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ARXIV:2602.05148 · LLM TRAINING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.05148LLM TRAININGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation.
Opportunity summary
Pain CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates.
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Extensive experimental results show that CoSA provides a principled perspective for efficient and expressive multi-scale model adaptation.
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
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CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation.
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Paper Pack
10.48550/arXiv.2602.05148CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation.
Abstract
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates. However, the low-rank assumption may restrict expressivity, particularly in task-specific adaptation scenarios where singular values are distributed relatively uniformly. To address this limitation, we propose CoSA (Compressed Sensing-Based Adaptation), a new PEFT method extended from compressed sensing theory. Instead of constraining weight updates to a low-rank subspace, CoSA expresses them through fixed random projection matrices and a compact learnable core. We provide a formal theoretical analysis of CoSA as a synthesis process, proving that weight updates can be compactly encoded into a low-dimensional space and mapped back through random projections. Extensive experimental results show that CoSA provides a principled perspective for efficient and expressive multi-scale model adaptation. Specifically, we evaluate CoSA on 10 diverse tasks, including natural language understanding and generation, employing 5 models of different scales from RoBERTa, Llama, and Qwen families. Across these settings, CoSA consistently matches or outperforms state-of-the-art PEFT methods.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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PROBLEM
CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates.
METHOD
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Extensive experimental results show that CoSA provides a principled perspective for efficient and expressive multi-scale model adaptation.
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such as LoRA and PiSSA, rely on low-rank decompositions of weight updates.
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. Extensive experimental results show that CoSA provides a principled perspective for efficient and expressive multi-scale model adaptation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training 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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CoSA offers an innovative PEFT technique leveraging compressed sensing for expressive and efficient LLM adaptation.
Segment
LLM Training
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Commercial read
3.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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partial
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missing
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Write integration checklist from prototype path and target workflow.
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ARTIFACTS
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DEFENSIBILITY
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