8 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…
Learning the ARTS of Search for Automated Discovery
Gurusha Juneja, Arnav Kumar Jain, Deepak Nathani +2
Scientific discovery can be formulated as an iterative search process over the space of hypotheses and experiments. Contemporary methods navigate this space using heuristics such a…
Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants
Deepak Nathani, Cheng Zhang, Chang Huan +7
Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders th…
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…
WildSci: Advancing Scientific Reasoning from In-the-Wild Literature
Tengxiao Liu, Deepak Nathani, Zekun Li +2
Recent progress in large language model (LLM) reasoning has focused on domains like mathematics and coding, where abundant high-quality data and objective evaluation metrics are re…
Adversarial Training for Process Reward Models
Gurusha Juneja, Deepak Nathani, William Yang Wang
Process Reward Models (PRMs) enhance reasoning ability of LLMs by providing step-level supervision. However, their widespread adoption is limited due to expensive manual step-level…