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30 result(s) for “okapi bm25 site www eyespy cy site www irs gov site does nvidia com site can site:arxiv.org” of 53 matched · 666 ms
Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
For each evaluation sample, we construct an Okapi BM25 index over its decoded text chunks; the index is therefore sample-specific rather than a shared external knowledge base
from the top result below
https://arxiv.org/html/2608.03048v1
for large language models, as recent recurrent-memory approaches face two inherent challenges: sequential chunk-wise updates can overwrite early critical evidence with later irrelevant content, and serial inter-chunk dependencies limit parallelism and cause latency to increase with context length
https://arxiv.org/html/2608.02613v1
6 Ingest Overhead: When Does Structured Memory Pay Off
https://arxiv.org/list/cs.AI/new
We demonstrate that it can self-assemble functional circuits from scratch and rapidly re-route logic around permanent, previously unseen hardware faults
https://arxiv.org/list/cs.CL/new
LG)
Half a million cuneiform clay tablets survive in museums worldwide, yet modern users can neither read nor write in the world's oldest writing system, leaving a 4,000-year cultural barrier that existing NLP tools have only partially addressed
https://arxiv.org/html/2501.13956v1
com
Abstract
We introduce Zep, a novel memory layer service for AI agents that outperforms the current state-of-the-art system, MemGPT, in the Deep Memory Retrieval (DMR) benchmark
https://arxiv.org/list/cs.IR/new
Our results show that skill representation itself matters, and that simply preserving the structure already present in skill files can substantially improve retrieval
https://arxiv.org/html/2608.01085v1
However, this collaboration introduces a vulnerability: backdoor behavior can be activated when peer evidence reaches a hidden threshold, rather than being determined by any single message
https://arxiv.org/html/2608.02668v1
com/hazdzz/deep_connection \ncudata[Correspondence]Jie Zhang (),
Correspondence: Cheng-Fang Su ()
“Geometry is not true, it is advantageous
https://arxiv.org/html/2608.03703v1
Dispersing the rebalance through the session, Korea’s chosen remedy, moves the order but not the reference that sizes it and can raise the toll
https://arxiv.org/list/cs.CV/new
CV)
Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seasonally, and modality-specific evidence can be ambiguous
https://arxiv.org/list/math/new
Their finite iterates can develop rapidly increasing numbers of linear pieces, making them a natural test case for the expressive power of deep neural networks
https://arxiv.org/abs/2608.03883
Our systems combine sparse BM25 retrieval, evidence-aware rescoring for self-referential symptom reports, embedding-based reranking, query-prototype expansion, and LLM-based reranking
https://arxiv.org/abs/2608.03860
The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation
https://arxiv.org/abs/2104.08663
Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs
https://arxiv.org/list/cs.IR/recent
02048 [pdf, html, other]
Title: SmartGR: Hierarchy and Beam-Aware Knowledge Distillation for Generative Recommendation
Ziheng Zhang, Yu Cui, Bohao Wang, Yong He, Chao Yu, Chuan Yuan, Wujie Sun, Can Wa
https://arxiv.org/tb/2005.14165
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https://arxiv.org/html/2407.16741v3
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https://arxiv.org/html/2607.28127v1
Consequently, the ability to effectively leverage these technologies to extract actionable insights from complex narratives and convert them into autonomous trading signals can provide a significant informational advantage, leading to improved price prediction (Peng and Jiang, 2016) and the development of effective
https://arxiv.org/html/2608.03494v1
Abstract
Vocabulary extension is an efficient way to adapt pretrained large language models (LLMs) to new languages, but the initialization of newly added token embeddings can strongly affect continued pre-training (CPT) efficiency
https://arxiv.org/html/2608.03035v1
For example, if a user planning a dinner first mentions that three guests are coming but later shares that two have canceled, an LLM assisting with the reservation must track that only one guest remains, a proposition that can only be determined using information from preceding context, and encode “Three guests are
https://arxiv.org/html/2608.01024v1
While the evolutionary methods can in principle be run under such non-uniform demand, their search is not informed by it: random mutation does not know which shelves are requested frequently, so placing the high-frequency shelves well is left to chance
https://arxiv.org/list/cs.DC/new
DC)
The artificial intelligence industry has constructed a USD 300 billion centralized data center infrastructure to serve a workload, large language model inference, that does not architecturally require centralization
https://arxiv.org/html/2608.00648v1
The framework is suitable for a broad range of distributed task-planning applications and can be flexibly transferred to related scenarios with different practical constraints and settings
https://arxiv.org/abs/2404.05321
265 (HEVC), AV1 (SVT-AV1) along with direct comparisons to the latest AV1 encoder inside NVIDIA GPUs (40 series), and AWS Mediaconvert's AV1 implementation
https://arxiv.org/abs/2609.14845
Finally, as a proof of concept for what experimental research can be done with the models, we have utilised them in two demonstrative numerical applications, quantifying application-level accuracy differences between AMD matrix cores and the NVIDIA tensor cores
https://arxiv.org/abs/2608.01891
We implement AFlex in SGLang and evaluate it on NVIDIA A800 GPUs using Qwen3-32B and Mixtral-8$\times$7B under production Conversation and Coding traces
https://arxiv.org/abs/2608.02122
8% on average on NVIDIA L40S and L4, and reduces EDP by 9
https://arxiv.org/list/cs.MM/new
This paper presents QualiVision, a design probe examining how an interactive, spreadsheet-backed workspace can support video-based qualitative analysis
https://arxiv.org/html/2509.02523v1
Low latency ASR models that are small enough to deploy on-device can empower a new generation of voice-driven applications, from real-time translation devices to intelligent conversational interfaces in automobiles (Rege et al
https://arxiv.org/list/cs.AR/new
Our analysis shows that, despite its limited write performance, HBF can significantly improve the batch size, throughput, and flexibility of LLM-serving systems while reducing the minimum GPU requirements, but realizing these benefits critically depends on sustaining HBM-comparable read bandwidth and requires