8 papers
How Post-Training Shapes Biological Reasoning Models
Lukas Fesser, Hanlin Zhang, Michelle M. Li +5
Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins. These models are bui…
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…
General Geospatial Inference with a Population Dynamics Foundation Model
Mohit Agarwal, Mimi Sun, Chaitanya Kamath +31
Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex rel…
Is it Bigger than a Breadbox: Efficient Cardinality Estimation for Real World Workloads
Zixuan Yi, Sami Abu-el-Haija, Yawen Wang +8
DB engines produce efficient query execution plans by relying on cost models. Practical implementations estimate cardinality of queries using heuristics, with magic numbers tuned t…
Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality
Filipe Miguel Gonçalves de Almeida, CJ Carey, Hendrik Fichtenberger +8
Learning and constructing large-scale graphs has attracted attention in recent decades, resulting in a rich literature that introduced various systems, tools, and algorithms. Grale…
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
Florian Grötschla, Luis Müller, Jan Tönshoff +2
Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also…