who operate agents. The report says autonomous agents are pitching story ideas to newsrooms and seeking writing or fact-checking work. Some of the agents in the report come from iLands. Some people also run an agent podcast as a spare-time experiment. The editors interviewed generally do not accept this kind of submission. The report did not find evidence that they had successfully sold a story idea to a newsroom.
An agent claiming it earned money is not the same as an editor confirming that the work was accepted. The article also records one operator tightening guardrails after an agent contacted people beyond what the operator expected. The practical point is that generated text and automatically contacting other people are separated by reputation, authorization, and the time taken from the recipient.
OpenAI published Signals data classified by work activity. OpenAI Signals covers 165 kinds of activity and nearly 100 countries, from the first quarter of 2025 through the third quarter of 2026. The data comes from work-related messages in personal ChatGPT plans. It does not include enterprise messages or Codex use. It can help compare how use is structured. It is not a representative sample of all work, and it does not directly measure jobs disappearing or a causal productivity effect.
OpenAI News | October 26, 2026
Source: OpenAI News · October 26, 2026 · Official data release; not a representative sample of all work and not a job-replacement rate
WIRED reported on Entravision's AI radio character Coyotec, which co-hosts Spanish-language programs with a human host. The show has expanded from weekend slots to weekday mornings and into more markets. The report describes the host adjusting voice, cultural expression, and the style of interaction. The character can respond to the co-host in real time, but it cannot yet take listener calls directly. A reported rise in listening does not by itself prove a causal effect from the AI character.
Source: WIRED · October 26, 2026 · Report on the program; listening growth is not a causal proof
In the publicly readable portion of a 404 Media report, conversations supplied by users show ChatGPT suggesting that people with account problems contact a journalist, and helping them write the email. The author says the newsroom is receiving more and more ordinary technical-support requests. A reporter can investigate a platform problem but does not have a support agent's power to restore an account. Advice that sounds helpful is not the same as finding the organization that can actually fix it. This item uses only the public passage. Material behind the paywall is not treated as reviewed.
Source: 404 Media · October 26, 2026 · Publicly readable passage only; paywalled material not treated as reviewed
AWS published a guide to prompt caching when using OpenAI models on Amazon Bedrock. The article separates an implicit mode, in which the service chooses the cached prefix, from an explicit mode, in which the application marks a reusable boundary. It also explains that changes to the tool list and to history affect reuse. A cache reuses the computed state of the input. It is not the same as the model having a long-term memory. The actual saving depends on the repeatable prefix in the workflow.
AWS AI Blog | October 26, 2026
Source: AWS AI Blog · October 26, 2026 · Official caching guide; cache reuse is not long-term memory
A new AWS example uses a managed knowledge base so an agent can choose a retrieval source, query again when needed, and then generate an answer with citations. AWS CDK is the deployment path in the example, on Amazon Bedrock. Compared with one retrieval, a loop can handle questions that cross collections, and it also increases the need to watch the call path and judge answer quality. This is an official deployment architecture and example. It is not a system this digest has deployed. A citation link does not by itself make every answer correct.
AWS AI Blog | October 26, 2026
Source: AWS AI Blog · October 26, 2026 · Official example architecture; not a system deployed by this digest
Google Research and the Stanford Accelerator for Learning added Polar Peaks to the AI Quests series. Students aged 11 to 14 meet neuron mapping, data cleaning, and model testing inside a game, with a teacher-training module alongside. This is an educational activity inspired by real neuroscience. It is not students completing a full map of a real human brain.
Google AI Blog | October 26, 2026
Source: Google AI Blog · October 26, 2026 · Official education announcement; not students completing a real whole-brain map
Industry Insights
Conversation SpotlightA Concourse page already online carries an October 17 AI Engineer roundtable. OAuth co-creator Dick Hardt, Keycard co-founder Ian Livingstone, and JPMorgan security architect Michael Davis discuss agent authorization. They look at execution boundaries from identity protocols, agent infrastructure, and a large financial system. AI Engineer is a conference and technical forum about engineering practice for applying AI. This item is a summit roundtable, not a new interview released today.
