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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
1K text-only ego-observed tokens/agent/day)
https://arxiv.org/html/2608.03048v1
com/JetAstra/PI-Mem
Introduction
The ability to reason over long contexts is increasingly important for large language models (LLMs), supporting applications such as long-document question answering (Gemini Team 2025; Yang et al
https://arxiv.org/html/2510.03215v2
In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences
https://arxiv.org/html/2608.03011v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.03063v1
Either clue alone is weak, and a pipeline that inspects text and behavioral statistics separately misses the case; only reading the wording jointly with the behavior reveals the fraud
https://arxiv.org/list/cs.AI/new
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Artificial Intelligence
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https://arxiv.org/list/cs.CL/new
1K text-only ego-observed tokens/agent/day)
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/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/html/2608.03486v1
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https://arxiv.org/html/2501.13956v1
contain raw input data in the form of messages, text, or JSON
https://arxiv.org/html/2407.16741v3
com/All-Hands-AI/OpenHands
Slack http://bit
https://arxiv.org/html/2608.02668v1
Thus, a natural question arises: which retraction should a de
https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license
arXiv:2608
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/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
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Information Retrieval
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https://arxiv.org/html/2608.02616v1
2 Related Work
De-identification and text anonymization
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/2608.03372v1
org GitHub
sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one
https://arxiv.org/html/2608.03446v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2608.02609v1
Figure 1: TabletCraft pipeline: English text is translated to Akkadian, converted to cuneiform signs, and rendered as a clay tablet, enabling bidirectional cultural interaction
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
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.02518v2
Just like how human developers typically select and examine one small piece of code at a time, the critics use a code snippet together with a text query to call relevant tools like web search and code interpreters
https://arxiv.org/html/2506.01844v1
The success of text-based models has thus been extended to other modalities, sparking interest towards multi-modal vision-language (VLMs) (Alayrac et al
https://arxiv.org/html/2607.22511v2
The source code, formal library, and run records are available at https://github
https://arxiv.org/html/2407.03956v3
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