Similarity Driven Reuse Mechanism

• Similarity Driven Reuse Mechanism is a strategy that identifies and reuses artifacts by measuring structural, syntactic, or semantic similarity.
• It employs specialized metrics and algorithms—like b-bit MinHash, Euclidean, and cosine distances—to efficiently index, compare, and adapt artifacts across domains.
• Empirical results demonstrate significant improvements in recall, runtime efficiency, and memory reduction, underscoring its scalability and robustness.

A similarity driven reuse mechanism is a formal strategy for identifying, ranking, and reusing artifacts—such as code fragments, model parameters, intermediate features, configurations, or prior results—by measuring quantitative similarity and applying this knowledge to guide computations, memory management, or adaptation pipelines. This paradigm leverages explicit or implicit structural, syntactic, or semantic similarity to enable efficient reuse, robust transfer, and scalable search in a variety of computational settings. It is prominent in source code analysis, deep learning, graph reasoning, continual learning, accelerator hardware, and generative models.

1. Formal Principles and Definitions

Central to similarity driven reuse is the selection of a precise similarity metric (or distance function) tailored to the domain:

• Syntactic or token-based similarity: For source code, b-bit minwise hashing of token trigrams enables fast Jaccard similarity estimation between file contents (Ishio et al., 2017).
• Semantic/structure-based similarity: In graph and neural models, Euclidean or cosine distance between learned embeddings captures architectural or behavioral proximity (Yang et al., 18 Jun 2025).
• Component aggregation: Individual file similarities aggregate into component-level scores (e.g., $S_Q(C) = \sum_{q \in Q} S(q, C)$) to accurately reflect holistic similarity for complex artifacts (Ishio et al., 2017).
• Feature or type similarity: In type-directed code reuse, the cost of conversion is set by multiset distances between atomic type features (Wang et al., 2016).
• Residual, output, or cache similarity: For model accelerators or transformers, L₁ or cosine similarity is used to detect redundancy in KV caches or block features, triggering cache reuse or computation skipping (Roy et al., 7 Dec 2025, Chen et al., 1 Aug 2025).
• Task contextual similarity: In continual learning, metric learning on KL scatter of feature anchors assesses task relatedness and guides dynamic expansion and pruning (Han et al., 28 Oct 2024).

Similarity serves as the gatekeeper predicate for reuse: artifacts exceeding a threshold are marked for reuse, routed for adaptation, or populate a candidate set for further processing.

2. Core Algorithms and Workflow Structure

The similarity driven reuse workflow consists of the following canonical phases, exemplified across domains:

1. Representation and Indexing:
   • Precompute compact representations (e.g., MinHash, graph embedding, autoencoded tensor, SIFT descriptor) of artifacts for rapid similarity computation (Ishio et al., 2017, Yang et al., 18 Jun 2025, Roy et al., 7 Dec 2025, Javaheri et al., 4 Feb 2025).
   • Index representations in scalable structures, e.g., inverted tables for files, HNSW graphs for image descriptors (Javaheri et al., 4 Feb 2025).
2. Similarity Estimation:
   • Use lightweight estimators (b-bit MinHash, reduced-dimensional embedding, summary statistics) for all-pairs or candidate-limited comparisons, deferring expensive exact measurement to top matches (Ishio et al., 2017).
3. Filtering and Aggregation:
   • Aggregate pairwise file or feature similarities into artifact-level scores (e.g., component sum-of-max, head-level L₁ for KV, decision distance vector in model reuse) to enable robust matching (Ishio et al., 2017, Roy et al., 7 Dec 2025, Li et al., 2021).
4. Thresholding and Selection:
   • Apply context-dependent thresholds to select candidates for reuse, balancing tradeoffs between recall, precision, memory, and fidelity (Roy et al., 7 Dec 2025, Javaheri et al., 4 Feb 2025).
5. Adaptation or Routing:
   • In domains requiring adaptation, trigger code synthesis, configuration transfer, or output modification based on detected similarity (e.g., code wrappers, LLM-driven solution adaptation) (Wang et al., 2016, Su, 6 Sep 2025).

