2026-10-07

LoGRA: Scaling LLM Reinforcement Learning with Low-Rank Gradient Sketches

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10/05 08:00

Reinforcement learning (RL) has greatly advanced the capabilities of large language models (LLMs), but its memory demands remain a barrier to broader adoption. We introduce LoGRA, an approach to RL post-training that reduces memory by retaining useful learning signals in low-rank gradient sketches.

Capability-Driven Self-Evolution of Agent Memory

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10/05 08:00

Memory self-evolution uses task feedback to iteratively improve executable memory programs that store and retrieve information from past interactions. Existing approaches typically adopt holistic evolution, deriving revision directions from mixed feedback and judging progress by overall performance.

Closing the Context Gap: Activation Alignment for Tabular In-Context Learning

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10/05 08:00

Tabular foundation models perform in-context learning (ICL) by conditioning predictions on labeled training examples provided as context. Unlike traditional models that separate training from inference, these models must process all training examples in every forward pass, making each prediction exp

Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution

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10/05 08:00

A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the incorrect answers together hold more probability. The power distribution raises each complete answer's probability to a power above one and renormalizes

Collaborative Personalized Preference Alignment for LLMs under Data Deficiency

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10/05 08:00

Real-world users often exhibit highly heterogeneous preferences over multiple objectives for LLM responses. A lightweight aligner can tailor these responses to individual preferences, but scarce user-specific feedback makes personalized training difficult. Learning shared initializations across user

Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks

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10/05 08:00

Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level semantic similarity, which is insufficient for increasingly complex search applications. Here, we investigate agentic retr

Adapting prior-data fitted networks for tabular anomaly detection

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10/05 08:00

While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the limited availability of strong deep representations. Recently, prior-data fitted networks (PFNs) have emerged as a promising source of such representa

SoK: Semantic Decision Engines in Network Control Loops

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10/05 08:00

A semantic decision engine such as Jev can return a valid answer and still miss a network deadline, select an infeasible action or leave the service unverified. We systematize 139 paper families by decision interface, execution path and check ownership. Fifty families claim that their engine fits a

What Matters for Latent Reasoning with Flow Matching

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10/05 08:00

Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer. We argue that an effective latent thought must meet five requirements: it should be useful, helping produce the correct answer rather than merely changing it, diverse, so that resampling yie

Optimizing the Optimizer: Language Models Discover Faster Molecular Relaxation

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10/05 08:00

Geometry optimization is a major cost in many quantum-chemical workflows: each optimization step requires one force evaluation, and at the density-functional level that evaluation dominates the wall time. Research in this area has produced a broad range of optimization methods, and we ask whether a

LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them

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10/04 08:00

Jev-style decision models return categorical probability distributions over predefined options without generating free-form text, enabling software systems to act on their outputs directly. In this work, we investigate the extent to which general-purpose LLMs already possess this capability out of t

UndoBench: Separating Task Competence from Recovery Capability in Tool-Using AI Agents

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10/04 08:00

Tool-using AI agents are increasingly deployed across enterprise software systems, yet widely used benchmarks primarily evaluate nominal task completion, conflating baseline planning competence with operational fault recovery. We introduce UndoBench, a benchmark spanning 36 base workflows and 36 fau

SearchJev: A Fast and Calibrated System-1 Model for Search Agents

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10/04 08:00

Search agents repeatedly make short decisions about relevance, evidence sufficiency, and search actions. Using generative language models for these decisions introduces latency and unreliable confidence. We present SearchJev, a fast and calibrated System-1 model that separates search decisions from

Agentic discovery of blood biomarker from distilled private health records

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10/04 04:30

Routine complete blood counts (CBCs) could yield new biomarkers, but the private records needed to evaluate candidates cannot be shared with frontier language model agents that excel at discovery. We distilled the evidence held in the Clalit Health Services panel of over 5.4 million patients into a

