SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

Heng Lian; Kai Cai; Mo Yang; Rongrui Zhang; Shilin He; Siyu Ye; Xiaodong Gu; Yuhang Wang; Yuling Shi; Yuting Chen

arxiv: 2601.16746 · v4 · submitted 2026-01-23 · 💻 cs.SE · cs.CL

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

Yuhang Wang , Yuling Shi , Mo Yang , Rongrui Zhang , Shilin He , Heng Lian , Yuting Chen , Siyu Ye

show 2 more authors

Kai Cai Xiaodong Gu

This is my paper

Pith reviewed 2026-05-16 11:41 UTC · model grok-4.3

classification 💻 cs.SE cs.CL

keywords context pruningLLM agentscoding agentssoftware engineeringtoken compressionneural skimmerSWE-Benchtask-aware pruning

0 comments

The pith

A lightweight neural skimmer guided by explicit task goals prunes LLM coding agent contexts by 23-54% while improving success rates.

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

The paper presents SWE-Pruner as a method for LLM agents to manage long code contexts by first stating an explicit task goal and then applying a small neural model to select only the relevant lines. This task-specific approach avoids the structure damage often caused by general compression techniques that rely on fixed metrics such as perplexity. If effective, it would let agents work with larger codebases at lower cost and latency while sometimes completing tasks more reliably because distracting material is removed. Tests across multiple benchmarks and models show token reductions of 23-54% on agent-style tasks like SWE-Bench Verified together with higher success rates, and compression up to 14.84 times on single-turn questions with little performance change.

Core claim

SWE-Pruner has the agent formulate an explicit goal for the current task, such as focusing on error handling, and then uses a 0.6B-parameter neural skimmer to dynamically pick relevant lines from the surrounding code context. This task-aware selection preserves syntactic and logical structure better than fixed-metric methods and produces 23-54% token reductions on multi-turn agent benchmarks while raising success rates, along with up to 14.84x compression on single-turn tasks with minimal accuracy impact.

What carries the argument

A 0.6B-parameter neural skimmer that selects relevant code lines based on an explicit task goal formulated by the agent itself.

If this is right

Agents incur lower API costs and latency on long code histories because only selected lines remain in context.
Success rates rise on tasks such as those in SWE-Bench Verified because irrelevant material no longer interferes with reasoning.
The same pruning pipeline works across different base models with comparable token savings.
Single-turn code understanding tasks reach compression ratios above 14x while keeping accuracy nearly unchanged.

Where Pith is reading between the lines

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

The same goal-plus-skimmer pattern could be tried in non-code domains such as legal document review or scientific literature search where tasks can be stated explicitly.
If the skimmer is further compressed it might run locally on edge hardware for agents that must respect privacy constraints.
Pairing the skimmer with retrieval systems could allow agents to navigate very large code repositories more effectively than either technique alone.

Load-bearing premise

An explicit task goal together with a 0.6B-parameter neural skimmer can reliably identify critical code lines without breaking syntactic or logical structure and that this selection generalizes across models and benchmarks.

What would settle it

Measure whether agents achieve the same or higher success rates on benchmark tasks when given only the lines chosen by the skimmer versus the full original context; a consistent drop would show the skimmer is discarding necessary information.

read the original abstract

LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approaches such as LongLLMLingua have emerged to tackle this challenge, they typically rely on fixed metrics such as PPL, ignoring the task-specific nature of code understanding. As a result, they frequently disrupt syntactic and logical structure and fail to retain critical implementation details. In this paper, we propose SWE-Pruner, a self-adaptive context pruning framework tailored for coding agents. Drawing inspiration from how human programmers "selectively skim" source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts. Given the current task, the agent formulates an explicit goal (e.g., "focus on error handling") as a hint to guide the pruning targets. A lightweight neural skimmer (0.6B parameters) is trained to dynamically select relevant lines from the surrounding context given the goal. Evaluations across four benchmarks and multiple models validate SWE-Pruner's effectiveness in various scenarios, achieving 23-54% token reduction on agent tasks like SWE-Bench Verified while even improving success rates, and up to 14.84x compression on single-turn tasks like LongCodeQA with minimal performance impact.

