AdaptFuse: Training-Free Sequential Preference Inference via Externalized Bayesian Computation
Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manner consistent with Bayesian inference. Existing solutions require fine-tuning on sensitive user interaction data, limiting their applicability in privacy-conscious settings. We propose \textsc{AdaptFuse}, a training-free framework that externalizes probabilistic computation entirely from the LLM, where a symbolic module maintains a Bayesian posterior over a discrete hypothesis set, while a frozen LLM contributes semantic reasoning via multi-sample Dirichlet aggregation. The two signals are combined through entropy-adaptive fusion, which automat...
| Authors | Fangzhou Lin, Zilin Dai, Shuo Xing et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 14 October 2026 |
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