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30 result(s) for “okapi bm25 site hotchips pp site www is gov site www consumer fjc gov site:arxiv.org” of 39 matched · 688 ms
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Did you mean okapi bm25 site hotchips pp site www is gov site www consumer fc gov site:arxiv.org?
Freshness: no result on this page carries a fetch time yet (indexed before the fetch epoch was stored beside the doc)
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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends
https://arxiv.org/src/2303.12712
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https://arxiv.org/pdf/2303.12712v5
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https://arxiv.org/html/2501.13956v1
Consequently, additional context is required to provide out-of-domain (OOD) knowledge and reduce hallucinations
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/2607.28847v1
Consumer Financial Protection Bureau scrutinises algorithmic lending for disparate impact, whilst the UK Financial Conduct Authority has issued guidance on fair treatment in automated decisions (UK, 2021)
https://arxiv.org/html/2608.03099v1
2 Inspectable Language Is Not Necessarily Correct or Used
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https://arxiv.org/src/2006.03318
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https://arxiv.org/src/2005.14165
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https://arxiv.org/src/2006.01598
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https://arxiv.org/src/2006.02833
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https://arxiv.org/pdf/2608.02976
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https://arxiv.org/pdf/2608.03709
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https://arxiv.org/src/2006.03720
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https://arxiv.org/pdf/2006.02833
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https://arxiv.org/src/2006.01318
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https://arxiv.org/html/2608.03504v1
1 Introduction
Consider a consumer who is searching online to hire a car in a popular destination
https://arxiv.org/list/cs.CL/new
Many practically important problems are naturally treated as black-box optimization (BBO) problems, in which only objective values are observable, and the functional form is unavailable
https://arxiv.org/pdf/2006.00064
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https://arxiv.org/abs/2608.03883
arXiv is committed to these values and only works with partners that adhere to them
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/abs/2608.03860
arXiv is committed to these values and only works with partners that adhere to them
https://arxiv.org/list/cs.IR/new
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Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and how to transfer the learned knowledge while the pretrained model is continually refreshed
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/pdf/2608.02978
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https://arxiv.org/src/2006.00064
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https://arxiv.org/pdf/2607.28577v1
) /Title (Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets) /Trapped /False /arXivID (https://arxiv
https://arxiv.org/html/2608.02800v1
Under free entry, consumer welfare is pinned down by τ\tau alone, so self-preferencing has zero direct effect on consumers
https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly
https://arxiv.org/src/2006.00441
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