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The paper proposes QUAL, a label-free framework that aligns quantiles of time-series foundation models using the model pool as a probability reference, then applies Huber aggregation for robust zero-shot forecasting, reducing average member MASE by 7.4% and CRPS by 9.8% on GIFT-Eval.
CAMEL introduces a cross-architecture distillation framework with an asymmetric masking strategy that transfers knowledge from large retinal foundation models like RETFound to ultra-lightweight student models, preserving accuracy while drastically reducing computational cost for resource-limited clinical deployment.
The paper provides a formal mathematical framework defining 'any-task inference' (zero-shot capable) foundation models, decomposing them via a universal task reduction into encoder, model, and decoder components and stratifying error into design and realized-model terms, illustrated with five real-world examples.
A recent paper worth noting proposes a “unified training” algorithm that integrates pre-training, mid-training, and post-training into a single algorithmic workflow, simplifying infrastructure and providing a framework for better scientific research.
A CMU-led paper introduces 'unified training,' showing that pretraining, midtraining, SFT, and RL can be collapsed into a single likelihood-based algorithm where RL objectives are treated as maximum-likelihood fitting to model-generated targets, outperforming staged training in several accuracy-compute and continual-learning experiments.
BridgeRouter 将多桥跨模态迁移建模为实例级算法选择问题，通过增益感知的轻量路由器在冻结的 EEG 基础模型桥路径间进行推理时路由，在 FOG 和 ISRUC 数据集上显著优于单一最佳桥路径。
OpenEBFM introduces Energy-Based Foundation Models that separate generation into energy steering and token generation, showing better data, parameter, and compute scaling than matched GPT baselines at up to 1B parameters, with full code, checkpoints, and an EBFM chatbot released as open source.
A paper unifying foundation-model training stages (pretraining, midtraining, SFT, RL) under a single likelihood-based algorithm that blends static data and RL generations, improving accuracy-compute tradeoff and capability retention.
Albert Gu announces a new version of hierarchical networks (H-Nets) that claim 3-5x efficiency gains in scaling law experiments, matching an open-source model trained on 11T tokens with roughly 10x less data and compute, as a step toward end-to-end learning from raw bytes.
Rutgers researchers audit 41 model entries from 17 language model families using 24 pretraining indicators from the 2025 Foundation Model Transparency Index, finding that later releases do not consistently preserve earlier pretraining disclosures, with stable scores often masking offsetting gains and losses.
A new arXiv paper introduces Functional Rank Allocation (FRA), an unlabeled method for compressing large fleets of LoRA adapters via functional rank-distortion frontiers and fleet-level minimax allocation under a shared rank budget, improving worst-case and tail utility over uniform spectral compression across 78 adapters from three public fleets.
A controlled evaluation of five public EEG foundation models plus an in-house JEPA-style model, showing that random-initialisation, band-power baselines, and validation-selected probe layers substantially alter reported pretraining gains on sleep, emotion, and motor imagery benchmarks.
This paper benchmarks the rollout reliability of seven PDE foundation models across seven 2D time-dependent PDE datasets, showing that while fine-tuned foundation models often beat scratch-trained baselines at short horizons, full-horizon rollouts suffer substantial error amplification (median 2.04x), arguing for horizon-resolved reporting in future benchmarks.
A preregistered causal audit finds that covariate-aware time series foundation models (Chronos-2, TiRex-2, TabPFN-TS, TimesFM-2.5) forecast associations rather than intervention effects, with a derived confounding gap that can be reduced up to 83% via brief interventional post-training with LoRA adapters.
该论文提出了一个统一的“工作智能体”（work agents）框架，将基础模型与运行时框架结合以可靠执行复杂数字任务，引入持久性与开放性两个维度，并系统分析能力族、模型与框架优化及评估方法。
A technical report evaluating GPT-6-Astra across 12 embodied AI benchmarks finds it achieves unprecedented manipulation and navigation performance, yet reveals large gaps—costly demonstrations, physical constraints, and irreversible failures—preventing reliable real-world deployment.
本文评估了五个天气基础模型作为冻结编码器在增强岩石风化（ERW）碳去除量化中的迁移能力，其中基于 Aurora 1.5 构建的 TerraNova-ERW 在未见农田上表现最佳，支持少样本预测并可显著减少土壤采样需求。
Rachad Alao of Cohere speaks at PyTorchCon 2026 about how open science can enable transparent and secure AI foundation model development, advocating for open architectures, independent auditing, and tools like PyTorch, TorchTitan, and vLLM.
Aaron Levie argues that autonomous, domain-specific agents represent the emerging form factor for AI, driven by better tool/computer use, improving model capabilities, and mature agent harnesses and infrastructure — positioning them as a major leap beyond simple LLM wrappers for enterprise workflows.
论文提出 Mediator，一种离散、本体无关的 EHR 分词器，将任意临床概念映射到共享向量量化码本，使封闭词汇模型可迁移至 eICU，零样本任务 AUPRC 相对提升 29%。