Search: “pretraining”

Efficient Test-Time Adaptation For Robot Policies

arXiv:2610.20318v1 Announce Type: cross Abstract: Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload

local-aiarxiv-cs-lg

Agents

FACT: A Multimodal Force-Action-Track Foundation Model for Human-to-Robot Dexterity

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DGX agent

arXiv:2610.19155v1 Announce Type: new Abstract: Human demonstrations offer a scalable alternative to costly robot data collection for dexterous manipulation, but most human datasets lack force measure

agentsarxiv-cs-ro

15 Oct 2026

HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation

arXiv:2610.19654v1 Announce Type: new Abstract: Simulated students let researchers test tutoring systems and teaching strategies before real learners are available, but only if the simulation reproduc

researcharxiv-cs-cl

Historical-Feature Residual Adaptation for Sequential Screening

arXiv:2610.19315v1 Announce Type: new Abstract: Sequential screening policies must use acquired feedback effectively under a fixed experiment budget. We study acquisition-time residual adaptation: a s

How to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking

arXiv:2610.19185v1 Announce Type: new Abstract: Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them.

IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks

model-releasesarxiv-cs-ro

arXiv:2610.19481v1 Announce Type: new Abstract: Wrist-worn inertial measurement units (IMUs) provide a common sensing modality for tasks ranging from activity and gesture recognition to pose estimatio

Learning Across Temporal Graph Representation Silos

arXiv:2610.19576v1 Announce Type: new Abstract: Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains

Learning Graph Energies for Finite-Time Generation

arXiv:2610.19946v1 Announce Type: new Abstract: Energy-based models can represent distributions over discrete structures such as molecular graphs, but an energy that is accurate at equilibrium need no

Lotus: Learning to Understand Time-series by Learning to Forecast

arXiv:2610.18877v1 Announce Type: cross Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervisio

researcharxiv-cs-cl

Agents

Narrowing the Collaboration Gap, Probably

arXiv:2610.19215v1 Announce Type: new Abstract: Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents c

agentsarxiv-cs-lg

Neural POS Tagging for the Low-Resource Saraiki

arXiv:2610.20279v1 Announce Type: new Abstract: Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications.

PairIT: Autoregressive Transformers for Low-Data Molecule Optimization

arXiv:2610.19123v1 Announce Type: new Abstract: Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, p

local-aiarxiv-cs-lg

RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations

arXiv:2610.16443v1 Announce Type: new Abstract: Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control re

safetyarxiv-cs-ai

RINO: Renormalization Group Distillation with No Labels

arXiv:2610.15919v1 Announce Type: cross Abstract: Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfait

researcharxiv-cs-ai

SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise

model-releasesarxiv-cs-ai

arXiv:2610.17093v1 Announce Type: new Abstract: A model diff credits post-training with whatever separates a base checkpoint from one post-trained descendant, presuming a rerun would land close by. On

SnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation

arXiv:2604.05656v2 Announce Type: replace Abstract: Vision-language-action (VLA) models with flow-matching action heads, such as {pi}0 and {pi}0.5, have become a standard recipe for generalist robot m

local-aiarxiv-cs-cv

Tutorials

What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books

arXiv:2610.20104v1 Announce Type: new Abstract: Autoregressive transformers trained on limit order book (LOB) message streams are increasingly proposed as foundation models for market microstructure.

tutorialsarxiv-cs-lg

When Does Task-Aware Imputation Matter? An Analysis of Robustness Under Train-Test Mismatch

arXiv:2610.20342v1 Announce Type: new Abstract: Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing appro

Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification

arXiv:2610.15359v1 Announce Type: new Abstract: Deep classifiers deployed in safety-critical services like autonomous-driving perception remain vulnerable to small adversarial perturbations. Certified

CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance

arXiv:2610.14936v1 Announce Type: cross Abstract: Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their abili

ConicFlow: constrained Inference-time optimal control for flow matching models

arXiv:2610.14307v1 Announce Type: cross Abstract: We propose ConicFlow, which frames constrained sampling from a flow matching model at inference time as an optimal control problem that balances devia

local-aiarxiv-cs-cv

Agents

Context-aware Bayesian Fusion for Multimodal Object Detection

arXiv:2610.18741v1 Announce Type: new Abstract: Reliable detection of pedestrians and road users is essential for the safe operation of autonomous vehicles. However, this task becomes increasingly cha

agentsarxiv-cs-cv

Contextual Representation Flow Across Depth in State-Space and Transformer Architectures

arXiv:2510.06640v3 Announce Type: replace-cross Abstract: State Space Models (SSMs) have emerged as efficient, linear-time alternatives to Transformer-Based Models (TBMs) for long-context processing.

