arXiv:2601. 03506v2 Announce Type: replace-cross Abstract: Recent large reasoning models (LRMs) have achieved strong performance on complex reasoning tasks by generating a long chain-of-thought (Long-CoT).
arXiv:2608. 10447v1 Announce Type: cross Abstract: Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher accuracy than fast-thinking models that predict directly.
arXiv:2508. 02178v3 Announce Type: replace Abstract: Large reasoning models (LRMs) often exhibit overthinking, producing verbose Chain-of-Thought (CoT) traces that increase inference cost and obscure the underlying reasoning process.
arXiv:2606. 07108v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from inefficiencies due to redundant reasoning, known as "overthinking".
arXiv:2610.00571v1 Announce Type: cross Abstract: Large reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive,...
Large language model-based recommender systems are increasingly adopting slow-thinking models that generate step-by-step reasoning before making predictions, often achieving higher accuracy than fast-thinking models that predict directly. However, their reasoning traces are often unnecessarily verbose, increasing inference costs without commensurate accuracy gains.
GRIP (Granular Reward-guided Interpolation of Parameters) is a lightweight framework that blends a reasoning-oriented large language model with an instruction-tuned model by assigning learnable interpolation ratios to individual modules. The ratios are optimized while keeping both source models frozen, using a reward signal that prefers correct and concise responses. Experiments demonstrate that GRIP improves the accuracy-efficiency trade-off compared to fixed or search-based merging baselines and uncover module-wise fusion patterns linked to efficient reasoning.
arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).
arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not...
The paper introduces LIFT, a lightweight vector‑intervention technique that transfers reasoning capability from a base large language model (LLM) to a vision‑language model (VLM) without retraining the VLM backbone. LIFT defines Reasoning Vectors as differences in hidden states between a reasoning path with an explicit trace and a solver path without it, and injects these vectors into the VLM’s language‑side activations. Experiments on two VLMs across six reasoning benchmarks show that vectors derived from the base LLM consistently outperform those derived from the aligned VLM, indicating that the base LLM is a more effective source for recovering degraded reasoning. "whyItMatters":"The study demonstrates that a simple, frozen‑backbone intervention can partially restore reasoning abilities in multimodal models, highlighting the value of leveraging the original language model’s reasoning power."
arXiv:2510. 06052v2 Announce Type: replace Abstract: Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of thought before producing an answer.
arXiv:2605.06165v2 Announce Type: replace Abstract: As the widespread adoption of Large Language Models (LLMs) accelerates, token consumption from intermediate reasoning traces increasingly contribut...
arXiv:2604. 06465v2 Announce Type: replace-cross Abstract: Reasoning models achieve strong performance on complex problems by leveraging long chains of thought, but this deliberate reasoning incurs substantial inference-time cost.