Search: “models”

Pretraining Exposure Explains Popularity Judgments in Large Language Models

arXiv:2605.12382v1 Announce Type: new Abstract: Large language models (LLMs) exhibit systematic preferences for well-known entities, a phenomenon often attributed to popularity bias. However, the exte

13 May 2026

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Safety

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation

DGX agent

arXiv:2603.15759v2 Announce Type: replace-cross Abstract: Robot learning requires adaptation methods that improve reliably from limited, mixed-quality interaction data. This is especially challenging

safetyarxiv-cs-lg

13 May 2026

Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models

arXiv:2605.10971v1 Announce Type: cross Abstract: Discrete diffusion language models (DLMs) generate text by iteratively denoising all positions in parallel, offering an alternative to autoregressive

researcharxiv-cs-cl

13 May 2026

A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models

arXiv:2605.08513v1 Announce Type: cross Abstract: Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expr

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study

arXiv:2605.09622v1 Announce Type: cross Abstract: Voxel-wise dose prediction is a critical yet challenging task in practical radiotherapy (RT) planning, as bespoke models trained from scratch often st

BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models

arXiv:2605.08110v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point

Compute Where it Counts: Self Optimizing Language Models

arXiv:2605.10875v1 Announce Type: cross Abstract: Efficient LLM inference research has largely focused on reducing the cost of each decoding step (e.g., using quantization, pruning, or sparse attentio

CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings

arXiv:2605.10337v1 Announce Type: new Abstract: Intracranial electrocorticography (ECoG) offers high-signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient

Local Ai

Data-driven Circuit Discovery for Interpretability of Language Models

arXiv:2605.09129v1 Announce Type: new Abstract: Circuit discovery aims to explain how language models (LMs) implement a specific task by localizing and interpreting a circuit, a computational subgraph

local-aiarxiv-cs-ai

Agents

EvoDriveVLA: Evolving Driving VLA Models via Collaborative Perception-Planning Distillation

arXiv:2603.09465v3 Announce Type: replace-cross Abstract: Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the v

agentsarxiv-cs-ai

Hardware

Forcing-KV: Hybrid KV Cache Compression for Efficient Autoregressive Video Diffusion Models

arXiv:2605.09681v1 Announce Type: new Abstract: Autoregressive (AR) video diffusion models adopt a streaming generation framework, enabling long-horizon video generation with real-time responsiveness,

hardwarearxiv-cs-cv

Improved Mean Flows: On the Challenges of Fastforward Generative Models

arXiv:2512.02012v2 Announce Type: replace Abstract: MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key chal

Internalizing Safety Understanding in Large Reasoning Models via Verification

arXiv:2605.08930v1 Announce Type: new Abstract: While explicit Chain-of-Thought (CoT) empowers large reasoning models (LRMs), it enables the generation of riskier final answers. Current alignment para

Learning Graph Foundation Models on Riemannian Graph-of-Graphs

arXiv:2605.09993v1 Announce Type: new Abstract: Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general-purpose reasoning across

Applications

LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models

arXiv:2605.10641v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) are successful in addressing a multitude of vision-language understanding tasks, such as Visual Question Answering

applicationsarxiv-cs-ai

Metacognitive Behavioral Tuning of Large Language Models for Multi-Hop Question Answering

arXiv:2602.22508v2 Announce Type: replace Abstract: Large Language Models (LLMs) often produce incorrect answers on multi-hop question answering even when the reasoning trace already contains a correc

Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models

arXiv:2605.08472v1 Announce Type: new Abstract: The effectiveness of Reinforcement Learning (RL) in Large Language Models (LLMs) depends on the nature and diversity of the data used before and during

Mitigating Watermark Forgery in Generative Models via Randomized Key Selection

arXiv:2507.07871v4 Announce Type: replace-cross Abstract: Watermarking enables GenAI providers to verify whether content was generated by their models. A watermark is a hidden signal in the content, w

Restoration-Aligned Generative Flow Models for Blind Motion Deblurring

arXiv:2605.08854v1 Announce Type: new Abstract: Generative flow models offer powerful priors learned from large-scale natural images, but directly adapting them to restoration tasks such as motion deb

SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models

arXiv:2605.10576v1 Announce Type: cross Abstract: Low-level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpr

SLAM: Structural Linguistic Activation Marking for Language Models

arXiv:2605.05443v2 Announce Type: replace-cross Abstract: LLM watermarks must be detectable without compromising text quality, yet most existing schemes bias the next-token distribution and pay for de

Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models

arXiv:2605.09241v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) provide a simpleframework for learning world models by predicting future latent representations.Howev

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning

arXiv:2601.20829v2 Announce Type: replace-cross Abstract: As Reinforcement Learning with Verifiable Rewards (RLVR) substantially improves the reasoning abilities of large language models (LLMs), a new

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents

arXiv:2605.08964v1 Announce Type: new Abstract: In this thesis, we develop algorithms with theoretical guarantees for ensuring reliability and accountability of Machine Learning (ML) systems. As ML sy

safetyarxiv-cs-lg

V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction

model-releasesarxiv-cs-lg

arXiv:2605.10896v1 Announce Type: new Abstract: Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public da

Virtual Personas for Language Models via an Anthology of Backstories

arXiv:2407.06576v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diver

What Will Happen Next: Large Models-Driven Deduction for Emergency Instances

arXiv:2605.08599v1 Announce Type: new Abstract: Traditional simulation methods reproduce occurred emergency instances through presetting to assist people in risk assessment and emergency decision-maki

Guidance Is Not a Hyperparameter: Learning Dynamic Control in Diffusion Language Models

arXiv:2605.07701v1 Announce Type: new Abstract: Classifier-Free Guidance (CFG) is a widely used mechanism for controlling diffusion-based generative models, yet its guidance scale is typically treated

Hierarchical Dual-Subspace Decoupling for Continual Learning in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.07512v1 Announce Type: new Abstract: Class-incremental learning aims to continuously acquire new knowledge while preserving previously learned information, thereby mitigating catastrophic f

InvThink: Premortem Reasoning for Safer Language Models

arXiv:2510.01569v3 Announce Type: replace Abstract: We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before gener

Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment

arXiv:2605.08064v1 Announce Type: new Abstract: Spatial intelligence in vision-language models (VLMs) attracts research interest with the practical demand to reason in the 3D world.Despite promising r

safetyarxiv-cs-cv

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts

arXiv:2605.07307v1 Announce Type: new Abstract: Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must

researcharxiv-cs-cl

STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation

arXiv:2605.08029v1 Announce Type: new Abstract: Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generat

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

arXiv:2605.07799v1 Announce Type: cross Abstract: Training foundation models is computationally intensive and often slow to converge.We introduce PIQL,Privileged Information for Quick and Quality Lear

Local Ai

Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models

arXiv:2605.04830v1 Announce Type: new Abstract: Diffusion models undergo a phase transition in a critical time window during generation dynamics, with two complementary diagnoses of criticality. The s

local-aiarxiv-cs-lg

SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models

arXiv:2601.08623v2 Announce Type: replace Abstract: Image generation models (IGMs), while capable of producing impressive and creative content, often memorize a wide range of undesirable concepts from

safetyarxiv-cs-cv

Threshold-Guided Optimization for Visual Generative Models

arXiv:2605.04653v1 Announce Type: new Abstract: Aligning large visual generative models with human feedback is often performed through pairwise preference optimization. While such approaches are conce

safetyarxiv-cs-lg

Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective

arXiv:2602.03396v3 Announce Type: replace Abstract: Proprietary large language models (LLMs) embody substantial economic value and are generally exposed only as black-box APIs, yet adversaries can sti

researcharxiv-cs-cl

Agents

Dual-Foundation Models for Unsupervised Domain Adaptation

arXiv:2605.03365v1 Announce Type: new Abstract: Semantic segmentation provides pixel-level scene understanding essential for autonomous driving and fine-grained perception tasks. However, training seg

agentsarxiv-cs-cv

FINER-SQL: Boosting Small Language Models for Text-to-SQL

arXiv:2605.03465v1 Announce Type: cross Abstract: Large language models have driven major advances in Text-to-SQL generation. However, they suffer from high computational cost, long latency, and data

Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization

arXiv:2605.01482v1 Announce Type: new Abstract: Multi-Hop Fact Verification (MHFV) necessitates complex reasoning across disparate evidence, posing significant challenges for Large Language Models (LL

Memory-Efficient Continual Learning with CLIP Models

arXiv:2605.03866v1 Announce Type: new Abstract: Contrastive Language-Image Pretraining (CLIP) models excel at understanding image-text relationships but struggle with adapting to new data without forg

Partially Observed Structural Causal Models

arXiv:2605.03268v1 Announce Type: new Abstract: Here we introduce Partially Observed Structural Causal Models (POSCMs) that formalize causal systems where latent contexts co-determine both the interac

Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models

model-releasesarxiv-cs-lg

arXiv:2602.01842v3 Announce Type: replace Abstract: Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive de

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

arXiv:2605.02937v1 Announce Type: new Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synt

Sparse Data Tree Canopy Segmentation: Fine-Tuning Leading Pretrained Models on Only 150 Images

arXiv:2601.10931v2 Announce Type: replace Abstract: Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real

When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models

arXiv:2605.02914v1 Announce Type: new Abstract: A guard model fine-tuned on entirely benign data can lose all safety alignment -- not through adversarial manipulation, but through standard domain spec

safetyarxiv-cs-lg

Applications

CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models