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ai Oct 19 MOSS: Multimodal Origami Swamp Robot System The task of developing robotic systems for environmental monitoring in wetlands and swamps requires operation across diverse terrestrial and aquatic conditions. Conventional fixed-geometry wheels cannot adapt their contact geometr... machine learning Oct 19 Steering beyond Language: Improving Multilingual Training-Free Text Embeddings via Paired-Residual Subspace Removal Deriving text embeddings directly from frozen large language models has emerged as a promising direction, yet existing methods focus almost exclusively on English, leaving multilingual settings largely unexplored. This work presen... ai Oct 19 Beyond SAEs: Consistent Concept Discovery with Sparse Coding Sparse autoencoders (SAEs) explain foundation model representations by decomposing activations into sparse combinations of learned dictionary atoms. However, the learned dictionaries can vary across training runs, limiting the rel... machine learning Oct 19 FuTransformer: FP8 prefill and INT4 decode for Llama-style inference on Hopper Sparse autoencoders (SAEs) explain foundation model representations by decomposing activations into sparse combinations of learned dictionary atoms. However, the learned dictionaries can vary across training runs, limiting the rel... ai Oct 19 DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery Sparse autoencoders (SAEs) explain foundation model representations by decomposing activations into sparse combinations of learned dictionary atoms. However, the learned dictionaries can vary across training runs, limiting the rel... machine learning Oct 16 SoftCache: Adaptive Step Size Caching for Diffusion Transformers Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Training-free caching accelerates them by reusing activations from recent evaluat... machine learning Oct 16 PathFM: Fast Generative Forecasting of Multivariate Trajectories Time series foundation models (TSFMs) are a hot topic for general-purpose forecasting, but most leading models predict marginal quantiles, leaving the dependence structure of future trajectories unspecified. This distinction matte... ai Oct 16 FDE-Gym: The Last Mile of AI Delivery Existing agent benchmarks typically evaluate agents on well-specified tasks defined by issue reports, specifications, or task tickets. Real-world customer delivery is different: forward-deployed engineers (FDEs) must clarify ambig... machine learning Oct 16 Rethinking Routing Exploration in Modern Mixture-of-Experts Mixture-of-experts (MoE) models have become a cornerstone approach for scaling neural networks beyond the limits of conventional dense architectures. By routing each input token to a subset of specialized computation paths, MoE sy... ai Oct 16 MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering The expansion of large language models beyond assistance roles into autonomous system development represents one of the most discussed shifts in contemporary artificial intelligence. As these models grow in capability, a natural p...