2026-10-06

Improving Atomic-Fact Recall via Focused Views in Unstructured Knowledge Editing

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10/02 08:00

Large language models (LLMs) increasingly serve as general-purpose interfaces to factual knowledge, but their parameters do not automatically reflect information that changes after pretraining. Knowledge editing (KE) provides a targeted alternative to costly retraining by modifying selected knowledg

Self-Supervised Scaling of Terminal Environments for Scientific Domains

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10/02 08:00

Terminal agents are increasingly deployed beyond software engineering in science and other specialized domains. Constructing training environments requires executable reference behavior and a domain-specific verifier that distinguishes semantic correctness from superficially plausible artifacts. Aut

4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes

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10/02 08:00

We introduce 4DCodeBench, a benchmark for 4D inverse graphics through code generation, in which agents reconstruct dynamic scenes from video as executable graphics programs. To accomplish this, agents must translate visual observations into compact representations of scene structure and dynamics, by

Language Models that Play Chess and Explain Their Moves

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10/02 08:00

Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language models (LMs) can generate plausible-sounding explanations, but their weak playing strength limits the utility of their explanations. We introduce Queen,

FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution

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10/02 08:00

LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as circle packing. However, prior work typically optimizes performance gain over a fixed number of iterations. We argue that practical optimization shou

Multilingual GSM-Symbolic: What determines capability transfer across languages?

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10/02 08:00

We understand little about how capabilities acquired in one language carry over to another, or what governs this transfer: evaluations rely on incomparable, saturation-prone datasets and rarely examine its determinants jointly. Identifying what predicts transfer would let us avoid exhaustive evaluat

Collective Bias Mitigation via Model Routing and Collaboration

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10/02 08:00

Large language models (LLMs) are increasingly deployed in public health, finance, and governance, requiring both accuracy and societal value alignment. Despite recent advances, LLMs often perpetuate or amplify bias embedded in their training data, posing challenges to fairness. While self-debiasing

Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability

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10/02 08:00

Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning comes at an increased inference cost, motivating efficient methods that train models to solve tasks using fewer tokens. However, a common concern is th

Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models

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10/02 08:00

Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and sh

HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents

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10/02 08:00

We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13 languages, written by native or highly proficient speakers. Questions are designed to be extremely challenging. Each question targets a concise, pub

RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations

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10/01 08:00

A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the past bears on the message in front of it. Testing this requires a real person's record, and such records are private, so benchmarks generate the pe

Latent-MOPD: Latent Multi-Teacher On-Policy Distillation

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10/01 08:00

On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through their output distributions. We introduce Latent-MOPD, to our knowledge the first representation-level multi-teacher OPD method for LLMs. It integrate

From Gradients to Capabilities: Understanding Multi-Teacher On-Policy Distillation

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10/01 08:00

Multi-teacher on-policy distillation (MOPD) aims to combine the strengths of RL-trained teachers in a single student, but how teacher signals affect parameter changes remains underexplored. We study Qwen3-1.7B with four domain teachers trained with RL from the same initialization as the student, com

Spatial Memory Intelligence: Endowing World Models with Understanding-Driven Long-Term Memory

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10/01 08:00

Long-video generation and world models have shown strong potential for interactive entertainment and embodied simulation by predicting future observations conditioned on user actions and historical memory. However, as memory sequences grow longer and their structures become increasingly complex, man

VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks

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10/01 08:00

As LLM agents undertake increasingly complex, long-horizon tasks, verifying their outputs becomes increasingly challenging. We study how verification capability can be strengthened with a fixed base model, without access to reference answers or grading rubrics at test time. Repeated sampling yields

Local Support Learning

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10/01 08:00

We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of each weight matrix, we uncover a natural retention objective under which updates produced by gradient-based optimizers are suboptimal. Following this

SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation

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10/01 08:00

Existing Simulink benchmarks mainly evaluate whether generated models compile, execute, or resemble a reference model. These criteria do not establish whether a model satisfies its engineering requirements. We introduce SimuVerity, a benchmark of 101 text-to-executable Simulink model-generation task

Can Computation from Earlier Problems Help LLMs Solve New Ones?

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09/30 08:00

Large language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answer this question, we first conduct preliminary experiments showing that retained history can raise or lower later-turn accuracy, even within the sam

Dream4ACT: A Shared Visual Action Interface for Multi-Embodiment Video-Action Modeling

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09/30 08:00

Video generation models (VGMs) offer strong spatiotemporal priors for embodied observation--action modeling. However, joint-space action vectors lack explicit image-space structure and vary in dimensionality and semantics across embodiments, making it challenging to directly leverage the rich spatio

Does Learning Protein Folding Generalize to Broader Reasoning?

