ProteinJEPA introduces a joint‑embedding predictive architecture that supplements masked language modeling (MLM) with a cosine loss to predict latent representations of a teacher model. On 19 protein tasks, MLM+JEPA outperforms compute‑matched and step‑matched MLM‑only training across 78 and 76 of 114 comparisons, achieving notable gains on structure‑ and homology‑sensitive tasks such as SCOPe‑40 retrieval and remote homology. Ablation studies show the cosine loss is superior to mean squared error and that latent prediction complements rather than replaces MLM.
arXiv:2606. 00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision.
arXiv:2606. 21876v2 Announce Type: replace-cross Abstract: The Categorical Jacobian of Zhang et al.
arXiv:2606. 06902v1 Announce Type: new Abstract: Targeted post-training aims to improve reasoning, math, and code without degrading strengths.
The paper introduces Murmur2Vec, a lightweight, alignment‑free embedding that uses k‑mer counts hashed with MurmurHash to create a compact representation for biological sequences. It provides a full theoretical analysis, including bias/variance formulas, a Johnson–Lindenstrauss‑style concentration bound, and an excess‑risk bound that clarifies the trade‑off between hash‑table size and classifier performance. Empirically, Murmur2Vec matches or surpasses a fine‑tuned 650M‑parameter ESM‑2 protein language model across several classification tasks, including SARS‑CoV‑2 spike lineage and HIV‑1 Env subtype identification.
NCP-ArchPreview is a latent‑space language model that extends standard next‑token prediction (NTP) with a Next Concept Prediction (NCP) objective, allowing the model to predict discrete concepts spanning multiple tokens. The architecture builds a product‑quantized concept vocabulary from hidden states, uses a dedicated Concept Module to forecast future concepts, and feeds these predictions back to guide token‑level generation, all trained jointly end‑to‑end. Trained on 5.73 T tokens with 8.9 B parameters, it achieves the final pretraining loss of OLMo‑3‑7B using only 51.3 % of the tokens, outperforms OLMo‑3‑7B on downstream tasks (including a 5.99‑point GSM8K gain), and demonstrates that the learned latent space enables lightweight domain adaptation and improved drafting performance.
Osprey is a target‑agnostic pre‑training method that bootstraps draft models for speculative decoding from existing small language models. By pruning to a shallow backbone, restoring language‑modeling capability with next‑token pretraining, and adapting via vocabulary alignment and distillation, Osprey reduces per‑target work to a lightweight adaptation step. Experiments show that a single Osprey backbone improves mean acceptance length by up to 22.7% and increases tokens per second by 17.5% across several large target models, especially on out‑of‑domain and multilingual data.
arXiv:2609.25541v1 Announce Type: new Abstract: Tabular foundation models learn to predict cell values in context, whereas world-model self-supervision asks for prediction in representation space (Le...
arXiv:2605. 10840v3 Announce Type: replace-cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories.
arXiv:2607. 18302v1 Announce Type: new Abstract: Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior.
Subspace-Decomposed JEPAs (SD-JEPA) split the latent space of Joint-Embedding Predictive Architectures into two orthogonal subspaces: a low-dimensional progression subspace trained with a cosine-margin triplet loss and a high-dimensional content subspace regularised by SIGReg. The authors prove that the anti-collapse forces act on disjoint coordinates, allowing additive composition rather than competition. SD-JEPA outperforms the LeWM baseline on most control benchmarks and the strongest non-LeWM JEPA baseline on Push‑T, with a subspace-ablation confirming the split as essential. The 1‑D angular progression coordinate serves as a scene-aware compass, advancing with task progress, regressing on backtracking, and relocalising under perturbations to separate surprise from meaning.
arXiv:2606. 16517v1 Announce Type: new Abstract: Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins.
arXiv:2606. 17579v1 Announce Type: cross Abstract: Adding LLM-generated node features to graph neural networks (GNNs) is widely reported to improve accuracy on standard benchmarks.