2 citations · 4 across the 11 of their papers we have counts for
11 papers
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…
Beyond Static Summarization: Proactive Memory Extraction for LLM Agents
Chengyuan Yang, Zequn Sun, Wei Wei +1
Memory management is vital for LLM agents in long-term and personalized interactions. Most previous work studies how to retrieve and use memory, but pays less attention to how memo…
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…
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…
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…
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…
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.…
ArcNeural: A Multi-Modal Database for the Gen-AI Era
Wu Min, Qiao Yuncong, Yu Tan +1
ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as gr…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution
Jiahui Li, Lin Li, Tai-wei Chang +4
Reinforcement learning from human feedback (RLHF) offers a promising approach to aligning large language models (LLMs) with human preferences. Typically, a reward model is trained…
Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View
Xuan Liu, Jie Zhang, Haoyang Shang +3
Large language models (LLMs) have been shown to face hallucination issues due to the data they trained on often containing human bias; whether this is reflected in the decision-mak…