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https://arxiv.org/html/2607.22511v2
2 A tour of the clusters
Graphs and structural models
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
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
https://arxiv.org/html/2506.01844v1
The success of text-based models has thus been extended to other modalities, sparking interest towards multi-modal vision-language (VLMs) (Alayrac et al
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
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
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
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