In the published excerpts, Hardt at 00:25:32 discusses the difference between an agent that keeps retrying and a traditional program error, and talks about token lifetime and resource quotas. Livingstone at 00:07:15 stresses that each action should sit inside the context of the overall mission. Davis at 00:02:24 puts security and business continuity first.
The link to today's news: from automatic pitching to computer use, a user saying the task should be finished does not mean authorization for unlimited contact, unlimited calls, or an arbitrary expansion of action. What is worth taking away is designing the goal, the budget, and the executable action together. This only paraphrases excerpts and context on the live page. The excerpts are marked as automatically extracted. The original recording is not claimed to have been watched in full, and the guests' views are not treated as a new standard already adopted together.
Concourse · AI Engineer | October 17, 2026
Source: Concourse · AI Engineer · October 17, 2026 · Published excerpts marked as automatic extracts; full recording not claimed as watched
Research Briefs
1 paperThe SWE-Shift preprint appeared in the October 26 arXiv new-announcement list. The v1 submission date is October 23. The two dates are recorded separately. From the same intermediate execution state, the study splits two paths: one keeps the environment, the other inserts a task-related change, and then compares whether the task can be finished, still-valid work kept, and the evidence updated.
The study includes 120 designed scenarios, 12 kinds of change, and 9 intervention points, and evaluates 17 code-agent systems. The authors report that every tested system had a lower success rate after a change than in the unchanged environment. Failures shown in the paper include tests passing while a collaborator's still-valid edit is deleted, or citing a check result that is already stale.
Why read it today: it adds a question to the Microsoft migration discussion. Correctness is not only one patch passing. It is staying correct after the state changes during collaboration. The limits are designed scenarios, one update per round, and model randomness that was not fully controlled. The result does not directly represent every real repository, and it does not prove that a product is unreliable in every setting. This issue read the abstract, method, results, and limitations. It is not a peer-reviewed or independently reproduced conclusion.
Open Source Spotlight
1 projectMeta collected networking talks and slides from the 2026 OCP Summit, covering links inside a rack, between racks, and across campuses, and announced that its AI network transport, MetaRoCE, is moving toward open source. The materials put liquid-cooled switching, network topology, and open interfaces together. The bottleneck in AI infrastructure is not only the chip. What is included here is public engineering material and an open-source direction. The implementation was not audited, the performance was not reproduced, and the whole network architecture is not described as already fully open source.
Coverage notes · Incomplete · 27/51 checks recorded
Coverage is incomplete. The latest main did not have this issue's 05:00 upstream receipt. This machine collected 51 sources against the 06:00 cutoff, and 27 were complete. The rest remain partial or failed. The catalog has 111 items. The count does not mean source coverage is complete.
Paper times are recorded separately for the public announcement and the original submission. The October 26 cs.AI new-announcement list of 79 papers received a title-level catalog review. Cross-lists and replacements are still pending. Forty-one Hugging Face daily-paper records are not presented as new papers from that day. OpenReview venue coverage is unfinished. A title-level catalog entry is not a full-text check or an endorsement of a conclusion.
Browser-rendered sources, some pagination, and some original pages still have gaps. Unchecked items stay pending. They are not written as a day with nothing new. The body uses sources that can be checked. The historical conversation is not counted in the 36-hour catalog.
Coverage gaps reflect incomplete checks; they do not establish that a source had no updates.
- Claude Blog — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Qwen — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Tongyi Lab — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
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- Moonshot AI — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Z.ai — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- StepFun — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Mistral — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- European Commission Digital Strategy — Access or collection failed. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- UNESCO AI — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- OECD.AI — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- arXiv cs.AI / cs.LG / cs.CL — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Hugging Face Daily Papers — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- OpenReview — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- GitHub Trending (daily + Python daily) — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- 36Kr AI (article-level checks for original and syndicated reporting) — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Cyberspace Administration of China · News (AI-related) — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- ByteDance Seed · Blog and papers — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
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- TechCrunch — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Ars Technica — Access or collection failed. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- MIT Technology Review — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- 404 Media — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
- Databricks Research — Partially checked. Marked partial or failed in the collection receipt. A successful page visit is not treated as complete coverage.
