VOL. 022
8 research signalsDAILY EDITION / SAVED SNAPSHOT
A dated Agentic RL Daily snapshot with 8 verified primary-source signals across papers, official releases, deployment evidence, and safety or alignment findings.
EDITOR'S VIEW
FULL EDITION
Archived / 2026-08-08
01
Resourced Authority A Mechanism-Design Model for Participatory Governance of Deployed AI Agents
We give a formal mechanism design model for the continuous participatory governance of a deployed AI agent.
02
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging.
03
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness.
04
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments.
05
alibaba/ROLL v0.3.0
ROLL发布了v0.3.0版本，新增Video RLVR、AgentRunner 2.0、MTP训练、Router Replay、Multi-Teacher OPD等重要特性。
06
GitHub ->07
GitHub ->08
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
官方来源补充信号：Despite advances in artificial intelligence (AI) across multiple sectors, today's AI tools, including deep learning and generative AI, still fail when embedded into physical systems, such as robots and vehicles operating under real-world physical laws. This stems from their inability to maintain reliable world models for long-horizon planning under uncertainty and generalize to unseen scenarios. In this context, wireless networks, through pervasive sensing and communication, can orchestrate physical intelligence. However, current architectures optimize throughput, latency, and reliability and