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https://arxiv.org/html/2608.03077v1
Protection and Intelligent Processing of Intangible Cultural
Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism
{yeyongshi,biaofu}@stu
https://arxiv.org/html/2508.02739v1
, Jian Li1
Abstract
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs)
https://arxiv.org/list/cs.SE/new
Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs
https://arxiv.org/list/cs.CV/new
0 achieves state-of-the-art or leading performance on text-to-3D generation and 3D editing benchmarks, while exhibiting strong understanding and part-generation capabilities
https://arxiv.org/abs/2608.03883
The task requires systems to rank candidate Reddit sentences according to their relevance to each of the 18 symptoms in the Adult ADHD Self-Report Scale (ASRS-v1
https://arxiv.org/abs/2104.08663
We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark
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/list/cs.IR/recent
CL) [pdf, html, other]
Title: SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
Kaysarul Anas Apurba, Md
https://arxiv.org/html/2608.03035v1
1 Introduction
As LLMs interact with humans in increasingly complex collaborative settings, they must be able to reliably determine the truth of a statement with respect to the conversational context (“contextual truth”)
https://arxiv.org/html/2608.03210v1
Based on this finding, we systematically identify and distill the characteristics of effective contexts and propose a black-box context-aware semantic-shift jailbreak framework with Iterative Context Optimization (ICO)
https://arxiv.org/html/2608.03275v1
LoRA experts, causing adapter storage to grow linearly with the number of experts and restricting adaptation to a fixed expert pool
https://arxiv.org/html/2608.02942v1
At inference, the student’s early parallel commitments alter the context of later predictions, so the states it actually visits drift away from the supervised ones—precisely when step compression is most aggressive
https://arxiv.org/html/2608.03577v1
Most fundamentally, the evaluation of the quality of translation at the level of isolated segments is problematic because it tends to miss out on cohesion, coherence, and stylistic and rhetorical text features
https://arxiv.org/html/2608.02625v1
Latency-window-matched flash-only controls indicate that these gains persist after targeted tuning of block autoregressive decoding
https://arxiv.org/html/2608.02975v1
The concept of LLM-as-a-Judge is simple: we just prompt a powerful LLM to evaluate a segment of text
https://arxiv.org/html/2608.02617v1
Switzerland Mary-Anne Hartley LiGHT Laboratory, EPFL, Lausanne, Switzerland
Abstract
We evaluate whether clinician pairwise preferences provide a reliable signal of clinical safety in large language model (LLM) evaluation, using expert feedback collected through MOOVE (Massive Open Online Validation and
https://arxiv.org/html/2608.03095v1
1 Judge Prompt In-Context Examples
B Discriminative Task Prompts
B
https://arxiv.org/abs/2609.18842?context=cs.LG
18842 (cs)
Title:Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data
Abstract:The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures have ridden these laws to remarkable results, activating only a
https://arxiv.org/abs/2609.18842
18842 (cs)
Title:Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data
Abstract:The scaling laws hold that a language model grows more capable with more parameters and more training data, and Mixture-of-Experts (MoE) architectures have ridden these laws to remarkable results, activating only a
https://arxiv.org/html/2504.19413v1
3 Performance Comparison of Mem0 and Mem0gsuperscriptMem0𝑔\texttt{Mem0}^{\tiny g}Mem0 start_POSTSUPERSCRIPT italic_g end_POSTSUPERSCRIPT Against RAG Approaches and Full-Context Model
4
https://arxiv.org/list/cs.SD/new
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New submissions (showing 14 of 14 entries)
[1] arXiv:2608
https://arxiv.org/html/2407.21307v1
of Computing and Decision Sciences Universidad Nacional de Colombia Medellín ANT COLOMBIA
kgsalaza@unal
https://arxiv.org/html/2407.10064v4
Highway bridge operation and maintenance enhance structural safety and efficiency through technological innovation and management strategies, realizing sensor monitoring 31, 70, drone detection, algorithmic optimization of disease identification, scientific evaluation methods 76, 13, and multilevel maintenance
https://arxiv.org/list/eess.IV/new
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New submissions (showing 9 of 9 entries)
[1] arXiv:2608
https://arxiv.org/list/eess.SP/new
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New submissions (showing 14 of 14 entries)
[1] arXiv:2608
https://arxiv.org/list/cs.DC/new
We then describe a reference architectural stack of eight components addressing weight distribution, sovereignty-aware routing, thermal-adaptive quantization, multi-tenant resource management, federated network inference, cryptographic provenance, privacy-preserving telemetry, and distributed context window extension
https://arxiv.org/html/2407.01757v1
A distributed instrument is more than the sum of its sensors; by strategically scheduling measurements across a region to capture data with spatial context, it can provide insight into spatially and temporally correlated phenomena that is impossible to obtain using monolithic rovers or orbiters
https://arxiv.org/list/cond-mat/new
The mixed anomaly between these is accommodated by the not-on-site action of the symmetries
https://arxiv.org/list/cs.CR/new
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New submissions (showing 32 of 32 entries)
[1] arXiv:2608
https://arxiv.org/html/2401.02063v2
A’ohoku Place, Hilo, HI 96720, USA Sarah Antier Affiliation: Observatoire de la Côte d’Azur, Université Côte d’Azur, Boulevard de l’Observatoire, 06304 Nice, France Erik Blaufuss Affiliation: Department of Physics, University of Maryland, College Park, MD 20742, USA P