arXiv AI
Where Should a Document Live: Context, Representations, or Parameters?
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arXiv Machine Learning
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Cartridges at Scale: Training Modular KV Caches over Large Document Collections
arXiv:2606. 04557v1 Announce Type: cross Abstract: Large Language Models can reason over long contexts, yet prefilling millions of tokens is wasteful as much of the content remains static across queries.
arXiv Computation and Language
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BudgetMem: Training-Free Selective Memory for Cost-Efficient Long-Context Processing in Language Models
arXiv:2511. 04919v3 Announce Type: replace Abstract: Processing long documents with large language models (LLMs) is expensive: a single query over a 100K-token document can cost from tens of cents to over a dollar in API fees, depending on the model, and memory grows linearly with context length.
arXiv Machine Learning
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PuzzleKV: Page-Wise Low-Rank Decomposition for KV Cache Compression
arXiv:2608.23843v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compressio...
arXiv AI
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Benchmarking KV-Cache Optimizations across Task Quality and System Performance for Long-Context Serving
arXiv:2607. 05399v1 Announce Type: cross Abstract: Large language model serving is increasingly limited by KV-cache growth under long-context workloads, yet existing KV-cache compression techniques are difficult to compare because they were evaluated on different models, tasks, budgets, and serving stacks.
arXiv Machine Learning
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MetaKV: Adaptive KV Cache Compression for Constrained LLM Inference
arXiv:2609.07966v1 Announce Type: new Abstract: Key--value (KV) cache compression is an effective way to reduce the memory overhead of large language model (LLM) inference, particularly for long-cont...
arXiv Computation and Language
Sep 10
Fine-Tuning a KV Cache Concatenation-Aware Model or Recomputing KV Caches? Why Not Both?
The paper addresses the challenge of long input contexts in Retrieval-Augmented Generation (RAG) systems, where concatenating many retrieved chunks increases prefill workload and time to first token (TTFT). It proposes a dual strategy: fine‑tuning the model to be aware of KV cache concatenation and selectively recomputing only part of the KV caches. Experiments on the RULER benchmark show that for a 124k‑token input, this combined method boosts the RULER score by 9.7 points over a baseline that recomputes caches only, while cutting TTFT by 80% compared with full attention.