New tools and tests for robust, tool-using language agents

This cluster highlights a wave of work on building and evaluating LLM-based agents that leverage tools, search, and long-term context. New benchmarks like SWE-bench, AgentProcessBench, and the PokeAgent Challenge stress-test real-world coding, step-level tool use, and long-horizon competitive decision-making. Methods such as TRUST-SQL, MR-Search, and layered tool orchestration aim to improve multi-turn reasoning, database querying over unknown schemas, and reliable tool pipelines. OpenSeeker and game-style environments like AI Dungeons & Dragons further democratize research on frontier search agents and rich simulations, pushing toward more capable and trustworthy AI assistants.