Agentic Monte Carlo enables RL-style optimization of black-box LLM agents by sampling from the optimal policy posterior using Sequential Monte Carlo.
ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Re- flection
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
UCE builds a typed, evolving library of Memory, Strategy, Workflow and Skill units from agent trajectories, improving ALFWorld success from 75.4% to 96.3% and WebShop score from 45.1% to 61.3% while transferring to new actor models.
SMH-Bench supplies 1,100 stratified tasks in a verifiable smart-home simulator to measure LLM performance on explicit control, scheduling, ambiguity, and personalization as environment complexity grows.
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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Unified Context Evolution for LLM Agents
UCE builds a typed, evolving library of Memory, Strategy, Workflow and Skill units from agent trajectories, improving ALFWorld success from 75.4% to 96.3% and WebShop score from 45.1% to 61.3% while transferring to new actor models.
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SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
SMH-Bench supplies 1,100 stratified tasks in a verifiable smart-home simulator to measure LLM performance on explicit control, scheduling, ambiguity, and personalization as environment complexity grows.