4 citations · 11 across the 9 of their papers we have counts for
9 papers
Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty
Antonis Antoniades, Deepak Nathani, Ritam Saha +6
Autonomous AI Research promises to accelerate the scientific progress of machine learning. To realise this goal, current Large Language Model (LLM)-based agents need to go beyond j…
Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
Zhaotian Weng, Antonis Antoniades, Deepak Nathani +3
Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reduc…
Self-Resource Allocation in Multi-Agent LLM Systems
Alfonso Amayuelas, Jingbo Yang, Saaket Agashe +4
With the development of LLMs as agents, there is a growing interest in connecting multiple agents into multi-agent systems to solve tasks concurrently, focusing on their role in ta…
SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints
Zekun Li, Shinda Huang, Jiangtian Wang +8
As language agents increasingly automate critical tasks, their ability to follow domain-specific standard operating procedures (SOPs), policies, and constraints when taking actions…
SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement
Antonis Antoniades, Albert Örwall, Kexun Zhang +3
Software engineers operating in complex and dynamic environments must continuously adapt to evolving requirements, learn iteratively from experience, and reconsider their approache…
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data
Xinyi Wang, Antonis Antoniades, Yanai Elazar +4
The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing v…
MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate
Alfonso Amayuelas, Xianjun Yang, Antonis Antoniades +3
Large Language Models (LLMs) have shown exceptional results on current benchmarks when working individually. The advancement in their capabilities, along with a reduction in parame…
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…
Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
Antonis Antoniades, Yiyi Yu, Joseph Canzano +2
State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained…