Search: “pretraining”

Safeguarding LLMs via Model-Agnostic Latent Safety Signals from Dark Knowledge

arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks incl

safetyarxiv-cs-ai

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Scale-Invariant Training for Time Series Foundation Models

DGX agent

arXiv:2610.07324v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This settin

model-releasesarxiv-cs-ai

7 Oct 2026

SimCortex v2: Joint Cortical Surface Reconstruction with Near-Zero Collisions and Self-Intersections

arXiv:2610.07378v1 Announce Type: new Abstract: Reconstructing cortical WM and pial surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based neuroanatomical analys

Spectra: Exact Component Transport for Test-Time Prior Adaptation in Simulation-Based Inference

arXiv:2610.08021v1 Announce Type: new Abstract: Simulation-based inference (SBI) has become a powerful approach to Bayesian inference in complex scientific models whose likelihoods are difficult or im

Stabilizing language models under continual learning via condition-anchored distillation

model-releasesarxiv-cs-cl

arXiv:2610.06940v1 Announce Type: new Abstract: Continual adaptation of language models can change their output distribution on prompts learned earlier, while retaining every old prompt-answer pair ma

TabiBERT: A Large-Scale ModernBERT Foundation Model and A Unified Benchmark for Turkish

model-releasesarxiv-cs-cl

arXiv:2512.23065v4 Announce Type: replace Abstract: The introduction of BERT established encoder-only transformer models as a foundational paradigm in natural language processing. Encoder-only models

Applications

TacZero: Training-Free Peg Insertion Using a General-Purpose Vision-Language Model with Tactile Feedback

arXiv:2610.07621v1 Announce Type: new Abstract: Robots that autonomously determine their actions from language instructions and sensory observations could perform new contact-rich manipulation tasks w

applicationsarxiv-cs-ro

The Standardization Trap: Certifying Joint Label Processing in Tabular Foundation Models

arXiv:2610.08314v1 Announce Type: new Abstract: Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each

researcharxiv-cs-ai

Tool-calling retrieval versus vector RAG for a small Greek--English knowledge base: accuracy and robustness to how users type Greek

model-releasesarxiv-cs-ai

arXiv:2610.08205v1 Announce Type: cross Abstract: Assistants grounded in a small, frequently edited knowledge base can retrieve through tool calls to a live data interface or through vector retrieval-

Towards One-for-All Foundation Model for Attributed Graph Clustering

arXiv:2610.07778v1 Announce Type: cross Abstract: Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes m

researcharxiv-cs-ai

Tracking Is Not Permanence: What Video World Models Keep of a Hidden Object

model-releasesarxiv-cs-cl

arXiv:2610.07355v1 Announce Type: cross Abstract: Video world models track objects they can see; we ask what they keep of objects they cannot. We hide an object from a frozen V-JEPA 2 predictor and co

VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two re

safetyarxiv-cs-lg

When Forgetting is not Catastrophic: On the Mechanics of Spurious Forgetting

arXiv:2610.08718v1 Announce Type: new Abstract: Knowledge that a language model appears to forget during finetuning often remains stored and can be recovered, a phenomenon called spurious forgetting.

researcharxiv-cs-cl

Which alloy composition,what process parameters? Inferring the recipe from optimized metallic microstructure and texture

arXiv:2610.08165v1 Announce Type: new Abstract: The mechanical properties of a metallic alloy are set by its microstructure and texture: the size and shape of its grains and the orientation of their c

researcharxiv-cs-ai

Whose Face Is It Anyway? A Multi-Model Audit of Facial Affect Recognition on Children, and Why the Gap Is the Head, Not the Features

arXiv:2610.08279v1 Announce Type: new Abstract: Facial affect models are trained almost entirely on adults, yet are increasingly applied to children in education, health, and developmental research. W

safetyarxiv-cs-cv

A Multimodal Sequence-to-Sequence Model for Cross-Subject Prediction of Brain Responses to Naturalistic Stimuli

arXiv:2507.18104v3 Announce Type: replace Abstract: Brain encoding models predict time-resolved neural activity from computational representations of ongoing experience, providing a principled framewo

A Unified Scaling Law for Time Series Foundation Models

arXiv:2610.05269v1 Announce Type: new Abstract: We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support foreca

Aligning the Query Space: Greedy Information Projection for Language Model Data Selection

arXiv:2603.13790v2 Announce Type: replace Abstract: Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundame

Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision

arXiv:2610.06180v1 Announce Type: new Abstract: Whether a time-series pattern is anomalous often depends on the operating regime of the monitored process. A missing event can signal a fault in one reg

Tutorials

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

tutorialsarxiv-cs-lg

BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series

arXiv:2610.05409v1 Announce Type: new Abstract: Action-conditioned time-series forecasting requires accounting for how future actions and exogenous forcings influence multiple targets through partiall

Bilinear Flow Policy: Distributional Extrapolation for Goal-Conditioned Visuomotor Imitation

arXiv:2610.05765v1 Announce Type: new Abstract: Goal-conditioned imitation learning (GCIL) with flow matching is a promising framework that can represent multimodal behaviors while adapting to diverse

safetyarxiv-cs-ro

CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research

arXiv:2610.05863v1 Announce Type: new Abstract: High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constraine

COMPASS: Comet Object Measurement Pipeline with Automated Selection and Scoring

arXiv:2610.04680v1 Announce Type: new Abstract: Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However

Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization

arXiv:2510.06842v2 Announce Type: replace Abstract: Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.

