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Notes on coding agents
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SciWalker: Synthesizing Scientific Coding Problems with Operator Graphs and Execution Feedback
The preprint describes a framework for synthesizing scientific coding problems by sampling operator chains from graphs built from scientific library interfaces and operation modes. An LLM generates problem statements…
CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
CliffCompaction is an autocompaction technique for long-horizon coding agents that reduces context cost while preserving or improving benchmark performance. It only truncates or drops content rather than rephrasing it…
Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents
Growing Harness learns the agent harness itself from a strategy-free scaffold. Function-level traces localize failures, an optimizer repairs bounded code surfaces, and a held-out success-first gate rolls back repairs…
SWE-Serve: Benchmarking Agentic Engineering For Production Inference Serving
SWE-Serve contains 53 repository-grounded tasks derived from production changes to SGLang, spanning six inference-engineering families. Tasks use hidden functional and regression tests, end-to-end serving tests…
CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
CodeMidas converts implemented functionality in open-source codebases into executable reinforcement-learning environments using source code as the only task-specific input. Agents formulate behavioral specifications…
Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair
The paper discusses the unreliability of looping in coding agents and the challenge of ensuring that correct patches are retained, verified, and submitted. The authors conducted several studies to understand this gap…
Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data
Two coding agents were tested on a production task involving Quranic verse identification and transcription splitting. Both agents independently developed similar algorithms, but one (OpenAI Codex) performed poorly due…
Heuristic Learning for Active Flow Control Using Coding Agents
A new method for active flow control is proposed in this research paper. Instead of optimizing neural network parameters, the authors use coding agents to search directly for explicit executable feedback laws. The…
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
The paper introduces RuBench 1.0, a new benchmark for coding agents that uses tasks specified in Russian rather than English. The benchmark is designed to measure the performance of coding agents when they are given…
Distributed Attacks in Persistent-State AI Control
Researchers introduce Iterative VibeCoding, a setting for AI control that studies the deployment of autonomous AI coding agents and their potential for distributed attacks. The study finds that gradual attacks, which…
Understanding Agent-Based Patching of Compiler Missed Optimizations
The paper presents a study on the effectiveness of AI agents in addressing missed optimizations in compilers. The authors found that while coding agents can often optimize given examples, the generated patches…
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
The paper evaluates three performance-optimization benchmarks (GSO, SWE-Perf, and SWE-efficiency) used to assess coding agents. The authors find that these benchmarks have issues with stability, scoring rules, and the…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Researchers collect and release a dataset of approximately 4,300 coding-agent sessions from their own use of Claude Code and Codex. The data shows that coding-agent workloads feature long autonomous loops, long…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Researchers have collected and released a dataset of 4300 coding agent sessions, containing around 350,000 LLM steps and 430,000 tool calls from their own use of Claude Code and Codex. The analysis shows that…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Researchers present TraceLab, a dataset of 4,300 coding-agent sessions collected from their own usage of Claude Code and Codex. The data reveals characteristics of these workloads, such as long autonomous loops, long…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper presents the Spec Growth Engine, a framework to address the structural failure modes introduced by AI coding agents in software development. These failure modes include context explosion and silent spec-code…
Bayesian control for coding agents
The paper proposes a Bayesian controller for coding agents that make decisions based on a belief over candidate correctness, rather than fixed rules. This method is found to be most useful when verification is costly…
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