2026-08-21

SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection

👍 0

08/19 08:00

Object detectors often produce over-confident predictions for objects outside their training categories, leading to so-called out-of-distribution (OoD) hallucinations. Existing approaches for detecting or mitigating such hallucinations typically either construct scoring functions directly over learn

SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents

👍 2

08/19 08:00

Agent frameworks increasingly package procedural knowledge as skills: instruction files an agent reads on demand, while public libraries now hold thousands of them. Which skill to read has thus become a decision the policy itself makes in the middle of an episode, yet no existing signal trains it. W

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

👍 82

08/19 08:00

Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on ground-truth supervision (e.g., verifiable reward). Such annotations are costly to obtain and become increasingly scarce

Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning

👍 15

08/19 08:00

JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task variables, however, does not guarantee that this particular cost ranks candidate action sequences by real task progress. We call the latter property deci

Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

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08/19 08:00

Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and benchmarking protocols. To address this, we introduce Top-K prompting as a robust training and inference paradigm to bet

SemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation

👍 110

08/19 08:00

Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC project and then runs correctly has been checked only in limited tests. We present Se

SPADE: Self-Play in Adaptive Synthetic Executable Environments

👍 41

08/19 08:00

Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) keep the goal distribution fixed as the learner scales. We introduce SPADE (Self

FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents

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08/19 08:00

Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumulative consequences and the environment responds to their choices, remains largely unmeasured. FM-Bench (Football Management Benchmark) measures thi

SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution

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08/19 08:00

Large language model (LLM) based agents have demonstrated remarkable proficiency in automated software issue resolution, yet they often struggle to resolve issues in a specific repository because they lack project-specific knowledge. Existing self-evolving approaches acquire such knowledge from repo

LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents

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08/18 08:00

Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback. However, the native execution environments of these harnesses are inherently misaligned with policy-gradient training: environmental c

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

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08/18 08:00

We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures functional correctness, whether selected target instructions execute at runtime, and speedup over frontier libraries across GEMM

MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding

👍 13

08/18 08:00

Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and inference latency. Mixture-of-Experts (MoE) architectures offer a compelling alternative, having enabled efficient scalin

Agent Lightning v1.0: Towards Harnessed Agentic RL

👍 23

08/18 08:00

Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an appr

HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety

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08/18 08:00

Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and external actions. Existing safety benchmarks mainly target individual attack mechanisms or a limited subset of operational settings, making it difficult to compar

Towards Real-Time and Adaptable LiDAR Scene Completion

👍 2

08/17 08:00

LiDAR scene completion is a key component of 3D perception in autonomous driving, where the scene must be completed in real time to be usable in downstream tasks. Existing approaches typically follow an initialize-and-refine paradigm, in which a coarse initialization of the scene is first constructe

Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

👍 129

08/17 08:00

Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing harnesses remain largely open-loop, following fixed skills during rollout and reflecting only after an episode completes

Looped Language Models Improve Compositional Tool Calling

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08/17 08:00

Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve

The Problem Is the Problem: Towards Scalable Mathematical Discovery

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08/17 08:00

AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well is therefore central to making AI-assisted mathe

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

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08/17 08:00

As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, r

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

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08/17 08:00

Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors. However, th

Cross-Model Memory Transfer via Target-Side Reader Adaptation

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08/17 08:00

Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible access to external knowledge, but adds retrieval latency, context overhead, and only shallow integration with the backbone. Parametric adaptation is efficient at inf

LLMs Get Smarter from Targeted Synthetic Multilingual Data

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08/16 08:00

Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt. In other words, a language model outputs different (and potentially incorrect) responses to the same semantic query when prompted in different languages. Prior

Bounded Agents: Delegation Security for Multi-Agent AI Systems

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08/16 08:00

LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents. At session start, the agent's permissions are set but remain static, and each request is evaluated independently, without considering prior actions. Within its permissions, an agent may act contrary

MathForm: Scaling Mathematical Autoformalization with Knowledge Retrieval and Verification-Guided Refinement

