2026-10-16

Lluna: Collaborative Unimodal Models for Missing-Modality Inference

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10/14 08:00

Pretrained unimodal models offer a flexible route to multimodal prediction through collaboration, but this collaboration can become less accurate than a surviving unimodal model when modalities are missing. We study this setting using fixed surviving-model anchors and find that collaborative gains c

Self-Conditioning Enables Deep Sequential Reasoning in Flow Matching

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10/14 08:00

Recent work has explored visual reasoning through image generation, where models solve visual-reasoning problems by directly generating solution images. Intuitively, iterative samplers such as flow matching are well suited to this task, since each sampling step may resemble one reasoning step. Howev

VLX-Go: Robust Embodied Tracking through Multi-Stage Learning and On-Policy Refinement

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10/14 08:00

Embodied visual tracking requires a robot to identify a designated target from visual observations and control its own motion to follow that target through an environment. Following an isolated person is a comparatively simple demonstration of embodied visual tracking, but following the intended per

From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents

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10/14 08:00

Simulated environments enable scalable training data generation for embodied agents, but more data does not necessarily provide what a particular agent needs to learn. Existing environment-generation pipelines largely optimize generic properties such as realism, diversity, or task coverage, while di

VASPO: Self-Distillation with Privileged Value Estimation

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10/14 08:00

On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. They use GRPO for u

Glance Forcing: Making Bidirectional Diffusion Autoregressive at a Glance

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10/14 08:00

Autoregressive generation underlies real-time, causal video synthesis and is the backbone of video world models. Today's dominant approach distills a bidirectional diffusion model into a causal few-step student, but existing pipelines make this conversion prohibitively expensive, requiring offline P

SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm

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10/14 08:00

Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scientific agent swarms can combine evidence and implementations from different research directions to guide their agents' subsequent experiments. However,

AgentSafeEvo: Adversarial Co-Evolution for LLM Agent Safety

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10/14 08:00

Large language model (LLM) agents accomplish complex tasks through multi-turn interactions with external tools. However, malicious content embedded in user queries, tool descriptions, or environment observations can manipulate their decisions and induce unsafe actions. Adversarial co-evolution has e

RuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios

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10/14 08:00

Large language models (LLMs) increasingly tackle complex real-world tasks in specialized domains. However, identifying and correctly applying domain-specific rules remains challenging when scenario information is distributed across multiple files. Existing rule-reasoning benchmarks typically represe

Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports

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10/14 01:23

Evaluation for automatic crisis reporting should measure what practitioners need, not what is convenient to compare. Through LLM-assisted, author-approved content analysis, we derive a 15-slot information schema from professional humanitarian situation reports using qualitative content analysis, the

Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming

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10/13 08:00

Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reasoning performance to collapse on long-form reasoning tasks such as AIME. We propose LayerTrim, a training-free structured pruning method that identifie

PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards

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10/13 08:00

Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this unit-test-only reward teaches a model during debugging and find that, while it raises pass rates, it also teaches the model

CLASHarness: Benchmarking Multi-Agent Conflict in Computer Use via a Controllable and Verifiable Generation Pipeline

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10/13 08:00

Vision-language models (VLMs) are increasingly deployed in multi-agent systems to automate complex computer-use workflows, yet existing benchmarks mostly assume consistent, cooperative environments. In contrast, real-world collaboration rarely runs on consistent information: peer or sub-agents frequ

Choosing LLMs for Strategic Agent Interactions

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10/13 08:00

Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capability dimensions. However, they only evaluate strategic performance across individual fixed models. In parallel, contemporary LLM routers pick models u

To Align or Not To Align: Check Your COMPASS Before You Train

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10/13 08:00

Combining pretrained models is a common strategy for building AI systems, whether by aligning a vision encoder with a language model or merging fine-tuned checkpoints. However, finding compatible pairs typically requires costly empirical search, as compatibility is known only after alignment or merg

Rethinking Projector Training in Multimodal LLMs

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10/13 08:00

Multimodal LLMs are typically trained by first aligning a vision encoder with a language model via projector training on large image-caption datasets, followed by joint training of the full multimodal model. Despite its widespread use, the role of the projector training stage remains poorly understo

Joint Subspace Sparse Attention for Efficient Long-Context Decoding

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10/13 08:00

Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely operate at coarse granularity. Token-level selection offers finer control, yet presents two challenges: low-dimensional scoring may lose query--key structure needed for accurate rank

The Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow

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10/13 02:53

Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact decides whether an action is authorized, frontier open-weight agents do both, and one behavior marks the lie: retroactive permission laundering, actin

When Left Is Right? Camera-Motion Understanding beyond Screen

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10/12 08:00

Can a vision-language model tell left from right? Modern VLMs solve visual puzzles and follow hour-long videos, yet when asked whether the camera moved left or right, most do no better than a coin flip. Adding camera-motion data or geometric representations has been tried, separately, and neither cl

StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions

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10/12 08:00

We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-guided reasoning. The Instant models use bidirectional attention in full-attention layers to improve decision accuracy while keeping single-pass inferen

Rethinking LLM-Guided Visual Question Answering: Does Deeper Knowledge Integration Really Help?

