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30 result(s) for “okapi bm25 site hotchips pp site github com site text of de site www content de site:arxiv.org” of 38 matched · 952 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
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/2510.03215v2
In T2T, the Coder’s ambiguous
text instruction fails to convey the structural semantics
of <p> as a paragraph separator, causing the Writer to misplace the
contenthttps://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
We present SeqLLM, a framework that injects behavioral-sequence modeling into a pretrained LLM while preserving its language ability, enabling joint use of content and behavioral sequences
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
2 Agent Runtime: How Execution of Actions Results in Observations
2
https://arxiv.org/list/cs.CL/new
Prior work enables one-way, scholar-oriented translation from Akkadian to English, but offers no path in the reverse direction: non-specialist users cannot compose new content in cuneiform, and therefore remain passive consumers of ancient culture rather than active participants
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
In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables
https://arxiv.org/html/2608.03486v1
3 Specification of the natural parameters
3 Identification via independent innovation analysis
3
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
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/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/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/new
We provide the core implementation of KGD at this https URL
https://arxiv.org/html/2608.02616v1
2 Related Work
De-identification and text anonymization
https://arxiv.org/html/2608.02609v1
Prior work enables one-way, scholar-oriented translation from Akkadian to English, but offers no path in the reverse direction: non-specialist users cannot compose new content in cuneiform, and therefore remain passive consumers of ancient culture rather than active participants
https://arxiv.org/html/2608.03372v1
The check compares the stored text against its source; the question is what the comparison is made of: a free, auditable word list, or an LLM call that must be trusted
https://arxiv.org/html/2508.19205v1
In our experiments, this highly efficient tokenizer maintains a speech-to-text token ratio of approximately 2:1, meaning two speech tokens are roughly equivalent to one BPE SHB (15) text token
https://arxiv.org/html/2608.03446v1
Predicting Multilingual Classification and Translation Performance of LLMs with Cross-Lingual Alignment – Is English Enough
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
For instance, in a study of Github’s Copilot, Pearce et al
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/2607.26245v1
The attempt did not produce a tradable edge: out-of-sample, a walk-forward logistic model over 43 microstructure features does not beat, and slightly underperforms, the probability already implied by Polymarket’s own order book, and simulated trading nets −0