arXiv Machine Learning

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.

arXiv AI

Jun 6

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.

By Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

llms fine-tuning

arXiv AI

Jun 9

Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks

arXiv:2606. 08676v1 Announce Type: cross Abstract: AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated directly into Integrated Development Environments (IDEs).

By Shi Ying Chang, Chiok Yew Ho, Yichen Li, Yintong Huo

llms fine-tuning

arXiv AI

Aug 3

RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment

arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.

By Marcos Fuster-Pena, David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms reinforcement-learning benchmarks

arXiv AI

Jun 16

Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

arXiv:2511. 08577v3 Announce Type: replace-cross Abstract: Improving the reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications.

By Tianyu Fu, Yichen You, Zekai Chen, Guohao Dai, Huazhong Yang, Yu Wang

llms fine-tuning benchmarks

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

arXiv AI

Jul 28

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback

arXiv:2507. 22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation.

By Qiushi Sun, Jinyang Gong, Lei Li, Qipeng Guo, Fei Yuan

llms agents fine-tuning benchmarks

arXiv AI

Jun 3

Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition

arXiv:2606. 03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data.

By Jinnuo Liu, Yue Peng, Jinhan Niu, Hongyi Wen

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action

arXiv:2506. 13932v3 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks.

By Saurabh Pujar, Ira Ceka, Irene Manotas, Gail Kaiser, Baishakhi Ray, Shyam Ramji

llms agents benchmarks

arXiv Machine Learning

Jun 10

CodeAlchemy: Synthetic Code Rewriting at Scale

arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.

By Ankit Gupta, Aditya Prasad, Rameswar Panda

llms benchmarks

arXiv AI

6d ago

Program Semantic Inequivalence Game with Large Language Models

arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.

By Antonio Valerio Miceli-Barone, Vaishak Belle, Ali Payani

llms agents fine-tuning benchmarks

arXiv AI

Jul 1

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.

By Gan Luo, Zihan Qin, Bin Dong, Wotao Yin

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 16

Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter

arXiv:2606. 16934v1 Announce Type: cross Abstract: Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation and iterative verification.

By Patomporn Payoungkhamdee, Napat Laosaengpha, Jenta Wonglertsakul, Pittawat Taveekitworachai, Pume Tuchinda, Panjapong Poobanchuen, Ekapol Chuangsuwanich, Can Udomcharoenchaikit, Samuel Cahyawijaya, Peerat Limkonchotiwat, Sarana Nutanong

llms reinforcement-learning fine-tuning benchmarks

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