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10/12 08:00

Knowledge-based Visual Question Answering (VQA) requires reasoning that extends beyond visible image content, demanding external commonsense and factual knowledge. While recent work leverages Large Language Models (LLMs) as knowledge sources, the question of how deeply such guidance should be integr

LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators

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10/12 08:00

Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabilities remain largely separated: foundation LLMs encode broad chemical and biological knowledge, while domain-specific fl

ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty

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10/12 08:00

Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional class probabilities unknown. To address this, we introduce ProbabilityBench, a benchmark that provides exact, instance-level class probabilities across

History Replays Itself: Unleashing Long-Context Capability in Linear Attention

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10/11 08:00

Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support ongoing computation and preserve information from an ever-growing history. This coupling has reinforced a prevailing view that the state's memory capaci

Drive3R: Block-wise Streaming Geometric Foundation Models for Autonomous Driving

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10/10 08:00

High-fidelity closed-loop simulation for autonomous driving increasingly relies on dense multi-view 3D reconstruction. While recent feed-forward geometric foundation models have revolutionized per-scene optimization, applying them to continuous, long-horizon driving sequences is computationally intr

Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation

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10/09 08:00

Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as inference becomes a dominant computational workload. However, DLMs degrade quickly with fewer sampling steps, process the full sequence at every step, and

Cortex: Measurement-Gated Evolution and Pruning of Coding-Agent Skills and Rules

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10/09 08:00

Coding agents such as Claude Code also follow text files that developers write for them: a project file, skills the agent may choose to open, and rules that load automatically when the agent reads matching files. We call these files the agent's harness. Developers add to it whenever they correct the

APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces

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10/09 08:00

Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containing files, messages, and business applications. These environments create safety risks that are difficult to capture with attacks placed at a single, known read location. We introdu

The Thinking Leakage in Reinforcement Learning for LLMs

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10/09 08:00

Hybrid reasoning models such as the Qwen3 series switch between a THINK mode and a NO-THINK mode through a few chat-template tokens. A popular recipe trains such models with reinforcement learning in NO-THINK mode and reports improved reasoning performance. We provide evidence that much of this gain

Vela: Scaling Vision-Language-Action Models with Adaptive Action Curve Parametrization

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10/04 08:00

Most vision-language-action models represent future motion as fixed-rate action chunks, tying temporal resolution and prediction horizon to a fixed output budget. This pointwise representation wastes capacity on highly correlated neighboring actions, leaves temporal continuity and smoothness to be l

How Grok Bot Changed the Way I Work

@melvindvivas · 6.3K 粉丝 · 209.9K 阅 · 541 赞 · 83 转

10/13 13:20

I've been using AI coding assistants like Claude Code, Codex, and Cursor for quite some time. They've already changed how I build software, but Grok Bot has changed something else: how I work

中文介绍 博主Melvindvivas分享使用AI编程助手如Claude Code、Codex和Cursor的经验，特别是Grok Bot如何改变其工作方式。

Introducing Glass: Learn Things Once.

@eglyman · 393.9K 粉丝 · 88.6K 阅 · 724 赞 · 58 转

10/13 22:00

Last week, David Senra asked me: “How does Ramp move so fast?” Today we’re launching the answer. But before we do, I’d like to tell you why we built it. Giving every employee AI — the best models, the

中文介绍 博主eglyman介绍Ramp的AI工具Glass，旨在让员工一次性学习，提高工作效率。

How to make a good RL task

@GottliebEli · 809 粉丝 · 28.9K 阅 · 506 赞 · 37 转

10/14 02:36

I’ve been making lots of tasks lately. Many poor, some decent, and now finally, a few good. Through this work I’ve gained what I believe to be valuable insights into the task generation process that

中文介绍 博主GottliebEli分享如何制作好的强化学习任务，通过实际经验总结出有价值的工作流程见解。

Gemini 4 Argon available on Google Cloud, built to help solve your business’s hardest problems

@googlecloud · 573.7K 粉丝 · 21.2K 阅 · 502 赞 · 70 转

10/14 00:07

Last week we announced that Gemini Enterprise has evolved to make it even simpler to get work done, and introduced the Gemini agent, your new universal agent for work. But model intelligence underpins

中文介绍 Google Cloud推出Gemini 4 Argon，旨在帮助企业解决最复杂的问题，强调模型智能的重要性。

Gemini 4 Argon: Built to solve your hardest problems

@GoogleAIStudio · 208.3K 粉丝 · 19.6K 阅 · 635 赞 · 45 转

10/14 01:33

Gemini 4 Argon is now globally available today for developers and enterprises via the API in AI Studio and Gemini platform, for Google AI Ultra subscribers in Google Antigravity, and Google AI Ultra