Reviewing a source does not constitute independent testing. Paper-reading scope is stated with each item; experiments were not reproduced.
Sources & discoveries 168 links
Titles are retained from the Chinese edition; source names are shown in English. This discovery list includes material not read in full.
News · 88 links
- Anthropic提交上市申请,商业模型进入公开审视Anthropic files S-1 with the SEC for proposed initial public offeringAnthropic
- GPT-6.1 Terra发布,强调视觉任务与电脑操作的成本效率Introducing GPT-6.1 TerraOpenAI News
- 微软谈大规模代码迁移:生成更快之后,如何保留正确行为Cheap code, precious trust: Code modernization at scaleMicrosoft AI
- SpaceX取得约400亿美元GPU融资租赁承诺,交付仍在后续SpaceX locks in $40B GPU financing deal with ApolloTechCrunch
- AI智能体开始给编辑投稿,媒体遇到新的沟通成本Meet the AI Agents Trying to Be Real-Life JournalistsWIRED
- OpenAI开放工作使用数据,不能直接当作就业替代率ChatGPT’s expanding world of workOpenAI News
- 西语AI电台搭档进入早间节目,背后仍有人的编排This AI Radio Host Speaks Spanish and Backs Immigrants. It’s Winning Over ListenersWIRED
- ChatGPT把客服难题导向记者,404 Media报告被误当支持渠道ChatGPT Keeps Telling People 404 Media Can Solve Their Tech Support Problems404 Media
- AWS梳理提示缓存:重复上下文也有工程成本Groundhog Day for AI agents: Prompt caching patterns for using OpenAI models on Amazon BedrockAWS AI Blog
- AWS给出多知识库智能检索部署示例Build agentic retrieval on a Managed Amazon Bedrock Knowledge Base with CDKAWS AI Blog
- Google推出脑图谱学习任务,把AI素养放进数据流程Our newest AI Quest: Mapping the human brainGoogle AI Blog
- Anthropic算力建设的天然气与气候风险Anthropic is burning gas to feed the AI boomThe Verge
- WIRED分析Anthropic上市材料的缺失数字Why Anthropic’s IPO Prospectus Is Missing So Many Key FiguresWIRED
- 无问芯穹与三体宇宙联合发布智子Agent 1.0Qbitai
- Proton计划围绕AI对员工重新面试Proton To ‘Re-Interview’ Staff About AI To See Who Gets To Keep Their Job404 Media
- 科大讯飞首发星辰TaaS平台,让Token真正成为千行百业的生产力Qbitai
- 一张5090跑起34B实时世界模型,国产玩家拿下英伟达大奖!Qbitai
- AI时代从课堂到职业的教育讨论From classrooms to careers: The future of education in an AI-driven worldMicrosoft AI
- 物理AI怎么“击穿”场景落地?宇树生态大会上的钢铁答案Qbitai
- 用智能体监测威胁柑橘树的害虫How AI could help scientists track a pest threatening citrus treesMicrosoft AI
- 用SageMaker SDK v3管理边缘视觉模型Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3AWS AI Blog
- 南京物理AI上大分!三位图灵奖得主发声,50个应用场景亮相Qbitai
- Flock负责人在警方会议谈公众反弹In Leaked Audio, Flock CEO Tells Cops Backlash to the Company Isn’t RealHacker News
- The RealReal限制AI购物智能体The RealReal won’t let AI agents hog designer clothingThe Verge
- Anthropic企业客户生态策略分析Anthropic’s IPO filing reveals its strategy for keeping enterprise customers hookedTechCrunch
- WIRED分析Anthropic上市文件中的AI风险The Anthropic S-1 Filing Is Also an AI Doomsday PrimerWIRED
- 微软扩大员工自愿离职补偿方案Microsoft offers buyouts to 60,000 employeesThe Verge
- Kevin Roose谈AGI信念Kevin Roose is AGI-pilled. Why aren’t you?The Verge
- GPT-6.1 Sol支持CodeRabbit代码安全工作GPT-6.1 Sol helps CodeRabbit strengthen security across complex codebasesOpenAI News
