Workflow selection for LLM agents is recast as a contextual bandit over a reusable pool, reaching an 81.37% average across five benchmarks while cutting MATH training-phase token overhead by at least 40.55%.
When an AI agent calls an external tool, retrieval previously ranked candidates by semantic relevance alone and could not tell whether a tool would actually work at runtime. RATED pairs a frozen dual-encoder for semantic matching with a lightweight contextual bandit that re-ranks candidates from each execution's success or failure feedback. Under 30% failure injection, fault selection drops from 86.2% for the ToolLLM API retriever to 2.9%, at 0.172 ms added latency per step.
The work introduces Trident, an agentic LLM red-teaming framework composed of a dynamic benchmark with isolated sandbox servers spanning CybORG CAGE 4 and CyberWheel, a dataset of over 13,000 high-fidelity red-blue interaction trajectories for RLVR, and a "Code-as-Policy" RLVR agentic architecture (Trident Agentic) that reformulates red-agent training as a contextual bandit via a Log Summarizer-Planner-Coder design, where a trainable Planner generates complete attack strategies from compressed execution logs and a frozen Coder translates them into executable Python policies deployed against live DRL defenders; empirical evaluation shows that with a single trainable 7B planner, Trident reduces blue-agent defensive performance by an average of 522% compared to static red-agent baselines whil
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