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ARXIV:2605.20936 · LLM ARCHITECTURE SEARCH · SUBMITTED 21 MAY · 20:29 UTC · FRESHNESS STALE
ARXIV:2605.20936LLM ARCHITECTURE SEARCHSUBMITTED 21 MAY · 20:29 UTCFRESHNESS STALEWeizhe Chen · Miao Zhang · Junpeng Jiang · Yaping Li · Weili Guan · Liqiang Nie · arXiv
A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes.
Opportunity summary
Pain A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes. Existing designs often rely on manual empirical rules or proxy-based selector signals for layer-wise operator allocation.
Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality, making hybrid architecture design a central problem. Existing designs often rely on manual empirical rules or…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent NAS-style systems such as Jet-Nemotron demonstrate the promise of automated hybrid architecture search. Code availability is flagged in the production record; the public…
LLM Architecture Search moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes.
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10.48550/arXiv.2605.20936A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes.
Abstract
Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality, making hybrid architecture design a central problem. Existing designs often rely on manual empirical rules or proxy-based selector signals for layer-wise operator allocation. Recent NAS-style systems such as Jet-Nemotron demonstrate the promise of automated hybrid architecture search. However, Jet-Nemotron's PostNAS search stages alone use 200B tokens, making such search pipelines difficult to use as routine methods for hybrid architecture design. We introduce DASH, a fast differentiable search framework for hybrid attention architecture design, which relaxes discrete layer-wise attention operator placement into continuous architecture logits, prepares reusable teacher-aligned linear candidates, and performs architecture-only search with model and operator weights frozen to significantly enhance search efficiency. On Qwen2.5-3B-Instruct, DASH consistently outperforms a comprehensive suite of existing selector-style hybrid attention design baselines, showing that direct differentiable search can discover stronger hybrid architectures. Moreover, DASH achieves stronger RULER performance than released Jet-Nemotron models while remaining competitive on overlapping short-context and general benchmarks. Notably, each DASH search run uses only 12.3M tokens and takes about 20 minutes on a single RTX Pro 6000 GPU, corresponding to merely 0.006% of the PostNAS search tokens reported by Jet-Nemotron. These results suggest that high-quality hybrid attention architectures can be obtained through minutes-level differentiable search, providing a promising direction for hybrid architecture design.
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PROBLEM
A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes. Existing designs often rely on manual empirical rules or proxy-based selector signals for layer-wise operator allocation.
METHOD
Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality, making hybrid architecture design a central problem. Existing designs often rely on manual empirical rules or proxy-based selecto...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent NAS-style systems such as Jet-Nemotron demonstrate the promise of automated hybrid architecture search. Code availability is flagged in the production record; the public repository link still needs...
WHY NOW
LLM Architecture Search moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes. Existing designs often rely on manual empirical rules or proxy-based selector signals for layer-wise operator allocation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality, making hybrid architecture design a central problem. Existing designs often rely on manual empirical rules or proxy-based selector signals for layer-wise operator allocation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recent NAS-style systems such as Jet-Nemotron demonstrate the promise of automated hybrid architecture search. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Architecture Search moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A fast, differentiable framework for designing efficient hybrid attention architectures in Large Language Models within minutes.
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LLM Architecture Search
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