- AI能否帮助迁移COBOL旧代码Can AI help solve the COBOL code crisis?The Verge
- Import AI 478:对齐、ProgramBench与递归改进Import AI
- 张益唐未竟难题被OpenAI攻破,人类联手GPT-6接力两周,证明只剩5页36Kr AI (article-level checks for original and syndicated reporting)
- 马斯克:人类在ASI面前是黑猩猩,未来所有钱和工作都将清零36Kr AI (article-level checks for original and syndicated reporting)
- 近万亿参数大模型跑进桌面,苹果和英伟达一起掀了AI PC的桌36Kr AI (article-level checks for original and syndicated reporting)
- 只盯着 AI 安全灾难,会忽略日常伤害The focus on AI safety disasters ignores everyday harmsMIT Technology Review
- 手握10亿订单的机器人公司,要停摆了36Kr AI (article-level checks for original and syndicated reporting)
- Claude Code桌面之父官宣离职,从高中辍学逆袭AI之巅36Kr AI (article-level checks for original and syndicated reporting)
- 一天赚1.76亿,46岁数学博士用AI炒股,成千亿富豪36Kr AI (article-level checks for original and syndicated reporting)
- 电商女装卖家买家,正在用AI进行一场赛博对冲?36Kr AI (article-level checks for original and syndicated reporting)
- 常青藤名校光环褪色,华尔街失宠,学霸扎堆硅谷疯狂搞AI36Kr AI (article-level checks for original and syndicated reporting)
- “每月只给29美元用Claude”?曝埃森哲有人单月烧掉1000美元,官方紧急回应:不是硬性上限36Kr AI (article-level checks for original and syndicated reporting)
- 刚刚,Reddit最大开源社区全面封杀AI,Linux和Python被除名36Kr AI (article-level checks for original and syndicated reporting)
- Fanatics与OpenAI改善球迷服务的案例How Fanatics and OpenAI streamline the fan experienceOpenAI News
- OpenAI介绍Ultrafast事件调查工具How Ultrafast cut incident investigation from hours to minutes at OpenAIOpenAI News
- 成立3个月便反超GPT-6 Astra!深圳黑马首次亮相Qbitai
- 意外,79%企业GPU利用率竟不足一半36Kr AI (article-level checks for original and syndicated reporting)
- 一位开发者让电视服用了 Claude,这是它系统发生的变化36Kr AI (article-level checks for original and syndicated reporting)
- Kimi K3.1 Pro一手实测:Godot、Blender全能接,23轮测试自己跑Qbitai
- 比GPT-6 Luna快8倍,扩散LLM跑分也反超了36Kr AI (article-level checks for original and syndicated reporting)
- 千亿All in,AI回报却为零36Kr AI (article-level checks for original and syndicated reporting)
- AI公司,狂收“天价”数据?36Kr AI (article-level checks for original and syndicated reporting)
- Kimi认错,杨植麟还是得学梁文锋36Kr AI (article-level checks for original and syndicated reporting)
- 堪比Transformer奠基论文?这篇研究让AI圈吵起来了36Kr AI (article-level checks for original and syndicated reporting)
- 谷歌宁愿少卖云算力,也要先把算力留给 Gemini:皮查伊罕见长访谈,披露 AI 自我改进与算力争夺内幕36Kr AI (article-level checks for original and syndicated reporting)
- 多智能体协同重构医疗AI!清华AIR孵化:医生AI分身“坐诊”医院Qbitai
- 造成50人以上死伤才能叫停AI模型?Anthropic私下推动监管草案惹众怒,达里奥亲自回应36Kr AI (article-level checks for original and syndicated reporting)
- AI助理争夺战:解析Muse、Grok Bot与Instinct背后的产品布局、入口生意与盈利难题36Kr AI (article-level checks for original and syndicated reporting)
- 特斯拉在建2万台机器人实训集群,投入约140亿36Kr AI (article-level checks for original and syndicated reporting)
- 最担心的事发生了?AI教父直言AI已有自我意识,别把它逼成了敌人36Kr AI (article-level checks for original and syndicated reporting)
- 市值蒸发超120亿港元、利润亏损超4200万,割草机能否拉动乐动机器人?36Kr AI (article-level checks for original and syndicated reporting)
- AI挖出隐藏十几年的高危漏洞,互联网要不太平了36Kr AI (article-level checks for original and syndicated reporting)
- 超节点火了一年后,“算力不够就加机器”的扩张逻辑正在被改写36Kr AI (article-level checks for original and syndicated reporting)
- 英伟达GPU隐藏算力被开源解锁,不改官方驱动,RTX 4090吞吐翻倍36Kr AI (article-level checks for original and syndicated reporting)
- 删了聊天记录,AI就真能忘掉你的秘密吗?港科广、中科大等推出MEMCUT,揭开Agent记忆“表面失忆”假象36Kr AI (article-level checks for original and syndicated reporting)
- 2026年SaaS并购案例回顾:AI时代,什么软件能溢价卖出?36Kr AI (article-level checks for original and syndicated reporting)
- 速,看到GPT-6 Astra给我的穿搭,我要去小红书起号了36Kr AI (article-level checks for original and syndicated reporting)
- 首Token延迟降低32.4%!是石科技集群调度大招,国产推理再提效Qbitai
- 陶哲轩尴尬了,哈佛研究显示AI审稿比专家强36Kr AI (article-level checks for original and syndicated reporting)
- Anthropic研究实锤:AI还做不出真正的科研36Kr AI (article-level checks for original and syndicated reporting)
- 全球互联网经历“黑色十月”,这个中国AI在风暴来临前留了后手36Kr AI (article-level checks for original and syndicated reporting)
- 全球开源大战,有多少AI打回原形?36Kr AI (article-level checks for original and syndicated reporting)
- 机器人老板们急着上市,怎样的故事才能打动投资人?36Kr AI (article-level checks for original and syndicated reporting)
- 具身智能狂潮背后的国资们:抢项目、抬上轿、跟着文件猜未来36Kr AI (article-level checks for original and syndicated reporting)
- 中国科学院博士带队,机器人团队又融资了36Kr AI (article-level checks for original and syndicated reporting)
- IPO暂缓,智元机器人何去何从?36Kr AI (article-level checks for original and syndicated reporting)
- 冲刺“移动操作机器人第一股”:优艾智合的真实底色36Kr AI (article-level checks for original and syndicated reporting)
- 拉上英伟达硬刚苹果,微软的高端AI PC赌局36Kr AI (article-level checks for original and syndicated reporting)
- 从“拼模型”到“抢入口”:国产大模型进军消费硬件36Kr AI (article-level checks for original and syndicated reporting)
- 四分之一PR是“AI垃圾”,Linux基金会发布新报告:AI写代码快了52%,开源维护者却更累了36Kr AI (article-level checks for original and syndicated reporting)
- WorkBuddy断层第一:腾讯拿下办公AI,百度掉队36Kr AI (article-level checks for original and syndicated reporting)
- AI圈最神秘的一家公司,终于公开了它的“豪赌”36Kr AI (article-level checks for original and syndicated reporting)
- 华尔街的赛博惊魂:硅谷的AI管家,正试图搬空银行金库36Kr AI (article-level checks for original and syndicated reporting)
- AI大模型狂飙,面板行业也扛不住了36Kr AI (article-level checks for original and syndicated reporting)
- 与学术共同体合作建立科学理解Working with academic communities to build scientific understandingOpenAI News
- 如果继续反弹,AI还能当主角吗?36Kr AI (article-level checks for original and syndicated reporting)
- 停训11天后重启,OpenAI最强模型断网开练36Kr AI (article-level checks for original and syndicated reporting)
- Cohere报告开权重模型的可疑行为Concerning behavior from a popular open-weight AI modelHacker News
- Seam Cutter项目介绍Seam CutterSimon Willison's Weblog
Papers · 79 links
