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ARXIV:2603.17917 · LLM COMPRESSION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.17917LLM COMPRESSIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEBorja Aizpurua · Sukhbinder Singh · Román Orús · arXiv
A novel approach to compress large language models by focusing on the relative rank of weights.
Opportunity summary
Pain A novel approach to compress large language models by focusing on the relative rank of weights.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel approach to compress large language models by focusing on the relative rank of weights. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker…
Large language models (LLMs) contain billions of parameters, yet many exact values are not essential. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
LLM Compression moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A novel approach to compress large language models by focusing on the relative rank of weights.
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10.48550/arXiv.2603.17917A novel approach to compress large language models by focusing on the relative rank of weights.
Abstract
Large language models (LLMs) contain billions of parameters, yet many exact values are not essential. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes. To reduce the number of unique weight values, we apply weight clustering to pretrained models, replacing every weight matrix with K shared values from K-means. For Llama 3.1-8B-Instruct and SmolLM2-135M, reducing each matrix to only 16-64 distinct values preserves strong accuracy without retraining, providing a simple, training-free method to compress LLMs on disk. Optionally fine-tuning only the cluster means (centroids) recovers 30-40 percent of the remaining accuracy gap at minimal cost. We then systematically randomize cluster means while keeping assignments fixed. Scrambling the relative ranks of the clusters degrades quality sharply-perplexity can increase by orders of magnitude-even when global statistics such as mean and variance are preserved. In contrast, rank-preserving randomizations cause almost no loss at mid and late layers. On the other hand, when many layers are perturbed simultaneously, progressive layer-by-layer replacement reveals that scale drift-not rank distortion-is the dominant collapse mechanism; however, an affine correction w' = aw + b with a > 0 (which preserves both rank order and overall weight distribution) can substantially delay this drift. This rank-based perspective offers a new lens on model compression and robustness.
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Dimensions overall score 3.0
PROBLEM
A novel approach to compress large language models by focusing on the relative rank of weights. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
METHOD
Large language models (LLMs) contain billions of parameters, yet many exact values are not essential. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
WHY NOW
LLM Compression moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel approach to compress large language models by focusing on the relative rank of weights. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) contain billions of parameters, yet many exact values are not essential. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than another-rather than precise magnitudes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Compression moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel approach to compress large language models by focusing on the relative rank of weights.
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LLM Compression
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