All tags
Model: "dgm-h"
not much happened today
claude gpt-5.2-pro dgm-h rllm anthropic meta-ai-fair agent-frameworks workflow-automation multi-agent-systems reinforcement-learning reward-models self-improving-agents benchmark-generation operational-efficiency closed-loop-feedback jenny_zhang jase_weston mikhail_parakhin jeremyphoward
Anthropic introduced Claude Cowork and Claude Code enabling desktop control of mouse, keyboard, and screen in a macOS research preview, expanding agent capabilities beyond APIs and browsers. The agent ecosystem is evolving towards long-running, parallel, tool-rich workflows with projects like Hermes Agent, T3 Code, Command Center, and Parchi enhancing multi-agent orchestration and autonomous task management. Operational challenges such as fragility and inefficiency in subagents, including GPT-5.2 Pro and Claude browser/computer use, highlight the need for closed-loop feedback systems. Research from Meta AI advances self-improving agents with Hyperagents / DGM-H enabling meta-level procedural improvements, and unifies reinforcement learning post-training with RLLM (RL + LM-as-RM) to improve reward modeling across task types. Additionally, WebArena-Infinity drastically reduces browser environment construction costs, accelerating benchmark and environment generation.