[Submitted on 18 Oct 2026]
Title:LAMB: Latent Multi-Hypothesis Beliefs for Long-Horizon Large Language Agents
View a PDF of the paper titled LAMB: Latent Multi-Hypothesis Beliefs for Long-Horizon Large Language Agents, by Peien Ding and 3 other authors
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Abstract:Long-horizon language agents require compact memories that retain interaction evidence while preserving competing hypotheses about unobserved states. Although effective, many belief-state methods encode memory in discrete natural language, where repeated compression can omit evidence or prematurely discard plausible alternatives. We introduce LAMB, a recurrent framework for latent multi-hypothesis beliefs. A Common--Delta factorization combines a shared Common state for interaction evidence with weighted Delta components for competing hypotheses. Relative evidence from action-outcome predictions guides recurrent hypothesis updates without generating textual memory. A context-conditioned readout exposes hypotheses separately to the policy through a fixed-token belief prefix in the default configuration. The belief modules and policy are jointly trained using action imitation and auxiliary state, outcome, and observation-discrimination objectives. Across ALFWorld, BabyAI, ScienceWorld, and Crafter, LAMB matches or outperforms PABU, improving ALFWorld SR@30 and ScienceWorld success by 6.0 and 22.0 percentage points, respectively, and Crafter achievement progress by 26.37 points. On ALFWorld, it also reduces the redundant-action rate by 17.70 percentage points. These results support latent multi-hypothesis beliefs as compact memory for effective long-horizon decision making
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Computation and Language (cs.CL)
Cite as: arXiv:2610.26955 [cs.CL]
(or arXiv:2610.26955v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.26955
arXiv-issued DOI via DataCite
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From: Peien Ding [view email] [v1] Sun, 18 Oct 2026 10:41:04 UTC (1,349 KB)
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