arxiv: 2605.14362 · v1 · submitted 2026-05-14 · 💻 cs.SE · cs.AI

Recognition: no theorem link

Correctness-Aware Repository Filtering Under Maximum Effective Context Window Constraints

Shweta Mishra

Authors on Pith no claims yet

Pith reviewed 2026-05-15 02:35 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords context windowrepository filteringLLMtoken reductioncode intelligenceheuristicsoftware engineeringcontext hygiene

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The pith

A size-based filter using only file metadata cuts tokens in LLM repository contexts by 80 to 89 percent and raises task accuracy from 25 to 72 percent.

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

The paper presents a pre-execution filter that uses basic operating-system file-size information to remove large non-code artifacts before any tokenization occurs. This keeps the maximum effective context window available for actual source code rather than letting logs, binaries, or datasets push relevant files out. The method needs no indexes or semantic analysis and finishes in under a millisecond per file. Tests on ten real repositories confirm large token savings, and a small evaluation with CodeLlama shows the filtered inputs produce more correct answers with far fewer hallucinations.

Core claim

The authors establish that the SizeFilter at a one-megabyte threshold achieves 79.6 percent mean token reduction at 0.30 milliseconds overhead across 22,046 files, while the HybridFilter reaches 89.3 percent reduction with the lowest variance. In an evaluation on 18 tasks using CodeLlama-7B-Instruct, file-level accuracy reaches 72 percent under filtering compared to 25 percent without it, and hallucination frequency drops from 61 percent to 17 percent.

What carries the argument

The SizeFilter, a pre-execution heuristic that applies an operating-system stat call to obtain file size and excludes files exceeding a chosen threshold such as one megabyte, preventing overflow of the maximum effective context window.

If this is right

LLM developer tools become practical on gigabyte-scale repositories without custom indexing infrastructure.
Context construction time drops to near zero compared to semantic retrieval techniques that build graphs or embeddings first.
Task-specific accuracy improves because the model receives only compact, relevant files within its effective window.
Models produce fewer hallucinations when large distracting files are removed early.
The method works independently of programming language or file content type.

Where Pith is reading between the lines

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

Extending the filter with a quick token-density estimate could refine decisions for borderline files without adding much cost.
This size proxy might transfer to other AI tasks involving large document collections where relevance correlates with file length.
Repository maintainers could adopt such filters as a standard preprocessing step to improve LLM-assisted code review and generation.
Further tests on different model sizes and task types would clarify the range where size alone suffices as a relevance signal.

Load-bearing premise

File size serves as a reliable indicator that a file is irrelevant to the current task, so discarding large files will not remove necessary code.

What would settle it

Observe whether accuracy on LLM tasks falls below the baseline when the size filter excludes a file that contains essential code for solving the task.

Figures

Figures reproduced from arXiv: 2605.14362 by Shweta Mishra.

**Figure 1.** Figure 1: Pre-execution heuristic filtering pipeline (six stages). Dashed red arc: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 4.** Figure 4: SizeFilter threshold sensitivity (±1σ band, 10 repositories). Star: recommended θ=1 MB. At 5 MB, σ=36.1 pp—not suitable for production use [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 3.** Figure 3: Mean token reduction by filter strategy (10 repositories). Error bars: [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 8.** Figure 8: HybridFilter(1 MB) token reduction across 10 repositories. Blue line [PITH_FULL_IMAGE:figures/full_fig_p005_8.png] view at source ↗

**Figure 7.** Figure 7: HybridFilter(1 MB) effectiveness by file size bucket. Large files [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗

read the original abstract

Context window efficiency is a practical constraint in large language model (LLM)-based developer tools. Paulsen [12] shows that all tested models degrade in accuracy well before their advertised context limits the Maximum Effective Context Window (MECW) which makes context construction a quality problem, not just a cost one. Modern software repositories routinely contain large non-code artifacts compiled datasets, binary model weights, minified JavaScript bundles, and gigabyte-scale log files that overflow the context window and push out task-relevant source code. We present a correctness-aware context hygiene framework: a pre-execution, size-based heuristic filter that intercepts repository scans before tokenization, using only OS-level stat() metadata with sub-millisecond overhead. Semantic retrieval approaches such as RepoCoder, GraphRAG, and AST-based chunking require index construction and query-time inference before any filtering decision is reached. Our framework, by contrast, requires no indexing and operates at <0.01 ms per file decision. Across 10 real open-source repositories (22,046 files, 5 languages), the proposed SizeFilter at \theta=1 MB achieves 79.6% (\pm13.2%) mean token reduction at 0.30 ms overhead: the HybridFilter achieves 89.3% (\pm9.0%) the lowest variance of any filter evaluated. A token-density study across 2,688 files confirms a strong linear correlation (Pearson r=0.997, k=0.250 tokens/byte). A limited-scope evaluation (18 tasks, CodeLlama-7B-Instruct) yields 72% file-level accuracy under filtering versus 25% at baseline; hallucination frequency declines from 61% to 17%. All code and data are released for reproducibility.

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

A simple size filter cuts tokens by 80% with tiny overhead and lifts accuracy on 18 tasks, but the correctness claim rests on an untested assumption that large files never matter.

read the letter

The main takeaway is that a basic file-size filter trims repository tokens by about 80% at 0.3 ms overhead and raises file-level accuracy from 25% to 72% on CodeLlama-7B while dropping hallucinations from 61% to 17%. The token-density correlation they measured is tight, and the approach is fast enough to run before any indexing. That practical side is the useful part here, though the link between size and task relevance is not directly checked.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a correctness-aware repository filtering framework using size-based heuristics (SizeFilter at θ=1 MB and HybridFilter) to manage context windows in LLM-based developer tools. It claims substantial token reductions (79.6% ±13.2% for SizeFilter, 89.3% ±9.0% for HybridFilter) across 10 open-source repositories with 22,046 files, sub-millisecond overhead, a strong linear token-density correlation (r=0.997), and improved performance in a limited evaluation of 18 tasks with CodeLlama-7B-Instruct, achieving 72% file-level accuracy versus 25% baseline and reducing hallucinations from 61% to 17%.

Significance. If the core assumption holds, the work provides a lightweight, indexing-free method for context hygiene that could meaningfully improve LLM reliability on repository-scale tasks by excluding non-code artifacts. The reported token-density correlation on 2,688 files and full release of code/data strengthen reproducibility. Significance is constrained by the narrow evaluation scope and lack of direct validation that size-based filtering preserves task-critical code.

major comments (2)

[Evaluation] Evaluation section: The reported 72% file-level accuracy and hallucination reduction (61% to 17%) rest on only 18 tasks with no task definitions, baseline controls, statistical tests, or ablation on whether any discarded files contained necessary code. This leaves open the possibility that gains are an artifact of task selection rather than filter correctness.
[Correctness-Aware Filtering] Correctness-aware filtering description: No manual inspection, dependency graph analysis, or ablation is described to verify that files >1 MB never contain task-critical code for the 18 tasks. The Pearson r=0.997 correlation on 2,688 files confirms size predicts token count but does not establish size as a reliable relevance proxy, which is load-bearing for the central claim.

minor comments (2)

[Abstract] Abstract: The phrasing 'the HybridFilter achieves 89.3% (±9.0%) the lowest variance of any filter evaluated' is grammatically incomplete and should be revised for clarity (e.g., 'and achieves the lowest variance...').
[Abstract] Abstract: Overhead is given as 0.30 ms for SizeFilter and <0.01 ms per file decision; clarify whether these refer to per-file or aggregate measurements.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address the major comments point by point below, acknowledging the limitations of the current evaluation while defending the core contributions on token reduction and filtering efficiency.

read point-by-point responses

Referee: [Evaluation] Evaluation section: The reported 72% file-level accuracy and hallucination reduction (61% to 17%) rest on only 18 tasks with no task definitions, baseline controls, statistical tests, or ablation on whether any discarded files contained necessary code. This leaves open the possibility that gains are an artifact of task selection rather than filter correctness.

Authors: We agree that the evaluation is limited in scope and that additional details would strengthen the claims. The 18 tasks were chosen to cover typical repository navigation and code understanding queries, but we did not provide explicit definitions in the manuscript. We will revise the Evaluation section to include task descriptions, clarify the baseline as the unfiltered full repository context, and add any applicable statistical tests for the accuracy improvement. Regarding the ablation on discarded files, we note that the filter is heuristic and the evaluation demonstrates empirical improvement; however, a full verification that no critical code was discarded would require task-specific manual review, which we will discuss as a limitation and plan for future work. The primary results on token reduction (79.6% and 89.3%) and sub-millisecond overhead remain robust and independent of the task-specific accuracy. revision: partial
Referee: [Correctness-Aware Filtering] Correctness-aware filtering description: No manual inspection, dependency graph analysis, or ablation is described to verify that files >1 MB never contain task-critical code for the 18 tasks. The Pearson r=0.997 correlation on 2,688 files confirms size predicts token count but does not establish size as a reliable relevance proxy, which is load-bearing for the central claim.

Authors: The SizeFilter operates as a pre-tokenization heuristic leveraging the observation that in the 10 studied repositories, files larger than 1 MB are overwhelmingly non-code artifacts (e.g., binaries, datasets, minified bundles), which is supported by the near-perfect linear correlation between file size and token count (r=0.997). This correlation validates size as a proxy for token density, enabling efficient filtering without semantic analysis. While we did not conduct manual inspection or dependency graphs for the specific 18 tasks, the framework's correctness-awareness stems from prioritizing the inclusion of smaller source files to fit within the MECW. We will expand the description to include examples of filtered file types from the repositories and explicitly state the heuristic assumptions, along with potential limitations where large source files might occur. revision: partial

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper reports direct empirical measurements: token reduction percentages, overhead times, Pearson correlation on 2,688 files, and task accuracy on 18 held-out tasks. No equations, fitted parameters, or self-citations reduce these outcomes to inputs by construction. The SizeFilter and HybridFilter are heuristic rules applied to OS metadata; their performance numbers are computed post-application rather than tautologically reproduced. The MECW reference to Paulsen [12] is external and does not underpin the filter correctness claims. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central performance claims rest on an empirically observed linear token-per-byte relationship and on the assumption that size is a sufficient relevance signal. The threshold θ is chosen by hand.

free parameters (1)

θ = 1 MB
Size threshold set to 1 MB for the SizeFilter experiments

axioms (2)

domain assumption File size is a sufficient proxy for task-irrelevance in the evaluated repositories
Invoked to justify discarding files above θ without semantic inspection
domain assumption Token count scales linearly with byte size (Pearson r=0.997)
Measured on 2,688 files and used to convert size decisions into token savings

pith-pipeline@v0.9.0 · 5617 in / 1609 out tokens · 29393 ms · 2026-05-15T02:35:17.174378+00:00 · methodology

discussion (0)

Reference graph

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