[GH-ISSUE #181] four dimensions regarding importance and connection strength #142

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opened 2026-10-26 21:13:00 +00:00 by bsakaguchi · 0 comments

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2026-10-26 21:13:00 +00:00

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Originally created by @gyljsj9988 on GitHub (Feb 20, 2025).
Original GitHub issue: https://github.com/deepseek-ai/Janus/issues/181

Regarding the importance of sparsity strategies in the long-range context, it is quite innovative. However, even in the case of sparse connections, does the article consider strong connections and weak connections? I think there are four dimensions regarding importance and connection strength: 1. Important and strong connection; 2. Important but weak connection; 3. Not important but strong connection; 4. Not important and weak connection. Giving priority to those that are important but with weak connections, and then those that are important and have strong connections would have the greatest enlightening effect on its context. Does the article take this into account? Is such consideration valuable? Is it achievable? Does it overall improve the performance of the model?
In the proposed NSA (Native Sparse Attention) method, the dynamic hierarchical sparsity strategy implicitly covers the trade-off between importance and connection strength in the following ways, but there are differences from the user's explicit four-dimensional priority design:
Architecture Design and Four-Dimensional Coverage Analysis:
Compression Path (CMP): By aggregating blocks to capture global coarse-grained information, it naturally favors blocks with strong semantic associations (strong connection and important).
Selection Path (SLC): It retains fine-grained important tokens, which are screened based on attention scores and tend to focus on locally strongly associated important tokens (strong connection and important).
Sliding Window (WIN): It forces attention on neighboring tokens (strong connection but not necessarily important).
It does not explicitly distinguish the scenario of "important but weak connection", for example, potential associations across documents may be covered by the compression path but not prioritized.
The Value of the User's Proposal:
Validity of the Motivation: Giving priority to important weak connections may break through the tendency of local attention and has the potential for added value in knowledge reasoning and cross-paragraph reasoning tasks.
Feasibility of Implementation: It is necessary to design a new scoring function. For example, introducing an indicator of "long-range relevance degree" (such as co-occurrence frequency + attention entropy) and combining it with the existing importance scores for dynamic weighting.
Performance Gain: Theoretically, it can enhance the capture of long-range sparse dependencies, but it is necessary to balance the computational cost and it may be more suitable for fine-tuning specific tasks rather than general pre-training.
Potential for Adaptive Improvement of the Existing NSA:
A "connection strength attenuation factor" (such as a relative distance penalty term) can be added to the scoring stage of the SLC path, so that important tokens at a long distance can obtain higher priority.
Adjust the gating weight (g_cmp, g_slc) learning strategy to make the model autonomously strengthen its focus on important information with weak connections.
Feasibility Verification:
Experimental Verification: It is necessary to construct a targeted evaluation set (for example, reasoning tasks that require 关联分散线索) and compare the performance differences before and after the improvement.
Implementation Challenges: It is necessary to add computational modules without undermining hardware alignment, which may introduce additional costs. For example, Locality-Sensitive Hashing (LSH) can be used for efficient connection strength evaluation.
Conclusion: The user's four-dimensional classification is an insightful and refined design for the sparse attention mechanism. The existing NSA partially covers it but does not explicitly optimize it. In scenarios such as knowledge Q&A for long documents and multi-hop reasoning, targeted improvements may enhance the model's performance, but it is necessary to balance computational efficiency and generality. The experiments in the document show that the NSA already has relatively strong comprehensive performance, and further introducing explicit connection strength modeling can be a valuable extension direction.

Originally created by @gyljsj9988 on GitHub (Feb 20, 2025). Original GitHub issue: https://github.com/deepseek-ai/Janus/issues/181 Regarding the importance of sparsity strategies in the long-range context, it is quite innovative. However, even in the case of sparse connections, does the article consider strong connections and weak connections? I think there are four dimensions regarding importance and connection strength: 1. Important and strong connection; 2. Important but weak connection; 3. Not important but strong connection; 4. Not important and weak connection. Giving priority to those that are important but with weak connections, and then those that are important and have strong connections would have the greatest enlightening effect on its context. Does the article take this into account? Is such consideration valuable? Is it achievable? Does it overall improve the performance of the model? In the proposed NSA (Native Sparse Attention) method, the dynamic hierarchical sparsity strategy implicitly covers the trade-off between importance and connection strength in the following ways, but there are differences from the user's explicit four-dimensional priority design: Architecture Design and Four-Dimensional Coverage Analysis: Compression Path (CMP): By aggregating blocks to capture global coarse-grained information, it naturally favors blocks with strong semantic associations (strong connection and important). Selection Path (SLC): It retains fine-grained important tokens, which are screened based on attention scores and tend to focus on locally strongly associated important tokens (strong connection and important). Sliding Window (WIN): It forces attention on neighboring tokens (strong connection but not necessarily important). It does not explicitly distinguish the scenario of "important but weak connection", for example, potential associations across documents may be covered by the compression path but not prioritized. The Value of the User's Proposal: Validity of the Motivation: Giving priority to important weak connections may break through the tendency of local attention and has the potential for added value in knowledge reasoning and cross-paragraph reasoning tasks. Feasibility of Implementation: It is necessary to design a new scoring function. For example, introducing an indicator of "long-range relevance degree" (such as co-occurrence frequency + attention entropy) and combining it with the existing importance scores for dynamic weighting. Performance Gain: Theoretically, it can enhance the capture of long-range sparse dependencies, but it is necessary to balance the computational cost and it may be more suitable for fine-tuning specific tasks rather than general pre-training. Potential for Adaptive Improvement of the Existing NSA: A "connection strength attenuation factor" (such as a relative distance penalty term) can be added to the scoring stage of the SLC path, so that important tokens at a long distance can obtain higher priority. Adjust the gating weight (g_cmp, g_slc) learning strategy to make the model autonomously strengthen its focus on important information with weak connections. Feasibility Verification: Experimental Verification: It is necessary to construct a targeted evaluation set (for example, reasoning tasks that require 关联分散线索) and compare the performance differences before and after the improvement. Implementation Challenges: It is necessary to add computational modules without undermining hardware alignment, which may introduce additional costs. For example, Locality-Sensitive Hashing (LSH) can be used for efficient connection strength evaluation. Conclusion: The user's four-dimensional classification is an insightful and refined design for the sparse attention mechanism. The existing NSA partially covers it but does not explicitly optimize it. In scenarios such as knowledge Q&A for long documents and multi-hop reasoning, targeted improvements may enhance the model's performance, but it is necessary to balance computational efficiency and generality. The experiments in the document show that the NSA already has relatively strong comprehensive performance, and further introducing explicit connection strength modeling can be a valuable extension direction.

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