From Drift to Coherence: Stabilizing Beliefs in LLMs
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
Researchers have identified and partially resolved a fundamental coherence problem in how large language models form beliefs during inference. Prior work showed LLMs violate the martingale property, a key assumption underlying implicit Bayesian reasoning, but only in synthetic settings. This study extends the analysis to realistic multiple-choice QA tasks and introduces prompted predictive resampling, a technique where models generate repeated answers to the same question. The method exposes early belief drift but reveals that models self-correct through resampling, eventually stabilizing their predictions. This finding matters for practitioners relying on LLM confidence calibration and for theorists building formal models of LLM reasoning under uncertainty.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The practical implication buried in this paper is that confidence scores from a single LLM forward pass may be systematically unreliable not because of calibration failures in the traditional sense, but because the model's internal belief state hasn't yet converged. Resampling is less a fix than a diagnostic that reveals how much inference-time variance practitioners have been quietly ignoring.
The coherence problem described here sits in productive tension with the SoftMoE paper covered the same day, which addresses how expert routing affects compute allocation during inference. Both papers, from different angles, are probing what actually happens inside a model during a single forward pass and whether the outputs can be trusted at face value. The dimensionality and modularity work ('Dimensionality Controls When Modularity Helps') adds a third angle: architectural choices that look principled may not behave as expected under realistic conditions. Together these papers suggest a quiet but consistent research pressure on the assumption that LLM outputs are stable, well-formed probability estimates rather than snapshots of an unsettled process.
Watch whether prompted predictive resampling gets adopted as a standard confidence-elicitation baseline in calibration benchmarks over the next two conference cycles. If it does, that would confirm the martingale framing has moved from theoretical curiosity to practical evaluation infrastructure.
This interpretation is generated from the summary above and the archive coverage cited below. Our methodology · Report an error
Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
SoftMoE addresses a fundamental constraint in sparse mixture-of-experts scaling: the discrete top-k routing operator that locks expert activation counts and wastes compute. By replacing hard routing with a differentiable soft relaxation, the technique lets models dynamically learn how many experts to activate per layer within a global budget. This matters because it decouples expert capacity…
MentionsLarge Language Models · Prompted Predictive Resampling
How this coverage is produced
Modelwire uses AI to generate summaries and context from source headlines, snippets, and selected archive coverage. Automated checks do not verify every claim, and items are not routinely reviewed by a person before publication. Zacaria Solis operates the site. Read the linked source for the full evidence and report errors through our corrections process.
Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.