arXiv — cs.AI preprintsInternational19 October 2026
Not All Test-Time Compute Is Evidence: Model-Specific Bias in LLM Reasoning
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.23213v1 Announce Type: new Abstract: Test-time scaling methods implicitly assume that additional computation from the same model provides independent evidence. We show that this assumption fails within a single reasoning model: errors are correlated across repeated samples, across reasoning depth, and in the model's own self-evaluation, so more compute from the same model is not more evidence. We introduce DIA (Disagreement-triggered Independent Arbitration), which treats disagreement between an intermediate-stage and the final-stage prediction as a per-question signal of reasoning
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides19 October 2026
- Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation19 October 2026
- From Events to Reasoning: Hierarchical Rule Composition over Explicit States19 October 2026
- ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation19 October 2026
- Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model19 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.