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  1. Home
  2. Signal Canvas
  3. VQKV: High-Fidelity and High-Ratio Cache Compression via Vec
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VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization

Fresh1d ago
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Viability
0.0/10

Compared to this week’s papers

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: partial

Distribution: unknown

Source paper: VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization

PDF: https://arxiv.org/pdf/2603.16435v1

Repository: https://github.com/LUMIA-Group/VQKV

First buyer signal: unknown

Distribution channel: unknown

Last proof check: 2026-03-19T20:22:25.60839+00:00

Starting…

Dimensions overall score 3.0

GitHub Code Pulse

Stars
3
Health
C
Last commit
3/18/2026
Forks
0
Open repository

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Prior Work
Leech Lattice Vector Quantization for Efficient LLM Compression
Score 3.0stable
Prior Work
SideQuest: Model-Driven KV Cache Management for Long-Horizon Agentic Reasoning
Score 3.0stable
Higher Viability
KVSculpt: KV Cache Compression as Distillation
Score 7.0up
Higher Viability
More Than a Quick Glance: Overcoming the Greedy Bias in KV-Cache Compression
Score 5.0up
Higher Viability
Where Matters More Than What: Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries
Score 7.0up
Higher Viability
Beyond Speedup -- Utilizing KV Cache for Sampling and Reasoning
Score 6.0up
Higher Viability
You Need an Encoder for Native Position-Independent Caching
Score 8.0up
Higher Viability
Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective
Score 8.0up

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Related Resources

  • How can KV cache compression techniques be integrated with adaptive reasoning?(question)

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