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arxiv: 2606.30573 · v1 · pith:5DPCPQT7new · submitted 2026-06-29 · 💻 cs.LG

SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions

Mohit Raghavendra , Anisha Gunjal , Aakash Sabharwal , Yunzhong He This is my paper

Pith reviewed 2026-06-30 06:53 UTC · model grok-4.3

classification 💻 cs.LG
keywords coding agentssoftware engineering benchmarksmulti-turn interactionsuser-driven workflowsrequirement evolutioninteractive goal discovery
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The pith

Top coding models solve roughly 50 percent of single-turn tasks but only 25 percent when requirements evolve through repeated user feedback.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SWE-Interact as a benchmark that replaces upfront complete specifications with a user simulator that begins with vague instructions and gradually surfaces details, inspects work, and issues revisions. Results across frontier models show that success rates halve compared with standard single-turn SWE tasks, indicating that autonomous implementation skills do not automatically support interactive goal discovery. The setup reveals two distinct failure modes: stronger models overcommit to early code and forget later constraints, while weaker models abandon tasks under initial ambiguity. This measures an additional capability axis beyond current autonomous benchmarks.

Core claim

SWE-Interact places agents inside long-horizon sessions where a simulator grounded in real interaction studies starts with incomplete instructions, inspects the workspace, and supplies targeted feedback plus new constraints until the full goal is conveyed. The evaluation finds that the best models reach about 50 percent on matched single-turn baselines yet only 25 percent on the interactive versions, with even the strongest models exhibiting over-agentic coding, requirement forgetting, and technical errors while weaker models give up early or ignore instructions.

What carries the argument

The user simulator that progressively reveals requirements, inspects the agent's workspace, and issues revisions until the complete task is handed off.

If this is right

  • Agents must maintain running records of changing constraints rather than treating early code as fixed.
  • Evaluation protocols need to include multi-turn sessions with partial information to capture real deployment conditions.
  • Stronger models still require improved mechanisms to avoid over-committing to initial implementations.
  • Weaker models require better handling of ambiguity before they can participate in iterative refinement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training pipelines that expose models only to complete specifications may systematically under-prepare them for live development workflows.
  • Adding user-simulator loops to existing benchmarks could surface capability gaps that single-turn tests miss.
  • Deployment of coding agents in practice may require separate calibration for the interactive-discovery phase before autonomous coding begins.

Load-bearing premise

The simulator produces interactions that match how real developers reveal intent and give feedback over time.

What would settle it

Direct comparison of model success rates on SWE-Interact against their success rates when the same tasks are completed with actual human developers providing the evolving requirements.

Figures

Figures reproduced from arXiv: 2606.30573 by Aakash Sabharwal, Anisha Gunjal, Mohit Raghavendra, Yunzhong He.

Figure 1
Figure 1. Figure 1: SWE-INTERACT converts SWE benchmarks from one-shot implementation tasks into interactive developer workflows. Standard single-turn benchmarks provide the full task specification upfront and evaluate autonomous implementation. SWE-INTERACT exposes requirements through a multi-turn interaction driven by a persona-conditioned user simulator: the simulator starts with an incomplete request, reviews the agent’s… view at source ↗
Figure 2
Figure 2. Figure 2: Our sandbox design separates the agent container, which holds the workspace, from the user-simulator container, which holds the persona, task instructions, and toolset. The user simulator has shell access to the agent’s workspace, while the agent can only send messages to the user. environments. We first analyzed thousands of real user messages from SWE-chat [3]. SWE-chat labels each coding session by user… view at source ↗
Figure 3
Figure 3. Figure 3: Average agent-user interaction count and average user tool calls per trial. to explore the agent’s workspace to give targeted feedback and revisions. We show a representative example A.2 resulting from our setup with, and a similar example trajectory from the actual SWE-chat dataset. One of the longest trajectory in our results had 27 user messages, with the user issuing 332 tool calls to the agent’s works… view at source ↗
Figure 4
Figure 4. Figure 4: Goal discovery across the agent’s initial plan, implementation trajectory, and final verifier outcome. 6 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Failure-mode distribution over independently assessed semantic failure labels. Labels are not mutually exclusive, so the donut is over assigned labels rather than trajectories. Model Resolve Rate Avg. agent steps Avg. user-agent turns Neutral Ours Neutral Ours Neutral Ours GPT 5.5 29.3% 25.3% (-4.0 pp) 265.0 339.7 (+28.2%) 3.67 6.99 (+90.5%) Opus 4.8 29.3% 22.7% (-6.7 pp) 108.7 161.4 (+48.5%) 3.60 6.88 (+9… view at source ↗
Figure 6
Figure 6. Figure 6: Revision churn and overhead metrics. Misinterpretation / bad assumption: returns validated errors User surfaced: The user specified that Invalid stores errors as an immutable tuple, from_failure(e) wraps one error as (e,), and applicative apply concatenates error tuples left-to-right. Failure evidence: The implementation treated a tuple passed to Invalid(...) as one single error instead of as the stored er… view at source ↗
read the original abstract

