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#memory-compression News & Analysis

6 articles tagged with #memory-compression. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles
AIBullisharXiv – CS AI · Jun 117/10
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Task-Aware Structured Memory for Dynamic Multi-modal In-Context Learning

Researchers introduce TASM (Task-Aware Structured Memory), a training-free framework that optimizes how multi-modal large language models compress and retrieve information during in-context learning. The method addresses critical scalability limitations by using task-aware compression, structure-preserving token merging, and dynamic memory hierarchies to maintain performance while reducing computational costs.

AIBullisharXiv – CS AI · May 117/10
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Reformulating KV Cache Eviction Problem for Long-Context LLM Inference

Researchers introduce LaProx, a novel KV Cache eviction strategy for long-context LLM inference that reformulates the problem from head-wise weight averaging to output-aware layer-wise matrix multiplication. The method achieves 2× accuracy loss reduction under extreme compression while maintaining performance with just 5% of the original KV cache.

AIBullisharXiv – CS AI · May 77/10
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RetentiveKV: State-Space Memory for Uncertainty-Aware Multimodal KV Cache Eviction

RetentiveKV introduces an entropy-driven optimization method for multimodal large language models that achieves 5x KV cache compression and 1.5x decoding acceleration by reformulating token eviction as continuous memory evolution rather than discrete pruning. The approach addresses limitations of existing compression methods by accounting for visual tokens that gain importance later in decoding and preserving spatial continuity of visual information.

AIBullisharXiv – CS AI · Mar 37/102
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RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers

Researchers introduce RMAAT (Recurrent Memory Augmented Astromorphic Transformer), a new architecture inspired by brain astrocyte cells that addresses the quadratic complexity problem in Transformer models for long sequences. The system uses recurrent memory tokens and adaptive compression to achieve linear complexity while maintaining competitive accuracy on benchmark tests.

AIBullisharXiv – CS AI · Jun 96/10
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OmniMem: Perturbation-aware Memory Compression for Streaming Audio-Visual LLMs

OmniMem is a new memory compression framework for audio-visual large language models that enables efficient long-form video understanding by using modality-aware memory allocation and perturbation-aware token selection. The approach achieves 2-4% accuracy improvements over existing compression methods while reducing memory requirements, with potential applications in real-time video AI systems.