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20242026
most citedSpatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts

1 citations · 2 across the 6 of their papers we have counts for

collaborators

9 papers

cs.AI20261 cited

Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts

Riyang Bao, Cheng Yang, Dazhou Yu +3

Geospatial reasoning is essential for real-world applications such as urban analytics, transportation planning, and disaster response. However, existing LLM-based agents often fail…

cs.CL2025

Heaven-Sent or Hell-Bent? Benchmarking the Intelligence and Defectiveness of LLM Hallucinations

Chengxu Yang, Jingling Yuan, Siqi Cai +2

Hallucinations in large language models (LLMs) are commonly regarded as errors to be minimized. However, recent perspectives suggest that some hallucinations may encode creative or…

physics.data-an2025

Automating High Energy Physics Data Analysis with LLM-Powered Agents

Eli Gendreau-Distler, Joshua Ho, Dongwon Kim +3

We present a proof-of-principle study demonstrating the use of large language model (LLM) agents to automate a representative high energy physics (HEP) analysis. Using the Higgs bo…

cs.IR20251 cited

Better with Less: Small Proprietary Models Surpass Large Language Models in Financial Transaction Understanding

Wanying Ding, Savinay Narendra, Xiran Shi +4

Analyzing financial transactions is crucial for ensuring regulatory compliance, detecting fraud, and supporting decisions. The complexity of financial transaction data necessitates…

cs.CL2025

INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance

Shisong Chen, Qian Zhu, Wenyan Yang +15

Insurance, as a critical component of the global financial system, demands high standards of accuracy and reliability in AI applications. While existing benchmarks evaluate AI capa…

cs.AI2025

Large model retrieval enhancement framework for construction site risk identification

Jiawei Li, Chengye Yang, Yaochen Zhang +3

This study addresses construction site hazard identification by proposing a retrieval-augmented framework that enhances large language models (LLMs) without requiring fine-tuning.…