VOL. 026
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 / 2026-08-12
01
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
The safety of large language model (LLM) agents depends not only on model weights but also on the agent harness that manages context, memory, tools, permissions, and runtime control.
02
Towards Expert-level Medical AI for Real-time Video Consultations
Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues.
03
DSLE: A Learning Environment for Dark Souls Boss Encounters
We introduce the Dark Souls Learning Environment (DSLE), a containerized platform that presents all 22 boss encounters of Dark Souls: Remastered as game-playing agent benchmarks through a Gymnasium-style interface.
04
Defining Decentralization: An Ontological Perspective
Decentralization as a concept in computer science has existed for over half a century.
05
Multi-Agent AI Safety as an Institutional Design Problem
AI agents increasingly work inside systems that govern how they delegate tasks, move information, execute actions, and use shared resources.
06
CEAA: A Cognitive Embodied Agents Architecture for Interactive Computing Systems
The development of embodied Intelligent Virtual Agents (IVAs) that have cognitive capabilities in real-time interactive virtual environments remains a challenge.
07
Stealing Reasoning Traces from Proprietary LLM APIs
Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage.
08
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose.