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ARXIV:2603.11873 · LLM OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11873LLM OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy.
Opportunity summary
Pain AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy. However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading…
The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable…
LLM Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy.
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10.48550/arXiv.2603.11873AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy.
Abstract
The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to decoding speeds slowing by over 2.5 times. Through a fine-grained performance analysis, we pinpoint the primary bottleneck not in the computation itself, but in the severe overhead from fragmented, sequential CUDA kernel launches required for conventional dynamic routing. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution. Departing from conventional layer-wise or block-wise routing, AdaFuse employs a token-level pre-gating strategy, which makes a single, global routing decision for all adapter layers before a token is processed. This "decide-once, apply-everywhere" approach effectively staticizes the execution path for each token, creating an opportunity for holistic optimization. We capitalize on this by developing a custom CUDA kernel that performs a fused switching operation, merging the parameters of all selected LoRA adapters into the backbone model in a single, efficient pass. Experimental results on popular open-source LLMs show that AdaFuse achieves accuracy on par with state-of-the-art dynamic adapters while drastically cutting decoding latency by a factor of over 2.4x, thereby bridging the gap between model capability and inference efficiency.
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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 3.0
PROBLEM
AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy. However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to dec...
METHOD
The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite minimal increases...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution.
WHY NOW
LLM Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy. However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to decoding speeds slowing by over 2.5 times.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to decoding speeds slowing by over 2.5 times.
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. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Optimization 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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AdaFuse optimizes dynamic adapter inference for LLMs, significantly reducing latency while maintaining accuracy.
Segment
LLM Optimization
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Commercial read
3.0/10 public viability
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