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Efficient Cache Optimization for Language Models

BACKGROUND
3 SIGNALSFIRST DETECTED 27 July 2026UPDATED 15 October 2026
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29 Sept
24 Aug
27 Jul
Other feeds and industry voices
9 Apr
HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference

arXiv:2604.05887v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have advanced unified reasoning over text, images, and videos, but their inference is

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14 Apr
MEMENTO: Teaching LLMs to Manage Their Own Context

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

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5 Oct
iS-KV: Online Low-Rank KV Cache Compression via Block-Incremental SVD

arXiv:2610.02815v1 Announce Type: new Abstract: Long chain-of-thought reasoning substantially increases KV-cache memory during autoregressive decoding, as every generated token

REPORT · HOW · ArXiv CS.AIOPEN STORY
27 Apr
Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

arXiv:2604.21952v1 Announce Type: cross Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines

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20 Mar
SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding

arXiv:2603.18567v1 Announce Type: cross Abstract: Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates

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18 Aug
Learning Agent Execution for KV-Cache Management in Agentic Serving

arXiv:2608.14624v1 Announce Type: new Abstract: Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed

REPORT · WHERE · ArXiv CS.AIOPEN STORY
6 Aug
Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning

arXiv:2608.04771v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) excel on complex tasks through long chain-of-thought (CoT) reasoning, but their lengthy intermediate

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11 Jun
Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents

arXiv:2606.11680v1 Announce Type: new Abstract: Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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