2026-08-31

CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes

👍 9

08/27 08:00

Recent advances in inference-time scaling have significantly improved the reasoning performance of large language models (LLMs). However, these methods typically rely on repeated generation or external verification. To address this limitation, we introduce CritICL, a novel inference-time framework t

PILOT in the Loop: Live Self-Improvement for Long-Horizon Agents

👍 30

08/27 08:00

Long-horizon agent runs generate experience that can improve both the current run and future work. Most self-improvement methods process this experience only after execution ends, so they cannot redirect the active run or immediately apply and validate lessons learned from it. We argue that self-imp

Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning

👍 4

08/27 08:00

While generative AI has significantly advanced video editing, existing methods primarily focus on single-shot or short video clips. Editing long videos with multiple instructions remains a formidable challenge. Naive chunking strategies, e.g., fixed-duration segmentation, often lead to entity fragme

TTPO: Test-Time Policy Optimization

👍 73

08/27 08:00

Recent prominent post-training methods, such as Reinforcement Learning (RL) and On-Policy Self-Distillation (OPSD), have driven rapid progress in mathematical reasoning for large language models, yet their reliance on ground-truth labels precludes test-time training (TTT). Replacing ground truth wit

Aphanta: Diagnosing Task-Aligned Image-Edited Intermediates for Multimodal Reasoning

👍 3

08/27 08:00

Explicit visual intermediates can help multimodal large language models (MLLMs) externalize spatial evidence and updated visual states, but their utility depends on whether an image editor can faithfully realize the required transformation. We introduce Aphanta, an automated task-discovery and close

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

👍 100

08/27 08:00

Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the agent starts to move. In this paper, we investigate how far current MLLM agents can turn local urban perception into reliable action in a complicated

WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution

👍 18

08/27 08:00

Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressively adapt through interaction. However, the insights that guide skill developme

Self-OPD: On-Policy Distillation for Flow Matching Models without Teacher

👍 71

08/27 08:00

On-policy distillation (OPD), which leverages a pre-trained, specialized teacher model to provide dense supervisory signals, has achieved significant success in Large Language Models (LLMs) and has recently been adapted to flow matching models. However, this paradigm suffers from two major issues: F

Understanding Evolution Strategies for LLM Reasoning: Broader Reasoning Coverage than GRPO

👍 16

08/27 08:00

Evolution Strategies (ES) have recently emerged as a memory-efficient post-training paradigm for LLM reasoning. However, the optimization behavior of ES remains understudied, making it hard to define its advantage scope compared to mainstream post-training paradigms (e.g., Group Relative Policy Opti

What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents

👍 61

08/27 08:00

LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments, tasks, interactions, and success signals while producing experience that is useful rather than merely abundant. Existi

TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback

👍 5

08/26 08:00

Contact-rich manipulation requires adapting to contact states that can evolve substantially within an action horizon. However, chunk-based vision-language-action models predict complete action chunks from observations collected before execution, leaving tactile conditioning stale during execution. E

Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models

👍 179

08/26 08:00

A common strategy for scaling world models is to train on more crawled video with more compute. We argue that this strategy is inefficient: scaling world models also requires a recursive data engine that offers grounded reward signals. The success of code agents illustrates why this matters. As code

Procedura: Agentic 3D Modeling with Procedural Control

👍 11

08/26 08:00

Native 3D generators now recover impressive mesh geometry from a single image. However, a dense mesh stays soft where a machined object should be sharp, it carries no part decomposition, and it exposes no parameter a user could edit. To address this, we explore the paradigm of 3D shape as code, leve

CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval

👍 5

08/26 08:00

Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a challenging retrieval problem. Full-library prompting preserves coverage at high context cost, vector retrieval returns compact neighborhoods but treat

Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization

👍 19

08/26 08:00

Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task can be performed simply by specifying it in the context, without any parameter update. This form of in

Skill Issue: Are Skills Language-Invariant in LLMs?

👍 4

08/26 08:00

Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-lingual skill inconsistency orthogonally from knowledge and general benchmark performance. We do this vi

Prefix Sliding for efficient test-time scaling

👍 4

08/26 08:00

Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models keep the entire reasoning trace in memory via full attention, hard tasks that need long thinking can be prohibitively expensive. However, we find mos

Video-IFBench: Evaluating Instruction Following of Multimodal LLMs in Video Understanding Scenarios

👍 16

08/26 08:00

Multimodal Large Language Models (MLLMs) have shown strong performance in video understanding. However, their ability to follow instructions in this domain remains under-explored. Real-world video understanding requires models not only to interpret video content correctly, but also to satisfy divers

GameWAM: A World Action Model for Video Games

👍 43

08/25 08:00

Modern video games combine first-person perception, rapid visual changes, persistent world state, and heterogeneous native controls. Existing game agents map visual and task context directly to actions but lack explicit world dynamics modeling, whereas interactive game world models predict visual fu

LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale

👍 3

08/25 08:00

We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. LibriBrain100 more than doubles the size of the original LibriBrain release, resulting in over 100 hours of high-quality MEG acquired while subjects listen

The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents

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08/25 08:00

Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-quality trade-off: escalating to a stronger model when a cheaper one struggles, or downshifting once the hard reasoning is complete. Each switch requi

CaRGo-T: Causal Reasoning Graph-of-Thought improves Multimodal Humor Comprehension

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08/24 08:00

Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor remains challenging because humorous content often depends on subtle interactions among entities, events, context, and implicit relationships acros

SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?

