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30 result(s) for “okapi bm25 site hotchips pp site hotchips pp site www is gov site plan standard ed site:arxiv.org” of 153 matched · 456 ms

Scope: Nishi’s own index (our crawl + Common Crawl) — not the whole live web. Some sites block all automated crawlers (anti-bot walls), so their pages may be absent here even though we never filter lawful content.

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

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
2 pp, exceeding MemSearch reader scaling (+10

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
This version is a work in progress; please feel free to contact me if you find a mistake, think I missed an important contribution, or just want to send a comment

Rainfall is rough

https://arxiv.org/html/2607.27099v1
4 Rainfall is rough at large time scales 1

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
, the meaning of a customized field) originates from users' domain knowledge and is rarely documented publicly

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
Using mortgage data with continuous and discrete features, we find that rejected female applicants require greater effort even when standard predictive-parity criteria are satisfied

1. Introduction

https://arxiv.org/html/2608.02909v1
This means that naively using these generated variables in a regression can generate endogeneity bias due to measurement error, as is well known from standard textbooks (e

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (ED)

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
Abstract Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs

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https://arxiv.org/html/2608.03545v1
While effective, the reward signal assigned from majority voting is highly sensitive to consensus strength, defined as the frequency of the most common answer within a rollout group

Internalizing Academic Writing Workflows for Introduction Generation via

https://arxiv.org/html/2608.03138v1
In academic paper writing, the introduction is the section where a study first presents its motivation, novelty and contribution to readers

Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
Furthermore, when there is no fear misspecification, the optimal decisions reduce to the standard Wald (1950) formulation of minimax decisions under ambiguity alone — the formulation underlying most of frequentist analysis

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https://arxiv.org/pdf/2608.03157
ÄÌ4ºû4àC0ÎøMÈ8$SÙH‹‘Rp¬Æk4>¢eñ8ŠQ�{Ñ›Pp/ªIŠö1˜œñšMõŠc2ââ…^ø¼Q#ïT#ÑáNrFRÂK“àclƒ ©‘w'�0I»ƒŸ}¢”ˆŸJüœãþUÞÁñ™h+`d@«WÙul“`¯r0=ÍÑhJl“ŒfÇ6'è‘ÏÅhÞ©&8bAŸƒ|¢ˆ šk³JˆžwÔ„ÔîØF�¹À´ÿù’LÅ·J6Q‰¶�‘†,'ø²¯ÎÄDÓUÁ {P½‰™_GObiw‚I®µ‰& zàk‚ýÑ�¾¥HÂF)Òª0^SÑogRÁ€¨“�ƒœ7™C¦¸ÈÁÑ6¿Ø8â¯W— lÀŸ2N *cåOÕ䂨8S

Testing the effects of an unobservable factor: Do marriage prospects

https://arxiv.org/html/2410.19947v2
In the National Longitudinal Survey of Youth (97) dataset (NLSY97), it is evident that college graduates with different majors have considerable differences in marital status during their 30s

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

https://arxiv.org/html/2608.01085v1
Figure 1: A backdoor in one agent is activated by a local input trigger ([TRIGGER] denotes a predefined word or phrase)

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https://arxiv.org/pdf/2608.02817
�R¼7c…‰å®RpJ –} Ž“�À\Êv>©åp—®fv)»çZ+ÈŠÐPP%:3ÎvÉåÈ÷ⱂT3ÞÙÌ™ŸIêj¸S× œL*û‹Wœ­oÔÌ5#ñF5šÏ½Ö `ظ,àÏ”ß5SY V 3™T4V²q ^6¶dµè�·q^«E�ЭZ6#Ç;mþxå_

Asking Questions the Right Way: A Multi-Agent Conversational System for

https://arxiv.org/html/2608.01366v2
The system’s primary contribution is architectural; to probe whether it behaves as intended, and to validate a measurement protocol for future work, we conducted an exploratory within-subjects study (N=4N=4) across four domain-diverse complex tasks, combining 32-channel EEG, NASA-TLX workload assessment, and

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction

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https://arxiv.org/pdf/2205.01882v5
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https://arxiv.org/pdf/2607.26188v1
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https://arxiv.org/pdf/2006.01128
9¶ûi‘R„%¦ûáPÙní'#ÎJ6µ«f ¾ýlÎ’çˆK¤;eD�›f¥�è® ‰ÅvûªLs8LwˆBÓ“†¹è«�¥ÆÔorn©Ê°EY Â×èÍåLšßÉ~VÖ@aÖµ2k) QÛš#†G½ð#~g0Óê Ÿ‡y©Ÿ•V²x‹ X‘܈©û,œ~ö‡OŸ:u5¤ž/¢ÛÅÍB€HQü»K�Z€­ ö\5,c®¦þ˜UÇ,™�ÎŒ+JÕûýÅÜýÀNm9Oº

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https://arxiv.org/src/2211.10435
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2608.01934
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https://arxiv.org/pdf/2102.13654
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/src/2006.03271
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https://arxiv.org/pdf/2608.03118
ZØz|ÐPG«-Tû*¾öÄö1ÇÆP¡8YîvkÚ©-'NÄŸéá`ÀPMÝŽ$&‡] L—'|*¡ûÍ�¥p7k7m¢E™7·€nÙîrøy09U=–PP#ü'#Þ„v2Zú$íþt;Ʀël�r5÷ð‡ð”

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
This project was begun on July 20, 2026; the present version is dated August 4, 2026

‹4›Õ^Üùh?Gðsz:¦¿ðSý;:Ãóðød|2�‡Ç£OŽ†Ã“ã³O¬£�ÇRù“%©[Ö'±

https://arxiv.org/src/2006.00508
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