arXiv AI
When Should Models Change Their Minds? Contextual Belief Management in Large Language Models
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arXiv AI
Oct 2
Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States
Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States introduces PoS, an inference-time framework that builds and maintains explicit belief states to guide large language model agents. Each belief state combines an estimate of the current world with unresolved task requirements, making clear what the agent still needs to learn and accomplish. PoS validates consistency, monitors task progress to detect Belief Trapping, and tailors recovery to the trapping pattern and unresolved requirements, achieving top performance across four benchmarks with all three LLM backbones.
arXiv AI
Oct 15
The belief-action gap in language model agents
arXiv:2610.17444v1 Announce Type: cross Abstract: Language model (LM) agents can fail for different reasons, requiring different explanations and interventions. We study how state beliefs, action val...
arXiv AI
Oct 15
Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Models
arXiv:2610.17310v1 Announce Type: new Abstract: Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral pattern...
arXiv Computation and Language
Oct 21
LAMB: Latent Multi-Hypothesis Beliefs for Long-Horizon Large Language Agents
The paper introduces LAMB, a recurrent framework that stores latent multi‑hypothesis beliefs for long‑horizon language agents. LAMB separates a shared Common state containing interaction evidence from weighted Delta components representing competing hypotheses, and updates these hypotheses using relative evidence from action‑outcome predictions without generating textual memory. Experiments on ALFWorld, BabyAI, ScienceWorld, and Crafter show that LAMB matches or surpasses prior methods, notably improving success rates and reducing redundant actions.
arXiv Machine Learning
Jun 5
ABBEL: Learning Natural-Language Belief States for Memory-Efficient Interaction
arXiv:2512. 20111v2 Announce Type: replace-cross Abstract: As the time horizons of sequential decision-making tasks grow, keeping full interaction histories in model context becomes increasingly costly.
arXiv AI
Jul 8
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
arXiv:2607. 06503v1 Announce Type: new Abstract: Large language model (LLM) agents solving multi-step tasks frequently commit to trajectories that are doomed to fail, yet continue to consume substantial inference compute before the failure becomes observable.