LongCodeZip: Hierarchical Code Compression

• LongCodeZip is a hierarchical code compression framework that preserves key semantic units in lengthy codebases.
• It employs a dual-stage process with function-level and block-level analysis using conditional perplexity and mutual information to retain relevant fragments.
• Empirical evaluations demonstrate up to 5.6× compression while maintaining or improving performance in code completion, summarization, and question answering tasks.

LongCodeZip is a hierarchical code compression framework designed to enable LLMs to efficiently process long contexts typical of real-world codebases. Unlike generic context pruning tools developed for natural language, LongCodeZip is specialized for source code: it leverages function-level and block-level structure to select and retain maximally relevant code fragments, guided by model-derived conditional perplexity. With empirically validated compression ratios up to 5.6×—without degradation of code completion, summarization, or question answering task quality—LongCodeZip advances the scalability and cost-efficiency of code intelligence applications (Shi et al., 1 Oct 2025).

1. Motivation and Problem Domain

LongCodeZip addresses the practical bottleneck faced by code LLMs when processing long contexts. Source code repositories often span tens of thousands of tokens, vastly exceeding the context windows of even advanced LLMs. High API costs and inference latency compound these technical limitations. Existing context pruning algorithms, such as LLMLingua, perform well for free-form text but fail to account for code-specific hierarchical structures and long-range interdependency—often resulting in omitted or fragmented semantic content vital for code generation tasks. The requirement is thus for a compression strategy that is aware of code structure, preserves crucial semantic units, and adapts retention to task relevancy (Shi et al., 1 Oct 2025).

2. Hierarchical Dual-Stage Compression Methodology

LongCodeZip consists of two stages: coarse-grained and fine-grained compression, hierarchically aligned with the syntactic and semantic organization of source code.

Coarse-Grained Compression:

The codebase is divided into function-level chunks. Each chunk is evaluated for relevance to a specific instruction (task query) via a conditional perplexity score. The approximated mutual information (AMI) for a function $c$ with respect to instruction $q$ is

$$\text{AMI}(c, q) = \text{PPL}(q) - \text{PPL}(q \mid c)$$

where $\text{PPL}(q)$ and $\text{PPL}(q \mid c)$ are the model's perplexity scores for the instruction alone and conditioned on the code chunk, respectively. The highest-AMI functions, subject to a coarse token budget, are retained.

Fine-Grained Compression:

Within selected functions, the code is further segmented into blocks by detecting local maxima in perplexity—a method that marks semantic or logical boundaries. Each function is allocated an adaptive token budget, weighted by its normalized AMI score: $$R_{\text{biased},i} = R_{\text{base}} \times (1 + \beta \cdot (2 \cdot \text{AMI}_{\text{norm},i} - 1))$$ where $\beta$ is a parameter modulating importance sensitivity. Within each function, block selection is formalized as a 0/1 knapsack optimization: each block $b$ has a value (normalized relevance score) and a weight (token count), and the dynamic programming solution maximizes the overall retained relevance under the assigned token limit (Shi et al., 1 Oct 2025).

3. Technical Implementation and Algorithmic Design

LongCodeZip is designed for plug-and-play use in code LLM pipelines. Its implementation includes:

• Syntactic Chunking: Function boundaries are identified via lightweight parsing routines, compatible with major programming languages.
• Model-Guided Scoring: Relevance scoring is computed via conditional perplexity APIs (supported by the target code LLM).
• Budget Allocation Algorithm: Adaptive function-level retention ratios are globally normalized so that the overall context does not exceed the available token budget.
• Block Segmentation and Selection: Local perplexity spikes are used to segment functions. Block selection within the allocation employs the classical 0/1 knapsack dynamic programming algorithm, with pseudocode and parameter choices outlined in the original paper.
• Integrated Context Assembly: The selected blocks from retained functions are concatenated to form the compressed context fed to the downstream LLM.

A plausible implication is that the entire framework is highly modular; it can be overlaid atop existing retrieval-based or chunking pipelines.

4. Empirical Evaluation and Compression Performance

LongCodeZip was extensively benchmarked on three representative code understanding tasks:

• Code Completion: On long context completion datasets, models using LongCodeZip matched or improved Exact Match (EM) and Edit Similarity (ES) scores compared to full-context inputs, with token reduction up to 5.6×.
• Code Summarization: Module-level summarization retained semantic content, with improved CompScore metrics, even after aggressive compression.
• Code Question Answering (RepoQA): Tasks requiring retrieval of relevant functions from long codebases showed maintained or improved retrieval accuracy relative to context pruning and retrieval-augmented baselines.

Performance improvements over baseline methods in all metrics were statistically significant. LongCodeZip was also compared against naive truncation, random block removal, function chunking, and RAG-based selection, outperforming all on retained semantic content and downstream generation quality (Shi et al., 1 Oct 2025).

Task	Compression Ratio	Performance Impact
Code Completion	up to 5.6×	Maintained/improved EM, ES
Summarization	~5×	Improved CompScore, semantic preservation
Code QA (RepoQA)	4–5×	Higher retrieval accuracy

5. Comparison to Prior Art and Code-Specific Challenges

Earlier pruning techniques (e.g., LLMLingua, random truncation) do not differentiate code structure or semantics, leading to catastrophic omission of dependencies (e.g., unused but required functions, class relations, macro calls). LongCodeZip’s dual-stage approach incorporates conditional perplexity and mutual information to select context elements most informative to the model for a given task. The adaptive token budget allocation enables non-uniform retention, assigning more tokens to highly relevant functions, in contrast to global uniform compression. Fine-grained block selection further avoids fragmentation of logical code blocks.

This suggests LongCodeZip is the first framework to offer empirically validated, code-structure-aware context reduction for LLMs, outperforming both text-centric and naive methods in token efficiency and downstream utility.

6. Implications, Extensions, and Future Directions

LongCodeZip enables efficient deployment of LLMs in real-world code intelligence settings, reducing both inference latency and API costs. By maintaining task-specific semantic granularity, it supports repository-level code completion, summarization, and QA in contexts previously inaccessible due to token limitations.

Potential extensions proposed for future work include integration with retrieval-augmented generation pipelines (RAG), generalization to additional programming languages, and improved fine-grained block boundary detection via learnable models. Cost-aware pipeline integration is also anticipated, allowing dynamic trade-offs between compression ratio, model latency, and generation quality.

A plausible implication is that LongCodeZip may be adapted for other forms of structured long-input data—such as configuration files or markup documents—provided that domain-specific block segmentation and perplexity refinement are implemented.

7. Summary

LongCodeZip advances code context compression for LLMs via a hierarchical, model-guided process: coarse-grained function selection followed by fine-grained block-level optimization using conditional perplexity and adaptive knapsack allocation (Shi et al., 1 Oct 2025). It achieves high compression ratios while preserving—and sometimes increasing—downstream task performance. The framework is modular, deployable as a pre-processing stage in code LLM pipelines, and represents a significant step forward in scaling code intelligence to production-scale repositories under stringent token budgets.