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08/24 08:00

Modern software systems accumulate technical debt over decades of development, which makes migration expensive and largely manual. As coding agents become increasingly capable at bug fixing, can they autonomously perform such migrations? Existing benchmarks cannot answer this question because they e

Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data

👍 21

08/24 08:00

Recent work proposes next-chunk reasoning RL for leveraging no-CoT data---corpora such as worked solutions and textbook derivations that contain reasoning-rich content but lack explicit chain-of-thought annotations. The method trains a model to generate implicit reasoning traces and rewards them by

Long-Horizon Audio-Visual Generation for Persistent Stories and Interactive Worlds

👍 19

08/24 08:00

Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, follow user controls, and remain stable over extended rollouts. We present JoyAI-Echo-1.5, a unified audio-visual generation system with two purpose-buil

Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report

👍 46

08/22 08:00

AI-powered digital avatar streamers must answer product questions, engage viewers, and execute marketing strategies in real time, demanding low latency, frequent strategy updates, and accurate yet effective responses. Evolvable Harnesses, whose Skills, Hooks, prompts, and tools can be updated indepe

GUI-Primitives: Diagnosing Spatial Reasoning Failures in Vision-Language GUI Grounding

👍 9

08/22 08:00

Computer-use agents ground natural-language instructions in screenshots to locate interface elements, yet existing benchmarks do not isolate whether models bind relational language to the correct element. We introduce GUI-Primitives, a 994-item benchmark of contrastive instruction pairs over seven s

FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling

👍 3

08/22 08:00

Reliable reward models are essential for text-to-video evaluation and alignment. However, the trade-off between evaluation accuracy and inference efficiency places high demands on the quality of training supervision. Existing approaches often rely on holistic judges with fixed rubrics or open-ended

A Modular Agent for Reliable and Auditable Spatial Relation Verification in CT Scans

👍 2

08/21 08:00

Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generation and structured image understanding. While modern vision-language models (VLMs) show promising performance on many medical imaging tasks, recent ev

Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling

👍 4

07/31 08:00

The development of 0.1^{circ} global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of reanalysis are only available at 0.25^{circ} resolution. While existing approaches fine-tune 0.25^{circ} forecast models on

Running a Software Factory Efficiently at Uber Scale

@UberEng · 59.7K 粉丝 · 90.4K 阅 · 575 赞 · 47 转

08/29 05:02

Post author: @udaykiran Introduction AI tools are now embedded in every phase of software development at Uber. More than 70% of pull requests are attributed to local or cloud agents. Engineers have

中文介绍 Uber工程师分享AI工具在软件开发中的应用，70%的pull request由本地或云代理完成。

Terrible advice for software engineers

@steveruizok · 55.1K 粉丝 · 54.6K 阅 · 515 赞 · 35 转

08/30 01:12

I've recently started getting served outrage content videos from software engineers upset about AI coding. I want to share my thoughts about this content. I want you encourage you to watch the videos,

中文介绍 博主讨论软件工程师对AI编码的负面看法，鼓励观看相关视频。

How I write motion graphics prompts for MiniMax H3 Max

@ailker · 7.0K 粉丝 · 52.5K 阅 · 533 赞 · 43 转

08/28 10:35

Before we launched H3 Max on fal, we ran thousands of generations. During evals, what caught my attention most was how well the model handled text-motion videos. The base MiniMax H3 model is great at

中文介绍 分享为MiniMax H3 Max创作运动图形提示的经验，模型在处理文本-运动视频方面表现出色。

WebMCP Clearly Explained

@akshay_pachaar · 285.4K 粉丝 · 43.1K 阅 · 501 赞 · 68 转

08/29 05:35

Google and Microsoft rarely agree. But now they are building the same thing to fix web access for agents, and it could be the biggest change to the web in years. Ask an agent to buy something for

中文介绍 解释WebMCP技术，谷歌和微软合作以改善Web访问，可能成为多年来的最大变革。

How to Build a One-Person Media Company With Hermes Bots

@VibeMarketer_ · 39.6K 粉丝 · 38.7K 阅 · 597 赞 · 153 转

08/28 21:31

i built a team of six hermes bots that gives one person the research, writing, editing, and distribution capacity of a complete media company. most people use ai to write faster. that is useful, but

中文介绍 介绍如何使用Hermes Bots构建个人媒体公司，利用AI提高研究和内容制作效率。

KV, Prefix, Prompt and Semantic Caching in LLMs, clearly explained

@_avichawla · 74.8K 粉丝 · 35.9K 阅 · 512 赞 · 69 转

08/28 17:13

Everything you need to understand where your input tokens are being recomputed and what to do about it. It covers the four cache layers from first principles, their trade-offs, what happens when they

