Search: “models”

From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning

arXiv:2605.06814v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) achieve high performance but can be opaque to humans, making it difficult to understand and compare the many proposed archi

safetyarxiv-cs-lg

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

DGX agent

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

safetyarxiv-cs-cl

11 May 2026

Hallucination Detection via Activations of Open-Weight Proxy Analyzers

model-releasesarxiv-cs-ai

arXiv:2605.07209v1 Announce Type: cross Abstract: We introduce a proxy-analyzer framework for detecting hallucinations in large language models. Instead of looking inside the generating model, our sys

Haven’t tried this but it seems very neat… Yet all of the demos (except maybe one) are the model being fun and/or annoying by correcting or …

applicationsethan-mollick--x

Haven’t tried this but it seems very neat… Yet all of the demos (except maybe one) are the model being fun and/or annoying by correcting or reminding in real time. There are obvious uses for this sort

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

safetyarxiv-cs-ai

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

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

model-releasesarxiv-cs-ai

arXiv:2605.06173v2 Announce Type: replace-cross Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems

Tutorials

SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning

arXiv:2602.03201v3 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of

tutorialsarxiv-cs-lg

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

Industry

Thinking Machines Lab details interaction models, which can think and respond in real time, letting users and AI interact continuously for better collaboration (Thinking Machines Lab)

Thinking Machines Lab: Thinking Machines Lab details interaction models, which can think and respond in real time, letting users and AI interact continuously for better collaboration — Today, we're an

industrytechmeme

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

researcharxiv-cs-ai

Local Ai

What is the best image model for seed variation out of the box?

local-air-stablediffusion

This discussion thread examines which image generation models provide the best native seed variation capabilities—the ability to generate diverse images from the same prompt by varying the seed parame

9 May 2026

Hardware

Improving Bash Generation in Small Language Models with Grammar-Constrained Decoding

Grammar-constrained decoding modifies language model generation by applying grammar constraints at each step to block structurally invalid tokens , ensuring syntactically correct Bash command generati

hardwarenvidia-developer

8 May 2026

Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization

model-releasesarxiv-cs-lg

arXiv:2603.08022v2 Announce Type: replace Abstract: A data mixture refers to how different data sources are combined to train large language models, and selecting an effective mixture is crucial for o

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

KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning

model-releasesarxiv-cs-ro

arXiv:2602.04129v2 Announce Type: replace Abstract: Heterogeneous multi-robot systems are increasingly used in long-horizon missions requiring coordinated planning across diverse capabilities. However

Norm Anchors Make Model Edits Last

arXiv:2602.02543v3 Announce Type: replace Abstract: Sequential Locate-and-Edit (L&E) model editing can fail abruptly after many edits. We identify and formalize this failure as a positive norm-feedbac

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

SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation

model-releasesarxiv-cs-cv

arXiv:2511.06754v3 Announce Type: replace-cross Abstract: Inspired by how humans reason over discrete objects and their relationships, we explore whether compact object-centric and object-relation rep

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

Boosting Team Modeling through Tempo-Relational Representation Learning

arXiv:2507.13305v2 Announce Type: replace Abstract: Team modeling remains a fundamental challenge at the intersection of Artificial Intelligence and Social Sciences. Although a variety of computationa

applicationsarxiv-cs-lg

CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

arXiv:2605.00930v1 Announce Type: cross Abstract: In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common represen

researcharxiv-cs-ai

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

safetyarxiv-cs-cl

Agents

From Experimental Limits to Physical Insight: A Retrieval-Augmented Multi-Agent Framework for Interpreting Searches Beyond the Standard Model

arXiv:2605.02491v1 Announce Type: cross Abstract: Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual ana

agentsarxiv-cs-ai

Google, Microsoft and xAI agree to allow government safety checks of their AI models prior to release

Google LLC, Microsoft Corp. and xAI have agreed to share unreleased versions of their artificial intelligence models with the U.S. Department of Commerce to ensure the technologies do not pose a threa

safetysiliconangle

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

safetyarxiv-cs-ai

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

Model Routing as a Trust Problem: Route Receipts for Adaptive AI Systems

arXiv:2605.01710v1 Announce Type: new Abstract: AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or safety handling before res

safetyarxiv-cs-ai

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

Personalized Digital Health Modeling with Adaptive Support Users

arXiv:2605.02004v1 Announce Type: new Abstract: Personalized models are essential in digital health because individuals exhibit substantial physiological and behavioral heterogeneity. Yet personalizat

applicationsarxiv-cs-ai

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

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

arXiv:2605.01925v1 Announce Type: new Abstract: We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD

applicationsarxiv-cs-cv

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

arXiv:2603.07615v2 Announce Type: replace-cross Abstract: Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixel

Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation

arXiv:2605.01846v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distr

safetyarxiv-cs-cl

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

Extending machine learning model for implicit solvation to free energy calculations

arXiv:2510.20103v2 Announce Type: replace-cross Abstract: The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its a

Extreme Weather Bench: A framework and benchmark for evaluation of high-impact weather

model-releasesarxiv-cs-lg

arXiv:2605.01126v1 Announce Type: new Abstract: Forecasting the wide variety of high-impact weather events experienced globally is a challenge for both Artificial Intelligence (AI) and Numerical Weath

Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

arXiv:2605.02665v1 Announce Type: new Abstract: In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classifica

researcharxiv-cs-cl

GEASS: Training-Free Caption Steering for Hallucination Mitigation in Vision-Language Models

arXiv:2605.01733v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at grounded reasoning but remain prone to object hallucination. Recent work treats self-generated captions as a unif

Tutorials

GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models

arXiv:2605.01829v1 Announce Type: new Abstract: Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Stand

tutorialsarxiv-cs-cv

H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models

arXiv:2605.00847v1 Announce Type: new Abstract: Representing and navigating hierarchy is a fundamental primitive of reasoning. Large language models have demonstrated proficiency in a wide variety of

applicationsarxiv-cs-cl