Jul 2026· 2026 3rd World Conference on Computer and Information Security (WCCIS)· pp. 70-73· 0 citations· 13 references
Abstract
Large-language-model (LLM) agents perform well in embodied benchmarks but are costly, stochastic, and difficult to audit. We propose an LLM-free zero-shot decision agent for ALFWorld that combines structured commonsense priors with adaptive calibration. The agent uses object-location priors, task templates, synonym mappings, and a hierarchical state controller over admissible commands. Because every decision is traceable to explicit knowledge entries, success and failure signals update only the responsible entries rather than all parameters. On 134 ALFWorld valid_unseen tasks, static priors obtain 67.2% success; symmetric calibration over P(obj,loc), M(target,entity), and S(word) improves this to 73.9%, with one-round convergence and CPU-only execution. We also observe that 34/134 tasks contain description-environment inconsistencies; on the consistent subset, our system reaches 93.0%. The results show that interpretable structured priors can be a practical alternative for well-specified embodied decision making.
It is suggested that reliable inference-time adaptation requires not only better proposals, but also a principled mechanism for deciding which updates should become part of the deployed system.
Yu-Hua Jiang, Jiaming Wang, Qing-Bin Liu et al.· 0 citations
The results suggest that systematically decomposing the team decision problem, using LLMs as tractable surrogates for otherwise intractable inference and planning computations, and retaining conventional verification for physical feasibility can improve both task coordination and the human experience.
Dong Hae Mangalindan, Anand Gokhale, Francesco Bullo et al.· 0 citations
This paper investigates whether model's internal representations provide stronger signals of eventual task success in multi-turn agentic setups, and introduces two complementary methods: Latent Trajectory Dynamics (LTD) and the Action Representation Probe (ARP), which predicts success from representations formed at act...
Priyanka Mary Mammen, Emil Joswin, Srujananjali Medicherla· 0 citations
An agentic framework enhanced with an experience memory designed for the sequential setting and addressing common challenges of sequential decision-making such as credit assignment is introduced, and it is shown that post-game reflection and rule extraction yield measurable improvements on tic-tac-toe without modifying...
Jakub Rada, Viliam Lisý AI Center, Department of Computer Science et al.· 0 citations
It is proved that the LLM paired with the BSE is a sound Markov policy on the belief MDP induced by the underlying POMDP, and inherits the Bellman optimality guarantees of classical POMDP theory, provided the LLM is never exposed to the raw history.
Self-Supervised Skill Optimization (SSO) is introduced, a comparative framework that learns a reusable skill from unlabeled task instances alone and outperforms existing GT-free prompt optimizers on both closed-ended and open-ended tasks.
Siran Peng, Cui-Yu Yang, Tianyu Fu et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