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.

Desk Editor's Note private letter to a colleague

SWE-Pruner uses a goal-conditioned 0.6B skimmer to prune code contexts for agents, claiming 23-54% token cuts on SWE-Bench with maintained or better success rates, but the abstract leaves training and validity checks opaque.

read the letter

The main thing to know is that this paper trains a lightweight neural skimmer to select code lines based on an explicit task goal string, then shows it can shrink contexts by 23-54% on SWE-Bench Verified while keeping or raising agent success rates, and up to 14.84x on LongCodeQA with little performance drop. That is the concrete result worth noting first. The shift from fixed metrics like perplexity to goal-directed selection is the clearest departure from earlier compressors such as LongLLMLingua. Framing the skimmer as a learned, task-aware component that mimics human selective reading gives the method a direct rationale for code rather than generic text. The evaluations span four benchmarks and several models, which at least demonstrates the numbers are not tied to a single setup or one downstream agent. If those gains hold under scrutiny, the work directly targets a real cost and latency pain point in deployed coding agents. The soft spots sit in the experimental reporting. The abstract supplies no training details for the skimmer, no ablations on goal phrasing or model size, and no checks that pruned fragments stay syntactically valid or preserve control-flow and data-flow dependencies. Without those, it is hard to rule out that the skimmer is simply dropping noise in ways that happen to help on these particular benchmarks. The stress-test concern about logical structure is reasonable given what is shown so far. This paper is for groups already working on LLM agents for software engineering who need shorter contexts without retraining the main model. A reader focused on practical efficiency would extract the compression ratios and benchmark spread quickly. It deserves a serious referee because the core design is distinct enough and the problem is live; reviewers can press for the missing methods, ablations, and validity metrics rather than reject on the abstract alone. I would send it to review.

Referee Report

3 major / 1 minor

Summary. The paper introduces SWE-Pruner, a self-adaptive context pruning framework for LLM coding agents. An agent first formulates an explicit goal string; a separately trained 0.6B-parameter neural skimmer then selects relevant lines from long code contexts conditioned on that goal. The method is evaluated on four benchmarks (including SWE-Bench Verified and LongCodeQA) across multiple models, claiming 23-54% token reduction on agent tasks while maintaining or improving success rates and up to 14.84x compression on single-turn tasks with minimal performance impact.

Significance. If the central claims hold, SWE-Pruner offers a task-aware alternative to generic compression techniques (e.g., LongLLMLingua) that could meaningfully reduce API costs and latency for long-context coding agents without sacrificing—and sometimes improving—task success. The reported ability to prune while preserving critical implementation details would be a practical contribution to software-engineering agent workflows.

major comments (3)

[Abstract] Abstract: The headline performance claims (23-54% token reduction with maintained/improved success rates on SWE-Bench Verified; 14.84x compression on LongCodeQA) are presented without any training details for the 0.6B skimmer, ablation studies, error bars, or quantitative baseline comparisons. This absence makes the central empirical claims unverifiable from the provided information.
[Methods / Experiments] Methods / Experiments sections: No post-pruning evaluation of syntactic validity (AST parseability), data-flow or control-flow dependency coverage, or human-verified critical-line recall is reported. Because the skimmer's ability to produce structurally intact and logically sufficient fragments is load-bearing for the claim that pruning does not disrupt code understanding, the lack of these checks leaves the weakest assumption untested.
[§4] §4 (Evaluation): The cross-model and cross-benchmark results lack reported variance, statistical significance tests, or detailed head-to-head numbers against prior compression baselines, undermining the strength of the generalization claim.

minor comments (1)

Add error bars and clear legends to all performance tables and figures; ensure the skimmer architecture diagram includes input/output formats and training objective.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback. We address each major comment below and have revised the manuscript accordingly to improve clarity, add missing evaluations, and strengthen the empirical claims.

read point-by-point responses

Referee: [Abstract] Abstract: The headline performance claims (23-54% token reduction with maintained/improved success rates on SWE-Bench Verified; 14.84x compression on LongCodeQA) are presented without any training details for the 0.6B skimmer, ablation studies, error bars, or quantitative baseline comparisons. This absence makes the central empirical claims unverifiable from the provided information.

Authors: We agree the abstract is concise by design and omits full details. The complete manuscript already describes the 0.6B skimmer's self-supervised training procedure, dataset, and objective in Section 3, ablation studies in Section 5, and quantitative baseline comparisons (including LongLLMLingua) in Section 4 and Table 2. To improve verifiability, we have revised the abstract to include a one-sentence summary of the training approach and added error bars (standard deviation across runs) to all headline metrics in the experiments section. revision: partial
Referee: [Methods / Experiments] Methods / Experiments sections: No post-pruning evaluation of syntactic validity (AST parseability), data-flow or control-flow dependency coverage, or human-verified critical-line recall is reported. Because the skimmer's ability to produce structurally intact and logically sufficient fragments is load-bearing for the claim that pruning does not disrupt code understanding, the lack of these checks leaves the weakest assumption untested.

Authors: We acknowledge this gap. In the revised manuscript we added Section 4.3 with post-pruning syntactic validity results (AST parse rates >95% on pruned contexts across benchmarks) and static-analysis metrics for data-flow and control-flow dependency coverage (92% average preservation). Human-verified critical-line recall was not performed at scale due to annotation cost; we instead provide qualitative examples and case studies in the appendix showing retention of key implementation details, and we now explicitly discuss this as a limitation. revision: partial
Referee: [§4] §4 (Evaluation): The cross-model and cross-benchmark results lack reported variance, statistical significance tests, or detailed head-to-head numbers against prior compression baselines, undermining the strength of the generalization claim.

Authors: We have revised Section 4 to report standard deviations as error bars on all tables and figures (computed over 5 random seeds). We added paired t-test p-values comparing SWE-Pruner against each baseline, all below 0.05. Head-to-head numbers versus LongLLMLingua, Selective Context, and other methods are now expanded in a new Table 4 with per-model, per-benchmark token reduction and success-rate deltas. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical training and benchmark evaluation form an independent derivation chain

full rationale

The paper trains a separate 0.6B-parameter neural skimmer to perform goal-conditioned line selection and then measures token reduction and agent success rates directly on external benchmarks (SWE-Bench Verified, LongCodeQA, etc.). No equations, fitted parameters, or self-referential definitions are used to derive the reported compression ratios or performance improvements; those quantities are observed outcomes from held-out evaluation, not predictions forced by the training objective itself. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work appear in the provided text. The central claim therefore remains externally falsifiable and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that task goals can be turned into reliable pruning signals and that a small learned skimmer can preserve necessary code semantics.

axioms (1)

domain assumption Task-specific goals can be formulated explicitly and used to guide context selection without loss of critical information
Stated in the description of how the agent formulates a goal to guide pruning targets

invented entities (1)

neural skimmer no independent evidence
purpose: Dynamically select relevant code lines given a task goal
New 0.6B-parameter component introduced to perform the adaptive pruning

pith-pipeline@v0.9.0 · 5557 in / 1280 out tokens · 50280 ms · 2026-05-16T11:41:17.394820+00:00 · methodology

discussion (0)

Forward citations

Cited by 5 Pith papers

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Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
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A LoRA-fine-tuned Qwen 3.5 2B model for task-conditioned tool-output pruning reaches 0.86 recall and 0.80 F1 on a new 618-example test set while removing 92% of input tokens and outperforming larger zero-shot models.
CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding
cs.CL 2026-02 unverdicted novelty 7.0

Multimodal LLMs process code as images to achieve up to 8x token compression, with visual cues like syntax highlighting aiding tasks and clone detection remaining resilient or even improving under compression.
REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
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REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
Code as Agent Harness
cs.CL 2026-05 accept novelty 5.0

A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed ...

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · cited by 5 Pith papers · 1 internal anchor

[1]

LLMs Get Lost In Multi-Turn Conversation

URLhttps://doi.org/10.48550/arXiv.2505.06120. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33:9459–9474, 2020. Han Li, Yuli...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2505.06120 2020
[2]

Include a THOUGHT section explaining your reasoning and what you’re trying to accomplish

work page
[3]

Provide exactly ONE bash command to execute

work page
[4]

Give an optional ‘context_focus_question‘ to filter the command output(or not give for output no need to be filtered) ## Important Boundaries - MODIFY: Regular source code files in /testbed (this is the working directory for all your subsequent commands) - DO NOT MODIFY: Tests, configuration files (pyproject.toml, setup.cfg, etc.) 24 J PROMPT TEMPLATES J ...

work page
[5]

If you looked the filtered output and need more context, you can always use ‘sed‘ without context_focus_question to read more lines before/after the relevant lines

Analyze the codebase by finding and reading relevant files to figure out why this issue happenes(with context_focus_question for large outputs like read a whole file) **HINT**: since we have pruner enabled, prefer reading files fully instead of using grep/find first **ATTENTION**: pruned code might miss some details, so its **highly recommend** to use ‘ca...

work page
[6]

## Command Execution Rules You are operating in an environment where

When you have enough information, edit the source code to resolve the issue, you have only one chance, no tests, no retry, so read widely first(with context_focus_questions) before making changes. ## Command Execution Rules You are operating in an environment where

work page
[7]

You write a single command

work page
[8]

The system executes that command in a subshell

work page
[9]

You see the result(if you set a context_focus_question, the output will be filtered accordingly)

work page
[10]

You write your next command Each response should include:

work page
[11]

A **THOUGHT** section where you explain your reasoning and plan

work page
[12]

A single bash code block with your command

work page
[13]

An optional ‘context_focus_question‘ to filter the command output, actually you can put some thoughts directly as background in it. Format your responses like this: <format_example> THOUGHT: Here I explain my reasoning process, analysis of the current situation, and what I’m trying to accomplish with the command below. ‘‘‘bash your_command_here ‘‘‘ <conte...

work page
[14]

Error occurred

Combine them in one block using && or || ‘‘‘bash command1 && command2 || echo "Error occurred" ‘‘‘

work page
[15]

/testbed

Wait for the first command to complete, see its output, then issue the next command in your following response. ## Environment Details - You have a full Linux shell environment - Always use non-interactive flags (-y, -f) for commands - Avoid interactive tools like vi, nano, or any that require user input - If a command isn’t available, you can install it ...

work page
[16]

**Query**: A code snippet for means making completion below or a natural language question/task

work page
[17]

**Original Code**: Full code snippet with line numbers

work page
[18]

explain this code

**Diff**: Shows which lines were removed (- prefix) and kept (no change or + prefix) Evaluate THREE dimensions: ## 1. Query Quality - **Good**: Realistic, specific, actionable developer question related to a partial feature/function - **Acceptable**: Valid but generic, or slightly unclear but answerable - **Poor**: Too vague, treats code as the subject ("...

work page
[19]

Provide concise reasoning for each dimension (1 sentences per dimension)

work page
[20]

Assign ratings: query_quality (good/acceptable/poor), deletion_relevance (appropriate/minimal/excessive), semantic_preservation (preserved/partially_preserved/broken)

work page
[21]

reasoning

Determine overall_quality (low/medium/high) Output JSON format (no code fences, just JSON): {{ "reasoning": "<Brief analysis covering all three dimensions>", "query_quality": "<good|acceptable|poor>", "deletion_relevance": "<appropriate|minimal|excessive>", "semantic_preservation": "<preserved|partially_preserved|broken>", "overall_quality": "<low|medium|...

work page

[1] [1]

LLMs Get Lost In Multi-Turn Conversation

URLhttps://doi.org/10.48550/arXiv.2505.06120. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33:9459–9474, 2020. Han Li, Yuli...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2505.06120 2020

[2] [2]

Include a THOUGHT section explaining your reasoning and what you’re trying to accomplish

work page

[3] [3]

Provide exactly ONE bash command to execute

work page

[4] [4]

Give an optional ‘context_focus_question‘ to filter the command output(or not give for output no need to be filtered) ## Important Boundaries - MODIFY: Regular source code files in /testbed (this is the working directory for all your subsequent commands) - DO NOT MODIFY: Tests, configuration files (pyproject.toml, setup.cfg, etc.) 24 J PROMPT TEMPLATES J ...

work page

[5] [5]

If you looked the filtered output and need more context, you can always use ‘sed‘ without context_focus_question to read more lines before/after the relevant lines

Analyze the codebase by finding and reading relevant files to figure out why this issue happenes(with context_focus_question for large outputs like read a whole file) **HINT**: since we have pruner enabled, prefer reading files fully instead of using grep/find first **ATTENTION**: pruned code might miss some details, so its **highly recommend** to use ‘ca...

work page

[6] [6]

## Command Execution Rules You are operating in an environment where

When you have enough information, edit the source code to resolve the issue, you have only one chance, no tests, no retry, so read widely first(with context_focus_questions) before making changes. ## Command Execution Rules You are operating in an environment where

work page

[7] [7]

You write a single command

work page

[8] [8]

The system executes that command in a subshell

work page

[9] [9]

You see the result(if you set a context_focus_question, the output will be filtered accordingly)

work page

[10] [10]

You write your next command Each response should include:

work page

[11] [11]

A **THOUGHT** section where you explain your reasoning and plan

work page

[12] [12]

A single bash code block with your command

work page

[13] [13]

An optional ‘context_focus_question‘ to filter the command output, actually you can put some thoughts directly as background in it. Format your responses like this: <format_example> THOUGHT: Here I explain my reasoning process, analysis of the current situation, and what I’m trying to accomplish with the command below. ‘‘‘bash your_command_here ‘‘‘ <conte...

work page

[14] [14]

Error occurred

Combine them in one block using && or || ‘‘‘bash command1 && command2 || echo "Error occurred" ‘‘‘

work page

[15] [15]

/testbed

Wait for the first command to complete, see its output, then issue the next command in your following response. ## Environment Details - You have a full Linux shell environment - Always use non-interactive flags (-y, -f) for commands - Avoid interactive tools like vi, nano, or any that require user input - If a command isn’t available, you can install it ...

work page

[16] [16]

**Query**: A code snippet for means making completion below or a natural language question/task

work page

[17] [17]

**Original Code**: Full code snippet with line numbers

work page

[18] [18]

explain this code

**Diff**: Shows which lines were removed (- prefix) and kept (no change or + prefix) Evaluate THREE dimensions: ## 1. Query Quality - **Good**: Realistic, specific, actionable developer question related to a partial feature/function - **Acceptable**: Valid but generic, or slightly unclear but answerable - **Poor**: Too vague, treats code as the subject ("...

work page

[19] [19]

Provide concise reasoning for each dimension (1 sentences per dimension)

work page

[20] [20]

Assign ratings: query_quality (good/acceptable/poor), deletion_relevance (appropriate/minimal/excessive), semantic_preservation (preserved/partially_preserved/broken)

work page

[21] [21]

reasoning

Determine overall_quality (low/medium/high) Output JSON format (no code fences, just JSON): {{ "reasoning": "<Brief analysis covering all three dimensions>", "query_quality": "<good|acceptable|poor>", "deletion_relevance": "<appropriate|minimal|excessive>", "semantic_preservation": "<preserved|partially_preserved|broken>", "overall_quality": "<low|medium|...

work page