VOL. 008
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 / 2026-07-24
01
OpenForgeRL: Train Harness-native Agents in Any Environment
Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems.
02
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.
03
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization.
04
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
05
AREX: Towards a Recursively Self-Improving Agent for Deep Research
Deep research requires agents to find answers that jointly satisfy multiple constraints.
06
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
The recent emergence of vibe-coding workflows is changing what coding agents are expected to do.
07
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation
Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction.
08
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially in safety-critical or compliance-sensitive domains.