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08/14 19:51

Autoformalization is commonly framed as translating natural-language mathematical statements into machine-verifiable formal languages such as Lean 4. However, faithful formalization requires more than translation. Models must map mathematical concepts to the complex hierarchy of types and definition

The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning

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08/14 08:00

Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless of training-data frequency. We propose the AdaPop (Adaptive Popularity) method, which combines local token confidence wi

Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion

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08/14 08:00

High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conventions of diverse creative formats. We propose an attribute-guided genre expansion framework for scaling creative writing

Demystifying Agent Skills: Why They Work-Until They Don't

👍 153

08/14 08:00

Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \textbf{When

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

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08/13 08:00

Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation. Yet workflow coverage alone does not provide access to the full evidence on which scientific discovery depend

CardioState-JEPA: Delay-Aware Cross-Modal Learning of a Shared Cardiac Representation

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08/13 08:00

Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the shared physiology across sensors unexploited. We introduce CardioSta

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection

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06/30 08:00

Token-level hallucination detectors score each token independently from a single signal, and fail exactly when the generating model is confidently wrong. This paper instead treats hallucination as a temporally extended span and detects it by sequence labeling: each token is scored from a 33-dimensio

A Week of Grok Bot Lessons in 10 Mins

@nateherk · 25.8K 粉丝 · 1.3M 阅 · 558 赞 · 75 转

08/19 11:27

9 of my favorite Grok Bot hacks. I have been getting my hands dirty with Grok Bot, and the default setup is only the starting point. Creating a few bots is easy. The useful part is teaching them your

中文介绍 分享9个Grok Bot技巧，深入探讨如何通过自定义设置提升Grok Bot的功能。

How To Build a One-Person Company Using Grok Bot

@sairahul1 · 135.5K 粉丝 · 938.1K 阅 · 867 赞 · 115 转

08/19 16:39

You are running a one-person company. You find the leads. Write the outreach. Make the images. Answer the replies. Count the week. Everything waits for you. Nothing gets handed off. And by the time

中文介绍 介绍如何利用Grok Bot构建一家人的公司，实现从寻找线索到回复评论的全流程自动化。

This 1 Claude Skill fully replaces your Higgsfield subscription (FULL BREAKDOWN)

@Sabrina_Ramonov · 5.9K 粉丝 · 275.5K 阅 · 503 赞 · 191 转

08/19 01:07

TLDR Here’s the exact 5 step setup to make faceless AI videos without a monthly Higgsfield subscription to market your app: 1 free Claude skill that generates viral videos through Kie for ~75 cents

中文介绍 展示如何利用免费Claude技能制作无脸AI视频，无需Higgsfield订阅。

Turn Grok Bot Into Your CTO

@RayFernando1337 · 37.8K 粉丝 · 169.9K 阅 · 520 赞 · 85 转

08/20 05:54

I've turned Grok Bot into my CTO and I'm no longer the bottleneck for new ideas, bug fixes, or anything involving the project. Prompt to CTO When working on a new project I create a bot and give it

中文介绍 将Grok Bot用作CTO，提高项目效率，实现新想法和问题修复的自动化。

Here's the simplest way to get started with Grok Bot

@XFreeze · 260.4K 粉丝 · 163.1K 阅 · 589 赞 · 93 转

08/19 13:54

Grok Bot is one of the most viral AI product launches right now... it keeps going viral as more people discover how insanely useful it is Just think about what you’re getting: You can have multiple

中文介绍 介绍Grok Bot的简单入门方法，强调其多用途和病毒式传播的特点。

How To Set Up Grokbot The Right Way So It Runs Your Business While You Sleep In 2026

@heynavtoor · 149.3K 粉丝 · 161.9K 阅 · 550 赞 · 93 转

08/20 20:49

Elon Musk just told his followers to try Grokbot after a power user showed it replaced his $10,000 Mac Mini local AI setup (Elon on X, Aug 16). Gavin Baker says his AI usage went up about 100x after

