✨SWE-RM: Execution-free Feedback For Software Engineering Agents

📝 Summary:
This paper introduces SWE-RM, a robust, execution-free reward model for software engineering agents. It overcomes limitations of execution-based feedback, improving coding agent performance in both test-time scaling and reinforcement learning. SWE-RM achieves new state-of-the-art results for open...

🔹 Publication Date: Published on Dec 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21919
• PDF: https://arxiv.org/pdf/2512.21919

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#SoftwareEngineering #AI #ReinforcementLearning #CodingAgents #RewardModels

✨ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?

📝 Summary:
ISO-Bench evaluates coding agents on real-world LLM inference optimization tasks using combined execution and LLM metrics. Agents often identify bottlenecks but fail to execute working solutions, highlighting that scaffolding is as important as the model itself.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19594
• PDF: https://arxiv.org/pdf/2602.19594

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#CodingAgents #LLMOptimization #AIResearch #Benchmarking #LargeLanguageModels

✨Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents

📝 Summary:
A task-conditioned tool-output pruning model effectively reduces input tokens for coding agents. It achieves 0.86 recall and 0.80 F1, removing 92% of tokens, outperforming larger zero-shot models and heuristic baselines.

🔹 Publication Date: Published on Apr 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04979
• PDF: https://arxiv.org/pdf/2604.04979
• Github: https://github.com/KRLabsOrg/squeez

🔹 Models citing this paper:
• https://huggingface.co/KRLabsOrg/squeez-2b

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#CodingAgents #LLM #TokenPruning #AI #MachineLearning

✨Scaling Test-Time Compute for Agentic Coding

📝 Summary:
This framework improves long-horizon agentic coding by using compact trajectory representations for test-time scaling. It employs Recursive Tournament Voting and adapted Parallel-Distill-Refine to significantly boost coding agent performance on benchmarks.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16529
• PDF: https://arxiv.org/pdf/2604.16529

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#AgenticAI #CodingAgents #MachineLearning #AIResearch #DeepLearning

🔥 FastContext: Training Efficient Repository Explorer for Coding Agents

💡 The paper introduces FastContext, a dedicated exploration subagent designed to improve the efficiency of repository exploration in large language model coding agents. The problem addressed is that repository exploration is a major bottleneck in coding agents, consuming a substantial token budget and polluting the agent's context with irrelevant code snippets.

The method involves separating repository exploration from code solving using specialized exploration models. FastContext is invoked on demand and issues parallel tool calls to return concise file paths and line ranges as focused context. The exploration models used in FastContext are powered by 4B-30B parameters and are bootstrapped from strong reference-model trajectories. They are then refined with task-grounded rewards for broad first-turn search, multi-turn evidence gathering, and precise citation generation.

The results show that integrating FastContext into a coding agent improves end-to-end resolution rates by up to 5.5 percent while reducing coding-agent token consumption by up to 60 percent, with minimal overhead. The paper demonstrates that repository exploration can be effectively handled by specialized models, separate from the code solving process. The code and data for FastContext are made available, allowing for further research and development in this area. Overall, the paper presents a significant contribution to the field of coding agents and software engineering, providing a more efficient and effective approach to repository exploration.

📅 Published on Jun 12

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.14066
• PDF: https://arxiv.org/pdf/2606.14066
• Project Page: https://huggingface.co/microsoft/FastContext-1.0-4B-SFT

🤖 Models citing this paper:
• https://huggingface.co/microsoft/FastContext-1.0-4B-SFT
• https://huggingface.co/microsoft/FastContext-1.0-4B-RL

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📢 By: https://xn--r1a.website/PaperNexus

#EfficientRepositoryExploration #CodingAgents #LargeLanguageModels #RepositoryExplorationSubagents #SpecializedExplorationModels