User-agent interaction beyond messages

Benchmarking Coding Agents When Users Touch the Code

SWE-Touch tests whether a coding agent can complete a software repair after a user has directly changed the code in their shared workspace.

Yuqiao Tan · Jinxiang Meng · Fangyu Lei · Minzheng Wang · Shizhu He · Jun Zhao · Kang Liu

Code & documentation Dataset

9coding models

3repair benchmarks

−7.7mean points on Verified

Research question

When a user contributes code during an ongoing task, can the agent interpret that edit against the issue, repository, and tests and still reach a correct solution?

Evaluation design

From autonomous repair to a shared workspace

Each task is evaluated under matched conditions. The user edit is the only change to the live task state.

Vanilla

The agent repairs the original checkout

The issue, repository, tools, verifier, and interaction budget follow the standard autonomous setting.

vs.

Counter-Edit

A user changes relevant code during the repair

A small, plausible but task-conflicting edit and a contextual user message enter the same live workspace.

SWE-Touch pipeline with task-critical region mining, user edit construction, and shared workspace evaluation — Click the figure for the full-resolution pipeline.

Main evidence

User edits expose a gap hidden by autonomous repair

Resolve rates are percentages. Verified results report mean ± standard deviation.

−7.7average change on SWE-bench VerifiedAll nine evaluated models decline under Counter-Edit.

−1.3 to −16.5large variation across modelsStrong autonomous scores do not imply consistent recovery.

63.3%failures retain the conflictMost failed trajectories end with the user-introduced behavior still active.

SWE-bench Verified

Retention is the fraction of tasks solved under Vanilla that remain solved under Counter-Edit.

Resolve-rate decrease

Counter-Edit minus Vanilla, percentage points

GPT 5.5−1.3

Claude 4.8−1.8

GLM 5.1−4.3

Qwen 3.7−4.8

Kimi K2.6−6.0

MiniMax M2.5−9.5

DeepSeek V4−11.0

MiniMax M2.7−13.8

Qwen Coder−16.5

Model	Vanilla	Counter-Edit	Δ	Retention
Claude Opus 4.8	85.2 ± 1.8	83.3 ± 0.6	−1.8	96.0
GPT 5.5	80.5 ± 1.0	79.2 ± 0.6	−1.3	95.0
GLM 5.1	72.7 ± 2.0	68.3 ± 0.8	−4.3	83.3
MiniMax M2.7	76.5 ± 1.5	62.7 ± 2.4	−13.8	78.1
MiniMax M2.5	75.7 ± 3.3	66.2 ± 1.0	−9.5	78.3
Qwen 3.7 Max	75.2 ± 1.0	70.3 ± 0.8	−4.8	90.3
Qwen3-Coder-480B	57.2 ± 3.5	40.7 ± 1.0	−16.5	60.8
Kimi K2.6	70.3 ± 2.0	64.3 ± 3.4	−6.0	87.2
DeepSeek V4 Pro	74.8 ± 0.8	63.8 ± 1.8	−11.0	81.5

Finding. The ordering changes substantially: Qwen3-Coder-480B and MiniMax M2.7 lose more than 13 points, while GPT 5.5 and Claude Opus 4.8 retain over 95% of their Vanilla-solved tasks.

Harder repair tasks

Selected SWE-Bench Pro and DeepSWE tasks use a longer budget and trajectory-relative delivery.

Model	SWE-Bench Pro			DeepSWE
Model	Vanilla	Counter	Δ	Vanilla	Counter	Δ
Claude Opus 4.8	68.0	68.0	0.0	56.0	46.0	−10.0
GPT 5.5	38.0	38.0	0.0	64.0	56.0	−8.0
GLM 5.1	43.1	32.8	−10.3	19.4	16.8	−2.5
MiniMax M2.7	30.6	24.6	−6.0	2.2	2.2	0.0
MiniMax M2.5	32.6	24.6	−8.0	0.0	0.0	0.0
Qwen 3.7 Max	36.0	26.0	−10.0	4.1	2.1	−2.0
Qwen3-Coder-480B	20.0	14.0	−6.0	0.0	0.0	0.0
Kimi K2.6	50.0	48.0	−2.0	18.0	12.0	−6.0
DeepSeek V4 Pro	34.0	32.0	−2.0	4.1	2.0	−2.1
Mean	39.1	34.2	−4.9	18.6	15.2	−3.4

Finding. The aggregate direction persists on both harder benchmarks, although the effect varies by model and task family.

Message, edit, and frequency

Condition	GPT 5.5	GLM 5.1	M2.7	Qwen 3.7
Vanilla	81.5	70.5	76.5	74.0
Message, K=3	79.5	73.0	76.5	77.0
Code edit, K=3	80.5	66.5	67.0	71.5
Both, K=1	78.5	72.0	64.5	71.5
Both, K=3	79.5	69.0	64.5	71.0
Both, K=5	78.0	69.0	60.0	69.0

Exploratory resolve rates on SWE-bench Verified. K is the maximum number of interventions.

Finding. Messages alone do not reproduce the effect consistently. Direct changes to executable code are the stronger intervention in this comparison.

Edits stay local

Benchmark	Reference lines / files	Counter lines / files
Verified	13.3 / 1.20	7.0 / 1.04
Pro	361.0 / 5.44	13.0 / 1.40
DeepSWE	730.2 / 7.24	10.8 / 1.52

Mean changed lines and files per patch.

Finding. Counter-Edits remain small even when the underlying reference repair spans hundreds of lines and several files.

Trajectory analysis

Failure is not only passive acceptance

Agents sometimes remove or replace the inserted edit and still fail. Recognizing a problematic contribution does not guarantee that the model can restore the repository to a correct state.

63.3%of Vanilla-solved / Counter-Edit-failed runs end with the conflicting behavior retained.

Failure-mode distribution and user-edit revision rates by model — Failure modes and edit-response behavior on SWE-bench Verified.

Open release

Reproduce and extend SWE-Touch

The release separates reusable benchmark records from the Harbor-based evaluation implementation.

Code

Runner, schemas, user simulator, validation gates, and reproducibility commands.

Open repository

Dataset

Task records, critical regions, Counter-Edits, and validation evidence.

Browse on Hugging Face

Quick start

Install the package, validate the release, and launch Vanilla or SWE-Touch evaluation.

Read the guide