2026-08-20

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

👍 2

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

👍 1

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

👍 12

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

👍 16

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

👍 7

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

Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation

👍 44

08/18 08:00

Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing methods often force VLMs into unnatural action spaces that misalign with their 2D pre-training priors, compounded by rigid reasoning schedules and in

From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation

👍 9

08/18 08:00

Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation. A central challenge is not only how to curate each task-specific corpus, but also how to organize heterog

Security Assessment of DeepSeek Harness with A.I.G: Evaluating Resistance to Indirect Prompt Injection

👍 4

08/18 08:00

We assess indirect prompt injection in DeepSeek Harness (DSH), using AI-Infra-Guard (A.I.G) to construct tests, deliver controlled taint, execute DSH, collect traces, and judge outcomes. The study covers 14,560 controlled executions over 16 indirect-content channels, text and file carrier modes, 35

ASI-Bench: At the Dawn of Artificial Superintelligence

👍 51

08/18 08:00

Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results. However, the capabilities of today's AI systems are still largely built on learning, compressing, and applyi

Abra: Scaling Diffusion Image Training

👍 7

08/18 08:00

Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic scaling law study for text-to-image diffusion models using Abra, a controlled family of flow-matching transformers trained across three orders of m

StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows

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08/18 08:00

Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on researcher-selected tasks, leaving uncertain whether such progress extends to the work that real-world users actually dema

aDSL: Agentic 3D Creation via Joint Agent-Program Design

👍 2

08/18 08:00

Programmatic representations provide a compelling paradigm for 3D content creation, enabling fine-grained edits, interpretability, and explicit structural control. Yet, agentic workflows that rely on large language models (LLMs) to author 3D programs remain brittle, often failing to translate high-l

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

👍 91

08/18 08:00

Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing several limitations of RL: its heavyweight backpropagation-based training stack makes it impractical to f

The Problem Is the Problem: Towards Scalable Mathematical Discovery

👍 2

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

👍 15

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

👍 1

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

Unifying Graph Neural Networks Through a Common Layer Equation

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08/17 08:00

Graph neural networks are commonly described through family-specific equations whose notation obscures shared computations and structural differences. We introduce a common layer equation that represents covered architectures through seven components: an update domain, channel set, propagation bank,

FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution

👍 58

08/17 08:00

Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the

From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents

👍 6

08/17 08:00

Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local signals, such as token probabilities, predictive entropy, or per-step confidence, and therefore overlook the long-range

StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding

👍 5

08/17 08:00

Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compression, yet a training-free sliding-window baseline already matches them. We therefore fix a memory-free recent-window proto

Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning

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08/16 08:00

Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments. By generating intermediate reasoning images, Visual CoT provides an intuitive mechanism for visual foresight but introduces substantial inference overh

Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents

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08/15 08:00

Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness eva

StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

👍 373

08/15 08:00

Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. We bet on harness scaling to improve the execution system around an ag

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

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

👍 129

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

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

👍 1

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

AVA-Encoder: Towards Agent-Native Video Representation Learning

👍 37

08/12 08:00

Creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key challenge is the absence of a structured video representation that is both faithful to film content and directly usable for agentic reasoning and manipu

Accuracy and Order Sensitivity Diverge Under Label-Free Strategies

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08/12 08:00

Multiple-choice benchmarks are widely used to evaluate large language models, but MCQ scores conflate knowledge with sensitivity to option order, which makes them unreliable measures of model knowledge. In this paper, we test whether preventing a model from seeing option labels while committing to a

HarmProfile: Characterizing Harmful Distributions in Frontier LLMs

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06/11 08:00

Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of

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 nateherk分享9个Grok Bot技巧，从默认设置出发，创建多个机器人并教会它们任务。

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 sairahul1介绍如何利用Grok Bot管理一家个人公司，从寻找线索到回复回复，一应俱全。

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 XFreeze介绍Grok Bot，强调其病毒式传播和实用性，适用于多种场景。

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 milesdeutscher发布Grok Bot终极指南，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 mattyp探讨Grok Bot的单输出消息模式，以及其独特的流程和工作方式。

Securing the Infrastructure of Intelligence

@JensenHuang · 1.0M 粉丝 · 110.9K 阅 · 1.9K 赞 · 234 转

08/17 20:40

Land, power and shell: The next critical resource for AI factories. AI factories are the defining infrastructure of the AI era—where compute transforms energy and data into intelligence that powers

中文介绍 JensenHuang讨论AI工厂的基础设施，强调计算如何转化为智能能源和数据。

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 0xCodez发布Grok Bot自动化教程，通过10个步骤实现生活自动化。

Update to Pi App Studio Creation Pricing

@PiCoreTeam · 4.3M 粉丝 · 74.1K 阅 · 850 赞 · 222 转

08/18 06:19

Starting August 24, 2026, Pi App Studio will update how app creation and editing are priced. Until now, Pi has heavily subsidized the cost of creating and editing apps in App Studio for all creators.

