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Showing 26–50 of 264 papers

TitleStatusHype
Web-Bench: A LLM Code Benchmark Based on Web Standards and FrameworksCode3
CodeMixBench: Evaluating Large Language Models on Code Generation with Code-Mixed Prompts—0
Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis—0
Rewriting Pre-Training Data Boosts LLM Performance in Math and CodeCode1
The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models—0
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement—0
DataDecide: How to Predict Best Pretraining Data with Small ExperimentsCode3
Type-Constrained Code Generation with Language Models—0
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs—0
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency—0
Can LLMs Enable Verification in Mainstream Programming?—0
Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models—0
RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox TestingCode1
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Enhancement Protocol—0
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?—0
ThrowBench: Benchmarking LLMs by Predicting Runtime ExceptionsCode0
KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for CodingCode3
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge—0
Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval—0
Nexus: A Lightweight and Scalable Multi-Agent Framework for Complex Tasks AutomationCode2
CodeCriticBench: A Holistic Code Critique Benchmark for Large Language ModelsCode1
Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code Generation CapabilitiesCode1
UnitCoder: Scalable Iterative Code Synthesis with Unit Test Guidance—0
MasRouter: Learning to Route LLMs for Multi-Agent SystemsCode2
CopySpec: Accelerating LLMs with Speculative Copy-and-Paste Without Compromising QualityCode0
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