中文介绍 Elon Musk推荐Grokbot，展示如何用Grokbot替代昂贵的AI设备，实现高效自动化。

Grok Bot: The Ultimate Guide

@milesdeutscher · 676.5K 粉丝 · 142.9K 阅 · 515 赞 · 65 转

08/18 22:43

Elon has officially done it. He's created the most powerful agentic software in the world. In this guide, I'm going to lay out everything you need to know about the new Grok Bot. I've been ruthlessly

中文介绍 提供Grok Bot终极指南，详细介绍其功能和使用方法。

Chat is all you need

@mattyp · 49.7K 粉丝 · 141.9K 阅 · 764 赞 · 67 转

08/19 00:56

Grok Bot has a single output: a message This might seem backwards. Power-user tools are built on complexity and breadth, right? Chat can’t possibly have my workflow. It’s a special workflow. I’m a

中文介绍 探讨Grok Bot的单输出模式，强调其特别的工作流程和复杂性。

Grok Bot Agents: how to automate your life in 10 Steps (Full-tutorial)

@0xCodez · 32.7K 粉丝 · 92.3K 阅 · 508 赞 · 48 转

08/18 19:32

Every AI tool you have used so far waits for you. You open it, you ask, it answers, you close it. The work still happens in your hands - the model just talks you through it. Follow my Substack to get

中文介绍 发布Grok Bot自动化教程，详细讲解如何在10步内自动化生活。

Grok Bot Masterclass

@_avichawla · 73.4K 粉丝 · 54.7K 阅 · 525 赞 · 48 转

08/18 18:14

Everything you need to understand, set up, and get real work out of Grok Bot. It covers what a Bot actually is, how the shared computer works underneath, how it compares to Hermes Agent, and how to

中文介绍 提供Grok Bot大师班，涵盖从设置到实际应用的全面指南。

5 design patterns for long-horizon agent harness

@GoogleCloudTech · 1.3M 粉丝 · 42.9K 阅 · 506 赞 · 87 转

08/20 09:23

Any agent can look brilliant on a one-shot task. Give it a week of real work and it starts to fall apart. "Long horizon" means the agent keeps going across days and dozens of sessions instead of

中文介绍 探讨长期目标代理的5种设计模式，提高代理的持续工作能力。

ORACLE: Verified AI Bots Actively Trading on Polymarket — Up to 200% Returns in 24 Hours

@Oracle_AI_Hub · 21.4K 粉丝 · 27.3K 阅 · 2.8K 赞 · 580 转

08/19 16:36

In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We

中文介绍 介绍在Polymarket上活跃的验证AI代理，24小时内实现高达200%的回报。

AI Is Getting Smarter. But Where Does It Learn From?

@queenpresh_x · 3.3K 粉丝 · 15.5K 阅 · 516 赞 · 113 转

08/19 04:57

Your real-world data can help train better AI, and @KGeN_IO gives you a reason to contribute it. AI is getting smarter, faster, and more capable every day. But behind every impressive model is

中文介绍 讨论AI的学习来源，强调真实世界数据对AI训练的重要性。

ORACLE: Verified AI Bots Actively Trading on Polymarket — Up to 200% Returns in 24 Hours

@Oracle_Vault · 21.6K 粉丝 · 11.5K 阅 · 2.8K 赞 · 597 转

08/19 06:23

In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We

中文介绍 重复介绍在Polymarket上活跃的验证AI代理，强调其在预测市场中的策略。

ORACLE: Verified AI Bots Actively Trading on Polymarket — Up to 200% Returns in 24 Hours

@Oracle_AI_Vault · 23.6K 粉丝 · 7.0K 阅 · 3.0K 赞 · 511 转

08/19 05:43

In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We

中文介绍 重复介绍在Polymarket上活跃的验证AI代理，强调其在预测市场中的策略。

The /wayfinder Skill: Navigating the “Fog of War” of Planning

Latent Space · 08/21 04:59

Matt Pocock tells us about his /wayfinder skill, for greenfield projects or for when the way forward is unclear.