中文介绍 PiCoreTeam更新Pi App Studio定价策略，自2026年8月24日起调整应用制作和编辑的定价方式。

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 _avichawla提供Grok Bot大师班课程，涵盖Bot的设置和使用，以及与Hermes Agent的比较。

Bring your spreadsheet data to life

@GoogleAI · 2.4M 粉丝 · 49.3K 阅 · 516 赞 · 53 转

08/18 03:39

Turn your spreadsheets into live, interactive mini-apps with Sheets canvas and Gemini 3.7 Flash — no coding required. How does it work? Let’s dive into it: Most AI UI tools produce prototypes that

中文介绍 GoogleAI介绍如何使用Sheets canvas和Gemini 3.7 Flash将电子表格数据转化为互动式迷你应用。

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 queenpresh_x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 Oracle_Vault介绍在Polymarket上活跃验证的AI机器人，24小时内回报率高达200%。

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 Oracle_AI_Vault重申在Polymarket上活跃验证的AI机器人，24小时内回报率高达200%。

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重申为零数据保留政策提供支持，并预览了不损害数据隐私的私有安全处理技术。

LFM2.5 Q4\_0 Checkpoints from Quantization-Aware Distillation

Hugging Face Blog · 08/19 21:48

中文介绍 Hugging Face博客发布了LFM2.5 Q4 0 Checkpoints的量化蒸馏信息。

[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支持的免费模式，让任何人都能将想法转化为工作软件。

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

TLDR AI · 08/19 08:00

中文介绍 GLM-5.3 API、Cerebras的新芯片、OpenAI网络速度放缓。

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广告扩展至31个欧洲市场，帮助广告商接触潜在客户。

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进其路由系统。

Strengthening democratic oversight in national security

OpenAI News · 08/19 03:00

OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, and expertise.

中文介绍 OpenAI推出加强国家安全中AI民主监督的倡议，为政府机构提供工具、培训和专业知识。

How Much Memory Does Your Agent Actually Need?

Hugging Face Blog · 08/19 02:09

中文介绍 AI代理实际需要的内存量。

Partnering with CodeAI to prepare the first AI generation

OpenAI News · 08/18 19:00

OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it responsibly.

中文介绍 OpenAI与CodeAI合作，帮助学生建立AI素养，批判性地思考AI，并发展负责任地使用和塑造AI的技能。

Pacing model development in an era of cyber-critical capabilities

OpenAI News · 08/18 19:00

OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development.

中文介绍 OpenAI加强了对前沿AI模型的监控、对齐和安全，以指导模型发展的步伐。

Introducing ChatGPT for Teens: Built for learning, backed by protections

OpenAI News · 08/18 19:00

ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features, and additional controls for parents.

中文介绍 ChatGPT for Teens帮助青少年学习、批判性思考和自信地使用AI，提供更强的内置保护、健康使用功能和家长控制。

We still don’t know how people are really using AI

MIT Tech Review AI · 08/18 18:06

AI companies like Anthropic and OpenAI regularly publish reports on how people are using products like Claude and ChatGPT, but they only release the data they want us to see, AI researchers say. “There is no independent source to corroborate it,” says Anka Reuel, a computer science PhD candidate at

中文介绍 AI公司如Anthropic和OpenAI定期发布关于人们如何使用Claude和ChatGPT等产品的报告，但它们只发布我们想要看到的数据，AI研究人员表示。

AI’s recursive self-improvement might not come so quickly after all

MIT Tech Review AI · 08/18 17:00

The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight. LLMs can already write code, generate synthetic data for training, and optimize the computer chips they run on. Forecasts of explosive AI progress predict that what researchers c

中文介绍 AI行业目前最宏伟的承诺是AI将很快自我改进，几乎不需要人类监督。

Asana cleared 5 years of engineering work in 2 weeks with Codex

OpenAI News · 08/18 15:00

Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.

中文介绍 Asana使用OpenAI Codex在两周内完成了预计需要五年时间的工作，花费约12K美元。