VOL. 004
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 / 2026-07-14
01
Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming
Production LLM agents such as Claude Code and Codex operate over untrusted content, files, commands, and workspace state, making safety failures directly actionable. Red-teaming must therefore keep pace with evolving models and tools. Existing approaches mainly optimize attack success and preserve a
02
An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory
Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams. These scams often span over multiple weeks or months, gradually build trust and request for money or sensitive information. Existing scam-detection systems mainly focus on iso
03
alibaba/ROLL v0.3.0
A verified open source signal preserved from the original daily snapshot. The primary source is linked for full context, while the archive keeps the original publication date and source attribution intact.
04
Auditing the Risk Claims of Distributional Reinforcement Learning
Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring. We ask a question theory anticipates but that has not been measured directly: are the risk claims of a trained d
05
StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use. However, real-world tasks are often long-horizon and involve evolving contexts containing accumulated observations, intermediate edits, failed attempts
06
Technical Report on the CVPR 2026@AdvML Workshop Challenge
Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs. Built on DriveLM-style multi-view visual quest
07
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful execution environment spanning 500+ tools across 16 application domains, supporting multi-image, multi-turn tasks where agents must ground progressively arri
08
Multi-Agent Reinforcement Learning for C-V2X RAT Selection
Vehicles are increasingly equipped with advanced V2X communication capabilities. While early V2X apps utilized services such as Cooperative Awareness Messages, recent developments have allowed more advanced applications including cooperative driving, shared perception, and sensor-sharing services. T