The Numerical Linear Algebra of Large Language Models

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10/03 08:00

Numerical Linear Algebra (NLA) has consistently played a vital role in advancing science by providing tools to solve fundamental problems encountered in scientific and engineering applications. Over the decades, it has continually evolved to meet the demands driven by successive waves of scientific

LMBuild: Evaluating LLM Agents for Generating Buildable and Functional Structures

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10/03 08:00

LLM-based agents are increasingly capable of generating complex 3D structures, with the potential to reshape how objects are designed and realized in the physical world. Yet, producing elegant geometry is fundamentally different from producing objects that can be built and perform their intended fun

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

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10/02 08:00

Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retrieve external news, standard retrieval-augmented approaches struggle with high nois

Learning Latent Protein Languages for Autoregressive Generation

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10/02 08:00

Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino acid tokens encode residue identities without explicit contextual semantics, while backbone coordinates require a discrete representation in our fram

Foresight: planning future perception in streaming VLMs without retraining

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10/02 08:00

Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed throughout inference. Consequently, they cannot adapt computation to evolving scene dynamics, where different future events demand different levels and

Intent Interpretation at RIC Timescales: Jev Decision Models versus Large Language Models in 6G Open RAN

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10/02 08:00

Intent-based Open RAN needs an interpreter that turns intents into A1 policies within the loop of the RAN intelligent controller (RIC). Decision models such as Jev-1.13.0 return typed policy fields, whereas generative large language models (LLMs) produce the policy token by token. We ask whether the

The AI Theorist reveals excitonic structure in α-RuCl_3

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10/01 08:00

Advances in experimental instrumentation and automation generate increasingly rich datasets, but turning experimental observations into microscopic understanding remains a bottleneck in scientific discovery. To accelerate this process, we introduce AI Theorist, a system of artificial intelligence (A

World Editing: Intervening on Executable Worlds at Increasing Depth

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10/01 08:00

Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that should remain unchanged, a

DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents

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10/01 08:00

Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually similar controls compete for attention. Existing training data rarely pair such scenes with dense annotations or vary them in a controlled way. We intr

MEA: A Reward-Driven Multi-Agent System for Faithful Model Explanations

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10/01 08:00

Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their predictions. While post-hoc explanation methods offer a lens into this model behavior, wielding them effectively demands ex

OmniReasoning: Pushing the Limits of Audio-Visual Joint Reasoning

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09/30 08:00

Recent advances have enabled unified omni-modal models in understanding audio, vision, and language. However, existing benchmarks, training data, and learning methods largely treat the modalities independently, leaving the capability of audio-visual joint reasoning poorly evaluated and insufficientl

Video2Skill: From Streaming Experience to Reusable Embodied Skills

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09/29 08:00

Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embodied agents generalize to new tasks. Yet an agent can only plan with skills it knows. Recovering skills from observed experience, the inverse of plann

ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context

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09/29 08:00

Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the way matter. Progress Reward Models (PRMs) score how far a task has come at every step, and serve as dense rewards, verifiers and monitors. Yet in long

Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym

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09/29 08:00

Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or undermine trust. This work proposes foundations for designing, realizing, and evaluating proactive LLM agents around three joint principles (3T): T

When Does Selection Replace Extraction? A Pre-Registered Test of Agent Memory with a Typed Decision Model

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09/28 08:00

Does conversational memory need LLM-extracted facts, or is selecting the right raw turns enough? Published results disagree. Extraction-based systems report gains from distilled facts. Recent studies find raw history with good ranking does as well, but disagree about whether ranking matters. We ran

Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment

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09/15 08:00

Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable biometric systems. Existing state-of-the-art FR-integrated FIQA methods suffer from temporal instability: as the feature space evolves during train

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 探讨如何雇佣Grok Bot，区别于普通聊天机器人，Grok Bot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发展初期，预测未来将更加疯狂，呼吁关注AI的早期阶段。

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 语言学习将迎来变革，AI导师让每个人都能拥有个性化的学习体验。

AI won't take your job anytime soon. In 10 yrs, only 5% of what humans do will be replaced by AI

@mustafasuleyman · 1.2M 粉丝 · 99.2K 阅 · 587 赞 · 104 转

10/06 22:16

This is the prediction Nobel laureate Daron Acemoglu makes in the first issue of The Humanist Review, our new magazine exploring the future of AI, published by MAI. He argues we need to stop

中文介绍 诺贝尔奖得主预测，10年内AI将取代人类工作的比例仅为5%。

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，首次实现一周内将金属产品设计成实物。

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 分享Web3、AI和Web2的Hackathon列表，筛选出值得参与的竞赛。

If you have too many interests, build this AI system in 1 hour

@Hesamation · 90.6K 粉丝 · 21.4K 阅 · 618 赞 · 51 转

10/06 17:46

if you have an ADHD brain, you probably have six different goals, you get overwhelmed, and you miss days without working on any one of them. that's me. I love agents, harnesses, RL, LLM architecture,

中文介绍 针对多兴趣人士，介绍如何构建AI系统来管理多重目标。

Atlassian and OpenAI expand partnership to turn enterprise knowledge into action

OpenAI News · 10/07 00:00

Atlassian and OpenAI are expanding their partnership to connect frontier models with enterprise knowledge and help teams plan, build, and deliver work.

中文介绍 Atlassian与OpenAI扩大合作，将前沿模型与企业知识连接，助力团队规划、建设和交付工作。

How Jump Trading is scaling quant research with ChatGPT

OpenAI News · 10/06 20:00

Jump Trading uses OpenAI to expand quantitative research. See how longer-running AI workflows combine multiple data sources with human review.

中文介绍 Jump Trading利用OpenAI扩展定量研究，通过结合多个数据源和人工审核的AI工作流程。

Sharing AI progress in mathematics

OpenAI News · 10/06 20:00

OpenAI publishes new results on open problems in mathematics from an internal frontier model and shares Lean proof formalizations and research details on GitHub.

中文介绍 OpenAI发布关于数学开放问题的内部前沿模型新结果，并在GitHub上分享Lean证明形式化和研究细节。

Advancing computer use with Ironclad

OpenAI News · 10/06 18:00

Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.

中文介绍 OpenAI与Ironclad合作，通过训练和评估AI代理在复杂合同工作流程上的应用，推进计算机在专业工作中的应用。

Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance

Hugging Face Blog · 10/06 14:44

中文介绍 Falcon-Emirati：当一个LLM学习方言、文化和细微差别。

[AINews] Reflection Beam - 501B-A23B American Open Model

Latent Space · 10/06 14:28

A small win for US open source

中文介绍 美国开源模型[AINews]Reflection Beam - 501B-A23B取得小胜。

Instinct group chats 💬, Reflection’s 501B model 🧠, OpenAI text watermarks 🏷️

TLDR AI · 10/06 08:00

中文介绍 本能小组讨论，Reflection的501B模型，OpenAI文本水印。

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 将AI代理连接到企业知识。企业AI代理往往缺乏知识，需要理解数据在特定组织背景中的意义。

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阐述遵守欧盟文本溯源规则的方法，介绍水印应用、检测方式及为何研究者优先获取访问权。

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 将预测分析引入代理AI时代。企业AI的核心问题是使预测系统能够根据自身结论行动，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中推出新的视觉广告格式，并扩大测量工具、归因合作伙伴关系和品牌适用性。

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 人们真的很讨厌AI，但他们为何不能获得足够的应用？

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 EmTech Future 2026：当AI遇到一切。Yossi Matias探讨了AI如何开始重塑生物、基础设施、制造和科学，以及为什么它最大的影响可能发生在与其他领域的交叉处。

Prime Inference 📈, agent population 📈, Claude academy 🎓

TLDR AI · 10/05 08:00

中文介绍 Prime Inference、代理人群、Claude学院。

The Agent Said It Was Done. The Database Disagreed.

Hugging Face Blog · 10/04 06:56

中文介绍 代理说已完成，但数据库不同意。