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09/30 08:00

Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can lea

LexReward: A Taxonomy-Driven Reward Framework for Legal Language Models

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09/30 08:00

Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, often rely on coarse-grained holistic judgments, providing limited domain specificity and interpretability. We introduce L

Tail-Influence Sampling for CVaR Policy Evaluation

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09/29 08:00

Policies with similar mean returns can differ sharply in rare failures, yet estimating lower-tail conditional value-at-risk (CVaR) accurately can require many costly rollouts. When different conditional components of a stochastic workflow can be queried separately, we ask how to allocate a fixed eva

WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents

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09/29 08:00

Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of a broader agentic interaction system comprising the environment, task, agent harness, and evaluator. Scaling environments in isolation, however, do

MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation

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09/29 08:00

Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs

WM-VLM: Probing Internal World Models for Interleaved Visual-Textual Reasoning

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09/28 08:00

Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primarily through language. We investigate whether VLMs can solve spatial problems by reasoning with both text and generated visual states. To this end, w

MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception

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09/27 08:00

Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models capture such structure broadly through physics-motivated dynamical formulations or learning-motivated architectures. The former provide stronger prior

Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features

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09/27 08:00

Supervised fine-tuning (SFT) learns most aggressively from tokens that the model deems least likely. This helps acquire new behaviors, but also amplifies noisy or conflicting supervision and can overwrite useful pretrained knowledge. Through a unified policy-loss view, we revisit existing token-rewe

Equal Ranking Quality, Different Decisions: Measuring and Reducing Order Dependence in LLM Scorers

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09/26 08:00

In passage reranking, response ranking and multi-document question answering, LLMs can score several candidate documents or responses together in one prompt, each still receiving its own score. Such scorers are selected on ranking quality, but their scores determine a decision: what a score threshol

Receiver-Conditioned Latent Communication gives 94% CacheBack

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09/25 08:00

Multi-agent systems distribute large contexts across agents that communicate to solve a task. Text messages are compact but require decoding and may omit evidence the receiving agent needs. Recent latent communication instead transfers KV caches. This avoids text generation and can improve accuracy

Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification

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09/24 08:00

Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable hallucination detection in high-stakes clinical settings. Despite the urgency of reliable and transparent medical fact ver

How To Hire A Grok Bot: Experience From The First Zero Human Company.

@BrianRoemmele · 489.9K 粉丝 · 1.1M 阅 · 533 赞 · 85 转

10/05 02:36

You do not build a Grok Bot. You hire one. The difference is the whole article. A chatbot answers a question and forgets the room. A Grok Bot has a name, a job, a conversation that persists, and a

中文介绍 探讨如何招聘Grok机器人，区别于传统聊天机器人，Grok机器人拥有持续对话能力。

He Who Controls the Compute Controls the Universe

@stevenmarkryan · 458.8K 粉丝 · 124.9K 阅 · 541 赞 · 84 转

10/05 13:25

Incredible things are happening in AI. What’s more incredible is that it’s still EXTREMELY early. As in, Day 0.000. Things are only going to get crazier from here. In case you’ve been living under a

中文介绍 AI发展早期，技术潜力巨大，未来将更加疯狂。

Every language learner gets a tutor now

@TryLiveAvatar · 1.1K 粉丝 · 123.4K 阅 · 692 赞 · 188 转

10/06 02:45

The best way to learn a language was always a patient tutor. For the first time, everyone can have one. Here's what that changes for learners, teachers and schools. The hardest part is speaking Ask

中文介绍 语言学习进入个性化时代，每个人都能拥有耐心导师。

VIBE MANUFACTURING IS HERE

@gregisenberg · 724.2K 粉丝 · 44.1K 阅 · 569 赞 · 53 转

10/04 21:25

For the FIRST TIME in HISTORY, you can describe a physical product in one sentence and, a week later, hold it in your hands, cut from metal and built to your exact measurements. That's VIBE

中文介绍 VIBE制造技术突破，可描述产品并在一周后持在手中。

Evals: how to know whether an AI system actually works

@sermakarevich · 549 粉丝 · 33.0K 阅 · 520 赞 · 64 转

10/04 02:38

An article for everyone who ships, buys, or signs off on software built on large language models (LLMs): engineers, product managers, and the CEO. Written in plain language, from the big picture down

中文介绍 评估AI系统实际工作情况，面向工程师、产品经理和CEO。

LIST OF HACKATHON FOR OCTOBER.

@onlyonealexia · 1.6K 粉丝 · 26.5K 阅 · 509 赞 · 53 转

10/04 10:08

A List of Web3, AI & Web2 Hackathons Compiled by @onlyonealexia There are a lot of hackathon lists that look impressive until you actually open the links. I compiled a list of ongoing and upcoming

中文介绍 整理Web3、AI和Web2黑客马拉松活动列表。

All games are open source now

@rehan_shei · 11.9K 粉丝 · 24.9K 阅 · 520 赞 · 49 转

10/04 08:52

A week ago I pointed Opus 5.5 at Terraria before bed. I woke up to a fully working, reverse engineered version of the game. Then I added a homing missile launcher, a tactical nuke and a bunch of new

中文介绍 游戏开源趋势，Terraria游戏被反向工程并加入新元素。

Reinforcement Learning from scratch #1

@goyal__pramod · 11.4K 粉丝 · 24.7K 阅 · 504 赞 · 59 转

10/03 12:45

Now I am aware you must have been looking forward to the second part of my CUDA blog (if you haven't read it yet, check it out!), but hey! A man can have varied interests. And I believe if you are

中文介绍 从头开始学习强化学习，分享CUDA博客第二部分。

Connecting AI agents to enterprise knowledge

MIT Tech Review AI · 10/05 23:47

For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations. AI agents need this understanding to re

中文介绍 企业AI代理缺乏对数据的理解，需要更多知识来提升性能。

Our approach to EU text provenance rules

OpenAI News · 10/05 23:00

How OpenAI is approaching text watermarking under EU rules. Learn where watermarks apply, how detection works, and why access starts with researchers.

中文介绍 OpenAI探讨在欧盟规则下文本水印的应用、检测方式及为何研究人员的访问是关键。

Bringing predictive analytics to the agentic AI era

MIT Tech Review AI · 10/05 21:29

In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from pr

中文介绍 企业AI预测模型性能提升，关键在于如何让系统自主行动而不偏离商业意图。

Building advertising for the way people use AI

OpenAI News · 10/05 18:00

OpenAI introduces a new visual ad format in ChatGPT and expands measurement tools, attribution partnerships, and brand suitability for advertisers.

中文介绍 OpenAI在ChatGPT推出新的广告格式，并扩展测量工具和品牌适宜性。

People really hate AI, so why can’t they get enough?

MIT Tech Review AI · 10/05 16:00

Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI…

中文介绍 尽管人们对AI存在偏见，但Springboards等初创公司正努力提供更多样化的AI响应。

EmTech Future 2026: When AI Meets Everything

MIT Tech Review AI · 10/05 12:00

Yossi Matias, Vice President & Head of Google Research, explores how AI is beginning to reshape biology, infrastructure, manufacturing, and science, and why its greatest impact may come when it intersects with other fields. Step inside the newsroom with our MIT Technology Review editors for sharp an

中文介绍 谷歌研究副总裁Matias探讨AI如何重塑生物、基础设施、制造和科学，并与其他领域交叉。

Prime Inference 📈, agent population 📈, Claude academy 🎓

TLDR AI · 10/05 08:00

中文介绍 Prime Inference、agent population、Claude academy的最新动态。

The Agent Said It Was Done. The Database Disagreed.

Hugging Face Blog · 10/04 06:56

中文介绍 数据库与AI代理在任务完成上的分歧。

[AINews] not much happened today

Latent Space · 10/03 16:45

a quiet day.

中文介绍 今日AI新闻相对平静。

A model guide for the GPT-6 family

OpenAI News · 10/03 00:15

Learn how startups can choose GPT-6 models, tune reasoning effort, improve prompts and skills, coordinate tools, and prepare workflows for production.

中文介绍 OpenAI发布GPT-6系列模型构建指南，包括选择模型、调整推理努力、改进提示和技能等。

Redefining enterprise intelligence with autonomous AI

MIT Tech Review AI · 10/02 23:49

Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year. F

中文介绍 企业AI已全面投入运营，预计2026年全球AI投资将达25万亿美元，增长44%。

Open-sourcing AstaBrief, the fast report-generation model in Asta

Hugging Face Blog · 10/02 23:19

中文介绍 Hugging Face开源Asta中的快速报告生成模型AstaBrief。

Inside-Out AI: Rebuilding Airbnb Behind the Scenes and Across the Guest Experience

Latent Space · 10/02 22:04

After leading Meta’s Llama models, Ahmad Al-Dahle is now transforming Airbnb with AI — from how its teams develop products to how it serves guests.

中文介绍 前Meta的Llama模型负责人Ahmad Al-Dahle用AI重塑Airbnb，从产品开发到客户服务。

Don’t be fooled—LLMs don’t reason

MIT Tech Review AI · 10/02 16:00

On an afternoon in Seoul in March 2016, I watched a program I helped build put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a…

中文介绍 LLMs不具备推理能力，它们的行为可能看似荒谬。

[AINews] Pi 1.0, Pi Durable, and AIE NYC

Latent Space · 10/02 14:40

the minimalist harness goes stable... and TypeScript!

中文介绍 Pi 1.0、Pi Durable和AIE NYC的最新动态。