ThinkFuse is a training‑free test‑time fusion framework designed for small reasoning models (SRMs). It selectively intervenes in unreliable reasoning segments by comparing segment‑level uncertainty shifts with trajectory‑level uncertainty trends, and fuses auxiliary reasoning paths into the primary model’s trajectory. Experiments show ThinkFuse outperforms baselines on mathematical and knowledge‑intensive benchmarks, requires fewer fusion triggers, and generates fewer tokens, demonstrating efficient selective triggering.
arXiv:2608. 10928v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by allocating additional inference-time compute to generate extended chain-of-thought reasoning.
arXiv:2601. 20379v2 Announce Type: replace Abstract: Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption.
arXiv:2608. 15065v1 Announce Type: new Abstract: Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inference is a prerequisite for reliable deployment.
arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.
arXiv:2609.16055v1 Announce Type: cross Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...
The paper introduces TTT‑Consolidate, a framework that leverages multi‑agent reasoning trajectories as adaptation data to improve subsequent reasoning. It consolidates these trajectories into parametric representations via test‑time training and then generates a new solution conditioned on the consolidated information, with meta‑learning used to optimize the consolidation process. Using the newly created OpenParallelThinking corpus of 12,810 math problems and trajectories from six LLMs, TTT‑Consolidate outperforms existing aggregation baselines by up to 20 percentage points on challenging math and STEM benchmarks, especially as model size grows.
arXiv:2607. 10386v1 Announce Type: cross Abstract: Large language models (LLMs) excel at generating long chains of thought, but long reasoning traces are often verbose and memory-inefficient.
The paper investigates how monitoring the internal representations of Large Reasoning Models (LRMs) during their Chain-of-Thought (CoT) reasoning can predict future behavior. By evaluating a probe at each generated token, the authors construct probe trajectories that capture the continuous evolution of a concept’s probability, and extract signal‑processing features to characterize volatility, trend, and steady‑state behavior. These trajectory features improve predictive performance over static probes, adding an average of 21.7 AUROC points, and the study demonstrates that template‑based training data and pooling operations are essential for effective monitoring across multiple reasoning models and datasets.
arXiv:2606. 06915v1 Announce Type: cross Abstract: Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.
arXiv:2510. 13940v4 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency.
arXiv:2610.18547v1 Announce Type: new Abstract: People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeate...
arXiv:2611.08403v1 Announce Type: new Abstract: Self-consistency (SC) is a widely used decoding strategy for improving chain-of-thought reasoning, but it requires sampling many complete reasoning tra...