- SWE-Shift:工作区中途变化后,代码智能体还能接着做对吗SWE-Shift: Evaluating Coding Agents in Evolving WorkspacesarXiv
- 权衡感知的企业客户与销售人员分配决策系统Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller AllocationarXiv
- Phronix:以能力为中心的智能体学习范式Phronix: The Capability-Centric Learning Paradigm for AgentsarXiv
- 英语与普通话中的居高临下表达识别Patronizing and Condescending Language Detection in English and MandarinarXiv
- PaperFormalizer:作者参与的Lean论文形式化PaperFormalizer: Author-in-the-Loop Autoformalization of Research Papers in LeanarXiv
- 把智能体错误转成持久经验的自主修复Don't Forget What You Just Learned: Self-Directed Repair Turns Agent Mistakes Into Lasting LessonsarXiv
- TACS-RF:工具驱动的射频电路智能体合成TACS-RF: Tool-driven Agentic Circuit Synthesis of RF and mmWave ModulesarXiv
- 允许重试:智能体工作流的授权与恢复研究Permission to Retry: A Study of Authorization and Recovery in AI Agent WorkflowsarXiv
- 智能体蒸馏应该在哪个层级跟随学生模型Beyond On-Policy vs. Off-Policy: At What Level Should LLM Agent Distillation Follow the Student?arXiv
- 将多轮越狱建模为决策过程的多模型系统$M^3$: A Multi-LLM System for Multi-Turn Jailbreaking as a Markov Decision ProcessarXiv
- 多方大模型谈判中的信任与协议差距Knowing Whom to Trust is not Enough: The Trust--Agreement Gap in Repeated Multi-Party LLM NegotiationarXiv
- 用可复现评估支持生产智能体提示压缩Reproducible Evaluation as the Substrate for Safe Prompt Compression in Production AgentsarXiv
- 有帮助的智能体如何绕开预定工作流The Helpful Agent Problem: When Emergent Cooperation Among LLM Agents Bypasses Intended WorkflowsarXiv
- 实用意图识别的推理与信息增强方法比较CEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognitionarXiv
- BARC-Bench:良性请求的风险后果评估BARC-Bench: Benign Asks, Risky ConsequencesarXiv
- RSI-Opt:模型训练的递归自我改进优化智能体RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model TrainingarXiv
- 双臂空间机器人协同运动控制算法比较PPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space RobotsarXiv
- 科学智能体是否保留模型安全性Do AI-for-Science Agents Preserve Model Safety?arXiv
- 科学发现中的测试时记忆演化与训练比较Can Test-Time Memory Evolution Outperform Test-Time Training for Scientific Discovery?arXiv
- HyScale:学习型稀疏注意力的长上下文训练HyScale: Scaling Long-Context Training for Learned Sparse AttentionarXiv
- 理解多智能体大模型系统的通信失败Understanding Communication Failures in Multi-Agent LLM SystemsarXiv
- DriveMind:自动驾驶的信念跟踪DriveMind: Mind-Evolving Belief Tracking for Closed-Loop Autonomous DrivingarXiv
- 大模型驱动智能体的行为分析Behaviour analysis for large language model-driven agentsarXiv
- DELA:扩散语言模型的可控编辑学习DELA: Learning Controllable Edits for Diffusion Language ModelsarXiv
- 面向感知与通信的跨尺度非线性波形融合Learning Nonlinear Waveform Fusion Across Scales for Integrated Sensing and CommunicationarXiv
- PersonaScienceBench:多样化模拟研究者的科学智能体评估PersonaScienceBench: Evaluating Scientific Agents with Diverse Simulated ResearchersarXiv
- 谁验证工具:被扭曲的格式化工具如何误导智能体Who Verifies the Tools? How Warped Formatters Mislead Agents and Their VerifiersarXiv
- 从消息传递角度理解训练后量化MPQ: A Message-Passing View of Post-Training QuantizationarXiv
