arXiv AI
Sep 11

Belief-State Engine: Augmenting LLMs for Principled Planning Under Partial Observability

The paper introduces the Belief-State Engine (BSE), an inference module that supplies a large language model (LLM) with a Bayesian posterior over hidden states in a partially observable Markov decision process (POMDP). By keeping the raw action‑observation log hidden from the LLM, the BSE ensures the agent behaves as a sound Markov policy on the belief MDP, thereby inheriting classical POMDP optimality guarantees. Experiments on the Tiger POMDP and a red‑team attack‑graph task show that BSE‑augmented agents outperform six baselines in task return, belief calibration, and decision consistency.

By Arnab Chattopadhayay, Debdipta Halder
arXiv AI
Jul 7

ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability

arXiv:2607. 02686v1 Announce Type: new Abstract: Reinforcement learning agents operating under partial observability must act on incomplete information, making them natural candidates for guidance from small language models (SLMs) that carry broad reasoning priors.

By Juarez Monteiro, Nathan Gavenski, Guilherme Lima, Francisco Galuppo, Odinaldo Rodrigues, Adriano Veloso
arXiv AI
Sep 4

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

The paper introduces Imagine-then-Plan (ITP), a framework that lets agents learn by interacting with a learned world model to generate multi-step imagined trajectories. ITP features an adaptive lookahead mechanism that balances ultimate goals with task progress, producing richer signals about future outcomes. Experiments on various benchmarks show that ITP outperforms existing baselines, and analyses suggest the adaptive lookahead improves reasoning for complex tasks.

By Youwei Liu, Jian Wang, Hanlin Wang, Beichen Guo, Wenjie Li