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arxiv: 2604.04979 · v1 · submitted 2026-04-04 · 💻 cs.SE · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents

\'Ad\'am Kov\'acs

Authors on Pith no claims yet

Pith reviewed 2026-05-13 16:59 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords tool-output pruningcoding agentstask-conditioned pruningSWE-benchLoRA fine-tuningtoken efficiencyagent benchmarksQwen model
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The pith

Task-conditioned pruning lets a fine-tuned 2B model retain 0.86 recall while dropping 92% of tool output tokens for coding agents.

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

Coding agents must process lengthy tool outputs but only a small part is usually relevant to the current task. The paper develops a task-conditioned pruning approach that, given a query, extracts the minimal verbatim evidence block from each tool output. They create a large benchmark from SWE-bench repository interactions plus synthetic multi-ecosystem data and fine-tune a 2B Qwen model with LoRA. This yields 0.86 recall and 0.80 F1 while removing 92 percent of tokens, beating both heuristic baselines and a 35B zero-shot model. Efficient pruning of this kind would let agents maintain longer histories without context limits or performance loss.

Core claim

Given a focused query and a tool output, return the smallest verbatim evidence block the agent should inspect next. By fine-tuning Qwen 3.5 2B with LoRA on a benchmark of 11,477 examples with a 618-example curated test set, the model achieves 0.86 recall and 0.80 F1 while removing 92% of input tokens, outperforming zero-shot Qwen 3.5 35B and all heuristic baselines.

What carries the argument

Task-conditioned tool-output pruning, which identifies and returns the smallest relevant verbatim block from a tool observation based on the agent's current query.

If this is right

  • Tool outputs can be reduced to 8% of original size with minimal loss of relevant information.
  • Small specialized models outperform larger general models on pruning tasks.
  • Heuristic methods are outperformed by learned task-conditioned approaches.
  • The new benchmark supports development of better pruning techniques for agents.

Where Pith is reading between the lines

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

  • This pruning strategy may apply to other domains where agents receive verbose observations, such as web browsing or data analysis agents.
  • Integrating the pruner into agent loops could enable longer-running tasks without token budget exhaustion.
  • Performance on the synthetic data suggests potential robustness across different programming ecosystems.

Load-bearing premise

The curated 618-example test set together with the synthetic data accurately captures the variety of tool outputs seen by real coding agents in practice.

What would settle it

Running the model on a fresh collection of tool outputs from actual coding agent sessions in repositories outside the SWE-bench set and checking if the recall stays above 0.75.

Figures

Figures reproduced from arXiv: 2604.04979 by \'Ad\'am Kov\'acs.

Figure 1
Figure 1. Figure 1: illustrates the overall pipeline. The benchmark input is a pair (q, o), where q is a Input Query: “Find the traceback that explains the ImportError.” Tool output: 501 lines from read file, grep, pytest, git log, . . . Grounded spans 183: ): 184: super(). init ( 185: import name=... 191: self.name = name 193: self.subdomain = ... Gold: (start line, end line) Generative output <relevant lines> 184: super(). … view at source ↗
Figure 3
Figure 3. Figure 3: Compact qualitative example from a kubectl observation. The full output has 250 lines; the gold evidence has two. 7 Limitations The benchmark evaluates pruning quality on single tool observations rather than full agent trajectories. It therefore measures evidence preservation directly, but not the downstream effect on end-to-end task completion. In ad￾dition, usefulness is approximated with span overlap, w… view at source ↗
read the original abstract

Coding agents repeatedly consume long tool observations even though only a small fraction of each observation matters for the next step. We study task-conditioned tool-output pruning: given a focused query and one tool output, return the smallest verbatim evidence block the agent should inspect next. We introduce a benchmark of 11,477 examples built from SWE-bench repository interactions and synthetic multi-ecosystem tool outputs, with a manually curated 618-example test set. We fine-tune Qwen 3.5 2B with LoRA and compare it against larger zero-shot models and heuristic pruning baselines. Our model reaches 0.86 recall and 0.80 F1 while removing 92% of input tokens, outperforming zero-shot Qwen 3.5 35B A3B by 11 recall points and all heuristic baselines by a wide margin.

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 Squeez, a task-conditioned tool-output pruning method for coding agents. Given a focused query and a tool output, the goal is to return the smallest verbatim evidence block needed for the next agent step. The authors construct a benchmark of 11,477 examples from SWE-bench repository interactions plus synthetic multi-ecosystem data, with a manually curated 618-example test set. They fine-tune Qwen 3.5 2B via LoRA and report that the resulting model achieves 0.86 recall and 0.80 F1 while removing 92% of input tokens, outperforming zero-shot Qwen 3.5 35B by 11 recall points and all heuristic baselines.

