418 citations · 3.5k across the 221 of their papers we have counts for
12 papers · 1 filter
Telco-GAIA: Bilingual Benchmark for Agents in Telecom Domain
Dmitrii Khizbullin, Zaid Alyafeai, Abdelrahman Eldesokey +4
We introduce Telco-GAIA, a bilingual, multi-modal benchmark for evaluating tool-using agents on the data of a real-world telecommunications operator. Telco-GAIA comprises 100 human…
DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization
Tong Zhang, Motasem Alfarra, Carlos Hinojosa +2
As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, furthe…
SETA: Scaling Environments for Terminal Agents
Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru +19
Large language models (LLMs) are rapidly shifting toward agents that solve tasks through diverse interfaces, including web and graphical user interfaces (GUIs). Among these, the te…
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
Jhon Lopez, Carlos Hinojosa, Bernard Ghanem
Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim. However, ensuring spatial correctness and v…
FloorplanQA: A Benchmark for Spatial Reasoning in LLMs using Structured Representations
Fedor Rodionov, Abdelrahman Eldesokey, Michael Birsak +3
We introduce FloorplanQA, a diagnostic benchmark for evaluating spatial reasoning in large language models (LLMs). FloorplanQA is grounded in structured representations of indoor s…
AVA: Attentive VLM Agent for Mastering StarCraft II
Weiyu Ma, Yuqian Fu, Zecheng Zhang +2
We introduce AVACraft, a multimodal StarCraft II benchmark supporting both Multi-Agent Reinforcement Learning (MARL) and Vision-Language Model (VLM) paradigms. Unlike SMAC-family e…
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents
Tianqi Xu, Linyao Chen, Dai-Jie Wu +13
The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites…
Can Large Language Model Agents Simulate Human Trust Behavior?
Chengxing Xie, Canyu Chen, Feiran Jia +11
Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in social science and role-playing applications. However, one fundamental quest…
Mindstorms in Natural Language-Based Societies of Mind
Mingchen Zhuge, Haozhe Liu, Francesco Faccio +23
Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each o…
LLM as A Robotic Brain: Unifying Egocentric Memory and Control
Jinjie Mai, Jun Chen, Bing Li +3
Embodied AI focuses on the study and development of intelligent systems that possess a physical or virtual embodiment (i.e. robots) and are able to dynamically interact with their…
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani +2
The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conver…
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
Guocheng Qian, Xuanyang Zhang, Guohao Li +5
The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which i…