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ARXIV:2604.02816 · LLM COMPRESSION · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02816LLM COMPRESSIONSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNXinhao Wang · Zhonyu Xia · Zhiwei Lin · Zhe Li · Yongtao Wang · arXiv
A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs.
Opportunity summary
Pain A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs.
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A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent…
Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques,…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. In this paper, we show that these two techniques are strongly coupled: naively applying semantic-based token pruning to PTQ-optimized MLLMs can discard activation outliers…
LLM Compression moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs.
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10.48550/arXiv.2604.02816A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs.
Abstract
Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent optimizations. In this paper, we show that these two techniques are strongly coupled: naively applying semantic-based token pruning to PTQ-optimized MLLMs can discard activation outliers that are important for numerical stability and thus worsen quantization errors in low-bit regimes (\textit{e.g.}, W4A4). To address this issue, we propose a quantization-aware vision token pruning framework. Our method introduces a lightweight hybrid sensitivity metric that combines simulated group-wise quantization error with outlier intensity. By combining this metric with standard semantic relevance scores, the method retains tokens that are both semantically informative and robust to quantization. Experiments on standard LLaVA architectures show that our method consistently outperforms naive integration baselines. At an aggressive pruning ratio that retains only 12.5\% of visual tokens, our framework improves accuracy by 2.24\% over the baseline and even surpasses dense quantization without pruning. To the best of our knowledge, this is the first method that explicitly co-optimizes vision token pruning and PTQ for accurate low-bit MLLM inference.
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PROBLEM
A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent optimizations.
METHOD
Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, th...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. In this paper, we show that these two techniques are strongly coupled: naively applying semantic-based token pruning to PTQ-optimized MLLMs can discard activation outliers that are important for numerical...
WHY NOW
LLM Compression moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent optimizations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal Large Language Models (MLLMs) have shown strong reasoning ability, but their high computational and memory costs hinder deployment in resource-constrained settings. While Post-Training Quantization (PTQ) and vision token pruning are standard compression techniques, they are usually treated as independent optimizations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. In this paper, we show that these two techniques are strongly coupled: naively applying semantic-based token pruning to PTQ-optimized MLLMs can discard activation outliers that are important for numerical stability and thus worsen quantization errors in low-bit regimes (\textit{e.g.}, W4A4).
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 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for co-optimizing vision token pruning and quantization to enable efficient deployment of multimodal LLMs.
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