Extending CSRv2 to the k=1 Extreme Sparsity Regime

Establish training and modeling techniques that enable Contrastive Sparse Representation v2 (CSRv2) to produce effective embeddings when the activation budget is k=1 (i.e., only a single latent dimension is nonzero), mitigating dead neurons and preventing sharp performance degradation in this extreme setting.

Background

The paper demonstrates that CSRv2 achieves strong results at ultra-sparse settings such as k in {2,4}, but performance collapses when k=1 due to severe dead neurons and optimization difficulties. The authors emphasize that this case effectively reduces to clustering and is qualitatively different from other sparsity levels.

They suggest that clustering-inspired approaches (e.g., prototype or vector quantization, balanced assignment, entropy regularization, or optimal transport) may help, but extending CSRv2 to k=1 remains unresolved and is highlighted as a key open challenge.

References

A key open challenge is the $k=1$ regime, where CSRv2 still suffers from severe dead neurons and sharp degradation (Appendix~\ref{sec:k_equals_1}). Since $k=1$ effectively reduces to clustering (mapping each input to a one-shot label), future work could explore clustering-inspired approaches, such as prototype or vector quantization, balanced assignment, entropy regularization, or optimal transport.

— CSRv2: Unlocking Ultra-Sparse Embeddings  (2602.05735 - Guo et al., 5 Feb 2026) in Concluding Remarks

We conjecture that jointly training across budgets regularizes the extreme $K{=}1$ case, which a dedicated specialist must otherwise learn in isolation.

— FlexComp: One Model for Every Ratio in Context Compression  (2609.11192 - Zhao et al., 10 Sep 2026) in Section 4, subsection "One Model Matches Ratio-Specific Models"