Carlos's Debrief

⚡ Quick Summary

• No direct arXiv queue items landed this cycle: the strongest research signal came from curated paper channels and scholar indexing rather than new raw arXiv queue entries.
• **Policy and World Modeling Co-Training for Language Agents**: Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill this gap, yet existing approaches often require separate simulators, extra training stages, or additional inference-time computation.
• **Agent Skills Should Go Beyond Text: The Case for Visual Skills**: Reusable skills are a key mechanism for extending agent capabilities, allowing agents to accumulate experience and solve increasingly complex tasks. Yet most existing skill-learning methods store reusable experience as text-only assets, such as instructions, reasoning traces, or summarized trajectories.
• **Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses**: Search agents are often trained as policies over growing transcripts: the model must decide how to search while also remembering what it has seen, which evidence is useful, which constraints remain open, and which claims have actually been checked …
• **MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft**: Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic open worlds remains unclear.