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30 result(s) for “okapi bm25 site hotchips pp site github com site text of de site sign have se site:arxiv.org” of 38 matched · 979 ms
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The query asks the model to recall the topic of an earlier conversation about Tesla; the gold answer is anchored in one specific session about Pooja’s dig site
from the top result below
https://arxiv.org/html/2608.02613v1
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 8787/77/4848 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
https://arxiv.org/html/2608.03048v1
To address these issues, we propose PI-Mem (Parallel-Iterative Memory), a mechanism that processes all chunks in parallel and iteratively refines a shared memory over a bounded number of turns
https://arxiv.org/html/2510.03215v2
However, current text-to-text (T2T) interfaces restrict information exchange among LLMs, particularly when conveying rich or diverse semantic interpretations of a shared context
https://arxiv.org/html/2608.03011v1
AS] 04 Aug 2026
Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling
Ye-Xin Lu, Xin Wang, Yang Ai, Hui-Peng Du, Zhen-Hua Ling, and Junichi Yamagishi This work is the extended version of our conference paper [1] published at IEEE International Conference on
https://arxiv.org/html/2608.03063v1
Yet the two capabilities have long lived in disjoint model families: large language models (LLMs) reason fluently over text but cannot natively consume behavioral sequences (Caffagni et al
https://arxiv.org/html/2608.03486v1
We find modest asymmetries with respect to the sign of the shock and state of the economy
https://arxiv.org/list/cs.AI/new
However, their performance heavily depends on the clarity and completeness of data semantics
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/html/2407.16741v3
edu
Abstract
Software is one of the most powerful tools that we humans have at our disposal; it allows a skilled programmer to interact with the world in complex and profound ways
https://arxiv.org/list/cs.CL/new
(3) Search latency bites only at very small reader: on a Spark GB10 edge node, memory-search adds a moderate and fixed 87/7/48 ms (BM25-RAG/Memobase/MemSearch) that composes a small part of TTFT for most reader-backend combinations
https://arxiv.org/html/2608.02668v1
Both prove to be members of a one-parameter family of angular retractions, pp-SpheretNorm, whose rotation angle saturates rather than growing without bound
https://arxiv.org/html/2509.22186v2
ocr, PP-StructureV3), establishing new performance records in text recognition, formula recognition, table recognition, and reading order prediction
https://arxiv.org/html/2608.02918v1
Biscainho are with SMT, DEL/Poli & PEE/COPPE, Federal University of Rio de Janeiro, Brazil (corresponding e-mail: wallace
https://arxiv.org/html/2407.04503v4
team, Université de Bordeaux, France
Abstract
As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next
https://arxiv.org/abs/2608.03883
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy
https://arxiv.org/abs/2104.08663
Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors
View PDF HTML (experimental)
Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of
https://arxiv.org/abs/2608.03860
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy
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/list/cs.IR/new
We provide the core implementation of KGD at this https URL
https://arxiv.org/html/2608.02609v1
Code and pretrained model: https://github
https://arxiv.org/html/2608.02616v1
2 Related Work
De-identification and text anonymization
https://arxiv.org/html/2608.03372v1
Hedging and attribution have open-ended realizations, so vocabulary plateaus near half recall, and a one-question LLM witness recovers +17+17 and +15+15 points of cue-detection recall at equal precision
https://arxiv.org/html/2508.19205v1
1 Introduction
While recent advancements in Text-to-Speech (TTS) synthesis have achieved remarkable success in generating high-fidelity, natural-sounding speech for single speakers in short utterances WCW+ (23); SJT+ (23); ACC+24a ; LVS+ (23); CNM+ (24); DWC+24a ; JCC+ (25); YZC+ (25), a significant frontier remains
https://arxiv.org/html/2608.03446v1
cz
Abstract
Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the given language are more aligned to English within the model
https://arxiv.org/html/2210.00131v4
where X𝑋Xitalic_X & Y𝑌Yitalic_Y are the dataset’s text-based features & labels, B𝐵Bitalic_B & G𝐺Gitalic_G are common causes of X𝑋Xitalic_X & Y𝑌Yitalic_Y: one a shortcut and one intended, and W𝑊Witalic_W & S𝑆Sitalic_S are not causes of Y𝑌Yitalic_Y, but included due to their
https://arxiv.org/html/2608.02612v1
2 Behavioral Analysis of LLMs as Meta-Solvers
Sensitivity to Problem Domains and Difficulties
https://arxiv.org/html/2407.02518v2
Pretrained on a massive amount of text data on very deep Transformer-based architectures, these models have shown impressive performance in many natural language tasks
https://arxiv.org/html/2607.22511v2
2 A tour of the clusters
Graphs and structural models
https://arxiv.org/html/2407.03956v3
edu
Abstract
Prior research has enhanced the ability of Large Language Models (LLMs) to solve logic puzzles using techniques such as chain-of-thought prompting or introducing a symbolic representation
https://arxiv.org/html/2608.00785v1
com
Abstract
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs