1 citations · 3 across the 16 of their papers we have counts for
13 papers · 1 filter
Belief Cascades Drive Persuasion in LLM Agent Networks
Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit +4
Multi-agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making agent-to-agent persuasion a basic but undermeasured…
Dont Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination
Prafulla Kumar Choubey, Kung-Hsiang Huang, Pranav Narayanan Venkit +5
Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a scalable Enterpri…
MMGR: Multi-Modal Generative Reasoning Benchmark and Evaluation
Zefan Cai, Haoyi Qiu, Tianyi Ma +13
Modern multimodal generative models can synthesize visually compelling images and videos, but it remains unclear whether this visual fluency reflects genuine reasoning: when prompt…
Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion
Haoyi Qiu, Yilun Zhou, Pranav Narayanan Venkit +4
As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2…
GUI-KV: Efficient GUI Agents via KV Cache with Spatio-Temporal Awareness
Kung-Hsiang Huang, Haoyi Qiu, Yutong Dai +2
Graphical user interface (GUI) agents built on vision-language models have emerged as a promising approach to automate human-computer workflows. However, they also face the ineffic…
DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou +3
Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing ci…
Benchmarking Deep Search over Heterogeneous Enterprise Data
Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath +3
We present a new benchmark for evaluating Deep Search--a realistic and complex form of retrieval-augmented generation (RAG) that requires source-aware, multi-hop reasoning over div…
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback
Thai Hoang, Kung-Hsiang Huang, Shirley Kokane +12
Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involv…
CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions
Kung-Hsiang Huang, Akshara Prabhakar, Onkar Thorat +6
While AI agents hold transformative potential in business, effective performance benchmarking is hindered by the scarcity of public, realistic business data on widely used platform…
Multimodal Cultural Safety: Evaluation Framework and Alignment Strategies
Haoyi Qiu, Kung-Hsiang Huang, Ruichen Zheng +2
Large vision-language models (LVLMs) are increasingly deployed in globally distributed applications, such as tourism assistants, yet their ability to produce culturally appropriate…
SafeWorld: Geo-Diverse Safety Alignment
Da Yin, Haoyi Qiu, Kung-Hsiang Huang +2
In the rapidly evolving field of Large Language Models (LLMs), ensuring safety is a crucial and widely discussed topic. However, existing works often overlook the geo-diversity of…
CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments
Kung-Hsiang Huang, Akshara Prabhakar, Sidharth Dhawan +6
Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data. Integrating AI agents into CRM…
Evaluating Cultural and Social Awareness of LLM Web Agents
Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal +4
As large language models (LLMs) expand into performing as agents for real-world applications beyond traditional NLP tasks, evaluating their robustness becomes increasingly importan…