We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks. Existing frontier SWE benchmarks typically provide complete requirements upfront and evaluate agents on autonomous implementation. In contrast, SWE-Interact places agents in a realistic developer workflow: a carefully designed user simulator starts with vague or incomplete instructions, progressively reveals requirements, inspects the agent's workspace, and provides targeted feedback, revisions, and new constraints until the full task goal has been handed off. Grounded in large-scale studies of real coding-agent interactions, this setup tests whether agents can discover user intent, adapt to evolving requirements, and build on their own prior work. Across a suite of frontier and open-weight models, we find that strong performance on single-turn SWE tasks does not reliably transfer to multi-turn, user-driven workflows: the best-performing models solve roughly 50% of single-turn baseline tasks but only 25% of the corresponding SWE-Interact tasks. The strongest models in our evaluation, including Opus 4.8 and GPT 5.5, start strong even in the face of vague initial instructions, persevere until all the requirements are surfaced by the user, integrate them better and write clean code. However, they still suffer from over-agentic coding, forgetting requirements and technical mistakes. Weaker models start poorly under ambiguity, give up early, forget or ignore instructions and rework their code more. Overall, SWE-Interact measures an orthogonal, real-world capability axis for frontier model development: interactive goal discovery and iterative refinement with a user in the loop.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces SWE-Interact, a benchmark that replaces single-turn SWE tasks (complete requirements given upfront) with multi-turn sessions driven by a user simulator. The simulator begins with vague instructions, progressively reveals requirements, inspects workspaces, and supplies feedback until the goal is met. Across frontier models the headline result is that ~50% success on single-turn baselines drops to ~25% on the corresponding SWE-Interact tasks; the authors interpret this gap as evidence that single-turn capability does not transfer to interactive, requirement-evolving workflows.

Significance. If the simulator faithfully reproduces the statistical properties of real developer–agent interactions, the work identifies an orthogonal evaluation axis—interactive goal discovery and iterative refinement—that current autonomous SWE benchmarks miss. The empirical comparison across multiple models supplies concrete evidence that existing leaderboards may overstate readiness for realistic coding sessions.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (Evaluation Setup): the central transfer claim rests on the reported 50% vs. 25% gap, yet the manuscript supplies no information on task selection criteria, number of tasks per condition, statistical significance testing, or error analysis. Without these details the numerical comparison cannot be assessed for robustness.
  2. [§4] §4 (User Simulator): the simulator is stated to be 'grounded in large-scale studies of real coding-agent interactions,' but no quantitative validation is presented (e.g., KL divergence or distributional match on turn counts, requirement granularity, feedback type frequencies, or abandonment rates). Because simulator fidelity is the load-bearing assumption for interpreting the performance drop as a genuine capability gap rather than a benchmark artifact, this omission directly affects the main conclusion.
minor comments (2)
  1. [Abstract] Model names 'Opus 4.8' and 'GPT 5.5' appear without version or release dates; clarify exact checkpoints used.
  2. [Abstract] The abstract states that weaker models 'give up early' and 'rework their code more,' but no quantitative metrics (e.g., average turns before abandonment, edit counts) are referenced; add these to the results tables if present in the full evaluation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which identify areas where additional detail will improve the clarity and credibility of our claims. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (Evaluation Setup): the central transfer claim rests on the reported 50% vs. 25% gap, yet the manuscript supplies no information on task selection criteria, number of tasks per condition, statistical significance testing, or error analysis. Without these details the numerical comparison cannot be assessed for robustness.

    Authors: We agree that the current description of the evaluation setup is insufficient for assessing robustness. In the revised manuscript we will expand §3 with: explicit task selection criteria (random stratified sample from SWE-Bench verified tasks meeting complexity and domain filters), the precise number of tasks per condition, statistical significance testing (McNemar’s test with bootstrap confidence intervals on the 50 % vs. 25 % gap), and a dedicated error-analysis subsection that quantifies the frequency of the failure modes already mentioned in the abstract (over-agentic coding, requirement forgetting, technical mistakes). revision: yes

  2. Referee: [§4] §4 (User Simulator): the simulator is stated to be 'grounded in large-scale studies of real coding-agent interactions,' but no quantitative validation is presented (e.g., KL divergence or distributional match on turn counts, requirement granularity, feedback type frequencies, or abandonment rates). Because simulator fidelity is the load-bearing assumption for interpreting the performance drop as a genuine capability gap rather than a benchmark artifact, this omission directly affects the main conclusion.

    Authors: We concur that quantitative validation of the simulator is necessary to support the central interpretation. Although the design is derived from the cited large-scale studies, the original submission does not report distributional matches. We will add a new subsection (or appendix) to §4 that presents KL-divergence and frequency comparisons on turn counts, requirement granularity, feedback-type distributions, and abandonment rates, computed against the statistics reported in the grounding studies. This addition will directly address the concern that the observed gap might be an artifact of simulator mismatch. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical measurements on a new benchmark

full rationale

The paper introduces SWE-Interact as a new testbed and reports raw performance percentages (50% single-turn vs 25% interactive) as direct empirical results from running models on the benchmark. No equations, fitted parameters, or derived quantities are presented as predictions. The simulator is described as grounded in external large-scale studies without any self-citation chain or uniqueness theorem invoked to justify the central transfer claim. The reported gap is a straightforward measurement rather than a reduction to prior inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim depends on the unverified fidelity of the user simulator and the assumption that single-turn baselines are directly comparable to the new multi-turn tasks; no free parameters or invented entities are visible in the abstract.

axioms (1)
  • domain assumption The user simulator accurately models real developer interactions and requirement evolution.
    Abstract states the simulator is 'grounded in large-scale studies' but does not demonstrate equivalence to actual user behavior.

pith-pipeline@v0.9.1-grok · 5831 in / 1178 out tokens · 30237 ms · 2026-06-30T06:53:00.701301+00:00 · methodology

discussion (0)

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Reference graph

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