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20232026
most citedAdvances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

16 citations · 73 across the 18 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024

Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions

Jiarui Zhang, Ollie Liu, Tianyu Yu +2

Multimodal large language models (MLLMs) have made rapid progress in recent years, yet continue to struggle with low-level visual perception (LLVP) -- particularly the ability to a…

cs.AI2024★ 15 cited

Game-theoretic LLM: Agent Workflow for Negotiation Games

Wenyue Hua, Ollie Liu, Lingyao Li +9

This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several…

cs.CL2024

MatViX: Multimodal Information Extraction from Visually Rich Articles

Ghazal Khalighinejad, Sharon Scott, Ollie Liu +4

Multimodal information extraction (MIE) is crucial for scientific literature, where valuable data is often spread across text, figures, and tables. In materials science, extracting…

cs.AI2024★ 2 cited

IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations

Deqing Fu, Ruohao Guo, Ghazal Khalighinejad +5

Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs. But do their capabilities change depending on the…

cs.AI2024★ 2 cited

DeLLMa: Decision Making Under Uncertainty with Large Language Models

Ollie Liu, Deqing Fu, Dani Yogatama +1

The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face chall…