Significance. If the empirical claims hold under more rigorous validation, the work could meaningfully advance efficient agentic coding systems by demonstrating that targeted fine-tuning on a modest model can deliver substantial token reduction and accuracy gains over both larger zero-shot models and simple heuristics. This has direct implications for reducing context-window pressure and inference cost in long-horizon tool-using agents.

major comments (2)
  1. [Benchmark construction (abstract and associated section)] The performance numbers (0.86 recall, 0.80 F1, 92% token reduction) are measured exclusively on the 618-example manually curated test set described in the abstract. No inter-annotator agreement, curation protocol, label-validation procedure, or quantitative diversity statistics (output length distribution, task-category coverage, ecosystem balance) are provided. Because the central claim of outperformance rests on the quality and representativeness of this held-out set, the absence of these details is load-bearing.
  2. [Evaluation (abstract and results section)] No error bars, standard deviations, or statistical significance tests accompany the reported metrics or the 11-point recall margin over the 35B zero-shot baseline. With a test set of only 618 examples, it is impossible to determine whether the observed gains are robust or sensitive to the particular curation choices.
minor comments (2)
  1. [Abstract] The abstract states that the 11,477 examples were built from SWE-bench interactions and synthetic data but gives no breakdown of the split or labeling procedure; a short paragraph clarifying this would aid reproducibility.
  2. [Model and training] Training details (LoRA rank, learning rate, number of epochs, exact prompt format) are not mentioned; adding them would improve clarity without altering the core claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on benchmark transparency and evaluation rigor. We address each major point below and will revise the manuscript to incorporate additional details and analyses where feasible.

read point-by-point responses

  1. Referee: [Benchmark construction (abstract and associated section)] The performance numbers (0.86 recall, 0.80 F1, 92% token reduction) are measured exclusively on the 618-example manually curated test set described in the abstract. No inter-annotator agreement, curation protocol, label-validation procedure, or quantitative diversity statistics (output length distribution, task-category coverage, ecosystem balance) are provided. Because the central claim of outperformance rests on the quality and representativeness of this held-out set, the absence of these details is load-bearing.

    Authors: We agree that the current manuscript provides insufficient detail on how the 618-example test set was constructed. In the revision we will expand the benchmark section with: (1) the full curation protocol, including selection criteria from the 11,477-example pool and annotation guidelines; (2) quantitative diversity statistics (output-length histograms, task-category distribution, and ecosystem balance); and (3) a description of the label-validation steps performed. Because curation was carried out by a single primary annotator, inter-annotator agreement statistics are not available; we will explicitly note this limitation and report any secondary review performed on ambiguous cases. revision: yes

  2. Referee: [Evaluation (abstract and results section)] No error bars, standard deviations, or statistical significance tests accompany the reported metrics or the 11-point recall margin over the 35B zero-shot baseline. With a test set of only 618 examples, it is impossible to determine whether the observed gains are robust or sensitive to the particular curation choices.

    Authors: We accept that the absence of uncertainty estimates and significance testing weakens the presentation. We will recompute all metrics with bootstrap confidence intervals (or standard errors) and add a statistical comparison (bootstrap test or McNemar’s test on per-example outcomes) between our model and the 35B zero-shot baseline. These results will be reported in the revised results section together with a brief discussion of sensitivity to the particular test-set curation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark evaluation with independent test set

full rationale

The paper introduces a new benchmark (11,477 examples from SWE-bench plus synthetic data, with a 618-example manually curated test set) and reports empirical performance of a fine-tuned Qwen 3.5 2B model against zero-shot baselines and heuristics. No equations, derivations, or predictions are present that reduce by construction to fitted parameters defined on the same data. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked. All reported metrics (0.86 recall, 0.80 F1, 92% token reduction) are direct measurements on the held-out test set, rendering the evaluation chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that a small fine-tuned model can learn to identify relevant verbatim spans better than larger zero-shot models or heuristics; this depends on standard supervised-learning assumptions plus the unstated claim that the benchmark labels are reliable.

axioms (1)
  • domain assumption Supervised fine-tuning on task-specific labeled spans improves extraction quality over zero-shot use of larger models
    Invoked by the decision to fine-tune Qwen 3.5 2B and compare against zero-shot 35B baseline

pith-pipeline@v0.9.0 · 5439 in / 1400 out tokens · 35523 ms · 2026-05-13T16:59:00.475600+00:00 · methodology

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Reference graph

Works this paper leans on

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