Efficient Test-Time Scaling via Temporal Reasoning Aggregation

• URL: http://arxiv.org/abs/2604.17304v1
• Date: Sun, 19 Apr 2026 07:39:40 GMT
• Title: Efficient Test-Time Scaling via Temporal Reasoning Aggregation
• Abstract summary: We propose TRACE, a training-free framework for efficient test-time scaling.<n>TRACE determines when to terminate reasoning based on temporal aggregation of multi-step evidence.<n>We show that TRACE reduces reasoning token usage by 25-30% on average while maintaining accuracy within 1-2% of full-length reasoning.
• Score: 5.87118425760756
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Test-time scaling improves the reasoning performance of large language models but often results in token-inefficient overthinking, where models continue reasoning beyond what is necessary for a correct answer. Existing dynamic early-exit methods typically rely on single-step confidence signals, which are often unreliable for detecting reasoning convergence in multi-step settings. To mitigate this limitation, we propose TRACE, a training-free framework for efficient test-time scaling that determines when to terminate reasoning based on temporal aggregation of multi-step evidence rather than instantaneous signals. TRACE detects reasoning convergence over time by aggregating two complementary signals across recent reasoning steps: answer consistency, capturing the persistence of predicted answers, and confidence trajectory, modeling the temporal evolution of model confidence. Benefiting from these two factors, TRACE can accurately determine whether the reasoning process has converged, thereby promptly halting inference and effectively avoiding redundant reasoning steps. Extensive experiments on multiple challenging benchmarks show that TRACE reduces reasoning token usage by 25-30% on average while maintaining accuracy within 1-2% of full-length reasoning, consistently outperforming existing dynamic reasoning methods.

Related papers

• SABER: Stability-Aware Early Exit for LLM Reasoning via Adversarial Branch Probing [32.867599324353456]
  SABER is a training-free framework for stability-aware early exit via adversarial branch probing.<n>It reduces reasoning token consumption by 30.2%--39.8% on average while maintaining competitive accuracy with full-length reasoning.
  arXiv  Detail & Related papers  (2026-08-28T06:13:23Z)
• ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling [60.48279351544583]
  ThinkRetrieve is a test-time scaling framework for large reasoning models.<n>It retrieves relevant exemplars at each intermediate step and injects them directly into the thinking trace.<n>It consistently improves accuracy over standard test-time scaling.
  arXiv  Detail & Related papers  (2026-08-11T13:58:07Z)
• When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning [52.21239821135325]
  We introduce Dynamic Thought Sufficiency in Reasoning (DTSR), a novel framework for efficient reasoning.<n>Inspired by human metacognition, DTSR operates in two stages: Reflection Signal Monitoring, and Thought Sufficiency Check.<n>DTSR reduces reasoning length by 28.9%-34.9% with minimal performance loss, effectively mitigating overthinking.
  arXiv  Detail & Related papers  (2026-04-08T07:56:28Z)
• Early Stopping for Large Reasoning Models via Confidence Dynamics [55.67938134245981]
  Large reasoning models rely on long chain-of-thought generation to solve complex problems.<n>Key challenge is determining when the model should stop reasoning and produce the final answer.<n>We propose CoDE-Stop, an early stopping method that leverages the dynamics of intermediate answer confidence to decide when to terminate reasoning.
  arXiv  Detail & Related papers  (2026-04-06T17:59:45Z)
• enhancing reasoning accuracy in large language models during inference time [1.0282918759603745]
  Large Language Models (LLMs) often exhibit strong linguistic abilities while remaining unreliable on multi-step reasoning tasks.<n>We study inference-time techniques to improve the reasoning accuracy of LLMs.
  arXiv  Detail & Related papers  (2026-03-22T16:00:07Z)
• Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution [79.98699884805636]
  Reasoning Execution by Multiple Listeners (REMUL) is a multi-party reinforcement learning approach.<n>REMUL builds on the hypothesis that reasoning traces which other parties can follow will be more faithful.<n>Speakers are rewarded for producing reasoning that is clear to listeners.
  arXiv  Detail & Related papers  (2026-02-18T02:55:55Z)
• Understanding and Steering the Cognitive Behaviors of Reasoning Models at Test-Time [22.9491443902816]
  We study the structure of reasoning trajectories and uncover specialized attention heads that correlate with distinct cognitive behaviors.<n>We propose CREST, a training-free method for Cognitive REasoning Steering at Test-time.<n>CREST adaptively suppresses unproductive reasoning behaviors, yielding both higher accuracy and lower computational cost.
  arXiv  Detail & Related papers  (2025-12-31T02:46:04Z)
• Tracing the Traces: Latent Temporal Signals for Efficient and Accurate Reasoning [21.58053831545995]
  We introduce Latent-Trajectory signals that characterize the temporal evolution of a model's internal representations.<n>We show that these signals predict solution accuracy more reliably than both cross-layer metrics and output-based confidence measures.
  arXiv  Detail & Related papers  (2025-10-12T08:03:56Z)
• Fractured Chain-of-Thought Reasoning [61.647243580650446]
  We introduce Fractured Sampling, a unified inference-time strategy that interpolates between full CoT and solution-only sampling.<n>We show that Fractured Sampling consistently achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling gains in Pass@k versus token budget.
  arXiv  Detail & Related papers  (2025-05-19T11:30:41Z)
• ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning [64.93140713419561]
  Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs.<n>Existing fine-tuning-based compression methods either operate post-hoc pruning, risking disruption to reasoning coherence, or rely on sampling-based selection.<n>We introduce ConCISE, a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, and Early Stopping to terminate reasoning when confidence is sufficient.
  arXiv  Detail & Related papers  (2025-05-08T01:40:40Z)

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.