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Research questionHow can AI agents adapt execution routes as runtime evidence invalidates their planned continuation?Agents often commit to a multi-step execution route before intermediate outcomes are known. When those outcomes change what should happen next, stale actions or broad replanning can waste computation and discard useful progress.
AI
AI Agents
AI Memory
Alignment & Safety
Code Generation & Program Synthesis
Evaluation & Benchmarks
Inference Optimization
Multi-agent Systems
Reasoning
Latest papersRecent research connected to this question, newest first.TROVE: Adaptive Agent Skill Orchestration via Trace-Grounded Route Validation and EditingThe source studies LLM-based agents using workflow-search traces and outcome-conditioned route decisions. Evidence comes from code-generation, question-answering, and math-reasoning benchmarks across different LLM backbones, with comparisons to dataset-level optimization, query-level architecture selection, and graph-constrained scheduling; conclusions are limited to these benchmark and orchestration settings.research paper · Sep 4, 2026Fresh Memory, Stale Plans: Dependency-Scoped Validation for Distributed LLM-Agent MemoryApplies to distributed LLM-agent teams coordinating through shared public records and executing external actions after planning. Evidence comes from 30 controlled live workflows with post-plan revisions and controlled replay; it supports safety and systems-cost conclusions, not general task-accuracy gains.research paper · Sep 3, 2026REPOT: Recoverable Program-of-Thought via Checkpoint RepairThe evidence concerns Program-of-Thought Python programs that print primitive-action plans, evaluated on PuzzleZoo-775, PlanBench Blocksworld, and Derail-550 with closed and open-weight language models. Recovery results vary across models and benchmarks, and the reported Adaptive RePoT results are preliminary.research paper · May 28, 2026
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