Search: “models”

LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training

arXiv:2511.19931v2 Announce Type: replace-cross Abstract: Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various dom

18 May 2026

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Model Releases

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders

DGX agent

arXiv:2602.17050v3 Announce Type: replace Abstract: Embedding tables are critical components of large-scale recommendation systems, facilitating the efficient mapping of high-cardinality categorical f

model-releasesarxiv-cs-lg

18 May 2026

Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests

arXiv:2601.07820v2 Announce Type: replace Abstract: In human conversation, both interlocutors play an active role in maintaining mutual understanding. When listeners are uncertain about what speakers

safetyarxiv-cs-cl

18 May 2026

Agents

Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling

arXiv:2605.15812v1 Announce Type: cross Abstract: Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet mos

agentsarxiv-cs-ai

18 May 2026

Hardware

EMA: Efficient Model Adaptation for Learning-based Systems

arXiv:2605.13942v1 Announce Type: new Abstract: Machine learning (ML) is increasingly applied to optimize system performance in tasks such as resource management and network simulation. Unlike traditi

hardwarearxiv-cs-lg

HeatKV: Head-tuned KV-cache Compression for Visual Autoregressive Modeling

arXiv:2605.14877v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models have recently demonstrated impressive image generation quality while maintaining low latency. However, they suffer fr

safetyarxiv-cs-cv

Applications

MeMo: Memory as a Model

arXiv:2605.15156v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Ma

applicationsarxiv-cs-ai

Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework

arXiv:2605.14550v1 Announce Type: new Abstract: Artificial intelligence in high-stakes tabular domains cannot be evaluated by predictive performance alone, yet current practice still assesses explaina

safetyarxiv-cs-lg

BEHAVE: A Hybrid AI Framework for Real-Time Modeling of Collective Human Dynamics

arXiv:2605.12730v1 Announce Type: new Abstract: Existing AI systems for modeling human behavior operate at the level of individuals or detect events after they occur. As a result, they systematically

safetyarxiv-cs-ai

14 May 2026

Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification

arXiv:2605.12699v1 Announce Type: cross Abstract: Existing multiplex graph models often assume homophily, where connected nodes tend to belong to the same class or share similar attributes. Consequent

tutorialsarxiv-cs-ai

14 May 2026

Proximal-Based Generative Modeling for Bayesian Inverse Problems

arXiv:2605.13278v1 Announce Type: cross Abstract: Score-based diffusion models demonstrate superior performance in generative tasks but encounter fundamental bottlenecks in inverse problems due to the

safetyarxiv-cs-lg

14 May 2026

A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods

arXiv:2605.11585v1 Announce Type: new Abstract: This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree reg

CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration

arXiv:2605.11186v1 Announce Type: new Abstract: Auto-regressive decoding in Large Language Models (LLMs) is inherently memory-bound: every generation step requires loading the model weights and interm

Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification

arXiv:2509.20899v3 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but exte

Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach

model-releasesarxiv-cs-cl

arXiv:2605.12177v1 Announce Type: new Abstract: [Abridged] Production LLM deployments receive feedback from a non-random fraction of users: thumbs sit mostly in the tails of the satisfaction distribut

From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review

arXiv:2605.12303v1 Announce Type: cross Abstract: High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted

One-Step Generative Modeling via Wasserstein Gradient Flows

arXiv:2605.11755v1 Announce Type: cross Abstract: Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it

Agents

Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling

arXiv:2605.12411v1 Announce Type: cross Abstract: AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant ne

agentsarxiv-cs-cl

What makes a word hard to learn? Modeling L1 influence on English vocabulary difficulty

arXiv:2605.12281v1 Announce Type: new Abstract: What makes a word difficult to learn, and how does the difficulty depend on the learner's native language? We computationally model vocabulary difficult

tutorialsarxiv-cs-cl

BetaEdit: Null-Space Constrained Sequential Model Editing

arXiv:2605.09285v1 Announce Type: new Abstract: Null-space-based methods have garnered considerable attention in model editing by constraining updates to the null space of the pre-existing knowledge r

researcharxiv-cs-cl

Local Ai

Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction

arXiv:2605.08276v1 Announce Type: new Abstract: Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain sh

local-aiarxiv-cs-cv

BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization

model-releasesarxiv-cs-cv

arXiv:2605.10345v1 Announce Type: new Abstract: Geometric differences between cross-view images, such as drone and satellite views, significantly increase the challenge of Cross-View Geo-Localization

