REINS: A Self-Evolving Agent Harness for Real-Time Trajectory Planning
Overview
This research introduces REINS, a runtime harness designed for real-time trajectory planning that wraps an LLM's reasoning loop with tool dispatch, verification, and persistent memory. The system grounds LLM outputs to a learned variable-level dynamics model, verifies them via calibrated predicates and forward rollout, and consolidates results into an indexed rule memory serving future scenes in O(log n) time. By closing a self-evolution loop where familiar scenes resolve via memory lookup and novel scenes invoke the LLM to enrich memory, REINS achieves over 98% compliance, nearly 50% collision reduction, and 1.9-4.6 ms latency on simulated and real-world benchmarks without modifying LLM weights.
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“vel scenes invoke the LLM, whose verified outputs enrich the memory. Without modifying LLM weights, REINS attains 98%+ compliance, nearly 50% collision reduction, and 1.9-4.6 ms latency on simulated and real-world benchmarks at the tactical decision layer, with LLM invocation rate monotonically declining as memory matures. Code: https://github.com/ZhihongCui/REINS”
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“vel scenes invoke the LLM, whose verified outputs enrich the memory. Without modifying LLM weights, REINS attains 98%+ compliance, nearly 50% collision reduction, and 1.9-4.6 ms latency on simulated and real-world benchmarks at the tactical decision layer, with LLM invocation rate monotonically declining as memory matures. Code: https://github.com/ZhihongCui/REINS”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalREINS: A Self-Evolving Agent Harness for Real-Time Trajectory Planning
This research introduces REINS, a runtime harness designed for real-time trajectory planning that wraps an LLM's reasoning loop with tool dispatch, verification, and persistent memory. The system grounds LLM outputs to a learned variable-level dynamics model, verifies them via calibrated predicates and forward rollout, and consolidates results into an indexed rule memory serving future scenes in O(log n) time. By closing a self-evolution loop where familiar scenes resolve via memory lookup and novel scenes invoke the LLM to enrich memory, REINS achieves over 98% compliance, nearly 50% collision reduction, and 1.9-4.6 ms latency on simulated and real-world benchmarks without modifying LLM weights.
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