Agentic Monte Carlo enables RL-style optimization of black-box LLM agents by sampling from the optimal policy posterior using Sequential Monte Carlo.
ADaPT: As-needed decomposition and planning with language models
3 Pith papers cite this work. Polarity classification is still indexing.
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SPIKE dual-controller framework raises success rates 5-9 points and cuts tokens 55% in StarDojo agents by reusing strategic plans across stable segments and escalating only at detected events.
Full-horizon planning with on-demand replanning achieves accuracy parity with single-step planning in tool-calling agents for knowledge base and multi-hop question answering while consuming 2-3 times fewer tokens.
citing papers explorer
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Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
Agentic Monte Carlo enables RL-style optimization of black-box LLM agents by sampling from the optimal policy posterior using Sequential Monte Carlo.
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SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents
SPIKE dual-controller framework raises success rates 5-9 points and cuts tokens 55% in StarDojo agents by reusing strategic plans across stable segments and escalating only at detected events.
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Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Full-horizon planning with on-demand replanning achieves accuracy parity with single-step planning in tool-calling agents for knowledge base and multi-hop question answering while consuming 2-3 times fewer tokens.