[2603.24755] SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

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Computer Science > Software Engineering

arXiv:2603.24755 (cs)

[Submitted on 25 Mar 2026 (v1), last revised 7 May 2026 (this version, v2)]

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

Authors:Gabriel Orlanski, Devjeet Roy, Alexander Yun, Changho Shin, Alex Gu, Albert Ge, Dyah Adila, Nicholas Roberts, Frederic Sala, Aws Albarghouthi<br>View a PDF of the paper titled SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks, by Gabriel Orlanski and 9 other authors

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Abstract: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.

Comments:<br>Code and Leaderboards are located at this https URL

Subjects:

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2603.24755 [cs.SE]

(or<br>arXiv:2603.24755v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2603.24755

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arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.5281/zenodo.18405900, https://doi.org/10.5281/zenodo.19257129

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Submission history<br>From: Gabriel Orlanski [view email]<br>[v1]<br>Wed, 25 Mar 2026 19:26:44 UTC (138 KB)

[v2]<br>Thu, 7 May 2026 20:39:58 UTC (219 KB)

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View a PDF of the paper titled SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks, by Gabriel Orlanski and 9 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

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