Challenge: Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics.
Approach: They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency.
Outcome: The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss.

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Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning (2025.findings-naacl)

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Challenge: Existing decoding strategies for chain-of-thought reasoning do not exploit prior information about question difficulty.
Approach: They propose a decoding strategy called self-consistency to improve reasoning performance by adjusting the number of samples based on the posterior distribution of a set of pre-samples.
Outcome: The proposed method outperforms baseline methods on arithmetic, commonsense and symbolic reasoning tasks while achieving comparable performance.
Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency (2025.emnlp-main)

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Challenge: Recent studies show that Test-Time Scaling (TTS) can improve reasoning performance without retraining the model.
Approach: They propose a step-wise pruning strategy that identifies and removes redundant chains using inter-chain similarity at the thought level.
Outcome: The proposed method reduces inference latency and KVC usage by up to 45% and 26% with R1-Distill while maintaining or improving accuracy.
Let’s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs (2023.emnlp-main)

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Challenge: Existing methods for improving the correctness of output from large language models generate a constant number of samples per question, but Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%.
Approach: They propose a model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion.
Outcome: The proposed technique reduces sample budget by 7.9 times with an average accuracy drop of less than 0.1%.
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling (2025.naacl-long)

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Challenge: Large Language Models (LLMs) generate reasoning paths before answers, but lack a systematic approach to determine optimal number of samples or select the most faithful rationale.
Approach: They propose a framework that evaluates the quality of reasoning and consistency of answers for each generated sample and uses criteria-based stopping and weighted majority voting to guide early stopping decisions and rationale selection.
Outcome: The proposed framework outperforms existing methods while maintaining accuracy.
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection (2026.findings-acl)

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Challenge: Weighted majority voting requires a critic to evaluate each candidate’s reasoning trace to produce the answer’s confidence score.
Approach: They propose a lightweight framework that uses a measure of semantic similarity to filter reasoning traces that are semantically equivalent to others, degenerate, or hallucinated.
Outcome: The proposed framework reduces token usage by 47% while maintaining or exceeding the accuracy of CISC.
ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026.acl-demo)

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Challenge: Existing TTC scaling strategies and reasoning scorers are fragmented and evaluated under inconsistent protocols.
Approach: They propose a framework for seamless test-time compute scaling of large language model reasoning . they use a modular Python library to implement state-of-the-art scaling strategy and scorer families .
Outcome: The proposed framework evaluates performance and computational efficiency on mathematical and coding tasks.
EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving (2026.acl-long)

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Challenge: Large Language Models (LLMs) have recently advanced the field of Automated Theorem Proving (ATP) Existing cost analyses regulate only the number of sampling passes, ignoring the substantial disparities in sampling costs.
Approach: They propose to integrate two complementary methods into a unified EconRL pipeline to increase pass rates under constrained sampling passes.
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Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)

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Challenge: Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective.
Approach: They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism.
Outcome: The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches .
Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024.findings-acl)

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Challenge: Recent studies have shown that self-consistency decoding can improve performance for complex reasoning tasks with large language models.
Approach: They propose a self-consistency decoding strategy that generates multiple paraphrases for each test question and then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding.
Outcome: The proposed strategy reduces the sampling number and improves performance on complex reasoning tasks.
Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning (2026.findings-acl)

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Challenge: Self-consistency improves reasoning reliability but incurs substantial inference cost . Adaptive self-consistent methods rely on count-based stopping rules that treat all responses equally .
Approach: They propose a method that reframs adaptive sampling from response counting to evidence sufficiency by leveraging response-level confidence.
Outcome: The proposed method reduces inference cost by up to 70% while preserving accuracy on GSM8K.

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