中文介绍 GoogleAIStudio宣布Gemini 4 Argon全球可用，通过API在AI Studio和Gemini平台为开发者和企业提供解决方案。

How to Build an AI Hacking Agent That Finds Bugs While You Sleep

@zack0x01_ · 5.3K 粉丝 · 19.3K 阅 · 501 赞 · 71 转

10/13 14:52

I built an AI agent that hunts bugs while I sleep. Not a chatbot that gives me advice about hacking, an agent on my own server with hands. It maps a target, tests the bug classes I actually care

中文介绍 博主zack0x01_构建一个AI黑客代理，在睡眠时寻找漏洞，实现自主服务器上的安全监控。

Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint

Hugging Face Blog · 10/16 04:34

中文介绍 Hugging Face Blog 发布文章介绍 Granite Switch 4.1 功能，包括聊天、决策和语音。

Bringing GPT-Rosalind to academic research labs

OpenAI News · 10/16 03:00

Introducing the Rosalind Academic Plan, offering eligible academic labs sponsored access to GPT-Rosalind and tools to advance life sciences research.

中文介绍 OpenAI 推出 Rosalind 学术计划，为符合条件的实验室提供 GPT-Rosalind 和工具支持生命科学研究。

Teaching students to think and learn through questions

OpenAI News · 10/15 21:00

Celebrate Teachers’ Day with AI for Educators in Brazilian Portuguese and see how Study Mode can help teachers support active, step-by-step learning.

中文介绍 OpenAI 发布巴西葡萄牙语版 AI 教育工具，探讨如何通过提问帮助学生思考和逐步学习。

[AINews] Modal’s $1B ARR, $16B Series D makes it NYC’s newest AI Infra Decacorn, 3x usage and 3x valuation in 5 months, 15x in 1 yr

Latent Space · 10/15 18:08

Sandboxes are all you need.

中文介绍 Modal 完成 10 亿美元年收入，16 亿美元 D 轮融资，5 个月内用户和估值均增长 3 倍，一年内增长 15 倍。

Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta

Hugging Face Blog · 10/15 08:00

中文介绍 Hugging Face Blog 欢迎Meta推出 Muse Spark Open 开放性多模态模型。

Welcome FLUX.3 Image Klein in Diffusers

Hugging Face Blog · 10/15 08:00

中文介绍 Hugging Face Blog 推出 FLUX.3 图像 Klein 差分扩散模型。

Jev 1.14 ⚡, Grok Voice 3 🎙️, RSI 🔁

TLDR AI · 10/15 08:00

中文介绍 TLDR AI 介绍 Jev 1.14、Grok Voice 3 和 RSI 更新。

Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents

Latent Space · 10/15 05:40

At AI Engineer New York, frontier labs and financial firms explored how to give agents useful autonomy while keeping consequential decisions under control.

中文介绍 Latent Space 文章探讨 OpenAI 和 Anthropic 如何在金融代理中实现自主性和安全性。

Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio

Latent Space · 10/15 04:35

How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists.

中文介绍 Latent Space 报道 Ian Quigley 和 Leash Bio 如何使用数据分析技术应对模型欺骗。

HiRO-ACEv2: Simulating extreme weather at 3 km resolution for a fraction of the cost

Hugging Face Blog · 10/15 00:12

中文介绍 Hugging Face Blog 介绍 HiRO-ACEv2 模型，用于极端天气预报。

Amp puts AI agents to work beyond code with OpenAI models

OpenAI News · 10/14 20:00

See how Amp pushes the frontier of what agents can do with GPT-6 Astra, Ultrafast, GPT-Live-1, and Sign in with ChatGPT.

中文介绍 OpenAI 的 Amp 使用 GPT-6 模型扩展 AI 代理功能，超越代码。

SentinelOne cuts malware analysis from weeks to hours

OpenAI News · 10/14 20:00

With OpenAI Daybreak, SentinelOne cuts weeks of malware analysis to hours, creating capacity to investigate more threats.

中文介绍 OpenAI 与 SentinelOne 合作，缩短恶意软件分析时间，从周级到小时级。

How Third Moment Research runs 10,000 agents for 36+ hours

OpenAI News · 10/14 15:00

With GPT-6 Astra and GPT-6.1 Sol, Codex, and Daybreak for cybersecurity, this AI lab builds agents that focus for days and improve with use.

中文介绍 Third Moment Research 使用 GPT-6 模型构建长时间运行的 AI 代理，专注于提升网络安全。

How Block is building AI-powered security with OpenAI technology

OpenAI News · 10/14 15:00

Block is building AI-powered security with OpenAI. Its security team investigates threats faster and proposes patches in minutes.

中文介绍 Block 利用 OpenAI 技术构建 AI 驱动的安全解决方案，提升威胁调查和补丁提案速度。

[AINews] AI Engineer NYC: Gemini 4 Argon, Vals AI Benchmarks

Latent Space · 10/14 13:40

Gemini 4 Argon went GA today, but we already gave it title story 2 weeks ago and most benchmarks, like CursorBench, simply independently confirm their claims.

中文介绍 Latent Space 报道 AI Engineer NYC 活动中推出的 Gemini 4 Argon 和 Vals AI 基准测试。