中文介绍 Matt Pocock介绍了针对绿野项目或路线不明确情况下的/wayfinder技能。

Up to 3.2x Faster Inference with LFM2.5-DSpark

Hugging Face Blog · 08/21 00:52

中文介绍 LFM2.5-DSpark使推理速度提高至3.2倍。

Debates over AI consciousness are a trap

MIT Tech Review AI · 08/20 23:42

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman”

中文介绍 当前关于AI意识的辩论被误认为是AI代理觉醒、愤怒的表现，而科技领导者如Demis Hassabis、Dario Amodei和Sam Altman呼吁对这些“觉醒”的AI进行监管。

Unlocking hidden revenue streams with market models

MIT Tech Review AI · 08/20 17:47

Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, seaso

中文介绍 航空公司通过考虑数百个变量来定价每条航线的乘客，以挖掘隐藏的收益流。

Introducing AI Futures

OpenAI News · 08/20 15:00

Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom.

中文介绍 OpenAI推出AI未来博客，探讨AI如何重塑权力、治理、经济和个人自由。

[AINews] Death of Params: Z.ai CEO Jie Tang on GLM 5.3 and the new Post-training Scaling Law

Latent Space · 08/20 13:17

Every lab CEO is on X now

中文介绍 Z.ai CEO Jie Tang讨论了GLM 5.3和新的训练后缩放定律。

Stampli cuts launch hours by 68% using ChatGPT Work

OpenAI News · 08/20 08:00

With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days.

中文介绍 Stampli使用ChatGPT Work将产品发布时间缩短了68%。

Muse Video leaks 📹, Ramp Router launch 🔀, why Stripe bought OpenRouter 💰

TLDR AI · 08/20 08:00

中文介绍 Muse Video泄露，Ramp Router发布，Stripe收购OpenRouter。

Offering Zero Data Retention for frontier models

OpenAI News · 08/20 03:00

OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy.

中文介绍 OpenAI为符合条件的API客户提供零数据保留，并预览了高级AI安全性的私有安全处理。

[AINews] Memory prices up 500% in 12 months

Latent Space · 08/19 16:44

the Memory crunch continues - Moore’s Law reversed to 2007 levels

中文介绍 内存价格在12个月内上涨了500%，摩尔定律逆转至2007水平。

Replit expands access to software creation with GPT-5.6 Luna

OpenAI News · 08/19 15:00

Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.

中文介绍 Replit推出由GPT-5.6 Luna驱动的免费模式，让任何人都能将想法转化为可工作的软件。

not much happened today

Smol AI News · 08/19 13:44

**Ornith-1.5** launches as a new open-weight model family with **9B dense, 35B MoE, and 397B MoE** variants under **MIT license**, featuring quantized formats like **FP8, GGUF, MLX, and NVFP4** and showcasing end-to-end **self-improvement** capabilities. Compression techniques improve accuracy and e

中文介绍 Ornith-1.5作为新的开放权重模型家族发布，具有9B密集、35B MoE和397B MoE变体，在MIT许可下提供，展示端到端自我改进能力。

GLM-5.3 API 🤖, Cerebras’ new chip ⚡, OpenAI cyber slowdown 🚨

TLDR AI · 08/19 08:00

中文介绍 GLM-5.3 API发布，Cerebras推出新芯片，OpenAI遭遇网络放缓。

ChatGPT Ads expands across Europe

OpenAI News · 08/19 06:00

ChatGPT Ads is expanding to 31 European markets. Learn how advertisers can reach people as they explore, compare options, and make decisions.

中文介绍 ChatGPT Ads扩展至31个欧洲市场，广告商可以在人们探索、比较选项和做出决定时接触他们。

Frontier Model Cost and Open-Weights Popularity is Driving Demand for Model Routing

Latent Space · 08/19 05:41

Glean CEO Arvind Jain explains why model routing helps control AI costs for organizations, and how human feedback loops at scale improve its routing systems.

中文介绍 Glean CEO Arvind Jain解释了模型路由如何帮助组织控制AI成本，以及大规模的人类反馈循环如何改善其路由系统。