Recognition: no theorem link
Deep Think with Confidence
Pith reviewed 2026-05-16 11:26 UTC · model grok-4.3
The pith
DeepConf uses model-internal confidence to filter weak reasoning traces, reaching 99.9% accuracy on AIME 2025 while cutting tokens by 84.7%.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Deep Think with Confidence (DeepConf) enhances both reasoning efficiency and performance at test time by leveraging model-internal confidence signals to dynamically filter out low-quality reasoning traces during or after generation. It requires no additional model training or hyperparameter tuning and can be seamlessly integrated into existing serving frameworks. Across a variety of reasoning tasks and recent open-source models, DeepConf@512 achieves up to 99.9% accuracy on AIME 2025 and reduces generated tokens by up to 84.7% compared with full parallel thinking.
What carries the argument
Model-internal confidence signals used for dynamic filtering of individual reasoning traces.
Load-bearing premise
Model-internal confidence scores reliably mark which reasoning traces are correct or high-quality.
What would settle it
A controlled run on AIME 2025 or a similar benchmark where keeping only the high-confidence traces produces lower final accuracy than keeping all traces.
read the original abstract
Large Language Models (LLMs) have shown great potential in reasoning tasks through test-time scaling methods like self-consistency with majority voting. However, this approach often leads to diminishing returns in accuracy and high computational overhead. To address these challenges, we introduce Deep Think with Confidence (DeepConf), a simple yet powerful method that enhances both reasoning efficiency and performance at test time. DeepConf leverages model-internal confidence signals to dynamically filter out low-quality reasoning traces during or after generation. It requires no additional model training or hyperparameter tuning and can be seamlessly integrated into existing serving frameworks. We evaluate DeepConf across a variety of reasoning tasks and the latest open-source models, including Qwen 3 and GPT-OSS series. Notably, on challenging benchmarks such as AIME 2025, DeepConf@512 achieves up to 99.9% accuracy and reduces generated tokens by up to 84.7% compared to full parallel thinking.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces Deep Think with Confidence (DeepConf), a training-free method that uses raw model-internal confidence signals to dynamically filter low-quality reasoning traces during or after generation. It claims this yields substantial gains in both accuracy and efficiency over standard self-consistency with majority voting, with the headline result being up to 99.9% accuracy and an 84.7% reduction in generated tokens on AIME 2025 using DeepConf@512 across models such as Qwen 3 and GPT-OSS series.
Significance. If the core filtering mechanism proves reliable, the work would provide a lightweight, plug-in improvement to test-time scaling that reduces token usage while raising accuracy on hard reasoning benchmarks. The absence of additional training or hyperparameter tuning is a practical strength, but the result hinges entirely on an unverified empirical correlation between internal confidence and trace correctness.
major comments (3)
- [Methods] Methods section (confidence extraction): The manuscript provides no explicit definition or formula for the per-trace confidence signal (e.g., mean token probability, normalized logit sum, or entropy-based measure). Without this, it is impossible to reproduce the filtering step or assess whether the signal is raw or post-processed.
- [Experiments / AIME 2025] Results, AIME 2025 experiments: The reported 99.9% accuracy and 84.7% token reduction at DeepConf@512 are presented without accompanying calibration plots, per-trace accuracy-vs-confidence curves, or Pearson/Spearman correlation coefficients between confidence and correctness. On hard out-of-distribution math problems, LLMs are typically miscalibrated; the absence of these diagnostics leaves the central filtering claim unsupported.
- [Experiments] Experimental controls: No ablation is shown on the choice of filtering threshold, the number of traces retained, or controls for post-hoc selection bias. It is therefore unclear whether the accuracy lift is due to genuine quality filtering or to selective reporting of high-confidence subsets.
minor comments (2)
- [Abstract / Results] The abstract and results tables should explicitly state the exact number of traces generated before filtering (e.g., 512) and the final retained count for each reported accuracy number.
- [Methods] Notation for the confidence threshold and the @512 suffix is introduced without a formal definition; a short equation or pseudocode box would improve clarity.
Simulated Author's Rebuttal
We thank the referee for their constructive comments. We will revise the manuscript to provide greater clarity on the confidence signal and additional experimental diagnostics to support our claims.
read point-by-point responses
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Referee: [Methods] Methods section (confidence extraction): The manuscript provides no explicit definition or formula for the per-trace confidence signal (e.g., mean token probability, normalized logit sum, or entropy-based measure). Without this, it is impossible to reproduce the filtering step or assess whether the signal is raw or post-processed.
Authors: We agree with this observation. The revised manuscript will include an explicit definition and formula for the per-trace confidence signal in the Methods section. We will specify that it is the average log-probability of tokens in the trace and provide the exact computation used in our experiments along with pseudocode for the filtering process. revision: yes
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Referee: [Experiments / AIME 2025] Results, AIME 2025 experiments: The reported 99.9% accuracy and 84.7% token reduction at DeepConf@512 are presented without accompanying calibration plots, per-trace accuracy-vs-confidence curves, or Pearson/Spearman correlation coefficients between confidence and correctness. On hard out-of-distribution math problems, LLMs are typically miscalibrated; the absence of these diagnostics leaves the central filtering claim unsupported.
Authors: We will add the requested diagnostics to the revised paper. This includes calibration plots, accuracy versus confidence curves for individual traces, and the correlation coefficients (both Pearson and Spearman) computed on the AIME 2025 results. These additions will directly address concerns about miscalibration and provide empirical support for the correlation between internal confidence and correctness. revision: yes
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Referee: [Experiments] Experimental controls: No ablation is shown on the choice of filtering threshold, the number of traces retained, or controls for post-hoc selection bias. It is therefore unclear whether the accuracy lift is due to genuine quality filtering or to selective reporting of high-confidence subsets.
Authors: We will incorporate ablations on the filtering threshold and the number of retained traces. Additionally, we will include a control experiment comparing our confidence-based selection against random selection of an equivalent number of traces. This will demonstrate that the performance gains arise from the quality of the filtered traces rather than from post-hoc selection bias. revision: yes
Circularity Check
No circularity; empirical heuristic with no equations or self-referential derivations.
full rationale
The paper introduces DeepConf as a practical filtering method based on raw model-internal confidence signals, with no mathematical derivations, equations, fitted parameters renamed as predictions, or load-bearing self-citations. Claims rest on empirical benchmark results (e.g., AIME 2025 accuracy and token reduction) rather than any chain that reduces to its own inputs by construction. This is a standard non-circular empirical contribution.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Model-internal confidence signals reliably indicate the quality of reasoning traces
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discussion (0)
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