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Solving Zebra Puzzles Using Constraint-Guided Multi-Agent Systems

https://arxiv.org/html/2407.03956v3
edu Abstract Prior research has enhanced the ability of Large Language Models (LLMs) to solve logic puzzles using techniques such as chain-of-thought prompting or introducing a symbolic representation

1 Introduction

https://arxiv.org/html/2608.03486v1
3 Specification of the natural parameters 3 Identification via independent innovation analysis 3

Report GitHub Issue

https://arxiv.org/html/2607.26245v1
The attempt did not produce a tradable edge: out-of-sample, a walk-forward logistic model over 43 microstructure features does not beat, and slightly underperforms, the probability already implied by Polymarket’s own order book, and simulated trading nets −0

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

Report GitHub Issue

https://arxiv.org/html/2608.03077v1
Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism {yeyongshi,biaofu}@stu

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

1 Introduction

https://arxiv.org/html/2506.01844v1
Effect of pretraining and multitask learning