智能体生态观察
智能体生态动态速递
追踪 AI 智能体（Agent）生态的模型发布、框架开源、行业落地与安全治理动态。
Iris: Climbing to the Search Frontier
We present Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together with the data…
DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most long-horizon …
Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning
Despite the remarkable prowess of Vision-Language Models (VLMs) in general multimodal tasks, they remain fundamentally `…
HarvestBench: Measuring Whether LLM Agents Will Pay to Avoid Killing Animals
Benchmarks for the side effects an agent causes on the way to a goal already exist, but HarvestBench is the first to put…
MaxKernel: Agentic Kernel Generation for TPUs
Designing and authoring high-performance custom kernels for accelerators is a complex task that requires deep hardware-l…
Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal
Safety alignment is usually posed as a topic-level question: is this subject harmful? Deployments ask a narrower one. A …
Unlocking Lossless Speedups in LLMs via Discrete Diffusion
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) str…
RoboTok: An Internet-Scale Data Engine for Human Demonstration Retrieval and Dexterous Manipulation Learning
Robot learning increasingly depends on broad and diverse demonstrations, yet collecting robot data remains expensive and…
From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution
Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value…
Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through …
VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement
Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is incapable of …
ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models
Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existi…
MasterControl Seventeen Every Time
We study a governed approach to enterprise analytics: a language model interprets the question, while deterministic poli…
NVIDIA and CrowdStrike Strengthen Agentic Cybersecurity Frontier
“We’re at an inflection point in cybersecurity,” Jensen Huang told a sold-out crowd at CrowdStrike’s Fal.Con 2026 in Las…
EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions, but lon…
Using Grounded Theory for Agent Behavior Analysis at Scale
Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns in long, …
Dr. Claw: An AI Scientist Workspace for Vibe Research
Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, y…
AgenticGen: Reward-Guided Agentic Video Generation for Advertising
Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose …
Scaling Automatic Research Agents via World Models
Automating empirical research is a long-standing direction of AI. Recent automatic research (AutoResearch) agents bring …
AgentKernel: The Trust-Native Agentic Operating System
Modern AI agents routinely cross trust boundaries: they ingest untrusted content, combine it with privileged instruction…