Conformal Prediction Links Reasoning Traces to Answers in LRMs with Finite-Sample Guarantees
Introduces CP over reasoning-answer pairs plus Shapley explanations to quantify and trace uncertainty origins in LRMs with statistical guarantees synthesizing conformal prediction Shapley value and o1-era reasoning literature.
Large Reasoning Models achieve gains in complex tasks yet lack statistically rigorous uncertainty sets that connect reasoning steps to final outputs according to primary source Li et al. (arXiv:2604.13395).
The paper proposes a conformal prediction methodology that constructs uncertainty sets over the joint reasoning-answer structure providing distribution-free guarantees absent in token-level probability baselines. It identifies that prior CP applications ignored logical dependencies between trace and answer while failing to disentangle reasoning quality from correctness two shortcomings also evident in OpenAI o1 system card which reported reasoning gains without calibrated uncertainty metrics (OpenAI 2024). The framework further supplies a Shapley value explanation that isolates a minimal sufficient subset of training examples and pivotal reasoning steps preserving the coverage guarantees.
Experiments on challenging reasoning benchmarks confirm tighter sets than conventional CP. Related work on conformal prediction establishes the finite-sample validity foundation (Angelopoulos Bates arXiv:2107.07511) while Lundberg Lee demonstrate Shapley values for feature attribution (arXiv:1705.07874); the current synthesis applies both to LRMs exposing how training data influences uncertainty coverage a link mainstream coverage of o1-style models routinely omitted.
This directly addresses the editorial lens by delivering quantifiable uncertainty measures for high-stakes decisions where prior literature left trustworthiness gaps unaddressed.
AXIOM: Li et al. supply finite-sample uncertainty sets that tie full reasoning traces to answers in LRMs and isolate influential training steps via Shapley values enabling calibrated trustworthiness scores for high-stakes deployment where prior o1 evaluations provided none.
Sources (3)
- [1]Quantifying and Understanding Uncertainty in Large Reasoning Models(https://arxiv.org/abs/2604.13395)
- [2]OpenAI o1 System Card(https://openai.com/index/learning-to-reason-with-llms/)
- [3]Conformal Prediction: A Gentle Introduction(https://arxiv.org/abs/2107.07511)