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30 result(s) for “okapi bm25 site hotchips pp site www uscourts gov site www uses gov site:arxiv.org” of 118 matched · 403 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
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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/html/2608.03048v1
PI-Mem gathers evidence from all chunks in parallel and uses it to update a shared memory over a bounded number of turns
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
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
https://arxiv.org/html/2407.16741v3
2 OpenHands Architecture
2.1 Agent Definition and Implementation
2.2 Agent Runtime: How Execution of Actions Results in Observations
2.3 Agent Skills: The Extensible Agent-Computer Interface
2.4 Agent Delegation: Cooperative Multi-agent
https://arxiv.org/list/cs.AI/new
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https://arxiv.org/html/2608.02668v1
Sphere pp-Angular Retraction Normalization
https://arxiv.org/html/2607.27188v1
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arXiv:2607
https://arxiv.org/html/2608.02909v1
For example, an LLM’s estimate of an article’s slant or the emotional content of text may only be a proxy for the true value, as it uses a limited sample and subject to idiosyncratic (e
https://arxiv.org/html/2607.28577v1
org perpetual non-exclusive license
arXiv:2607
https://arxiv.org/html/2608.00458v1
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arXiv:2608
https://arxiv.org/html/2607.28410v1
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arXiv:2607
https://arxiv.org/html/2608.02971v1
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https://arxiv.org/html/2608.02918v1
org perpetual non-exclusive license
arXiv:2608
https://arxiv.org/html/2407.10811v1
II RELATED WORK
II-A Traditional methods
II-B RL methods.
III PRELIMINARIES
IV METHOD
IV-A RL Setup
IV-B Network design
IV-C Training
IV-D Theoretical Analysis
V EXPERIMENT
V-A Towards Real-world Dataset and Simulation
V-B Benchmark
https://arxiv.org/html/2608.03063v1
8 pp in a production fraud detector serving billion-scale transaction traffic
https://arxiv.org/html/2608.00648v1
mdgam uses an extended graph attention mechanism to jointly update node and edge features, and employs multiple decoders to generate task-selection decisions and communication messages
https://arxiv.org/html/2608.03099v1
Taxonomy and Evidence Audit of Language Grounding in Embodied Agents
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https://arxiv.org/html/2608.03655v1
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https://arxiv.org/html/2609.03159v1
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https://arxiv.org/html/2407.00575v3
1 Introduction
1.1 Related Work
1.2 Outline and Notation
2 Problem Formulation
2.1 Applications
2.1.1 Quadratic Global Objective
2.1.2 Weighted Resource Allocation Games
3 Game Control Algorithm
3.1 Performance Guarantees
4 Convergence
https://arxiv.org/html/2608.01085v1
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https://arxiv.org/html/2607.28847v1
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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.
https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset
M Additional Empirical Application: Fishing-site Choice
M
https://arxiv.org/pdf/2402.03300
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https://arxiv.org/pdf/2005.14165
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https://arxiv.org/pdf/2608.02758
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https://arxiv.org/pdf/2407.02510
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https://arxiv.org/pdf/2608.03118
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