Dataset-Free Compliant Humanoid Loco-Manipulation with Dynamic Online Posture

arXiv:2610.05678v1 Announce Type: new Abstract: Most humanoid loco-manipulation controllers require human motion data to learn whole-body coordination and posture, leaving policies reliant on external

safetyarxiv-cs-ro

Local Ai

Discovered, Not Designed: Population Evolution for Collaborative and Compute-Intensive Model Discovery

arXiv:2610.05950v1 Announce Type: new Abstract: LLM-driven evolution enables iterative model development, but two practical goals remain underexplored: finding model designs that transfer across relat

local-aiarxiv-cs-lg

ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training

arXiv:2610.04140v1 Announce Type: new Abstract: Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part chan

safetyarxiv-cs-lg

FADE: Frame-Aware Diffusion-Transformer-based Multi-Concept Erasure for Video Unlearning

arXiv:2610.03980v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can reproduce copyrighted, violent, or explicit content, which motivates concept erasure: removing designated conce

Fitting Vision Adapters at Frontier Scales

arXiv:2610.05897v1 Announce Type: new Abstract: Training a small projector between a frozen vision encoder and language model is an established approach to multimodal learning. As the parameter count

ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies

arXiv:2610.04607v1 Announce Type: cross Abstract: Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. How

safetyarxiv-cs-cv

FreeSpeed: Training-Free Speed Control for Generative Robot Policies

arXiv:2610.05734v1 Announce Type: new Abstract: Online control of execution speed is essential for deploying robot policies in real-world scenarios, as robots may need to speed up under time constrain

safetyarxiv-cs-ro

From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval

arXiv:2610.05993v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task e

Hardware

Frozen Factor or Spectral Band? Disentangling Two Choices in Low-Rank LoRA

arXiv:2610.06621v1 Announce Type: new Abstract: Spectral variants of low-rank adaptation (LoRA) choose both a subspace and which factor to freeze. We separate these choices by freezing the input facto

hardwarearxiv-cs-lg

How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification

arXiv:2610.06034v1 Announce Type: cross Abstract: We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB)

Hardware

Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models

arXiv:2610.03817v1 Announce Type: cross Abstract: Purpose: To evaluate the performance-feasibility tradeoffs of foundation models (FMs) and task-specific convolutional neural networks (CNNs) trained f

hardwarearxiv-cs-cv

Improving Proactive AI Assistance with Hierarchical Procedural Understanding

arXiv:2610.06505v1 Announce Type: cross Abstract: Proactive AI assistants continuously observe a user's activity and decide whether to provide new guidance or remain silent. They should provide approp

KALEIDO: Input-Space Adaptation of a Vision Model for Time-Series Forecasting Through Gated Fold Geometries

arXiv:2610.04786v1 Announce Type: cross Abstract: Time-series foundation models buy zero-shot forecasting with large temporal corpora; a vision model needs none, since a natural image implicitly embed

Applications

Kepler4D: Controllable Future Video Generation via 4D Scene State Evolution

arXiv:2610.04152v1 Announce Type: new Abstract: Video world models aim to preserve scene structure and predict how dynamic objects evolve beyond visual observations. We present Kepler4D, a framework f

applicationsarxiv-cs-cv

Latent Flow Matching for Molecular Graph Generation

arXiv:2610.06468v1 Announce Type: new Abstract: Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become c

Learning Latent Protein Languages for Autoregressive Generation

arXiv:2610.03978v1 Announce Type: new Abstract: Autoregressive transformers remain comparatively weak for protein sequence and structure generation. We study the role of target representation: amino a

Tutorials

Learning Topological Representations of Protein Structure and Dynamics

arXiv:2606.14737v2 Announce Type: replace-cross Abstract: Modern protein representation models support tasks such as enzyme design and drug discovery, but their reliance on static data such as sequenc

tutorialsarxiv-cs-lg

Tutorials

Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models

arXiv:2610.06813v1 Announce Type: new Abstract: Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of s

tutorialsarxiv-cs-cv

LinearPFN: Amortized Variable Selection for Linear Models with Interactions

arXiv:2610.06580v1 Announce Type: new Abstract: Spike-and-slab regression is a standard Bayesian formulation of variable selection: it returns a posterior distribution over which candidate effects are

LoRA's Second Descent Extends Beyond Parameter Parity

arXiv:2610.04507v1 Announce Type: new Abstract: Double descent has sparked considerable interest, with recent work relating it to the data, the model and the learning configuration. Practical fine-tun

MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation

arXiv:2610.04036v1 Announce Type: new Abstract: Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic E

safetyarxiv-cs-cv

MAGIC: Topology-Aware Analytic Graph Few-Shot Class-Incremental Learning

arXiv:2610.04963v1 Announce Type: new Abstract: Graph few-shot class-incremental learning (GFSCIL) requires a model to continually recognize emerging classes from only a few labeled nodes while preser

Mind the Execution Gap: Action-Semantic Mismatch in World-Model Control

arXiv:2610.06582v1 Announce Type: cross Abstract: World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronousl