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Collections including paper arxiv:2602.02361

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

Paper • 2506.18403 • Published Jun 23, 2025 • 3
ReCode: Updating Code API Knowledge with Reinforcement Learning

Paper • 2506.20495 • Published Jun 25, 2025 • 10
SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution

Paper • 2507.23348 • Published Jul 31, 2025 • 12
LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering

Paper • 2509.09614 • Published Sep 11, 2025 • 7

SWE-Universe: Scale Real-World Verifiable Environments to Millions

Paper • 2602.02361 • Published Feb 2 • 61
LongCodeZip: Compress Long Context for Code Language Models

Paper • 2510.00446 • Published Oct 1, 2025 • 108
Code2World: A GUI World Model via Renderable Code Generation

Paper • 2602.09856 • Published Feb 10 • 201
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Paper • 2601.11868 • Published Jan 17 • 37

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

Paper • 2601.08955 • Published Jan 13 • 13
EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines

Paper • 2601.09465 • Published Jan 14 • 42
MAXS: Meta-Adaptive Exploration with LLM Agents

Paper • 2601.09259 • Published Jan 14 • 96
Toward Efficient Agents: Memory, Tool learning, and Planning

Paper • 2601.14192 • Published Jan 20 • 57

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 107
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Paper • 2310.11511 • Published Oct 17, 2023 • 79
In-Context Learning Creates Task Vectors

Paper • 2310.15916 • Published Oct 24, 2023 • 43
Matryoshka Diffusion Models

Paper • 2310.15111 • Published Oct 23, 2023 • 46

dataset related

SWE-Universe: Scale Real-World Verifiable Environments to Millions

Paper • 2602.02361 • Published Feb 2 • 61

LLM-in-Sandbox Elicits General Agentic Intelligence

Paper • 2601.16206 • Published Jan 22 • 87
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization

Paper • 2601.13118 • Published Jan 19 • 1
SWE-Universe: Scale Real-World Verifiable Environments to Millions

Paper • 2602.02361 • Published Feb 2 • 61
Think Anywhere in Code Generation

Paper • 2603.29957 • Published Mar 31 • 25

CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging

Paper • 2502.05664 • Published Feb 8, 2025 • 24
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

Paper • 2312.13010 • Published Dec 20, 2023 • 6
HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale

Paper • 2409.16299 • Published Sep 9, 2024 • 11
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI

Paper • 2505.19443 • Published May 26, 2025 • 15

The Debugging Decay Index: Rethinking Debugging Strategies for Code LLMs

Paper • 2506.18403 • Published Jun 23, 2025 • 3
ReCode: Updating Code API Knowledge with Reinforcement Learning

Paper • 2506.20495 • Published Jun 25, 2025 • 10
SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution

Paper • 2507.23348 • Published Jul 31, 2025 • 12
LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering

Paper • 2509.09614 • Published Sep 11, 2025 • 7

dataset related

SWE-Universe: Scale Real-World Verifiable Environments to Millions

Paper • 2602.02361 • Published Feb 2 • 61

SWE-Universe: Scale Real-World Verifiable Environments to Millions

Paper • 2602.02361 • Published Feb 2 • 61
LongCodeZip: Compress Long Context for Code Language Models

Paper • 2510.00446 • Published Oct 1, 2025 • 108
Code2World: A GUI World Model via Renderable Code Generation

Paper • 2602.09856 • Published Feb 10 • 201
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Paper • 2601.11868 • Published Jan 17 • 37

LLM-in-Sandbox Elicits General Agentic Intelligence

Paper • 2601.16206 • Published Jan 22 • 87
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization

Paper • 2601.13118 • Published Jan 19 • 1
SWE-Universe: Scale Real-World Verifiable Environments to Millions

Paper • 2602.02361 • Published Feb 2 • 61
Think Anywhere in Code Generation

Paper • 2603.29957 • Published Mar 31 • 25

Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models

Paper • 2601.08955 • Published Jan 13 • 13
EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines

Paper • 2601.09465 • Published Jan 14 • 42
MAXS: Meta-Adaptive Exploration with LLM Agents

Paper • 2601.09259 • Published Jan 14 • 96
Toward Efficient Agents: Memory, Tool learning, and Planning

Paper • 2601.14192 • Published Jan 20 • 57

CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debugging

Paper • 2502.05664 • Published Feb 8, 2025 • 24
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation

Paper • 2312.13010 • Published Dec 20, 2023 • 6
HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale

Paper • 2409.16299 • Published Sep 9, 2024 • 11
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI

Paper • 2505.19443 • Published May 26, 2025 • 15

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 107
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Paper • 2310.11511 • Published Oct 17, 2023 • 79
In-Context Learning Creates Task Vectors

Paper • 2310.15916 • Published Oct 24, 2023 • 43
Matryoshka Diffusion Models

Paper • 2310.15111 • Published Oct 23, 2023 • 46

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