arXiv:2606.17832v1 Announce Type: new Abstract: Large language models (LLMs) are often hypothesized to perform implicit Bayesian inference, yet a key coherence condition, the martingale property of predictive beliefs, has been shown to fail in controlled synthetic in-context learning settings. We revisit this question in a more typical usage regime: generic multiple-choice question answering. Exploiting the discrete answer space, we compute exact predictive distributions and study belief dynamics induced by autoregressive answer resampling. We introduce prompted predictive resampling (PPR), wh
The paper addresses a known limitation of LLMs (belief instability) at a time when their applications are rapidly expanding and their deployment in critical systems is increasing.
Improved belief coherence makes LLMs more reliable and trustworthy for complex tasks, potentially broadening their utility in high-stakes environments and accelerating the development of more advanced AI agents.
The proposed 'prompted predictive resampling (PPR)' method offers a concrete mechanism to stabilize LLM beliefs, moving them closer to theoretical ideals of rational inference.
- · AI developers
- · LLM-powered applications
- · Researchers in AI safety and alignment
Increased reliability and trustworthiness of large language models for complex inferential tasks.
Faster adoption of LLMs in environments requiring high epistemic coherence, such as scientific discovery or financial modeling.
The development of more robust, autonomous AI agents capable of maintaining consistent and evolving belief states over extended interactions.
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Read at arXiv cs.LG