2026-08-13

InSight-doc: Agentic Visual Perception for Long-Document Understanding

👍 5

08/11 08:00

Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot. In this work, we propose InSight-doc, an agentic visual perception framework that treats visual resolution as an adaptive reasoning-time resource. InSight-doc starts

DistilVDR: A Compact End-to-End Visual Document Retriever via Dual-Student Distillation

👍 5

08/11 08:00

Visual document retrieval (VDR) is dominated by multi-billion-parameter models that are slow to index at full corpus scale and expensive to serve. Prior compression routes either train a smaller multi-vector encoder from scratch or distil only the query side; neither yields a compact single-vector r

Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation

👍 5

08/11 08:00

We study reference-free post-training for multilingual machine translation with open large language models. Starting from the supervised-finetuned MiLMMT-46-v0.1 models, we apply Group Relative Policy Optimization (GRPO) with a reward that averages two reference-free quality estimation models and is

ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

👍 166

08/11 08:00

After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication. Yet neither explains whether the person forgot, is confused, has side effects, or deliberately refused, nor what support is appropriate. This reveals a structural

SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure

👍 6

08/11 08:00

Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, examples, and warnings, while common action sequences are copied rather than reused. The resulting skill becomes expensive to i

DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?

👍 2

08/11 08:00

Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments. Yet existing benchmarks lack re

SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information

👍 5

08/11 08:00

Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps) to complete user instructions. However, due to the lack of dedicated benchmarks, their capabilities remain poorly unders

VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?

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08/11 08:00

Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments. Everyday life assistance is different. A task runs for weeks rather than minutes. The world keeps changing while the ag

Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference

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08/10 08:00

The Large Language Model from Power Law Decoder Representations (PLDR-LLM) and its attention, Power Law Graph Attention (PLGA), replace the fixed bilinear form of scaled dot-product attention (SDPA) with a learned, input-generated bilinear operator G_{LM}, built from a positive tensor A_{LM} by elem

Decoding-Level Taboo: A Diagnostic Stress Test for LLM Robustness

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08/10 08:00

Large language model evaluations typically focus on performance under nominal conditions, creating an illusion of capability where models comfortably walk a narrow, highly optimized generation corridor. In real-world deployments, however, complex system prompts, safety guardrails, and structural con

Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Design

👍 108

08/10 08:00

Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedback. This survey focuses on co-evolution in agentic systems, a multi-component form of self-evolution in which multiple

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

👍 552

08/10 08:00

We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update the model's recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verba

360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents

👍 4

08/09 08:00

We present 360CityArena, a benchmark for evaluating the urban exploration capabilities of embodied agents within a photorealistic environment constructed from 360-degree videos. Existing outdoor benchmarks either lack sufficient photorealism or complexity, resulting in a considerable gap from real-w

Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents

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08/09 08:00

Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional evidence often declines. This leads to unnecessary token cost, higher latency, and noisier inputs for final report generation.

VectraYX-Vision-1B: A Sub-2B Spanish/LATAM Cybersecurity Vision-Language Model with Structured Visual Reasoning and Native Tool Use

👍 6

08/09 08:00

We present VectraYX-Vision-1B, a sub-2B vision-language model (VLM) for Spanish/LATAM cybersecurity imagery, coupling a frozen SigLIP-so400m encoder to a 1.04B Spanish/LATAM security decoder via an MLP. To our knowledge, it is the first sub-2B VLM specialized for cyber UI (IDA, Ghidra, Wireshark, Nm

On-Policy Self-Distillation without Any Supervision

👍 185

08/09 08:00

On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language models (LLMs). However, existing methods still rely heavily on external supervision, including ground-truth signals, environmental feedback, or guidance from larger models, and therefore fall short

Business Arena: Benchmarking LLM Agents in a Realistic Marketplace

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08/09 08:00

Running a business is a challenging form of intelligent work. Operators must infer opportunities from partial signals, commit capital under uncertainty, adapt to delayed outcomes in a changing market, and satisfy regulatory obligations before trading legally. Frontier LLM agents can increasingly com

Gaming Without an Attacker: Benchmark Fingerprinting in LLM-Driven Search Under Selection Pressure

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08/09 08:00

Benchmarks for systems that are optimized against the evaluation signal measure something different from what they claim. We document this concretely in two GPU-kernel-optimization suites with held-out generalization gates: Metal-Sci (10 scientific-compute tasks) and Metal-ZK (12 zero-knowledge/cryp

SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification

👍 5

08/09 08:00

Large language models (LLMs) increasingly serve as data-driven reasoners, yet their chains-of-thought (CoT) can be unfaithful even when final answers are correct. Most existing ``verification'' signals are not diagnostic: answer matching observes only the outcome, LLM-as-judge provides subjective an

TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity

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08/08 08:00

The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, classification, and generation tasks. Recent models, especially foundation models, benefit from time-series dataset similarity due to its significant rol

A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization

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08/08 08:00

In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control flow) and continuous parameters. While LLMs can be good at the first, they are not efficient at the second, wasting tokens taking discrete jumps in

CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG

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08/07 08:00

Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, while significant information redundancy and noise still remain in the coarse-grained chunks. This paper optimizes the Pare

iFAN: Inference-Aware Learning for Plain Mask Transformers

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08/07 08:00

Query-based mask transformers assemble segmentation outputs through pixel-wise competition among query predictions of the final layer, yet this inference process is not explicitly optimized during training. We identify two key mismatches: the query with the highest probability-mask score does not ne

Mendel Gödel Machine: Recursive Self-Improving Coding Agents via Comparative Evolution

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08/07 08:00

Self-improving coding agents that iteratively rewrite their own source code have demonstrated impressive performance on coding tasks. However, existing solutions generally derive self-modification from a single failure trajectory at a time, overlooking rich comparative signals available in the agent

Beyond Starry Night: Shortcut-Aware Control-State Planning for Artist-Grounded Text to Image Generation

👍 5

08/07 08:00

Artist-grounded image generation requires more than appending an artist name to a prompt. Image models often respond to artist names through canonical shortcuts, such as recurring motifs, generic palettes, or overrepresented period signatures, rather than preserving the user's intended scene. We int

MirrorWorld: Taming Video Diffusion Models for Mirror Reflection Generation

👍 5

08/07 08:00

Recent advances in video diffusion models (VDMs) have enabled high-fidelity video synthesis. However, generating mirror reflections remains challenging because the content within a mirror must remain consistent with the surrounding scene. Existing VDMs are not specifically designed to model scene-to

The Next Screenshot Knows: Gated Hindsight Distillation for Mobile GUI Agents

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08/06 08:00

GUI agents are commonly trained offline from successful interaction trajectories. Standard training decomposes each trajectory into prefix-action pairs: the agent predicts an action from the current screen and interaction history, while the subsequent observation is discarded. This removes the ratio

JigShape: Evaluating Visual-Geometric Reasoning in VLMs through Jigsaw Puzzles

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08/04 08:00

Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that create ambiguous ground truth in texture-repeated regions. We introduce \ours{}, a benchmark with tab-and-blank interlocking pieces where geometric const

Omega-S: A Functional Resilience Index for LLM Fine-Tuning

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08/04 08:00

Fine-tuning a large language model on new data degrades what it previously learned. We present Omega-S, a drop-in penalty computed from the weight matrix alone: it needs no previous-task data, no Fisher matrix and no stored copy of the old weights. It is three lines in an existing training loop and

MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models

👍 5

08/01 08:00

Multimodal Large Language Models (MLLMs) have achieved strong performance on a wide range of vision-language tasks, but often fail under imperfect or shifted contexts. A reliable MLLM should refuse truly out-of-context (OOC) questions with subject-level context shifts while still answering shifted i

To FDE, or not to FDE?

@thejessezhang · 85.7K 粉丝 · 270.4K 阅 · 510 赞 · 30 转

08/11 23:24

Two-thirds of our deployment work is now done autonomously by our own product (via Duet). This post is about why that is, our philosophy on building a product + deployment motion, and how that's

中文介绍 探讨产品构建与部署哲学，分享如何实现自动化部署，强调自主产品Duet的作用。

Agentic Code Quality

@addyosmani · 408.3K 粉丝 · 175.5K 阅 · 509 赞 · 65 转

08/12 14:36

For much of human history, we've evaluated code quality via code review: someone reads what you wrote and makes sure it's clean, thoughtful, fast, understandable, and tests well. For agents, that

中文介绍 分析代码质量评估方法，探讨智能体（agent）的代码质量评估问题。

How to Build a Shared AI Harness for Your Team

@VibeMarketer_ · 30.1K 粉丝 · 107.1K 阅 · 528 赞 · 29 转

08/10 21:36

I am going to show you how to turn the models, agents, skills, and automations living across your team's tools and workflows into one coordinated system. What most teams have today looks very

中文介绍 展示如何构建团队共享的AI工具集，实现模型、智能体、技能和自动化流程的协调。

NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI

@nvidia · 2.6M 粉丝 · 59.5K 阅 · 548 赞 · 66 转

08/11 21:41

The new lightweight open model and routing library delivers greater control over AI, data and workflows across edge devices, PCs, workstations, data centers and the cloud. Source: NVIDIA Blog By Kari

中文介绍 介绍NVIDIA Nemotron 3.5和NeMo Switchyard，提供更高效的智能体AI模型和路由库。

7 rules for self-improving agent loops every AI engineer should know

@GoogleCloudTech · 1.3M 粉丝 · 46.8K 阅 · 505 赞 · 63 转

08/11 01:57

By @Saboo_Shubham_ and @secchi_elia When coding agents write the code, defining what's good is the real engineering job. Coding agents can now build and improve other agents. Write the instructions,

中文介绍 分享7条自我提升智能体循环的规则，适合AI工程师参考。

/show-me: compact visual representations for coding agents

@dexhorthy · 30.0K 粉丝 · 42.9K 阅 · 635 赞 · 31 转

08/12 23:59

tl;dr make your agent converse visually instead of in walls of prose. Lighter and faster than HTML, good enough for most dev-work shaped problems. Coding agents are pretty much unreadable The

中文介绍 介绍用于编码智能体的紧凑视觉表示方法/show-me，提升开发效率。

Intro to Grok Bot

@mattyp · 44.5K 粉丝 · 33.5K 阅 · 651 赞 · 48 转

08/12 02:59

I’m always chasing better tools. Notes apps, workflows, optimizations, and now, personal agents. It’s an easy trap to fall into - always building the “custom” thing and sacrificing the work as a

中文介绍 介绍Grok Bot，探讨如何利用个人智能体提升工作效率。

Grok 4.6 – A field guide

@ericzakariasson · 80.4K 粉丝 · 32.2K 阅 · 719 赞 · 52 转

08/12 23:46

Grok 4.6 is out! I've used it for a few weeks as my daily driver across the normal mix of coding and knowledge work, and built a few projects with it specifically to push on where it holds up. It's

中文介绍 发布Grok 4.6版本，分享使用心得和项目案例。

Scaling AI agents with trustworthy data

MIT Tech Review AI · 08/13 00:51

Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges o

中文介绍 企业和技术领导者普遍认可智能体AI时代已到来，组织迅速采用智能体，少有高管怀疑该技术改变工作的潜力，但许多组织发现实现预期投资回报具有挑战。

Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

Hugging Face Blog · 08/13 00:14

中文介绍 介绍OlmoEarth Studio的定制嵌入导出，用于下游分析。

Putting sign language AI into users’ hands

DeepMind Blog · 08/12 22:01

Introducing sign-language-to-text (SL2T), our breakthrough model powering new sign language features for Deaf and hard of hearing users.

中文介绍 DeepMind推出新的手语到文本模型SL2T，为听障和听力受损用户提供新的手语功能。

LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge

Hugging Face Blog · 08/12 22:00

中文介绍 介绍LFM2.5-VL-3B，用于边缘的更快速视觉能力。

[AINews] How to steal a Reasoning Trace

Latent Space · 08/12 15:11

Speculative Decoding by any other name would distil as sweet

中文介绍 Latent Space介绍了如何进行推理跟踪的投机解码。

From assistance to execution: How enterprises put AI to work

OpenAI News · 08/12 14:00

OpenAI research reveals how enterprises are adopting agentic AI, using ChatGPT and Codex, and how frontier firms are pulling ahead in AI adoption.

中文介绍 OpenAI研究揭示了企业如何采用智能体AI，使用ChatGPT和Codex，以及前沿公司在AI采用方面的领先。

How RingCentral builds AI-native work from engineering to ops

OpenAI News · 08/12 08:00

See how RingCentral uses ChatGPT Work and Codex to accelerate AI product development and centralize operational intelligence across engineering and operations.

中文介绍 RingCentral展示如何使用ChatGPT Work和Codex加速AI产品开发并集中运营情报。

Cursor’s GitHub rival 👨‍💻, OpenAI COO leaves 👋, Gemini 1B milestone 📈

TLDR AI · 08/12 08:00

中文介绍 OpenAI COO离职，Gemini 1B里程碑达成。

🔬The BioAI Phase Shift - Matthew McPartlon & Neil Patil, Chai Discovery

Latent Space · 08/12 05:03

Pharma is suddenly paying for Bio × AI tools, and Chai is leading the pack with four deals closed this summer. Cofounder Matt McPartlon and Product leader Neil Patil explain why.

中文介绍 Chai Discovery的BioAI阶段转变，通过四个夏季交易引领，联合创始人Matt McPartlon和产品负责人Neil Patil解释原因。

Thinking of ACE? We Can Do It with Fewer Tokens

Hugging Face Blog · 08/11 21:37

中文介绍 介绍ACE，通过更少的标记实现。

Testing ads in ChatGPT

OpenAI News · 08/11 18:00

OpenAI begins testing ads in ChatGPT to support free access, with clear labeling, answer independence, strong privacy protections, and user control.

中文介绍 OpenAI开始在ChatGPT中测试广告，以支持免费访问，具有清晰的标签、答案独立性、强大的隐私保护和用户控制。

Daybreak models are now available on AWS

OpenAI News · 08/11 18:00

OpenAI and AWS are making Daybreak cybersecurity capabilities available through Amazon Bedrock to support enterprise security workflows.

中文介绍 OpenAI和AWS将Daybreak网络安全能力通过Amazon Bedrock提供给企业安全工作流程。

[AINews] Muse Glimmer and Spark: Open Weights return Personal Superintelligence promise

Latent Space · 08/11 13:16

a small win for american open models - Glimmer runs on a fits on a single RTX 3090!

中文介绍 Muse Glimmer和Spark：Open Weights回归个人超级智能的承诺。

Muse Glimmer ✨, OpenAI Cyber 🛡️, Claude vs Riemann Hypothesis 🧠

TLDR AI · 08/11 08:00

中文介绍 Muse Glimmer、OpenAI Cyber、Claude与黎曼猜想。

AI professors are negotiating the new realities of academic research

MIT Tech Review AI · 08/11 04:00

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising

中文介绍 AI教授正在协商学术研究的新现实。