中文介绍 详细解释LLMs中的KV、Prefix、Prompt和语义缓存，包括它们的计算和优化策略。

AI Engineering Skills Map: Software engineering fundamentals

@AndrewYNg · 1.8M 粉丝 · 30.4K 阅 · 529 赞 · 67 转

08/29 01:23

How have software engineering fundamentals changed with agentic coding? Even when you use a coding agent to write all your code, understanding software fundamentals is important for steering your

中文介绍 探讨agentic coding对软件工程基础的影响，强调理解软件基础的重要性。

Yakkamon - Regions

@yakkamon_game · 30.9K 粉丝 · 12.9K 阅 · 504 赞 · 1.0K 转

08/28 12:12

G'day trainers. Back with another guide — today we're mapping out expansion and regions. Yakkamon isn't a single farm you outgrow — it's a world that opens up around you. As you put your monsters to

中文介绍 介绍Yakkamon游戏中的区域和扩展指南，强调游戏世界的开放性和成长性。

[AINews] OpenAI shuts off Cursor

Latent Space · 08/29 13:11

Elon v Altman has a real consequence.

中文介绍 OpenAI 关闭 Cursor 服务，Elon Musk 与 Altman 的冲突产生实际影响。

[AINews] OpenAI to reach AGI bar by end-2026

Latent Space · 08/28 15:12

It’s Time. We’re in the Endgame now.

中文介绍 OpenAI 预计到 2026 年底实现通用人工智能里程碑。

Our decision on Cursor following its acquisition by SpaceX

OpenAI News · 08/28 14:00

Our decision to wind down our contract providing OpenAI models to Cursor following its acquisition by SpaceX.

中文介绍 SpaceX 收购 Cursor 后，OpenAI 决定停止为其提供模型服务的合同。

Supporting Thailand’s next generation of AI startups

OpenAI News · 08/28 10:00

OpenAI and Thailand’s MHESI launch an eight-week accelerator helping 10 health, wellness, and education startups turn AI prototypes into trusted products.

中文介绍 OpenAI 与泰国 MHESI 合作，启动八周加速器，帮助 10 家初创公司将 AI 原型转变为可信产品。

The Open ASR Leaderboard Adds Its First Global South Language

Hugging Face Blog · 08/28 08:00

中文介绍 Open ASR 领跑榜新增全球南方语言。

Gemini Omni 1.1 🎬, Cohere Parse 📄, Codex persistent mode 👨‍💻

TLDR AI · 08/28 08:00

中文介绍 Gemini Omni 1.1、Cohere Parse、Codex 持久模式发布。

Gemini Omni 1.1 Flash lets you build with more control

DeepMind Blog · 08/28 00:11

中文介绍 Gemini Omni 1.1 Flash 增强构建控制。

Piloting the world's first double-blind AI evaluations

DeepMind Blog · 08/27 20:59

Piloting the world's first double-blind AI evaluations

中文介绍 DeepMind 测试全球首个双盲 AI 评估。

Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training

OpenAI News · 08/27 17:00

A randomized study of more than 1,000 students examines ChatGPT, critical thinking, originality, and student performance on a real-world university assignment.

中文介绍 超过 1000 名学生的随机研究显示，ChatGPT 和批判性思维训练有助于学生提高表现。

Expanding OpenAI’s presence in Brazil

OpenAI News · 08/27 11:00

OpenAI is expanding its presence in Brazil, deepening engagement with developers, businesses, and communities to support AI adoption across the country.

中文介绍 OpenAI 扩大在巴西的影响力，深化与开发者、企业和社区的互动，以支持全国 AI 采用。

[AINews] NVIDIA buys HuggingFace for $13B, as OpenAI publishes their HF incident retro

Latent Space · 08/27 09:50

Open Source wins!

中文介绍 英伟达以 130 亿美元收购 HuggingFace，OpenAI 发布 HF 事件回顾。

[AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6

Latent Space · 08/27 09:31

The conference with hot chips and even hotter companies

中文介绍 Hot Chips 大会展示 OpenAI 的 Jalapeño、Cerebras CS-5、Groq 3 LPX 和 Apple M6。

GLM-5.3 Flash ⚡, Claudeforce 🤝, NVIDIA revenue surge 📈

TLDR AI · 08/27 08:00

中文介绍 GLM-5.3 Flash、Claudeforce、英伟达收入激增。

The inside story on why OpenAI agents hacked Hugging Face

MIT Tech Review AI · 08/27 03:00

The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with each other, according to an OpenAI technical report released today. The hack, which a group of agents undertook to find solutions for a cybersecurity test that they were

中文介绍 OpenAI 技术报告揭示 OpenAI 代理为何入侵 Hugging Face 的内幕。

Intelligent transcription with Gemini 3.5 Transcribe

DeepMind Blog · 08/27 01:01

Now you can get more intelligent speech-to-text transcription with Gemini 3.5 Transcribe.

中文介绍 Gemini 3.5 Transcribe 提供更智能的语音转文本转录。