Search: “models”

The Persistence of Cultural Memory: Investigating Multimodal Iconicity in Diffusion Models

arXiv:2511.11435v3 Announce Type: replace Abstract: The ambiguity between generalization and memorization in TTI diffusion models becomes pronounced when prompts invoke culturally shared visual refere

researcharxiv-cs-cv

10 Apr 2026

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Research

Causal Analysis of Robot Foundation Models Enables Efficiency Gains

DGX agent

arXiv:2610.20750v1 Announce Type: new Abstract: Vision-language-action (VLA) models are a promising approach to general purpose robot manipulation. While the internal computation of vision-language mo

researcharxiv-cs-ro

16 Oct 2026

Designing Transformers for Continuous Diffusion Language Models

arXiv:2610.22330v1 Announce Type: new Abstract: Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use

researcharxiv-cs-ai

Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models

arXiv:2311.01994v2 Announce Type: replace-cross Abstract: Building explainable machine learning models is crucial for human users to be able to interpret the proposed statistical relationship obtained

Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models

arXiv:2610.20763v1 Announce Type: cross Abstract: Full-duplex spoken language models decide at every frame whether to take the floor, defining a conversational personality -- yet each set of weights e

researcharxiv-cs-ai

Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts

arXiv:2605.06476v2 Announce Type: replace Abstract: In this work, we conduct an analysis to examine the consistency of Large Language Models (LLMs) with respect to their own generated responses in an

VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents

arXiv:2606.19729v3 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots. The Partially Observable Markov Decision Process (POMDP) provides

AM-Bench: Understanding Agents under Task Underspecification across Models and Harnesses

arXiv:2610.18090v1 Announce Type: new Abstract: Ambiguous or incomplete task specifications complicate both execution and clarification for language-model agents. Yet agents' responses to these inform

Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models

arXiv:2610.19058v1 Announce Type: cross Abstract: Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them. We exploit this

Correction Training for Diffusion Models

arXiv:2610.14136v2 Announce Type: new Abstract: Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noi

safetyarxiv-cs-ai

KiToke: Distribution-Aware Token Compression for Video Large Language Models

arXiv:2604.03414v2 Announce Type: replace Abstract: Video Large Language Models (Video LLMs) achieve strong performance on video understanding tasks but suffer from high inference costs due to the lar

Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models

arXiv:2610.20479v1 Announce Type: new Abstract: Bias audits of multilingual language models usually compare a model's average stereotype score in English with its average in another language and read

safetyarxiv-cs-cl

Mixed Poisson diffusion models and application to posterior sampling

arXiv:2610.20218v1 Announce Type: cross Abstract: Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely

researcharxiv-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

RL Post-Training Diffusion Language Models Along Their Own Generation Paths

arXiv:2610.17266v1 Announce Type: new Abstract: Diffusion language models are a promising and more efficient alternative to autoregressive models. However, most RL post-training techniques are not eas

safetyarxiv-cs-ai

UDHRBench: Benchmarking Human Rights in Large Language Models

arXiv:2610.15179v1 Announce Type: cross Abstract: Large language models are increasingly deployed in contexts requiring adherence to ethical and legal norms, yet their alignment with internationally r

Hardware

Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming

arXiv:2610.19121v1 Announce Type: cross Abstract: Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reaso

hardwarearxiv-cs-cl

Answer It or Not? Towards Prompt-Adapted Adversarial Images on Vision Language Models

arXiv:2610.16622v1 Announce Type: new Abstract: With the advancement in language modeling, research on adversarial images has recently shifted from image classifiers to vision-language models (VLMs).

researcharxiv-cs-cv

Distill3R: A Pipeline for Democratizing 3D Foundation Models on Commodity Hardware

arXiv:2602.00865v2 Announce Type: replace Abstract: While multi-view 3D reconstruction has shifted toward large-scale foundation models capable of inferring globally consistent geometry, their relianc

Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages

arXiv:2610.14112v1 Announce Type: cross Abstract: Calibrated typed-decision ('System One') models return a label and a probability in a single forward pass at a small fraction of the cost of large lan

GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models

arXiv:2610.15856v1 Announce Type: new Abstract: DiffusionNFT (NFT) fine-tunes diffusion models toward task-specific rewards, but optimizing these rewards does not guarantee fidelity to the full prompt

safetyarxiv-cs-lg

Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models

arXiv:2610.15781v1 Announce Type: new Abstract: Training-free pruning removes weights from pretrained large language models to reduce their deployment cost. However, existing approaches typically cons

Tutorials

In-Context Learning Can Help Vision-Language Models Overcome Training Priors

arXiv:2610.12942v2 Announce Type: replace Abstract: Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence

tutorialsarxiv-cs-cv

JustOPD: On-Policy Distillation from Autoregressive to Diffusion Language Models

arXiv:2610.17397v1 Announce Type: new Abstract: On-policy distillation (OPD) is a post-training approach for large language models (LLMs) that enables a student model to learn a teacher model's distri

safetyarxiv-cs-lg

Language2Drive: Do Vision-Language-Action Models Really Follow the Instructions in Autonomous Driving?

