Search: “models”

Diffusion Language Models Know the Answer Before Decoding

arXiv:2508.19982v5 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and fle

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Geometric Properties of the Voronoi Tessellation in Latent Semantic Manifolds of Large Language Models

DGX agent

arXiv:2604.06767v1 Announce Type: new Abstract: Language models operate on discrete tokens but compute in continuous vector spaces, inducing a Voronoi tessellation over the representation manifold. We

safetyarxiv-cs-lg

10 Apr 2026

Linear Representations of Hierarchical Concepts in Language Models

arXiv:2604.07886v1 Announce Type: new Abstract: We investigate how and to what extent hierarchical relations (e.g., Japan subset Eastern Asia subset Asia) are encoded in the internal representat

tutorialsarxiv-cs-cl

MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction

arXiv:2604.06390v1 Announce Type: cross Abstract: Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treat

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Privacy Attacks on Image AutoRegressive Models

arXiv:2502.02514v5 Announce Type: replace Abstract: Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion m

Rethinking Data Mixing from the Perspective of Large Language Models

arXiv:2604.07963v1 Announce Type: new Abstract: Data mixing strategy is essential for large language model (LLM) training. Empirical evidence shows that inappropriate strategies can significantly redu

AI Scientists for Building Virtual-Cell Models

arXiv:2508.02276v3 Announce Type: replace-cross Abstract: Autonomous agents, from virtual laboratories to biological data-analysis agents and co-scientists, have begun to turn scientific goals into hy

agentsarxiv-cs-ai

ARC-SORT: Adaptive, Robust, Compensated Multi-Object Tracking with Interacting Multiple Models

model-releasesarxiv-cs-cv

arXiv:2610.22306v1 Announce Type: new Abstract: Multi-object tracking in sports broadcasts is hard for two reasons: athletes move fast and change direction abruptly, and the camera pans and zooms at t

Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models

model-releasesarxiv-cs-cl

arXiv:2610.21383v1 Announce Type: new Abstract: Block Influence (BI) ranks Transformer blocks for depth pruning from their input-output cosine change, but the ranking depends on the calibration distri

Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2610.21537v1 Announce Type: new Abstract: Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity. In contrast, continual learning

FusionAudit: Pathway-Conditioned Robustness Auditing for Native Multimodal Models

arXiv:2610.21667v1 Announce Type: new Abstract: As vision-language models (VLMs) transition from frozen-encoder pipelines to native multimodal architectures, the visual channel increasingly serves as

Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond

arXiv:2610.21539v1 Announce Type: new Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following

agentsarxiv-cs-ai

Skill-Decoupled On-Policy Expert Distillation for Unified Vision-Language Models

arXiv:2610.22217v1 Announce Type: cross Abstract: Reinforcement learning (RL) can elicit specialized visual capabilities in vision-language models (VLMs), yet jointly optimizing heterogeneous objectiv

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Towards Unbiased On-Policy Distillation for Block Diffusion Language Models

arXiv:2610.05373v2 Announce Type: replace Abstract: On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffu

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Two-Stage Score Estimation for Group-Specific Diffusion Models

arXiv:2610.20979v1 Announce Type: cross Abstract: Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution wit

Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning

arXiv:2610.19802v1 Announce Type: new Abstract: Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional sco

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Can You Trust Your Preference Models? Stackelberg Games for Alignment

arXiv:2610.15008v1 Announce Type: new Abstract: Language models are frequently aligned to human preferences via imperfect evaluators whose errors can be exploited during optimization. We train policie

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Local Ai

Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models

arXiv:2610.18990v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has made diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful

local-aiarxiv-cs-lg

ForkGRPO: Efficient Exploration and Fine-Grained Credit Assignment for Diffusion Language Models

arXiv:2610.19408v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) have advanced rapidly, yet strengthening their reasoning through reinforcement learning (RL) remains an open cha

tutorialsarxiv-cs-cl

Applications

ObjDream: Guiding Vision-Language-Action Models with Object-Level Understanding and Future State Prediction

arXiv:2610.20223v1 Announce Type: new Abstract: Vision-language-action (VLA) models have emerged as a powerful paradigm for robotic manipulation, enabling robots to generate actions from visual observ

applicationsarxiv-cs-ro

One Block Ahead: Promoting Sampler State in Diffusion Language Models

arXiv:2610.19510v1 Announce Type: new Abstract: Block-diffusion language models denoise a whole block of token positions in parallel so each forward pass can emit many tokens. However, blocks are stil

Selective Reasoning for Typed Decision Models: When to Start, Read, and Stop

arXiv:2610.19644v1 Announce Type: new Abstract: Typed decision models need an interface that can use additional reasoning while retaining candidate outputs. We train a shared candidate-answer reader o

Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost

arXiv:2610.20035v1 Announce Type: cross Abstract: We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward. We derive it

