LAMB: Latent Multi-Hypothesis Beliefs for Long-Horizon Large Language Agents
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 r...
| Authors | Peien Ding, Hui Li, Yuhan Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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