7 papers
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
STRIVE: Structured Spatiotemporal Exploration for Reinforcement Learning in Video Question Answering
Emad Bahrami, Olga Zatsarynna, Parth Pathak +3
We introduce STRIVE (SpatioTemporal Reinforcement with Importance-aware Variant Exploration), a structured reinforcement learning framework for video question answering. While grou…
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…
AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents
Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster +34
LLM agents hold significant promise for advancing scientific research. To accelerate this progress, we introduce AIRS-Bench (the AI Research Science Benchmark), a suite of 20 tasks…
What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
Alexis Audran-Reiss, Jordi Armengol-Estapé, Karen Hambardzumyan +17
AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is stil…
DocSLM: A Small Vision-Language Model for Long Multimodal Document Understanding
Tanveer Hannan, Dimitrios Mallios, Parth Pathak +5
Large Vision-Language Models (LVLMs) have demonstrated strong multimodal reasoning capabilities on long and complex documents. However, their high memory footprint makes them impra…