Search: “models”

MSDS: Deep Structural Similarity with Multiscale Representation

arXiv:2604.19159v1 Announce Type: new Abstract: Deep-feature-based perceptual similarity models have demonstrated strong alignment with human visual perception in Image Quality Assessment (IQA). Howev

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Tutorials

Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties

DGX agent

arXiv:2604.19383v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework repre

tutorialsarxiv-cs-ai

22 Apr 2026

Applications

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption

arXiv:2510.18333v2 Announce Type: replace-cross Abstract: Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap st

applicationsarxiv-cs-cl

Product-of-Experts Training Reduces Dataset Artifacts in Natural Language Inference

arXiv:2604.19069v1 Announce Type: cross Abstract: Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlatio

safetyarxiv-cs-ai

Quantum inspired qubit qutrit neural networks for real time financial forecasting

arXiv:2604.18838v1 Announce Type: new Abstract: This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Qua

researcharxiv-cs-ai

Local Ai

Reasoning Over Space: Enabling Geographic Reasoning for LLM-Based Generative Next POI Recommendation

arXiv:2601.04562v2 Announce Type: replace Abstract: Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain l

local-aiarxiv-cs-ai

Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization

arXiv:2604.19079v1 Announce Type: cross Abstract: Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in bo

researcharxiv-cs-ai

SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution

model-releasesarxiv-cs-ai

arXiv:2604.18982v1 Announce Type: new Abstract: Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such age

Streaming Structured Inference with Flash-SemiCRF

arXiv:2604.18780v1 Announce Type: new Abstract: Semi-Markov Conditional Random Fields (semi-CRFs) assign labels to segments of a sequence rather than to individual positions, enabling exact inference

researcharxiv-cs-lg

TabReX : Tabular Referenceless eXplainable Evaluation

arXiv:2512.15907v2 Announce Type: replace Abstract: Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into te

Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulations

arXiv:2601.09953v2 Announce Type: replace Abstract: Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. We investigate the predictive value o

Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs

model-releasesarxiv-cs-ai

arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv

TrEEStealer: Stealing Decision Trees via Enclave Side Channels

arXiv:2604.18716v1 Announce Type: cross Abstract: Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine c

researcharxiv-cs-lg

Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length

arXiv:2603.22608v2 Announce Type: replace Abstract: Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, a

researcharxiv-cs-ai

Unveiling Fine-Grained Visual Traces: Evaluating Multimodal Interleaved Reasoning Chains in Multimodal STEM Tasks

arXiv:2604.19697v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown promising reasoning abilities, yet evaluating their performance in specialized domains remains chall

VDPP: Video Depth Post-Processing for Speed and Scalability

arXiv:2604.06665v2 Announce Type: replace Abstract: Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-t

Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling

model-releasesarxiv-cs-ai

arXiv:2509.16343v2 Announce Type: replace-cross Abstract: Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inferen

When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification

arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r

Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations

arXiv:2604.02846v2 Announce Type: replace Abstract: Fourier-encoded implicit neural representations (INRs) have shown strong capability in modeling continuous signals from discrete samples. However, c

AeroRAG: Structured Multimodal Retrieval-Augmented LLM for Fine-Grained Aerial Visual Reasoning

arXiv:2604.17889v1 Announce Type: new Abstract: Despite recent progress in multimodal large language models (MLLMs), reliable visual question answering in aerial scenes remains challenging. In such sc

AeroScene: Progressive Scene Synthesis for Aerial Robotics

model-releasesarxiv-cs-ro

arXiv:2603.23224v2 Announce Type: replace Abstract: Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This

ArgBench: Benchmarking LLMs on Computational Argumentation Tasks

arXiv:2604.17366v1 Announce Type: new Abstract: Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection

Local Ai

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling

arXiv:2508.16745v2 Announce Type: replace Abstract: Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. We study this questio

local-aiarxiv-cs-lg

Bridging Coarse and Fine Recognition: A Hybrid Approach for Open-Ended Multi-Granularity Object Recognition in Interactive Educational Games

arXiv:2604.16785v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have enabled open-ended object recognition, yet they struggle with fine-grained tasks. In co

Applications

Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA

arXiv:2604.17316v1 Announce Type: new Abstract: Safe clinical deployment of Large Language Models (LLMs) requires not only high accuracy but also robust uncertainty calibration to ensure models defer

applicationsarxiv-cs-cl

Applications

Can we generate portable representations for clinical time series data using LLMs?

arXiv:2603.23987v2 Announce Type: replace Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we st

applicationsarxiv-cs-lg

CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval

arXiv:2601.17230v2 Announce Type: replace Abstract: Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where tr

CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streams

arXiv:2604.16665v1 Announce Type: new Abstract: Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional

Coevolving Representations in Joint Image-Feature Diffusion

arXiv:2604.17492v1 Announce Type: new Abstract: Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents

Agents

CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving

arXiv:2509.00789v2 Announce Type: replace Abstract: The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs):

agentsarxiv-cs-cv

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding

arXiv:2511.08480v3 Announce Type: replace Abstract: Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enh

DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching

arXiv:2602.05449v3 Announce Type: replace Abstract: While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computatio

DMax: Aggressive Parallel Decoding for dLLMs

arXiv:2604.08302v2 Announce Type: replace Abstract: We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggr

safetyarxiv-cs-lg

Do LLM-derived graph priors improve multi-agent coordination?

arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i

Document-as-Image Representations Fall Short for Scientific Retrieval

arXiv:2604.18508v1 Announce Type: cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning

arXiv:2510.00761v5 Announce Type: replace Abstract: Large language model (LLM) unlearning aims to surgically remove the influence of undesired data or knowledge from an existing model while preserving

safetyarxiv-cs-lg

EgoSound: Benchmarking Sound Understanding in Egocentric Videos

arXiv:2602.14122v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inher

Enhancing Glass Surface Reconstruction via Depth Prior for Robot Navigation

arXiv:2604.18336v1 Announce Type: cross Abstract: Indoor robot navigation is often compromised by glass surfaces, which severely corrupt depth sensor measurements. While foundation models like Depth A

EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations

arXiv:2604.18320v1 Announce Type: new Abstract: Self-evolution of multimodal large language models (MLLMs) remains a critical challenge: pseudo-label-based methods suffer from progressive quality degr

safetyarxiv-cs-cv

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning

arXiv:2508.07809v5 Announce Type: replace Abstract: Reinforcement learning with verifiable reward (RLVR) has become a promising paradigm for post-training large language models (LLMs) to improve their

FireScope: Wildfire Risk Prediction with a Chain-of-Thought Oracle

arXiv:2511.17171v4 Announce Type: replace Abstract: Predicting wildfire risk is a reasoning-intensive spatial problem that requires the integration of visual, climatic, and geographic factors to infer

Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens

arXiv:2604.17785v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied unifo

researcharxiv-cs-cl

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

arXiv:2604.16648v1 Announce Type: new Abstract: In this work, we present FRIGID, a framework with a novel diffusion language model that generates molecular structures conditioned on mass spectra via i

From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization

arXiv:2601.16397v2 Announce Type: replace Abstract: Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where e

researcharxiv-cs-cl

From log pi to pi: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight

arXiv:2603.14389v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics re

Geometric Stability: The Missing Axis of Representations

arXiv:2601.09173v4 Announce Type: replace-cross Abstract: Representational similarity analysis and related methods have become standard tools for comparing the internal geometries of neural networks a

safetyarxiv-cs-cl

GeometryZero: Advancing Geometry Solving via Group Contrastive Policy Optimization

arXiv:2506.07160v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has boosted mathematical reasoning, yet geometry remains challenging where auxiliary construction is

safetyarxiv-cs-cl

GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning

arXiv:2604.17822v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Langua