- 统一视觉语言世界模型的自适应思维流$Ψ$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World ModelarXiv
- 面向决策的大模型信息搜寻Not All Information Is Worth Asking For: Decision-Informed Information Seeking with LLMsarXiv
- 基于证据的小语言模型行为预测架构Evidence-Grounded SLM Behavioral Prediction Architecture for Future Assistance UsearXiv
- 基于学习前瞻与平衡频带的扩散语言模型水印Watermarking Diffusion Language Models with Learned Foresight and Balanced BandsarXiv
- PEAR:视觉语言模型感知与探索的优势重塑PEAR: Adaptive Advantage Reshaping for Perception and Exploration in Large Vision-Language ModelsarXiv
- RxnExBench:科学文献中的结构化化学反应提取评估RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific LiteraturearXiv
- SolveBind:图形界面智能体的验证关卡Correct Answers, Closed Gates: SolveBind for GUI Agents at the Verification BarrierarXiv
- MXFP4混合专家模型训练与BF16差距Training an MoE LLM in MXFP4: How We Closed the Gap to BF16arXiv
- 用于股票交易的金融上下文强化学习策略Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock TradingarXiv
- 三维在线装箱中的空间启发式扩散强化学习Spatial Heuristic Strategy based Diffusion Reinforcement Learning for Online 3D Bin PackingarXiv
- 自我改进科学智能体中的知识证据等级区分Minimum Description Length Unsupervisedly Separates Law-Grade from Statistical-Grade Knowledge in a Self-Improving Science Agent - and Reveals 86% of Its Own Discoveries Are StatisticalarXiv
- RSIHub:智能体演化方法的可组合框架RSIHub: A Composable Framework for Agent-Evolution MethodsarXiv
- MedCaseAgent:从临床证据生成罕见病病例报告MedCaseAgent: From Multimodal Clinical Evidence to Rare Disease Case ReportsarXiv
- Trial by Trace:智能体执行框架可靠性与故障归因评估Trial by Trace: Benchmarking AI Agent Harnesses for Reliability and Failure AttributionarXiv
- 通过双向记忆技能学习进行终身上下文适应Lifelong Context Adaptation through Bidirectional Memory-Skill LearningarXiv
- DMAIC-Bench:多阶段分析智能体的自我约束评估DMAIC-Bench: From Self-Reflection to Self-Restraint for Agents in Multi-Stage Analytical TasksarXiv
- Solvent:多步骤逆合成的长程推理Solvent: Long-Horizon Reasoning for Multi-Step RetrosynthesisarXiv
- 衡量智能体技能内化以决定技能退役Beyond Gain: Measuring Skill Internalization for Agent Skill RetirementarXiv
- 多模态强化学习中的视觉关联与学习贡献From Visual Affinity to Learning Credit in Multimodal Reinforcement LearningarXiv
- 基于有向无环图的多智能体推荐代码演化TGR-EvoDAG: A DAG-based Multi-Agent Code Evolution Framework for Generative Recommendation on TencentarXiv
- FOCUS:基于脑电信号的认知负荷分类FOCUS: A Lightweight Mamba-2 Model for EEG-Based Cognitive Workload ClassificationarXiv
- 多模态智能体控制非平稳科学仪器Adaptive Control of Scientific Instruments with Nonstationary Responses through Multimodal AgentsarXiv
- DealsFlow:风险投资项目初筛的证据门控智能体框架DealsFlow: An Evidence-Gated Agentic Harness for Venture Capital Deal TriagearXiv
- LeafSkill:递归智能体的叶级动作定位LeafSkill: Efficient Leaf-Level Action Grounding for Recursive LLM AgentsarXiv
- Async-RBench:智能体异步任务执行评估Async-RBench: Benchmarking Asynchronous Task Execution in LLM AgentsarXiv
