{"total":50,"rows":[{"id":"2503.02240","title":"OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale","created":"2025-03-04","cited_by_count":91,"area":null},{"id":"2504.04030","title":"OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs","created":"2025-04-05","cited_by_count":60,"area":null},{"id":"2504.01934","title":"ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement","created":"2025-04-02","cited_by_count":54,"area":null},{"id":"2510.06826","title":"Mid-Training of Large Language Models: A Survey","created":"2025-10-08","cited_by_count":21,"area":null},{"id":"2412.11990","title":"ExecRepoBench: Multi-level Executable Code Completion Evaluation","created":"2024-12-16","cited_by_count":20,"area":null},{"id":"2506.09003","title":"SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner","created":"2025-06-10","cited_by_count":20,"area":null},{"id":"2502.12982","title":"Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs","created":"2025-02-18","cited_by_count":18,"area":null},{"id":"2601.15892","title":"Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model","created":"2026-01-22","cited_by_count":13,"area":null},{"id":"2508.11953","title":"Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models","created":"2025-08-16","cited_by_count":12,"area":null},{"id":"2511.10192","title":"Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL","created":"2025-11-13","cited_by_count":11,"area":null},{"id":"2504.01986","title":"TuRTLe: A Unified Evaluation of LLMs for RTL Generation","created":"2025-03-31","cited_by_count":9,"area":null},{"id":"2508.14727","title":"Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis","created":"2025-08-20","cited_by_count":7,"area":null},{"id":"2601.13384","title":"From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning","created":"2026-01-19","cited_by_count":6,"area":null},{"id":"2507.22462","title":"IFEvalCode: Controlled Code Generation","created":"2025-07-30","cited_by_count":5,"area":null},{"id":"2511.07464","title":"Motif 2 12.7B technical report","created":"2025-11-07","cited_by_count":4,"area":null},{"id":"2602.09003","title":"Data Science and Technology Towards AGI Part I: Tiered Data Management","created":"2026-02-09","cited_by_count":4,"area":null},{"id":"2505.13098","title":"LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs","created":"2025-05-19","cited_by_count":3,"area":null},{"id":"2602.04289","title":"Proxy Compression for Language Modeling","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2505.11441","title":"Is Compression Really Linear with Code Intelligence?","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2506.10401","title":"HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration","created":"2025-06-12","cited_by_count":2,"area":null},{"id":"2510.06548","title":"Reusing Overtrained Language Models Saturates Scaling","created":"2025-10-08","cited_by_count":2,"area":null},{"id":"2602.00747","title":"Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2603.03683","title":"CONCUR: Benchmarking LLMs for Concurrent Code Generation","created":"2026-03-04","cited_by_count":2,"area":null},{"id":"2607.17620","title":"PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer","created":"2026-07-20","cited_by_count":2,"area":null},{"id":"2505.05063","title":"CodeMixBench: Evaluating Large Language Models on Code Generation with Code-Mixed Prompts","created":"2025-05-08","cited_by_count":1,"area":null},{"id":"2505.13766","title":"A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2506.07945","title":"ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols","created":"2025-06-09","cited_by_count":1,"area":null},{"id":"2510.13697","title":"On Pretraining for Project-Level Code Completion","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2510.17163","title":"TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework","created":"2025-10-20","cited_by_count":1,"area":null},{"id":"2511.03898","title":"Secure Code Generation at Scale with Reflexion","created":"2025-11-05","cited_by_count":1,"area":null},{"id":"2512.19228","title":"Generation of Programmatic Rules for Document Forgery Detection Using Large Language Models","created":"2025-12-22","cited_by_count":1,"area":null},{"id":"2601.15879","title":"Evaluating and Achieving Controllable Code Completion in Code LLM","created":"2026-01-22","cited_by_count":1,"area":null},{"id":"2602.07083","title":"Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2605.18025","title":"TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2605.31058","title":"Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2606.18286","title":"CODEBLOCK: Learning to Supervise Code at the Right Granularity","created":"2026-06-10","cited_by_count":1,"area":null},{"id":"2508.18462","title":"VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2509.04442","title":"Delta Activations: A Representation for Finetuned Large Language Models","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2509.14281","title":"SCoGen: Scenario-Centric Graph-Based Synthesis of Real-World Code Problems","created":"2025-09-16","cited_by_count":0,"area":null},{"id":"2512.11402","title":"REMODEL-LLM: Transforming C code to Java using LLMs","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2512.20328","title":"Toward Explaining Large Language Models in Software Engineering Tasks","created":"2025-12-23","cited_by_count":0,"area":null},{"id":"2601.02602","title":"SWaRL: Safeguard Code Watermarking via Reinforcement Learning","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2601.21766","title":"CoFrGeNet: Continued Fraction Architectures for Language Generation","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.01227","title":"Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2603.27164","title":"daVinci-LLM:Towards the Science of Pretraining","created":"2026-03-28","cited_by_count":0,"area":null},{"id":"2606.27369","title":"Reinforcement Learning without Ground-Truth Solutions can Improve LLMs","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2609.20842","title":"COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.33010","title":"Model-Aware Data Selection from In-and-Out Information Interplay","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.00656","title":"Lingtai: What Concept Geometry Reveals--and Does Not Reveal--About LLM Inference","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.13735","title":"Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}