The following table summarizes key algorithmic stages across representative domains:

Domain / Paper	Similarity Metric	Representation	Reuse Target
Clone-and-own Code (Ishio et al., 2017)	Jaccard over trigrams, b-bit MinHash	Token trigrams, b-bit signature	File/component source code
VLSI Knowledge Transfer (Yang et al., 18 Jun 2025)	Euclidean $\ell_2$ on graph embeddings	MP+PGT encoder output	EDA configuration/init
LLM KV Cache (Roy et al., 7 Dec 2025)	L₁ norm over K/V heads	Autoencoded KV tensors	KV-cache entries
Diffusion Blocks (Chen et al., 1 Aug 2025)	Cosine on block residuals	Block input/output features	Blockwise computation skip
Continual Learning (Han et al., 28 Oct 2024)	KL scatter on features	Feature anchors, task subnet	Neuron retention, expansion
Edge Caching (Javaheri et al., 4 Feb 2025)	Euclidean on SIFT/HNSW	1-d reduced SIFT	Computed results

3. Quantitative Evaluation and Scalability

Similarity driven reuse mechanisms are empirically validated for accuracy, scalability, and efficiency across large-scale real-world datasets:

• Software ecosystems: On 10 million Debian source files, similarity-driven search with b-bit MinHash achieves Recall@5=0.907 for component origin identification, substantially outperforming SHA-1 hash baselines (Recall@5=0.773) and cutting manual effort by 40% (Ishio et al., 2017).
• VLSI and graph transfer: Pieceformer achieves 24.9% MAE reduction in graph similarity, demonstrates up to 89% runtime reduction in partitioning, and is the only method to fully cluster real-world design groups (Yang et al., 18 Jun 2025).
• KV-Cache reuse: KV-CAR yields up to 12.5% head-level memory reduction from similarity reuse alone, with only ~0.4 perplexity increase and <2% loss in zero-shot accuracy (Roy et al., 7 Dec 2025).
• Accelerated generative inference: Sortblock provides a 2.0–2.4× speedup in DiT models with negligible FID and SSIM degradation; ParaStep achieves up to 6.56× speedup in parallel diffusion, with an order of magnitude lower communication than prior parallelization approaches (Chen et al., 1 Aug 2025, Wang et al., 20 May 2025).
• Edge environments: CReIS's similarity-indexed edge caching achieves 86% reduction in completion time for MNIST workloads, with HNSW search latency <0.03 ms for ~10⁴ images (Javaheri et al., 4 Feb 2025).
• Continual learning: SCA-SNN achieves 1.4–1.3× higher class- and task-incremental accuracy vs. baselines, while reducing energy consumption by up to 4× through similarity-moderated expansion/pruning (Han et al., 28 Oct 2024).
• Software synthesis: Hunter's type similarity + ILP mapping achieves 100% benchmark solve rate, with >6× reduction in development time vs. manual baseline (Wang et al., 2016).

4. Strengths, Limitations, and Trade-Offs

Strengths:

• Scalability: Methods such as b-bit MinHash, linear-transformer partitioning, and HNSW indexing scale linearly or sublinearly to millions of items or high-dimensional tensors, enabling practical deployment.
• Robustness to Minor Changes: Similarity driven mechanisms detect near-duplicates and tolerate small modifications (e.g., identifier renames, weight variation, or slight perceptual changes) missed by exact matching.
• Resource Efficiency: Structural redundancy removal, as in KV-CAR or Sortblock, yields substantial memory or compute savings without altering model architectures or incurring perceptible accuracy loss.
• Fairness and Bias Reduction: Self-supervised contrastive objectives (as in Pieceformer) avoid human-label-induced bias and produce embeddings with uniform comparability.
• Biological Plausibility: In SNN continual learning, similarity-adaptive neuron reuse offers both improved interpretability and direct biological motivation (Han et al., 28 Oct 2024).

Limitations:

• Threshold Sensitivity: Incorrect thresholds (e.g., τ in cache reuse or KV compression) can induce under- or over-reuse, harming performance or fidelity (Roy et al., 7 Dec 2025, Chen et al., 1 Aug 2025).
• Database Completeness: In component search, missing even a single original version can yield no exact hit (Ishio et al., 2017).
• Task Drift/Heavy Modification: Extreme refactoring, domain shift, or deep inlining may break the assumed similarity structure, causing reuse misses (Ishio et al., 2017, Jia et al., 2021).
• Adaptation Overhead: For recommend-then-adapt pipelines (e.g., LLM method reuse, code wrapper generation), adaptation steps remain bottlenecks if structural similarity is superficial (Su, 6 Sep 2025).
• Hardware/Scheduling Overheads: Fine-grained control or special scheduling logic (e.g., RLE packing, crossbar selector, parallel ring protocols) can introduce complexity and subtle bottlenecks at scale (Khadem et al., 2021, Wang et al., 20 May 2025).

5. Domain-Specific Implementations

Clone-and-Own Code Search

Employs b-bit minwise hashing to quickly index 10⁶–10⁷ source files and computes component-level similarity by aggregating per-file Jaccard similarities, enabling accurate identification of cloned origins amid minor modifications (Ishio et al., 2017).

Knowledge Transfer in VLSI

Pieceformer calculates Euclidean distances in an embedding space produced by a hybrid message-passing + partitioned linear transformer. This supports statistically unbiased retrieval and initialization of downstream EDA processes, yielding order-of-magnitude reductions in optimization time (Yang et al., 18 Jun 2025).

Transformer Memory Optimization

KV-CAR reuses KV cache entries at the head level using L₁ distance thresholding between corresponding keys (and values) across layers, reducing memory occupancy and enabling longer inference sequences with minimal accuracy degradation (Roy et al., 7 Dec 2025).

Feature & Output Reuse in Diffusion/Generative Models

Similarity metrics on block features (cosine or relative MAE) drive dynamic skipping, adaptive recomputation, and parallelization of generative model inference, substantially accelerating workflows on commodity or distributed hardware (Chen et al., 1 Aug 2025, Wang et al., 20 May 2025).

Continual Learning (SNNs)

KL-scatter–based similarity between tasks directly modulates both selective neuron pruning (higher similarity ⇒ higher retention) and discriminative expansion (higher similarity ⇒ restrained growth), optimizing accuracy and energy consumption (Han et al., 28 Oct 2024).

Type-Directed Code Synthesis and Adaptation

Hunter’s system calculates multiset-based atomic type similarity to guide an ILP that prescribes cost-minimal argument/result mappings, followed by type-driven code synthesis for seamless API adaptation (Wang et al., 2016).

6. Theoretical Foundations and Future Extensions

The generalization behavior and correctness of similarity driven reuse are underpinned by rigorous probability and statistical theory:

• Generalization bounds: Non-asymptotic similarity-aware bounds allow reusing holdout sets far beyond naive union-bound limits, as similarity clusters between models or hypotheses require only loose covering numbers (N_η) for error control (Mania et al., 2019).
• Adaptive expansion: In continual learning, similarity-moderated expansion delivers a formal tradeoff between knowledge reuse and network growth (Han et al., 28 Oct 2024).
• Similarity-driven simulation: In binary analysis, the presence of 1-to-n/n-to-n mappings due to inlining motivates advanced simulation and clustering strategies sensitive to redundancy and coverage (Jia et al., 2021).

Potential extensions span code provenance tracking in supply-chain security, automatic patch mapping for vulnerability remediation, analogical reasoning for LLMs, and hardware agnostic similarity-driven memory controllers.

7. Comparative Analysis and General Patterns

Across diverse instantiations, key patterns emerge:

• Compact surrogates for high-dimensional similarity (e.g., signatures, embeddings, reduced descriptors).
• Search and recommendation pipelines integrating efficient similarity filters before invoking costly adaptation or recomputation.
• Domain-specific aggregation strategies (per-file, per-head, per-block, per-neuron) tailored to artifact granularity.
• Robustness to minor perturbations via similarity metrics that are tolerant to local or moderate global changes.
• Statistically principled thresholds and validation that ensure high precision at controlled recall, with application-dependent tradeoffs.

Similarity driven reuse mechanisms facilitate scalable, robust, and efficient reuse in settings characterized by structural or statistical redundancy, providing a unifying abstraction across software, hardware, and learning systems (Ishio et al., 2017, Yang et al., 18 Jun 2025, Roy et al., 7 Dec 2025, Wang et al., 2016, Yeom et al., 3 Dec 2024, Chen et al., 1 Aug 2025, Javaheri et al., 4 Feb 2025).