arXiv Machine Learning

Narcissus: Program Synthesis Using Context-Aware LLM Approximations

arXiv AI

Jun 4

The Invisible Lottery: How Subtle Cues Steer Algorithm Choice in LLM Code Generation

arXiv:2606. 04057v1 Announce Type: cross Abstract: Large language models (LLMs) now generate substantial production code, often for tasks with multiple valid algorithmic solutions.

By Akanksha Narula, Mofasshara Binte Rafique, Laurent Bindschaedler

llms benchmarks

Hugging Face Trending Papers

Jun 19

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents

Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.

llms agents fine-tuning benchmarks

arXiv Machine Learning

Aug 3

Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination

arXiv:2607. 28947v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to solve complex problems by searching over program space, offering a general paradigm for scientific problems that can be naturally represented and solved as programs.

By Jingwen Fu, Zhen Liu, Yuhan Liu, He Zhang, Nanning Zheng

llms agents robotics

arXiv AI

Aug 5

TraceCompiler: Skill-Guided Mining and Compilation of LLM Agent Traces into Mostly Deterministic Workflows

arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.

By Salma El Yadouni (EPFL), Guanyi Li (Binome Technologies)

llms agents benchmarks

arXiv Machine Learning

Jul 31

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

arXiv:2410. 06458v2 Announce Type: replace-cross Abstract: Instruction following is a key capability for LLMs.

By Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng

llms benchmarks

arXiv AI

Aug 14

CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language

arXiv:2603. 14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones.

By Junhang Cheng, Fang Liu, Jia Li, Chengru Wu, Nanxiang Jiang, Li Zhang

llms rag agents benchmarks

arXiv AI

Jul 22

Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments

arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.

By Shuoming Zhang, Ruiyuan Xu, Haofeng Li, Qiuchu Yu, Yangyu Zhang, Chunwei Xia, Xiaobing Feng, Chenxi Wang, Huimin Cui, Jiacheng Zhao

llms agents data-engineering

arXiv AI

6d ago

PromptResponse: Optimizing Prompts for LLM Coding Tasks

arXiv:2608.21074v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in research workflows and software development pipelines, yet their output remains sensitive to in...

By Erik Thureck, Robert K\"uhnen, Tim Jacobowitz

llms benchmarks safety

arXiv AI

Jun 6

Enhancing Software Engineering Through Closed-Loop Memory Optimization

arXiv:2606. 05646v1 Announce Type: cross Abstract: Large language models (LLMs) have enabled powerful software engineering (SE) agents capable of navigating complex codebases and resolving real-world issues.

By Xuehang Guo, Zora Zhiruo Wang, Qingyun Wang, Graham Neubig, Xingyao Wang

llms agents benchmarks

arXiv AI

Jul 22

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

arXiv:2607. 18260v1 Announce Type: new Abstract: We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis.

By Soham Dan

llms rag agents benchmarks

arXiv AI

Aug 18

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.

By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo

llms fine-tuning benchmarks

arXiv AI

Jun 12

WildIFEval: Instruction Following in the Wild

arXiv:2503. 06573v3 Announce Type: replace-cross Abstract: Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge.

By Gili Lior, Asaf Yehudai, Ariel Gera, Liat Ein-Dor

llms benchmarks

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