arXiv AI
Jul 13
SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution
arXiv:2603. 13428v3 Announce Type: replace-cross Abstract: Real-world software must continuously evolve to meet ever-changing and open-ended requirements.
EvoClaw: Evaluating AI Agents on Continuous Software Evolution
arXiv:2603. 13428v2 Announce Type: replace-cross Abstract: With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environments.
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arXiv AI
Jul 16
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.
arXiv AI
Sep 2
HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution
HarnessEvolve is a self‑evolving framework that improves agent harnesses—prompts, skills, tools, and execution logic—by learning from reference trajectories. It separates execution, evaluation, optimization, and gating into independent modules, addressing credit assignment failure, shortcut learning, and catastrophic forgetting. The approach uses reference trajectories to extract error signals, applies quality and performance gates to candidate updates, and validates updates on held‑out data, consistently outperforming state‑of‑the‑art baselines across diverse benchmarks.
arXiv AI
3d ago
E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
arXiv AI
Jun 29
Agentic Hardware Design as Repository-Level Code Evolution
arXiv:2606. 28279v1 Announce Type: cross Abstract: We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution.
Hugging Face Trending Papers
Jul 15
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering.
arXiv AI
Jun 4
The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development?
arXiv:2606. 04455v1 Announce Type: new Abstract: Current AI benchmarks evaluate agents on task execution within human-designed workflows.
arXiv AI
Sep 10
Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course
arXiv:2609.08832v1 Announce Type: new Abstract: Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains lar...
arXiv AI
Jun 17
SEAGym: An Evaluation Environment for Self-Evolving LLM Agents
arXiv:2606. 17546v1 Announce Type: new Abstract: Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop.
arXiv Machine Learning
Jul 31
Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.
arXiv AI
Jun 9
SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
arXiv:2606. 07682v1 Announce Type: cross Abstract: AI agents are increasingly expected to complete long-horizon workflows that require sustained progress over hours, millions of tokens, and complex environments.
arXiv AI
Jul 22
Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.