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arXiv AI

Planning Commitment of LLM under Partial Observability

The paper studies how large language models (LLMs) plan under partial observability, comparing aggressive planners that commit to long plans based on unobserved assumptions with conservative planners that only use observed information. Both strategies, whether the LLM outputs a plan directly or generates a PDDL formulation, achieve similar accuracy across three LLMs on two domains. Conservative planners reduce failures but increase API calls, while aggressive planners fail more often but replan efficiently when priors are informative. The study also finds that LLMs treat priors almost as facts, risking failure if priors are unreliable, and proposes hybrid strategies with tunable hyperparameters to balance safety and efficiency.

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Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents

Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.

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arXiv AI
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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
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arXiv AI
Oct 19

Testing the Planning Claims of LLM-Based Agents against Formal Semantics

The paper evaluates the planning abilities of large language model (LLM) agents by comparing their performance against formal semantics defined in hidden PDDL models. Across five domains, four LLMs, six prompting strategies, and three agent architectures, the best setup solved 78% of solvable tasks offline but only 40% online, with limited recognition of unsolvable tasks and no clear benefit from more elaborate prompting. The study concludes that claims of LLM planning competence require validated action traces rather than end‑to‑end success rates.

By Oriol Miro-Lopez-Feliu, Oscar Mir\'o L\'opez-Feliu, Victor Gimenez-Abalos, Javier Vazquez-Salceda, Victor Carballo Araruna, Sergio Alvarez-Napagao
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