Inference-Time Scaling

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14 papers in the last four weeks, up 180% on the four weeks before. 0.1% of all new papers.

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CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
May 8, 2026James Petullo, Nianwen XueInference-Time Search Inference-Time Scaling
Brandeis University / Waltham, MA
PaT: Planning-after-Trial for Efficient Test-Time Code Generation
May 8, 2026Youngsik Yoon, Sungjae Lee, Seockbean Song +3Test-Time Optimization Execution-Guided Code Generation
Department of Computer Science and Engineering, POSTECH, South Korea · Graduate School of Artificial Intelligence, POSTECH, South Korea · Microsoft Research Asia, Beijing, China
Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
May 7, 2026Rachel Ma, Dylan Hadfield-Menell, Kristjan GreenewaldProcess Reward Models Inference-Time Scaling
MIT CSAIL · MIT-IBM Watson AI Lab · IBM Research
Recursive Agent Optimization
May 7, 2026Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang +2Agentic RL Inference-Time Scaling
Carnegie Mellon University · Amazon AGI Labs
Multi-Agent Reasoning Improves Compute Efficiency: Pareto-Optimal Test-Time Scaling
May 2, 2026Florian Valentin Wunderlich, Lars Benedikt Kaesberg, Jan Philip Wahle +2Self-Consistency Decoding Cost-Aware Inference
University of Göttingen, Germany
Compute Aligned Training: Optimizing for Test Time Inference
Apr 27, 2026Adam Ousherovitch, Ambuj TewariTest-Time Optimization Inference-Time Scaling
Department of Statistics University of Michigan
Process Supervision via Verbal Critique Improves Reasoning in Large Language Models
Apr 23, 2026Hao-Yuan ChenInference-Time Scaling Process Supervision
Mindify AI Research · University of London · Senate House, Malet Street, London WC1E 7HU, United Kingdom
Evaluation-driven Scaling for Scientific Discovery
Apr 21, 2026Haotian Ye, Haowei Lin, Jingyi Tang +22Large Language Model-Guided Optimization Inference-Time Scaling
Wizard Intelligence Learning Lab, Stanford University · Peking University, Tsinghua University
Hive: A Multi-Agent Infrastructure for Algorithm- and Task-Level Scaling
Apr 19, 2026Zizhang Luo, Yuhao Luo, Youwei Xiao +3LLM Inference Efficiency Multi-Agent LLM Systems
Peking University Beijing, China
FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time
Apr 17, 2026Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar +4Inference-Time Scaling Molecular Generation
Massachusetts Institute of Technology, Cambridge, MA, United States · Texas A&M University, College Station, TX, United States
Generalization in LLM Problem Solving: The Case of the Shortest Path
Apr 16, 2026Yao Tong, Jiayuan Ye, Anastasia Borovykh +1Inference-Time Scaling Length Generalization
National University of Singapore · Capital Fund Management · Google Research
LatSearch: Latent Reward-Guided Search for Faster Inference-Time Scaling in Video Diffusion
Mar 15, 2026Zengqun Zhao, Ziquan Liu, Yu Cao +5Video Diffusion Models Inference-Time Scaling
Queen Mary University of London · Huawei R&D UK · Imperial College London
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
Feb 13, 2026Wei-Lin Chen, Liqian Peng, Tian Tan +5LLM Inference Efficiency Inference-Time Scaling
University of Virginia · Google
ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
Nov 24, 2025Long Lian, Sida Wang, Felix Juefei-Xu +7RL for Language Model Reasoning Inference-Time Scaling
Meta Superintelligence Labs (MSL), Menlo Park, CA, United States · UC Berkeley, Berkeley, CA, United States · UCSF, San Francisco, CA, United States
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
Nov 15, 2025Austin Feng, Marius Alonso, Ambroise Odonnat +2Self-Consistency Decoding Inference-Time Scaling
1Yale University, New Haven, CT, USA · 2Noah’s Ark Lab, Paris, France · 3Inria, Rennes, France +1
Reasoning in machine vision by learning fast and slow thinking
Jun 27, 2025Shaheer U. Saeed, Yipei Wang, Veeru Kasivisvanathan +4Visual Reasoning Inference-Time Scaling
Centre for Bioengineering, Queen Mary University of London, London, UK · Digital Environment Research Institute, Queen Mary University of London, London, UK · School of Engineering and Materials Science, Queen Mary University of London, London, UK +6
Scaling Evaluation-time Compute with Reasoning Models as Evaluators
Mar 25, 2025Seungone Kim, Ian Wu, Jinu Lee +8LLM Evaluation Language Model Generation Evaluation
CMU · UIUC2 · KAIST AI3 +3

May 8, 2026, cs.CLJ/K move · Enter open · S save

CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation

James Petullo, Nianwen Xue

Brandeis University / Waltham, MA

Inference-Time Search Inference-Time Scaling Text-to-SQL Inference-Time Compute Allocation

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While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform well on the most challenging tasks in the Bird-Bench (BIRD) benchmark. This is due to inadequate solution space exploration, which is necessary to uncover promising candidate queries that can be further refined to produce the correct output. To address this challenge, we introduce CA-SQL, a novel Text-to-SQL pipeline that utilizes the estimated difficulty of a task to dynamically scale the breadth of the exploration for generating solution candidates. In addition, we use a custom prompt seeding method, based on principles of evolutionary search, to further elicit exploratory behavior from the base LLM and a novel voting method to select the best candidate solution at the end of the search. Experiments demonstrate that our solution achieves a state-of-the-art score of 51.72% on the "challenging" tier of BIRD development set problems, using only GPT-4o-mini, out-performing other in-context learning approaches, even those that leverage larger models. Overall, our method attains a competitive 61.06% execution accuracy and 68.77% Soft F1 score on the BIRD development dataset.

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