NishiSearch

Web This site Trusted

30 result(s) for “okapi bm25 site hotchips pp site www uscourts gov site whether gov site:arxiv.org” of 118 matched · 676 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: newest result fetched 24 d ago · median age of these results 24 d · 1 of 30 carry a fetch time

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
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

Computation and Language

https://arxiv.org/list/cs.CL/new
We evaluate five open-weight readers with Vanilla context, BM25-RAG, Oracle retrieval, Memobase, and MemSearch as memory backends

1 Introduction

https://arxiv.org/html/2501.13956v1
2 Knowledge Graph Construction 2.1 Episodes 2.2 Semantic Entities and Facts 2.2.1 Entities 2.2.2 Facts 2.2.3 Temporal Extraction and Edge Invalidation 2.3 Communities 3 Memory Retrieval 3.1 Search 3.2 Reranker 4 Experiments 4.1 Choice

Report GitHub Issue

https://arxiv.org/html/2608.03048v1
× Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
The implemented contract fixes evidence non-overwrite, branch isolation, grounded multi-subject timers, guarded state change, and declaration-ranked event ordering over time and space; an external runner still decides whether evidence becomes an authoritative move

Probing Character-level Transformers for the Spanish L-shaped Morphome

https://arxiv.org/html/2608.03452v1
To choose the right stem, one must therefore know two things: whether the verb belongs to the arbitrary L-shaped class, and which paradigm cell is being inflected

1. Introduction

https://arxiv.org/html/2608.02909v1
We illustrate the method by revisiting two applications: whether gendered speech affects legislative outcomes in the German Parliament, and whether political risk influences poverty alleviation programs in China

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
However, detection alone cannot establish whether the underlying model representations contribute functionally to behavior

Train Often, Deploy Selectively: Forward-Gated Model Replacement in

https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license arXiv:2607

Testing the effects of an unobservable factor: Do marriage prospects

https://arxiv.org/html/2410.19947v2
org perpetual non-exclusive license arXiv:2410

What Language Does and What the Evidence Supports: A Functional Role

https://arxiv.org/html/2608.03099v1
We therefore evaluate grounding claim by claim, asking whether the reported evidence supports the specific responsibility assigned to language

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
Examples include understanding whether introducing a minimum wage reduces or increases unemployment (Card and Krueger, 1994); understanding whether providing free health insurance improves access to health care services (Finkelstein et al

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
They can therefore overlook whether rejected applicants face unequal burdens in reaching future approval, a phenomenon we call masked inequality

Every Wrong Answer Counts: Option-Level Psychometrics for LLM Multiple

https://arxiv.org/html/2608.02966v1
-Choice Benchmarks Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML?

Can Large Language Models Execute Parent Orders?

https://arxiv.org/html/2607.28410v1
org perpetual non-exclusive license arXiv:2607

On the Diversity of Analogy Making in Large Language Models

https://arxiv.org/html/2608.03233v1
org perpetual non-exclusive license arXiv:2608

Report GitHub Issue

https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
org perpetual non-exclusive license arXiv:2608

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

https://arxiv.org/html/2608.01085v1
org perpetual non-exclusive license arXiv:2608

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

https://arxiv.org/html/2608.01366v2
org perpetual non-exclusive license arXiv:2608

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

https://arxiv.org/html/2412.12213v2
Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub arXiv is now an independent nonprofit! Learn more × Back to arXiv Why HTML? Report Issue Back to

Preying on Leveraged ETFsPreliminary and incomplete. This project was

https://arxiv.org/html/2608.03703v1
begun on July 20, 2026; the present version is dated August 4, 2026. Claude Opus 5 and Fable 5 provided research assistance throughout, spanning code development, data construction and empirical analysis, and the drafting of the manuscript.

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

%PDF-1.5 %� 152 0 obj > stream xÚmTËŽâ0¼ç+¼$æÀà$0Š� ‰Ã£­ö ‰a#A%

https://arxiv.org/pdf/2402.03300
áÀ߯«›ÀÌjDÕå²»«ífðãc;ZæÕÁŽÌ«Ÿ¶­®MfGÑÏ}í q•]/¶ìÞ­ÍmÞ¯¶o⣩²­íÄ0ZÇë²è^œx]fçkn{ÕÿE+{*ʧyÄpg�6;5’PìŠîìVž¤pH8$hù—mÚ¢*ß„z•R:")󨺠ÊÖß3‰qŸûX”ysO'Hî)-ò"ëî}³‹³‹ÍÛ[ÛÙ˺s �á3 4†{´¢p¿YôdšrýØëKæ‘+ˆ™ÇÞ a }ÀõàíÑ« W€‡Œ{ Fvm734…4˜‡¢´�A­«»èGÞÿc

Information Retrieval

https://arxiv.org/list/cs.IR/new
In this work, we study whether preserving this structure improves skill retrieval

Inverse Learning of Latent Risk-Neutral Densities from Irregular Option

https://arxiv.org/html/2607.27188v1
org perpetual non-exclusive license arXiv:2607

Report GitHub Issue

https://arxiv.org/html/2608.00458v1
org perpetual non-exclusive license arXiv:2608

Consensus Measures for Unstructured Biomedical Text Annotations

https://arxiv.org/html/2608.03529v1
The objective is not whether an automated system reproduces the exact words or codes selected by a human annotator, but whether both extractors recover the same underlying biomedical information

Mapping the City Through the Lens of Language Models

https://arxiv.org/html/2608.02971v1
A disjoint 512-city sample evaluates replication; direct ratings of complete 40-indicator profiles test whether marginal judgments cohere at the city level; and matched desirability judgments probe normative coupling

Report GitHub Issue

https://arxiv.org/html/2609.02852v1
Report GitHub Issue Description: arXiv is now an independent nonprofit