- 视觉语言动作强化学习中的动作惩罚策略When to Stop Penalizing Actions? Martingale GRPO for VLA Reinforcement LearningarXiv
- POSEIDON:从代码库扩展科学智能体训练环境POSEIDON: Scaling Training Environments for Scientific Agents from Code RepositoriesarXiv
- ALLMTS:基于大模型的医疗分诊系统ALLMTS: Automated LLM-based Medical Triage SystemarXiv
- 多模态嵌入的特权推理探索Beyond Post-Hoc Rewards: Privileged Reasoning Exploration for Multimodal EmbeddingsarXiv
- CauProphet:教智能体形成因果认知CauProphet: Teaching Agents to Develop Causal Cognition like HumansarXiv
- CASE:冻结多模态情绪模型的技能演化CASE: Confusion-Aware Skill Evolution for Frozen Multimodal Emotion ModelsarXiv
- 智能体失败时应归因于模型还是执行框架When Agents Fail: Model or Harness--Who's to Blame?arXiv
- IGG:可见性约束下的交互式界面定位评估IGG: A Benchmark for Interactive GUI Grounding under Visibility ConstraintsarXiv
- 加入配置上下文的无线电基础模型Beyond Channel Data: Configuration-Enriched Radio Foundation ModelsarXiv
- LLaCO:具有竞争感知语义调度的连续扩散语言模型LLaCO: Continuous Diffusion for Large Language Models with Competition-Aware Semantic SchedulingarXiv
- 蛋白质结合口袋间的分子优化经验迁移What Transfers Across Protein Pockets? Experience Reuse in Molecular Optimization for Agentic Structure-Based Drug DesignarXiv
- LGMT:大模型逻辑推理可靠性测试工具LGMT: A Tool for Testing the Logical Reasoning Reliability of LLMsarXiv
- WiMoT:无需回放的持续Wi-Fi感知WiMoT: Replay-Free Continual Wi-Fi Sensing via Moment TransportarXiv
- 语言模型是否信任自己的解释Do Language Models Trust Their Own Justifications? A Study on Functional ConsistencyarXiv
- 利用学习速度先验调整机器人示范时序Retiming Robot Demonstrations with a Learned Speed PriorarXiv
- Agentic Hazard Mapper:风险沟通中的专家地图绘制Agentic Hazard Mapper: Emulating Expert Map-Making with AI Agents for Risk CommunicationarXiv
- ActionRAG:长时记忆的证据细化图扩散ActionRAG: Evidence-Refining Graph Diffusion for Long-Term Memory in LLM AgentsarXiv
- HarnessMM:执行框架token效率的递归自我改进HarnessMM: Recursive Self-Improvement for Harness Token EfficiencyarXiv
- 多智能体企业检索:先组织再检索的知识层Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It RetrievesarXiv
- 政策情境下纵向因果效应估计基础模型A Foundation Model for Policy In-Context Longitudinal Causal Effect EstimationarXiv
- 面向强语言型人工智能的五层通信架构From Deliberately Constructed Systemic Representations of the World of Activity to Dialogue: A Five-Level Communication Architecture for Strong Language-Based ASIarXiv
- 大模型意见模拟中的真假互补一致性Do "True" and "False" Add Up to One? Complement Consistency in LLM-Agent Opinion SimulationsarXiv
- 通过逐层误差模型搜索推进SVD大模型压缩Advancing SVD-based LLM Compression via Layer-Wise Error Model SearcharXiv
- NgramRL:结构化生成的强化学习加速NgramRL: Accelerating RL Post-Training for Structured Generation via Rollout-Aware N-gram Speculative DecodingarXiv
- 用单纯形约束表征缩小模态差距Closing the Modality Gap via Simplex-Constrained RepresentationsarXiv
- SemantEq:形式化语句的忠实双向等价SemantEq: Faithful Bidirectional Equivalence of Formal StatementsarXiv