Continuum Robot Modeling with Action Conditioned Flow Matching

arXiv:2605.09216v1 Announce Type: new Abstract: Predicting the shape of tendon driven continuum robots (TDCRs) at steady state from actuation remains challenging due to continuous deformation, complex

tutorialsarxiv-cs-ro

Local Ai

DetRefiner: Model-Agnostic Detection Refinement with Feature Fusion Transformer

arXiv:2605.10190v1 Announce Type: new Abstract: Open-vocabulary object detection (OVOD) aims to detect both seen and unseen categories, yet existing methods often struggle to generalize to novel objec

local-aiarxiv-cs-cv

Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa

model-releasesarxiv-cs-cv

arXiv:2605.08113v1 Announce Type: cross Abstract: Accurate predictions of smallholder maize yields across national boundaries are critical for food security planning in sub-Saharan Africa, yet most pu

Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness

arXiv:2509.13332v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly adopted as automated judges in benchmarking and reward modeling, ensuring their reliability, effici

HairGPT: Strand-as-Language Autoregressive Modeling for Realistic 3D Hairstyle Synthesis

arXiv:2605.08824v1 Announce Type: cross Abstract: Hair is a rich medium of visual and cultural expression, yet its digital modeling remains challenging due to the duality of fluidity and structure. Ma

tutorialsarxiv-cs-cv

HapticLDM: A Diffusion Model for Text-to-Vibrotactile Generation

arXiv:2605.09971v1 Announce Type: cross Abstract: Text-to-vibration generation converts natural language into haptic feedback, enabling vibration-effect designers to get scenarios-fitted vibrations mo

safetyarxiv-cs-ai

Agents

Heteroscedastic Diffusion for Multi-Agent Trajectory Modeling

arXiv:2605.10717v1 Announce Type: cross Abstract: Multi-agent trajectory modeling traditionally focuses on forecasting, often neglecting more general tasks like trajectory completion, which is essenti

agentsarxiv-cs-cv

How Much Do Circuits Tell Us? Measuring the Consistency and Specificity of Language Model Circuits

arXiv:2605.08348v1 Announce Type: new Abstract: The circuits framework in mechanistic interpretability aims to identify causally important sparse subgraphs of model components, typically evaluated by

researcharxiv-cs-cl

Measuring Embedding Sensitivity to Authorial Style in French: Comparing Literary Texts with Language Model Rewritings

arXiv:2605.10606v1 Announce Type: cross Abstract: Large language models (LLMs) can convincingly imitate human writing styles, yet it remains unclear how much stylistic information is encoded in embedd

researcharxiv-cs-ai

Network-Efficient World Model Token Streaming

arXiv:2605.09886v1 Announce Type: new Abstract: Generative driving world models rely on compact latent state representations that must be efficiently transmitted and synchronized across distributed co

researcharxiv-cs-ro

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training

arXiv:2605.08144v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success across a wide range of generative tasks, yet their training paradigm largely treats injected noise a

researcharxiv-cs-ai

SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

model-releasesarxiv-cs-cl

arXiv:2603.14889v2 Announce Type: replace-cross Abstract: The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances an

Sequential Causal Discovery with Noisy Language Model Priors

arXiv:2506.16234v2 Announce Type: replace Abstract: Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data oft

Structure-Centric Graph Foundation Model via Geometric Bases

arXiv:2605.08689v1 Announce Type: cross Abstract: Graph foundation models (GFMs) seek transferable representations across graph domains but are limited by structural heterogeneity and incompatible nod

researcharxiv-cs-ai

TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications

arXiv:2605.09929v1 Announce Type: new Abstract: Deploying large language models in telecommunications requires more than task accuracy. In realistic workflows, a model may inherit partially completed

Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning

model-releasesarxiv-cs-cv

arXiv:2605.08911v1 Announce Type: new Abstract: Autonomous vehicles need to perceive not only physical elements in the driving scene, such as lane lines and traffic lights, but also logical elements l

Arrow: A Foundation Model for Causal Discovery

arXiv:2605.07204v1 Announce Type: new Abstract: We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an u

researcharxiv-cs-lg

Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation

arXiv:2602.20816v3 Announce Type: replace Abstract: The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distri

researcharxiv-cs-cl

FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting

model-releasesarxiv-cs-cl

arXiv:2603.19254v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in financial research workflows, where their role is evolving from single-model assistance fo

Flock: A Knowledge Graph Foundation Model via Learning on Random Walks

arXiv:2510.01510v3 Announce Type: replace Abstract: We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relatio

tutorialsarxiv-cs-lg

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

Hallucination Detection via Activations of Open-Weight Proxy Analyzers

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

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

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

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

Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization

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

7 May 2026

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