arXiv AI
Jul 21
Panache: One-Pass Motif Discovery at Every Window Length
arXiv:2607. 17481v1 Announce Type: new Abstract: Motif discovery, the search for recurring patterns within a time series, is a core primitive of exploratory data analysis.
Panache: One-Pass Motif Discovery at Every Window Length
Motif discovery, the search for recurring patterns within a time series, is a core primitive of exploratory data analysis. A pattern, however, is defined by its duration, which analysts rarely know in advance.
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arXiv Machine Learning
Jul 9
Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference
arXiv:2607. 07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantization, eviction, or offloading.
arXiv Machine Learning
Sep 25
A New Gap Sequence for Shellsort: RL-Driven Algorithm Discovery Beyond $N^{4/3}$
arXiv:2609. 29881v1 Announce Type: cross Abstract: Choosing Shellsort gaps is a well-known open problem.
arXiv Machine Learning
Sep 23
Memoization Without Keys: Compact, Out-of-Core Tables for Functions of Sorted Arguments
arXiv:2609.20276v2 Announce Type: replace-cross Abstract: Memoizing an expensive function of a sorted score vector is a data-structure problem before it is a numerical one: at a billion gridpoints, a...
arXiv AI
Aug 10
Autonomy-of-Heads: Data-Free Sparse Attention from Frozen Query-Key Geometry
arXiv:2608. 06849v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by quadratic attention computation and growing KV-cache costs.
arXiv Machine Learning
Aug 26
A Feature-Major Codebook for Memory-Efficient Sparse-Binary Self-Organizing Maps: Scaling a MEDLINE Atlas to 1.05 Million Neurons on a Single Consumer GPU
The paper presents a memory‑efficient sparse‑binary self‑organising map (SOM) that scales a MEDLINE atlas to over a million neurons on a single consumer GPU. By re‑ordering the codebook into a feature‑major layout, the authors accelerate the best‑matching‑unit search by 4.5–8.5× without increasing quantisation error, enabling training of a 1,048,576‑neuron SOM in 72 s on a 24 GB GPU. The approach outperforms existing cuSPARSE and CPU‑based SOM implementations, achieving the largest SOM reported to date and demonstrating that resolution limits are computational rather than data‑driven.
arXiv AI
Jun 24
Can Aggregate Invariants Accelerate Continuous Subgraph Matching? Limits, Laws, and a Dynamic Spectral Index
arXiv:2606. 24421v1 Announce Type: new Abstract: Spectral filtering recently delivered substantial pruning for \emph{static} subgraph matching: Laplacian interlacing rejects candidates whose neighborhoods cannot host the query.
arXiv Machine Learning
Sep 18
A Table-Free Index for Tapered Memoization Grids: Compact Out-of-Core Evaluation of Functions of Sorted Arguments
The paper presents a table‑free index for tapered memoization grids, enabling compact out‑of‑core evaluation of functions that depend on sorted arguments. By showing that the grid’s key set corresponds to multiset combinations, the authors derive a closed‑form O(d) ranking and unranking scheme that removes the need for large preprocessing tables and allows order‑free parallel construction. The resulting values‑only flat array uses significantly less memory than hash‑map memoization, offers faster query times once cache limits are exceeded, and remains operable with memory‑mapped storage beyond RAM.
arXiv Machine Learning
Aug 5
ATFlash: Per-RoPE-Wavelength Attention Windows for Compute/Memory-Efficient LLM Inference
arXiv:2608. 02947v1 Announce Type: new Abstract: The attention score with rotary position embeddings (RoPE) decomposes exactly into a sum over its 2D-rotation frequency pairs, and each pair's wavelength limits how far it can discriminate position.
arXiv Machine Learning
Aug 11
SPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform Coding
arXiv:2608. 07915v1 Announce Type: new Abstract: Large language models (LLMs) increasingly read long inputs in the agentic era, from whole documents and codebases to conversations across many turns.
arXiv Machine Learning
Sep 25
When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse
The paper investigates cache replacement strategies for large language model (LLM) prefix reuse, analyzing production traces from two companies and testing 14 eviction algorithms in both high-bandwidth memory (HBM) and large memory-pool environments. It finds that sophisticated policies designed for traditional caches offer little advantage over simple LRU, because prefix reuse is largely driven by the regular pacing of active sessions, making recency a strong predictor. The study also highlights new challenges such as heavy-tailed session footprints and variable miss costs, and proposes a compute-savings ratio along with two offline oracles to better quantify these effects, suggesting that effective prefix-cache management should combine recency with selective quick demotion, compute-aware partial eviction, and capacity-dependent granularity.
arXiv Machine Learning
Aug 4
HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval
arXiv:2606. 21633v2 Announce Type: replace Abstract: The KV cache dominates GPU memory in long-context LLM serving, crowding out batch capacity and leaving GPU compute idle.