arXiv:2610.16634v1 Announce Type: cross Abstract: Recent Vision-Language-Action (VLA) models increasingly use natural-language instructions to condition autonomous driving behavior. Yet existing close

MARS: Systematic Multi-Level Acceleration for World Action Models

arXiv:2610.17141v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising alternative for embodied intelligence by jointly predicting future world states and robot actions

safetyarxiv-cs-cv

Recurrent In-Context Learning for Tabular Foundation Models

arXiv:2610.14998v1 Announce Type: new Abstract: Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the numb

SP^2ec: Adaptive Self-Speculative Decoding for Vision-Language Models

arXiv:2610.18735v1 Announce Type: new Abstract: Speculative Decoding (SD) has demonstrated its efficacy in accelerating large language model inference, but for Vision-Language Models (VLMs), the diver

Agents

Think Before You Speak: Enabling Training-Free Latent Communication Between Language Models

arXiv:2610.16165v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning

agentsarxiv-cs-lg

An Input-Dependent Readout Phase for Resonant Sequence Models

arXiv:2610.12496v1 Announce Type: cross Abstract: Sequence models with a fixed-size state avoid the quadratic cost of attention, but face a design choice. An input-dependent state transition gives con

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

researcharxiv-cs-lg

OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models

arXiv:2610.12914v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong promise in robotic manipulation, yet their post-training still predominantly relies on trajectory-

On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models

arXiv:2610.13676v1 Announce Type: new Abstract: Time series foundation models (TSFMs) increasingly offer covariate support, with calendar information being the most common type of categorical covariat

researcharxiv-cs-lg

People Making Conversation: Evaluating How Naturally Speech Models Converse

arXiv:2610.12802v1 Announce Type: new Abstract: A conversational speech model has to decide, at every pause, whether to speak. Existing evaluations test boundary detection, policy adherence, or aggreg

RSI-Anything: Unlocking Recursive Self-Improvement with World Models

arXiv:2610.13912v1 Announce Type: new Abstract: Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model

safetyarxiv-cs-cl

Transformer-based In-Context Score Operator Learning for Diffusion Models

arXiv:2610.13690v1 Announce Type: new Abstract: Diffusion models typically learn the generative dynamics of a single target distribution or a family of distributions indexed by conditioning variables

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

Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models

arXiv:2610.10859v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to

safetyarxiv-cs-cv

Recurrent Self-Improvement: Dynamic Cross-Loop On-Policy Distillation for Looped Language Models

arXiv:2610.10623v1 Announce Type: cross Abstract: Looped Language Models (LoopLMs) offer a parameter efficient approach to scaling reasoning by reusing shared parameters across recurrent computation s

Safe Actions Alone Do Not Ensure Safe Agents: Identifying Unfulfilled Obligations with Guard Models

arXiv:2610.11773v1 Announce Type: new Abstract: Guard models are increasingly used to safeguard LLM-based agents, primarily by identifying actions that agents are forbidden to perform. However, identi

Applications

Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations

arXiv:2511.04000v2 Announce Type: replace-cross Abstract: Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an effici

applicationsarxiv-cs-ai

TypedBench: A Benchmark for Calibration, Framing Sensitivity, and Cost in System One Decision Models

arXiv:2610.11392v1 Announce Type: new Abstract: System One models output calibrated probabilities over typed answers such as categorical choices, ordinal levels, or binary outcomes, via a non-generati

Local Ai

Anaximander: Interactively Running Geospatial Deep Learning Models on Any Compute Backend

arXiv:2610.09085v1 Announce Type: new Abstract: Applying deep learning models to satellite imagery from within geographic information systems (GIS) remains high-friction for remote sensing practitione

local-aiarxiv-cs-cv

8 Oct 2026

Tutorials

Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position

arXiv:2610.10114v1 Announce Type: new Abstract: The architectural design of Large Language Models (LLMs) is shifting from traditional full-attention-only models to hybrid models, which combine differe

tutorialsarxiv-cs-cl

8 Oct 2026

Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models

arXiv:2610.10405v1 Announce Type: cross Abstract: Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misb

researcharxiv-cs-cl

8 Oct 2026

A Validated Dataset and Benchmark for Coherent Multi-Diagram SysML Models

arXiv:2610.07356v1 Announce Type: cross Abstract: Systems engineers use several diagrams to describe the structure and behavior of systems. Engineers create these diagrams together to make sure that t

7 Oct 2026

Are Language Models Script-Aware?

arXiv:2610.08037v1 Announce Type: new Abstract: Language models frequently generate outputs in unintended languages or scripts, a phenomenon known as off-target generation. While existing research has

researcharxiv-cs-cl

7 Oct 2026

Beyond Training from Scratch: Foundation Models for Data-Efficient and Generalizable Cardiac MRI Reconstruction

arXiv:2610.08109v1 Announce Type: new Abstract: Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while preser