7 papers
Fact-Augmented Lookahead Planning for LLM Agents
Samuel Holt, Max Ruiz Luyten, Thomas Pouplin +1
Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search…
GameTalk: Training LLMs for Strategic Conversation
Victor Conchello Vendrell, Max Ruiz Luyten, Mihaela van der Schaar
Strategic decision-making in multi-agent settings is a key challenge for large language models (LLMs), particularly when coordination and negotiation must unfold over extended conv…
The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving
Max Ruiz Luyten, Mihaela van der Schaar
State-of-the-art large language model (LLM) pipelines rely on bootstrapped reasoning loops: sampling diverse chains of thought and reinforcing the highest-scoring ones, mainly opti…
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar
Transformer-based large language models (LLMs) are constrained by the fixed context window of the underlying transformer architecture, hindering their ability to produce long and c…
G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon +1
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often st…
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten +1
The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on differen…