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.
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
National Key Laboratory of Parallel and Distributed Computing, College of Computer Science and Technology, National University of Defense Technology（国防科技大学并行与分布计算国家重点实验室，计算机科学与技术学院）
OmniDelta: Skill-Driven Budget Allocation for Token Compression in OmniLLMs
Zhejiang University（浙江大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；Alibaba（阿里巴巴）
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）；BandAI, Bytedance（字节跳动BandAI）
ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models
Tsinghua University（清华大学）；City University of Hong Kong（香港城市大学）；Peking University（北京大学）；Shenzhen University of Advanced Technology（深圳理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Scaling Near-Optimal SFT-RL Annotation Budget Allocation from Small to Large LLMs
National University of Singapore（新加坡国立大学）；Agency for Science, Technology, and Research (A*STAR)（新加坡科技研究局）；Singapore-MIT Alliance for Research and Technology Centre（新加坡-麻省理工研究与技术联盟中心）
LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；Stepfun