Foundation Models for Event Prediction Across Heterogeneous Event Logs

model-releasesarxiv-cs-lg

arXiv:2610.17503v1 Announce Type: new Abstract: Event logs are ubiquitous across real-world domains, including business processes, manufacturing workflows, healthcare, and many other operational syste

Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation

arXiv:2610.16992v1 Announce Type: new Abstract: Bayesian optimisation (BO) is a powerful framework for sample-efficient optimisation of expensive black-box functions, but its application to high-dimen

HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning

model-releasesarxiv-cs-lg

arXiv:2610.16589v1 Announce Type: new Abstract: Electroencephalography (EEG) representation learning is challenged by noisy signals, inter-subject variability, and heterogeneous electrode montages in

Jag: Direct Box Prediction for Efficient Visual Grounding

arXiv:2610.13239v2 Announce Type: replace Abstract: Visual grounding combines image--language understanding with geometric prediction, yet autoregressive multimodal models often express a box as a coo

local-aiarxiv-cs-cv

Novel View Synthesis using DDIM Inversion

arXiv:2508.10688v3 Announce Type: replace Abstract: Single-image novel view synthesis requires viewpoint-dependent structural changes while retaining source-scene information and inferring unobserved

researcharxiv-cs-cv

Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning

arXiv:2610.16427v1 Announce Type: new Abstract: Recursive reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and

RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories

arXiv:2610.18699v1 Announce Type: new Abstract: Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world mod

safetyarxiv-cs-ro

S^3-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors

arXiv:2610.18458v1 Announce Type: new Abstract: Modern image restoration methods increasingly rely on large-scale supervised training, yet clean targets are often prohibitively difficult to obtain in

researcharxiv-cs-cv

SPLICE: Sparse Physics Library Identification and Compositional Expansion

arXiv:2610.17029v1 Announce Type: new Abstract: Neural operator splitting composes a library of neural operators, one per physical mechanism, into zero-shot simulators of new equations. Existing appro

Steertuning Policies from a Few Human Videos

model-releasesarxiv-cs-ro

arXiv:2610.18711v1 Announce Type: new Abstract: Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies. In this work, we introduce St

TabGift: A Context-Efficient Foundation Model for Tabular Data Generation

arXiv:2610.16990v1 Announce Type: new Abstract: Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. Tabu

Applications

TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks

arXiv:2610.15699v1 Announce Type: new Abstract: Generative modeling for tabular data has advanced to the point where models can synthesize realistic records, recover missing values, and generate sampl

applicationsarxiv-cs-lg

VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

arXiv:2602.16681v2 Announce Type: replace Abstract: Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundat

safetyarxiv-cs-cv

What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies

arXiv:2610.15035v1 Announce Type: new Abstract: Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating misma

researcharxiv-cs-cv

Designing Protein Structures from Natural Language

arXiv:2610.13917v1 Announce Type: new Abstract: A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically

GIST: Generator-state-Informed Supervision for Tabular Foundation Models

arXiv:2610.12904v1 Announce Type: new Abstract: Tabular foundation models (TFMs) based on prior-data fitted networks typically use sampled hard query labels, leaving avoidable sampling variability in

How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference?

arXiv:2610.13764v1 Announce Type: cross Abstract: Prior-data fitted networks (PFNs) are trained to approximate the Bayesian posterior predictive distribution (PPD) of a synthetic prior, yet results ab

Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities

arXiv:2610.12610v1 Announce Type: cross Abstract: Ethnic and cultural identities rarely align with political borders, a tension made sharper by migration and long-standing cross-border ties. Communiti

Localized Generative Design of Ship Hulls with Diffusion Models and High-Fidelity Assessment

model-releasesarxiv-cs-ai

arXiv:2610.12870v1 Announce Type: cross Abstract: Practical ship design requires localized refinement and performance evaluation. This paper presents a reward-directed diffusion framework for localize

Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

model-releasesarxiv-cs-ai

arXiv:2610.12638v1 Announce Type: cross Abstract: Day-ahead electricity forecasts can help building operators schedule heating, ventilation, and other services around anticipated demand. Choosing a fo

Rethinking the Roles of Diffusion and Decoding: Predictive-State Flows for Parallel Language Generation

arXiv:2610.12898v1 Announce Type: new Abstract: In continuous diffusion language models (DLMs), latents generated by the diffusion prior are mapped almost deterministically to tokens, either by a deco

TabMF: Learning Dataset Representations with Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2610.13818v1 Announce Type: new Abstract: Representations of supervised datasets support higher-level machine-learning tasks such as dataset retrieval and distribution-shift monitoring. However,

Understanding Mamba-3 Through Oscillatory Dynamics

arXiv:2610.13341v1 Announce Type: new Abstract: Mamba-3 introduces input-dependent rotations into recurrent state updates, but their contribution beyond state retention remains unclear. We interpret t

Cross-Embodiment Robot Foundation World Models with Latent Actions

model-releasesarxiv-cs-ro

arXiv:2610.10846v1 Announce Type: new Abstract: The diversity of robot embodiments and action spaces makes it challenging to build robot world models that generalize across different embodiments. We i

9 Oct 2026

Hardware

Language Models as AI Research World Models