10 citations · 22 across the 12 of their papers we have counts for
7 papers · 1 filter
AIRA_2: Overcoming Bottlenecks in AI Research Agents
Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo +22
Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the ben…
COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game
Alkis Sygkounas, Rishi Hazra, Andreas Persson +2
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and gener…
Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition
Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires +1
Large Language Models (LLMs) have been touted as AI models possessing advanced reasoning abilities. In theory, autoregressive LLMs with Chain-of-Thought (CoT) can perform more seri…
Can Large Language Models Reason? A Characterization via 3-SAT
Rishi Hazra, Gabriele Venturato, Pedro Zuidberg Dos Martires +1
Large Language Models (LLMs) have been touted as AI models possessing advanced reasoning abilities. However, recent works have shown that LLMs often bypass true reasoning using sho…
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt
Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge". Yet, obtaining plans that are both feasible (grounded in affordanc…
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach
Rishi Hazra, Luc De Raedt
Despite numerous successes in Deep Reinforcement Learning (DRL), the learned policies are not interpretable. Moreover, since DRL does not exploit symbolic relational representation…