# SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

arXiv: https://arxiv.org/abs/2603.24755 · HTML: https://mlsanity.com/paper/2603.24755/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 54 of its 86 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 54)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://mlsanity.com/paper/2601.11868/) (2026)
- [RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems](https://mlsanity.com/paper/2306.03091/) (2023)
- [Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving](https://mlsanity.com/paper/2504.02605/) (2025)
- [SWE-Bench+: Enhanced Coding Benchmark for LLMs](https://mlsanity.com/paper/2410.06992/) (2024)
- [SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents](https://mlsanity.com/paper/2504.08703/) (2025)
- [NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents](https://mlsanity.com/paper/2512.12730/) (2025)
- [SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios](https://mlsanity.com/paper/2512.18470/) (2025)
- [FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation](https://mlsanity.com/paper/2503.06680/) (2025)
- [What's Wrong with Your Code Generated by Large Language Models? An Extensive Study](https://mlsanity.com/paper/2407.06153/) (2024)
- [Commit0: Library Generation from Scratch](https://mlsanity.com/paper/2412.01769/) (2024)
- [LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks](https://mlsanity.com/paper/2502.06215/) (2025)
- [FeatureBench: Benchmarking Agentic Coding for Complex Feature Development](https://mlsanity.com/paper/2602.10975/) (2026)
- [LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces](https://mlsanity.com/paper/2602.14337/) (2026)
- [Measuring The Impact Of Programming Language Distribution](https://mlsanity.com/paper/2302.01973/) (2023)
- [SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale](https://mlsanity.com/paper/2602.23866/) (2026)
- [HumanEvo: An Evolution-aware Benchmark for More Realistic Evaluation of Repository-level Code Generation](https://mlsanity.com/paper/2406.06918/) (2024)
- [ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation](https://mlsanity.com/paper/2503.07010/) (2025)
- [RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback](https://mlsanity.com/paper/2510.06186/) (2025)
- [SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration](https://mlsanity.com/paper/2603.03823/) (2026)
- [Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations](https://mlsanity.com/paper/2004.10777/) (2020)
- [Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios](https://mlsanity.com/paper/2410.12468/) (2024)
- [CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation](https://mlsanity.com/paper/2504.21751/) (2025)
- [Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](https://mlsanity.com/paper/2508.21634/) (2025)
- [CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation](https://mlsanity.com/paper/2503.22688/) (2025)
- [MaintainCoder: Maintainable Code Generation Under Dynamic Requirements](https://mlsanity.com/paper/2503.24260/) (2025)
- [Do Code Clones Matter?](https://mlsanity.com/paper/1701.05472/) (2017)
- [ProjDevBench: Benchmarking AI Coding Agents on End-to-End Project Development](https://mlsanity.com/paper/2602.01655/) (2026)

## Built on by (most cited first, 30 of 30)

- [Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments](https://mlsanity.com/paper/2606.05661/) (2026)
- [SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades](https://mlsanity.com/paper/2605.14415/) (2026)
- [Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement](https://mlsanity.com/paper/2609.01481/) (2026)
- [LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation](https://mlsanity.com/paper/2608.00267/) (2026)
- [SWE-Touch: Benchmarking Coding Agents When Users Touch the Code](https://mlsanity.com/paper/2608.02499/) (2026)
- [Repo0: Design-Driven Zero-to-All Code Generation](https://mlsanity.com/paper/2608.19854/) (2026)
- [LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering](https://mlsanity.com/paper/2608.28281/) (2026)
- [Is Agent Code Less Maintainable Than Human Code?](https://mlsanity.com/paper/2606.21804/) (2026)
- [EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments](https://mlsanity.com/paper/2607.02440/) (2026)
- [CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?](https://mlsanity.com/paper/2610.07557/) (2026)
- [Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study](https://mlsanity.com/paper/2605.20049/) (2026)
- [StaminaBench: Stress-Testing Coding Agents over 100 Interaction Turns](https://mlsanity.com/paper/2606.19613/) (2026)
- [Terminal Agents: A Survey of AI Agents in Command-Line Environments](https://mlsanity.com/paper/2608.20485/) (2026)
- [SA-Bench: Evaluating Semantic Alignment in LLM-Based Paper Reproduction](https://mlsanity.com/paper/2608.24252/) (2026)
- [EvoGenUI-Bench: Evaluating LLMs as Multi-Turn Generative UI Assistants](https://mlsanity.com/paper/2608.29387/) (2026)
- [TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization](https://mlsanity.com/paper/2607.18161/) (2026)
- [Benchmarking the Residual: What Long-Horizon Evaluations Add Beyond Matched Short-Task Performance](https://mlsanity.com/paper/2607.27283/) (2026)
- [Persistent Recursive Worlds Enable Autonomous Software Evolution](https://mlsanity.com/paper/2608.10450/) (2026)
- [Super Library Agent: Joint Generation and Maintenance of Multiple Applications Beyond the Single Codebase](https://mlsanity.com/paper/2608.29310/) (2026)
- [If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs](https://mlsanity.com/paper/2609.10123/) (2026)
- [Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills](https://mlsanity.com/paper/2609.12012/) (2026)
- [Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms](https://mlsanity.com/paper/2609.27321/) (2026)
- [Do Coding Agents Reuse Existing Code or Reinvent the Wheel?](https://mlsanity.com/paper/2609.35357/) (2026)
- [StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents](https://mlsanity.com/paper/2609.36319/) (2026)
- [Can Agents Design Libraries for Agents?](https://mlsanity.com/paper/2609.36730/) (2026)
- [SWE-Journey: Towards More Realistic Evaluation of Coding Assistants through Long-Horizon, Multi-Turn Interaction](https://mlsanity.com/paper/2610.11559/) (2026)
- [EvoWeb: A Benchmark for Coding Agents on Evolving Web Applications](https://mlsanity.com/paper/2610.16402/) (2026)
- [StateHarness: Maintaining Evolving Project State for Long-Horizon Coding Agents](https://mlsanity.com/paper/2610.17616/) (2026)
- [SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents](https://mlsanity.com/paper/2610.18206/) (2026)
- [Definition Grounding in Theory-Level Auto-formalization](https://mlsanity.com/paper/2610.19223/) (2026)

## Abstract (arXiv)

Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmarks attempt to remedy this but heavily constrain an agent's design decision space, making it impossible to faithfully measure how their decisions shape future extensions. We introduce SlopCodeBench, a benchmark of 36 problems and 196 checkpoints where agents repeatedly extend their own solutions. Unlike prior iterative benchmarks, our evolving specifications demand architectural decisions but leave internal structure to the agent. We measure two forms of degradation: structural erosion (concentrated complexity) and verbosity (redundant code). Evaluating 15 coding agents across open and closed models, we find that no agent fully solves any problem end-to-end, and the best agent passes 14.8% of checkpoints. Quality degrades across checkpoints, with structural erosion rising in 77% of trajectories and verbosity in 75.5%. Compared to 473 open-source Python repositories, agent code is 2.3x more verbose and 2.0x more eroded, and the human repositories degrade less often and by smaller margins across their git histories. Explicit quality guidance reduces initial verbosity and erosion by up to a third, without affecting degradation rates. SlopCodeBench provides the first measurement of code degradation under iterative extension, revealing that agents pass checkpoints while producing code that erodes and bloats with each turn.