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Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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Empirical Validation of Shadow-Price-Guided Inlining in MIR

Authors: Bilar, Daniyel Yaacov;

Empirical Validation of Shadow-Price-Guided Inlining in MIR

Abstract

This research was conducted with AI assistance from multiple large language models operating under the author's direction. Anthropic's Claude (Opus) served as the primary research partner for source code analysis, experimental design review, statistical methodology, and manuscript preparation. Anthropic's Claude (Sonnet) provided independent manuscript review, identifying methodological concerns including the fixed-RNG-seed limitation in the random condition, the oracle nature of synthetic profiling, and the distinction between prospective intervention and post-hoc reinterpretation in the Kelly/TCP analogy; these critiques led to substantive revisions in §5.2, §5.4, and §5.6. Google's Gemini ("Nova the Optimal AI") contributed proof-of-concept code generation, MIR IR construction, and engineering brainstorming. Additional AI systems contributed to early-stage ideation and literature review. All experimental decisions, hypothesis formulation, statistical interpretations, and scientific claims are the sole responsibility of the author. AI contributions are disclosed transparently in accordance with scientific integrity norms.

Full experimental validation of dual decomposition protocol for JIT compiler inlining on the MIR lightweight JIT compiler. Implements a three-phase pipeline: Phase 1 synthetic profiling (execution frequencies derived from benchmark structure), Phase 2 shadow-price-guided mutation pass (MIR_CALL → MIR_INLINE promotion), Phase 3 wall-clock benchmarking across five conditions (no inlining, blind inline-all, random 50%, shadow-price, inverted-price). Includes two benchmark suites (3-function and 8-function workloads), raw timing data (20 runs × 5 conditions per benchmark), statistical analysis (Cohen's d effect sizes), and publication-quality figures (PNG + SVG). Key results: P3 (shadow vs. inverted-price) confirmed with d = 2.65–4.42 across both benchmarks; shadow-price matches blind inline-all performance with half the mutations on the 8-function benchmark (4 vs. 8 promotions, d = −0.02); P1 (shadow vs. random) falsified on 3-function benchmark due to fixed RNG seed. Companion to theory paper (DOI: 10.5281/zenodo.18715390) and Paper 1 empirical consistency results (DOI: 10.5281/zenodo.18735516).

Keywords

empirical validation, dual decomposition, shadow pricing, JIT compilation, statistical hypothesis testing, compiler optimization, inlining, network utility maximization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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