arXiv AI
Where Should a Document Live: Context, Representations, or Parameters?
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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.
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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.
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PuzzleKV: Page-Wise Low-Rank Decomposition for KV Cache Compression
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CacheGraft: Enhancing Retrieval-Augmented Generation via Latent-Space Document Enrichment
Recent work in machine learning has demonstrated that transformer-based language model agents can communicate state not only through text, but also directly in latent space via key-value (KV) caches....
arXiv Machine Learning
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Compile the Table: Query-Calibrated Operator Compression for Tabular In-Context Learning
The paper introduces QCOC, a method for compressing the key‑value cache used in tabular in‑context learning. QCOC clusters in‑context example states into joint‑KV prototypes, calibrates them against query vectors, and preserves multiplicities to maintain accuracy while achieving high compression. Experiments on 64 OpenML‑CC18 datasets and seven long tables show QCOC outperforms other compression and retrieval methods, achieving up to a 10.5× compression ratio and 508× speed‑up over dynamic retrieval baselines while staying within 0.23 percentage points of full‑context accuracy.