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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...
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...
Lean Graph Search for Formal Theorem Proving
Formal theorem proving sits at the intersection of logic, mathematics, and machine learning. In systems like Lean 4, provers must construct machine-checkable proofs by drawing on large formalized libraries of mathematical results....
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
The Problem With Single-View Time-Series Language Models Time Series Language Models (TSLMs) promise to bring the reasoning power of large language models to temporal data. They can answer questions, forecast future behavior, and...
AI Cost Transparency Report Q4 2026: 10 providers, 25 criteria, open dataset
The AI Cost Transparency Report Q4 2026, frozen on 2026-10-13, maps what ten providers disclose about pricing against what 32 models actually cost across four declared workloads. The report scores each provider on 25 binary criter...
Dog-Breed Text-to-image Flow Matching from Pretraining to Fine-Tuning on 4 Consumer GPUs
We generally perceive that training text-to-image diffusion models requires heavy compute: it is true if we aim at a comprehensive frontier model. This understanding prevents people from training their own models when getting star...
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards RLVR relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported to approximately preserve progress per sample with o...
FrontierML: A challenging, end-to-end machine learning benchmark
Scaling capital access across agent runs FrontierML enters the landscape as a benchmark from Induction Labs that measures how effectively agents convert budget into model performance on end-to-end machine learning tasks. The proje...
AI Models Can Provably Hide Arbitrary Capabilities
Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilities can be elicited. Hickey and colleagues, together with Draguns and team, de...
Designing Protein Structures from Natural Language
A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically require a dedicated input representation for each property, limiting the desi...