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1 Introduction

https://arxiv.org/html/2407.15077v2
2 Problem Definition 3 Method 3.1 Monotonic Improvement of B2MAPO scheme 3.2 B2MAPO Framework 3.2.1 Batch Sequence Generation Layer 3.2.2 Batch by Batch Policy Optimization Layer 4 DAG-based B2MAPO Algorithm 4.1 Hierarchical DAG

Mathematics

https://arxiv.org/list/math/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 Mathematics New submissions Cross-lists Replacements See recent articles Showing

[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
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 Authors and titles for recent submissions Wed, 5 Aug 2026

Computational Geometry

https://arxiv.org/list/cs.CG/new
PR) The Johnson--Lindenstrauss lemma asserts that every set of $n$ points in $d$-dimensional Euclidean space embeds into $O(\varepsilon^{-2}\log n)$-dimensional Euclidean space with distortion at most $1+\varepsilon$

Information Retrieval

https://arxiv.org/list/cs.IR/new
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Mathematical Finance Apr 2021

https://arxiv.org/list/q-fin.MF/2021-04
A lacuna in the proof of the old Lemma 3

Data Structures and Algorithms

https://arxiv.org/list/cs.DS/new
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Hardware Architecture

https://arxiv.org/list/cs.AR/new
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Systems and Control

https://arxiv.org/list/cs.SY/new
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Cryptography and Security

https://arxiv.org/list/cs.CR/new
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Learning Equilibrium with Estimated Payoffs in Population Games

https://arxiv.org/html/2407.06328v2
I Introduction II Preliminaries and Problem Description II-A Task Allocation Game Model II-B Learning Rule, Stochastic Alarm Clock, and Evolutionary Dynamics Model II-C Payoff Vector Estimation III Convergence Analysis and Revision Rate

Forthcoming in SIAM Journal on Financial Mathematics Decentralised

https://arxiv.org/html/2309.08431v3
Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision 1 Introduction 2 Constant function markets and concentrated liquidity 2.1 Constant function markets 2.2 Concentrated liquidity markets 3 The wealth of

Eliminating Majority Illusion is Easy

https://arxiv.org/html/2407.20187v1
1 Introduction 1.1 The Illusion Elimination Problem 1.2 Our Contributions. 1.3 Related Work. 2 Preliminaries 3 Majority Illusion Addition Elimination (MIAE) 3.1 A First Attempt 3.2 A Second Attempt 3.3 A TDI System 3.4 A Polynomial Time

Maximizing utility in multi-agent environments by anticipating the

https://arxiv.org/html/2407.04889v1
behavior of other learners 1 Introduction Setting. Zero-sum games. General-sum games. 1.1 Preliminaries 1.2 Algorithms for the learner 1.3 Our results 1.3.1 Zero sum games Continuous-time games. Discrete-time games. 1.3.2 A lower bound

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https://arxiv.org/pdf/1811.12628
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Stochastic Games with Minimally Bounded Action Costs

https://arxiv.org/html/2407.18010v2
1 Introduction 2 Formulation 3 Existence of a Value and Saddle Point Equilibrium 4 Learning in Unknown Environments 4.1 Convergence using Linear Function Approximators 5 Intervention Budget Constraints 6 Discussion on Subcases 7 Related

Homogeneous Distributed Observers for Quasilinear Systems

https://arxiv.org/html/2407.05763v1
1 Introduction 2 Preliminaries 2.1 Graph Theory 2.2 Generalized Homogeneity 3 Problem Statement 4 Basic Idea of the Observer Design 5 Homogeneous Observer Design 5.1 Globally Homogeneous Distributed Observer 5.2 Locally Homogeneous