arXiv AI June 2, 2026 By Minki Kang, Jongwon Jeong, Jaewoong Cho

T1: Tool-integrated Verification for Test-time Compute Scaling in Small Language Models

arXiv:2504. 04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 16

Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention

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.

By Zhen Yang, Mingyang Zhang, Feng Chen, Ganggui Ding, Liang Hou, Xin Tao, Ying-Cong Chen

llms efficiency benchmarks

arXiv Machine Learning

Jun 24

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs

arXiv:2603. 15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification.

By Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu, Nina Narodytska, Guy Katz

llms fine-tuning

arXiv AI

Jul 21

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs

arXiv:2510. 22228v2 Announce Type: replace-cross Abstract: Layer pruning has emerged as a widely adopted technique for improving the efficiency of large language models (LLMs).

By Keyu Wang, Tian Lyu, Guinan Su, Lu Yin, Marco Canini, Jonas Geiping, Shiwei Liu

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 10

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

arXiv:2607. 07748v1 Announce Type: new Abstract: Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages~(LRPLs) such as Julia.

By Didula Samaraweera, Anjana Supun, Srinath Perera

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

5d ago

Memorization Diagnostics for Code LLMs Should be Scale-Aware

arXiv:2608. 12771v1 Announce Type: cross Abstract: The extent to which large language models for code rely on memorization over genuine understanding remains highly debated.

By Prateek Kumar Rajput, Abdoul Aziz Bonkoungou, Alberick Euraste Djir\'e, Xunzhu Tang, Yewei Song, Iyiola Emmanuel Olatunji, El Hacen Diallo, Jacques Klein, Tegawend\'e F. Bissyand\'e

llms agents benchmarks

arXiv AI

Jun 8

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

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.

By Vladislav Smirnov (MBZUAI), Chieu Nguyen (MBZUAI), Sergey Senichev (Independent Researcher), Minh Ngoc Ta (MBZUAI), Ekaterina Fadeeva (ETH Z\"urich), Artem Vazhentsev (MBZUAI), Daria Galimzianova (MBZUAI), Nikolai Rozanov (MBZUAI, Imperial College London), Viktor Mazanov (Innopolis University), Jingwei Ni (ETH Z\"urich), Tianyi Wu (NUS), Igor Kiselev (Accenture), Mrinmaya Sachan (ETH Z\"urich), Iryna Gurevych (MBZUAI), Preslav Nakov (MBZUAI), Timothy Baldwin (MBZUAI), Artem Shelmanov (MBZUAI)

llms rag benchmarks

Related stories

Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

Memorization Diagnostics for Code LLMs Should be Scale-Aware

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning