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Predicting Multilingual Classification and Translation Performance of

https://arxiv.org/html/2608.03446v1

com/KathyHaem/cla-metrics 2 Background and Related Work Cross-lingual alignment

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

1 Introduction

https://arxiv.org/html/2511.06451v1

Problem statement and motivation. Thesis: risk-neutral pricing as a structured operator. From selective scan to a risk-neutral operator layer. Geometry in the loop: Q-Align and convex–monotone decoding. Spectral stability as a first-class

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

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

Report GitHub Issue

https://arxiv.org/html/2608.03372v1

org GitHub sourcecontextcandidatememoryalign claimscontent-word overlapseven checkspolarity ⋅\cdot condition ⋅\cdot temporalhedge ⋅\cdot attributionbrittle ⋅\cdot truncatedflagsevidence + citationverdict policyhard-codedpasspass_w_flagsrewriterejectuncheckablenever a passwitness (optional)one sentence, one

Very Large-Scale Multi-Agent Simulation in AgentScope

https://arxiv.org/html/2407.17789v2

com/modelscope/agentscope/tree/main/examples/paper_large_scale_simulation to inspire further research and development in large-scale multi-agent simulations

Report GitHub Issue

https://arxiv.org/html/2607.26245v1

× 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

Report GitHub Issue

https://arxiv.org/html/2608.00785v1

com Abstract Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs

AgentScope 1.0: A Developer-Centric Framework for Building Agentic

https://arxiv.org/html/2508.16279v1

Such advancements have laid a robust foundation for developing powerful LLM-based agent applications that can effectively interface with the world through a variety of tools, to perform diverse and complex tasks with increased autonomy and precision (Qin et al

1 Introduction

https://arxiv.org/html/2403.08299v1

The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot

Underspecification in Language Modeling Tasks: A Causality-Informed

https://arxiv.org/html/2210.00131v4

Code and open-source demos available at https://github

Report GitHub Issue

https://arxiv.org/html/2608.03077v1

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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

1 Introduction

https://arxiv.org/html/2402.03300v3

com/deepseek-ai/DeepSeek-Math Abstract Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature

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

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

Software Engineering

https://arxiv.org/list/cs.SE/new

Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4

Tools to Evaluate LLMs in Multi-turn Settings 1 Introduction 2 Related work Biases in LLMs outputs Transmission chains featuring artificial agents Iterated Learning Evaluation of LLMs in social contexts 3 Methods 3.1 Experimental