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16 result(s) for “okapi bm25 site hotchips pp site does apache org site see caltech edu site:arxiv.org” · 386 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

Report GitHub Issue

https://arxiv.org/html/2608.02613v1
6 Ingest Overhead: When Does Structured Memory Pay Off

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

1. Introduction

https://arxiv.org/html/2608.02909v1
This approach is theoretically valid, easy to implement, and does not require new data

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

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

Artificial Intelligence

https://arxiv.org/list/cs.AI/new
Research artifact available under the Apache-2

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

Information Retrieval

https://arxiv.org/list/cs.IR/new
Skip to main content System maintenance August 4th and 5th Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Information Retrieval New submissions Cross-lists Replacements See recent

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

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

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

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

[2608.03860] SciRet: A Compute-Aware Empirical Study of Retrieval and

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

Information Retrieval

https://arxiv.org/list/cs.IR/recent
IR) Tue, 4 Aug 2026 (showing first 34 of 45 entries ) [17] arXiv:2608

1 Introduction

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

[astro-ph/0205387] Cosmological Parameters from Cosmic Background Imager

https://arxiv.org/abs/astro-ph/0205387
Sievers (Caltech) and 16 other authors View PDF HTML (experimental) Abstract: We report on the cosmological parameters derived from observations with the Cosmic Background Imager (CBI), covering 40 square degrees and the multipole range 300 < l < 3500

Astrophysics of Galaxies

https://arxiv.org/list/astro-ph.GA/recent
GA); General Relativity and Quantum Cosmology (gr-qc); High Energy Physics - Phenomenology (hep-ph); High Energy Physics - Theory (hep-th) [16] arXiv:2608

Distributed Instruments for Planetary Surface Science: Scientific

https://arxiv.org/html/2407.01757v1
Opportunities and Technology Feasibility 1 Introduction 1.1 What is a distributed instrument? 1.1.1 Distributed Instruments and Sensor Networks 1.1.2 Distributed Instruments and Swarms 1.2 Contribution and Organization 2 State of the Art