CoRefine: Confidence-Guided Self-Refinement for Adaptive Test-Time Compute

• URL: http://arxiv.org/abs/2602.08948v1
• Date: Mon, 09 Feb 2026 17:44:41 GMT
• Title: CoRefine: Confidence-Guided Self-Refinement for Adaptive Test-Time Compute
• Authors: Chen Jin, Ryutaro Tanno, Tom Diethe, Philip Teare,
• Abstract summary: CoRefine is a confidence-guided self-refinement method that achieves competitive accuracy using a fraction of the tokens.<n>The controller consumes full-trace confidence to decide whether to halt, re-examine, or try a different approach.<n>We extend this to CoRefine-Tree, a hybrid sequential-parallel variant that adaptively balances exploration and exploitation.
• Score: 10.548368675645403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) often rely on test-time scaling via parallel decoding (for example, 512 samples) to boost reasoning accuracy, but this incurs substantial compute. We introduce CoRefine, a confidence-guided self-refinement method that achieves competitive accuracy using a fraction of the tokens via a lightweight 211k-parameter Conv1D controller atop a frozen LLM. The controller consumes full-trace confidence to decide whether to halt, re-examine, or try a different approach, enabling targeted self-correction with an average of 2.7 refinement steps per problem and roughly 190-fold token reduction relative to 512-sample baselines. Across diverse reasoning benchmarks and three open-source models, the controller achieves 92.6 percent precision when it confidently halts, indicating that confidence dynamics reliably signal correctness without ground-truth verification. We extend this to CoRefine-Tree, a hybrid sequential-parallel variant that adaptively balances exploration and exploitation, with easy serving integration and verifier compatibility. By treating confidence as a control signal rather than a correctness guarantee, CoRefine provides a modular primitive for scalable reasoning and agentic settings with imperfect verifiers.

Related papers

• $V_1$: Unifying Generation and Self-Verification for Parallel Reasoners [69.66089681814013]
  $V_$ is a framework that unifies generation and verification through efficient pairwise ranking.<n>$V_$-Infer improves Pass@1 by up to $10%$ over pointwise verification.<n>$V_$-PairRL achieves $7$--$9%$ test-time scaling gains over standard RL and pointwise joint training.
  arXiv  Detail & Related papers  (2026-03-04T17:22:16Z)
• ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
  ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
  arXiv  Detail & Related papers  (2026-02-27T05:22:01Z)
• On Calibration of Large Language Models: From Response To Capability [66.59139960234326]
  Large language models (LLMs) are widely deployed as general-purpose problem solvers.<n>We introduce capability calibration, which targets the model's expected accuracy on a query.<n>Our results demonstrate that capability-calibrated confidence improves pass@$k$ prediction and inference budget allocation.
  arXiv  Detail & Related papers  (2026-02-14T01:07:45Z)
• Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning [20.371912257758634]
  Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost.<n>We propose Reliability-Aware Adaptive Self-Consistency (ReASC), which addresses this limitation by reframing adaptive sampling from response counting to evidence sufficiency.<n>ReASC consistently achieves the best accuracy-cost trade-off compared to existing baselines, yielding improved inference efficiency across model scales from 3B to 27B parameters.
  arXiv  Detail & Related papers  (2026-01-06T12:27:53Z)
• Annotation-Efficient Universal Honesty Alignment [70.05453324928955]
  Existing methods either rely on training-free confidence estimation or training-based calibration with correctness annotations.<n>Elicitation-Then-Calibration (EliCal) is a two-stage framework that first elicits internal confidence using inexpensive self-consistency supervision, then calibrates this confidence with a small set of correctness annotations.<n>EliCal achieves near-optimal alignment with only 1k correctness annotations (0.18% of full supervision) and better alignment performance on unseen MMLU tasks than the calibration-only baseline.
  arXiv  Detail & Related papers  (2025-10-20T13:05:22Z)
• Sample Smart, Not Hard: Correctness-First Decoding for Better Reasoning in LLMs [72.82403830490084]
  We argue that the decoding rule should be calibrated by correctness, not confidence alone.<n>We propose simple strategies that achieve this goal: Greedy-Threshold makes sampling greedy at very low confidence steps.<n>Together, our findings challenge prevailings about decoding under uncertainty and show gains across math and general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-10-07T14:46:12Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Trusted Uncertainty in Large Language Models: A Unified Framework for Confidence Calibration and Risk-Controlled Refusal [31.458406135473805]
  We present UniCR, a unified framework that turns heterogeneous uncertainty evidence into a calibrated probability of correctness.<n>UniCR learns a lightweight calibration head with temperature scaling and proper scoring.<n>Experiments on short-form QA, code generation with execution tests, and retrieval-augmented long-form QA show consistent improvements in calibration metrics.
  arXiv  Detail & Related papers  (2025-09-01T13:14:58Z)
• Robust Conformal Prediction with a Single Binary Certificate [58.450154976190795]
  Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability.<n>We propose a robust conformal prediction that produces smaller sets even with significantly lower MC samples.
  arXiv  Detail & Related papers  (2025-03-07T08:41:53Z)
• Distilling Calibration via Conformalized Credal Inference [30.8135853479509]
  One way to enhance reliability is through uncertainty quantification via Bayesian inference.<n>This paper introduces a low-complexity methodology to address this challenge by distilling calibration information from a more complex model.<n> Experiments on visual and language tasks demonstrate that the proposed approach, termed Conformalized Distillation for Credal Inference (CD-CI), significantly improves calibration performance.
  arXiv  Detail & Related papers  (2025-01-10T15:57:23Z)
• Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing [9.637143119088426]
  We show that a robust base classifier's confidence difference for correct and incorrect examples is the key to this improvement. We adapt an adversarial input detector into a mixing network that adaptively adjusts the mixture of the two base models. The proposed flexible method, termed "adaptive smoothing", can work in conjunction with existing or even future methods that improve clean accuracy, robustness, or adversary detection.
  arXiv  Detail & Related papers  (2023-01-29T22:05:28Z)
• Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition [86.21889574126878]
  We show how per-frame entropy values can be normalized and aggregated to obtain a confidence measure per unit and per word. We evaluate the proposed confidence measures on LibriSpeech test sets, and show that they are up to 2 and 4 times better than confidence estimation based on the maximum per-frame probability.
  arXiv  Detail & Related papers  (2022-12-16T20:27:40Z)
• Robustness and Accuracy Could Be Reconcilable by (Proper) Definition [109.62614226793833]
  The trade-off between robustness and accuracy has been widely studied in the adversarial literature. We find that it may stem from the improperly defined robust error, which imposes an inductive bias of local invariance. By definition, SCORE facilitates the reconciliation between robustness and accuracy, while still handling the worst-case uncertainty.
  arXiv  Detail & Related papers  (2022-02-21T10:36:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.