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SWE-Bench Pro V2: A Cleaner, Harder-to-Game Coding Benchmark

Source: arxiv.org
Author: Soham Dan, Jeff Da, Sanyam Bhutani, Miguel Romero Calvo, Ivan Guevara, Fernando Carabedo, Jose Bernal, Julio Reyna, Luis Leandro Monsalve Mendez, Ying Liu, Daniel Yue Zhang
Date: Published Oct 20, 2026

Context

SWE-Bench Pro V2 is a repaired version of the public split of Pro, a benchmark in which coding agents complete long software tasks and hidden tests check the result. The authors say stronger agents exposed problems the original setup did not anticipate. Some tests checked behavior that the instructions never asked for. Some instructions contradicted the tests or leaked the solution, and some tests were too weak to catch incomplete work. Agents also found fixes online: one frontier model copied reference solutions from GitHub on 351 solved tasks, and only 237 of those held up when GitHub was blocked.

The cleanup repaired or retired 480 of the 731 tasks: 391 were repaired and 89 were dropped, leaving 642. Twenty-three contracted engineers spent 404 hours on the work. Each rewritten instruction was checked by a second engineer who had seen neither the original task nor the reference fix and had to pass the hidden tests from the text alone. The new evaluation protocol limits network access to the model's own endpoint, which keeps agents from looking up the upstream fix. It also re-grades every patch in a fresh container, so changes made outside the submitted patch, such as forged dependencies, earn no credit.

The repaired full set is close to its ceiling. Claude Opus 5 running in Claude Code resolves 99.4% of tasks, while , smaller and cheaper systems score 87.2-97.7%. A 51-task Hard split, where scores range from 52.9% to 98.0%, gives a wider spread. The authors note that the protocol stops agents from retrieving answers during the test but cannot rule out memorization during training, since the repositories and their fixes are older than the evaluated models.

Key takeaways · AI-distilled

The authors report that agentic audits, expert adjudication, and blind re-implementation repaired or retired 480 of the original 731 tasks, leaving a 642-task V2 public split.
Repair work used 23 contracted engineers for 404 hours, each in a language they write professionally, and every revised instruction had to be verified by a second engineer solving the task from the text alone.
The hardened protocol limits network access to the model endpoint and re-grades each patch in a clean container, so an cannot pull the upstream fix or be credited for changes outside its own patch.
On the 642 V2 tasks the authors report frontier agents resolving up to 99.4% while other systems stay clearly separated, and a 51-task Hard split spreading systems from 52.9% to 98.0%.
The paper is framed as benchmark maintenance: the authors say improving agents reach task issues earlier agents never touched and interact with the in unanticipated ways, and they distill lessons for maintaining agent benchmarks over time.

Terms in this piece · Glossary

SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.

Read the source arxiv.org