Local Ai

When Does Depth Help? Understanding Layerwise Inference in Tabular Foundation Models

arXiv:2610.19814v1 Announce Type: new Abstract: Transformer-based tabular foundation models (TFMs) achieve state-of-the-art performance on small to medium-sized tabular prediction tasks while processi

local-aiarxiv-cs-lg

Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models

model-releasesarxiv-cs-lg

arXiv:2510.06439v2 Announce Type: replace Abstract: Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimizati

CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation

arXiv:2610.14720v1 Announce Type: new Abstract: World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision

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CineRecon: Benchmarking Frontier Models for Agentic Cinematic Reconstruction

model-releasesarxiv-cs-cv

arXiv:2610.16577v1 Announce Type: new Abstract: Reconstructing reference videos as editable dynamic 3D scenes is an important path toward controllable video generation for filmmaking: these scenes pro

Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data

arXiv:2610.14006v1 Announce Type: new Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretab

tutorialsarxiv-cs-lg

Local Ai

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

Detecting Chain-of-Thought Hijacking in Backdoored Multimodal Large Reasoning Models

arXiv:2610.16598v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning enables multimodal large reasoning models (MLRMs) to solve complex visual problems, but also exposes them to CoT hijack

Detecting Unreliable Classifications from Quantized Models

arXiv:2610.18575v1 Announce Type: new Abstract: Post-training INT8 quantization is typically validated by checking accuracy retention, which requires ground-truth labels often unavailable in low-resou

Applications

Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models

arXiv:2610.14138v1 Announce Type: cross Abstract: Retinal foundation models have demonstrated strong performance across a wide range of ophthalmic tasks, yet relatively little is known about how disea

applicationsarxiv-cs-cv

LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2610.18843v1 Announce Type: new Abstract: Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, ca

LLM2R: From Large Language Models to 3D Reassemblers

arXiv:2610.18604v1 Announce Type: new Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because

mathbf{S^2LM}: Towards Semantic Steganography via Large Language Models

model-releasesarxiv-cs-cv

arXiv:2511.05319v3 Announce Type: replace Abstract: Despite remarkable progress in steganography, embedding sentence-level information into cover media remains challenging. In this work, we present th

OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching

arXiv:2610.17688v1 Announce Type: new Abstract: Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous dri

agentsarxiv-cs-ro

Online learning of neural state-space models

arXiv:2607.17614v2 Announce Type: replace-cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) mod

Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification

model-releasesarxiv-cs-lg

arXiv:2610.14844v1 Announce Type: cross Abstract: We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enf

RM-WAM: Reliable Memory-Augmented World Action Models for Long-Horizon Robot Manipulation

arXiv:2610.15305v1 Announce Type: new Abstract: World Action Models (WAMs) improve robotic manipulation by jointly predicting future visual states and actions, yet most existing methods make decisions

tutorialsarxiv-cs-ro

Same Goal, Different Words: Spatial Instruction Sensitivity in World-Action Models

arXiv:2610.16242v1 Announce Type: new Abstract: World-action models should preserve the intended manipulation across equivalent descriptions of a spatial goal. We evaluate this consistency in 864 roll

researcharxiv-cs-ro

Self-improving data synthesis can scale embedding models across domains and tasks

arXiv:2610.14730v1 Announce Type: cross Abstract: Adapting embedding models to new tasks requires training data that reflect task-specific information needs and relevance criteria. However, manual dat

SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models

arXiv:2610.15645v1 Announce Type: new Abstract: Surgical world models must turn prospective robot actions into visually faithful, structurally consistent instrument motion. We introduce SurgCast, whic

Towards Cost-Efficient Video Dataset Construction with Large Language Models

arXiv:2610.16857v1 Announce Type: new Abstract: Research on video classification models has witnessed significant growth recently, accompanied by a significant increase in the demand for datasets to t

What DNA Foundation Models Learn Beyond Sequence Composition

arXiv:2610.15711v1 Announce Type: new Abstract: Do genomic foundation models (FMs) provide predictive information beyond simple sequence composition? We evaluate frozen representations from five DNA F

tutorialsarxiv-cs-lg

Dream to Remember: Continual Steering of Generalist Robot Policies with World Models

arXiv:2610.13816v1 Announce Type: cross Abstract: Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learn

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12 Oct 2026

Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn

arXiv:2610.12792v1 Announce Type: cross Abstract: Technologies like large language models make it easier to convert cultural material into analyzable variables, while LLM-driven agentic systems extend

agentsarxiv-cs-cl

12 Oct 2026

S-Space: Exploring Spatial Workspace in Multimodal Language Models

arXiv:2610.13527v1 Announce Type: cross Abstract: Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly underst

12 Oct 2026

Attributing HOW, Not Just WHICH: Counterfactual Response Trajectories for Diffusion Models

arXiv:2610.11238v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in image generation, yet tracing their outputs to individual training examples remains challenging. Ex