Search: “framework”

arXiv:2605.03830v1 Announce Type: new Abstract: Contactless fingerprint recognition has gained increasing attention due to its advantages in hygiene and acquisition flexibility. However, the absence o

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Imbalanced Classification under Capacity Constraints

DGX agent

arXiv:2605.03289v1 Announce Type: cross Abstract: In many classification settings, the class of primary interest is underrepresented, leading to imbalanced data problems that arise in applications suc

researcharxiv-cs-lg

6 May 2026

Instance-Level Costs for Nuanced Classifier Evaluation

arXiv:2605.03135v1 Announce Type: new Abstract: Standard classification treats all errors equally, but in content moderation, medical screening, and safety-critical applications, mistakes on clear-cut

safetyarxiv-cs-lg

InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories

arXiv:2604.04106v2 Announce Type: replace Abstract: The generation of realistic and controllable GPS trajectories is a fundamental task for applications in urban planning, mobility simulation, and pri

applicationsarxiv-cs-ai

Intervention Complexity as a Canonical Reward and a Measure of Intelligence

arXiv:2605.02175v1 Announce Type: new Abstract: The Legg--Hutter universal intelligence measure provides a rigorous scalar assessment of general intelligence as expected reward across all computable e

safetyarxiv-cs-ai

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy

arXiv:2507.02974v3 Announce Type: replace-cross Abstract: As major progress in LLM-based long-form text generation enables paradigms such as retrieval-augmented generation (RAG) and inference-time sca

researcharxiv-cs-cl

ISAAC: Auditing Causal Reasoning in Deep Models for Drug-Target Interaction

arXiv:2605.02962v1 Announce Type: new Abstract: Deep learning models for drug--target interaction (DTI) prediction often achieve strong benchmark performance without necessarily relying on mechanistic

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

arXiv:2605.02965v1 Announce Type: new Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content,

Khala: Scaling Acoustic Token Language Models Toward High-Fidelity Music Generation

arXiv:2605.01790v1 Announce Type: cross Abstract: A common design pattern in high-quality music generation is to handle structure and fidelity in different representation spaces: a generator first mod

safetyarxiv-cs-ai

LangPrecip: Language-Aware Multimodal Precipitation Nowcasting

arXiv:2512.22317v2 Announce Type: replace-cross Abstract: Short-term precipitation nowcasting is an inherently uncertain and under-constrained spatiotemporal forecasting problem, especially for rapidl

researcharxiv-cs-cv

Large Language Models are Universal Reasoners for Visual Generation

arXiv:2605.04040v1 Announce Type: new Abstract: Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM bac

safetyarxiv-cs-cv

Latent State Design for World Models under Sufficiency Constraints

arXiv:2605.01694v1 Announce Type: new Abstract: A world model matters to an agent only through the state it constructs. That state must preserve some information, discard other information, and suppor

agentsarxiv-cs-ai

Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective

arXiv:2605.03373v1 Announce Type: new Abstract: Classical optimization theory establishes that zeroth-order (ZO) algorithms suffer from a dimension-dependent slowdown, with convergence rates typically

Hardware

Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute

arXiv:2504.17816v3 Announce Type: replace Abstract: Subject-driven video generation (SDV-Gen) aims to produce videos of a specific subject by adapting a pretrained video model, enabling personalized a

hardwarearxiv-cs-cv

Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks

arXiv:2605.01293v1 Announce Type: new Abstract: Foundation model-driven agents often struggle with long-horizon planning due to the transient nature of purely prompting-based reasoning. While existing

agentsarxiv-cs-ai

LitVISTA: A Benchmark for Narrative Orchestration in Literary Text

model-releasesarxiv-cs-cl

arXiv:2601.06445v2 Announce Type: replace Abstract: Computational narrative analysis aims to capture rhythm, tension, and emotional dynamics in literary texts. Existing large language models can gener

LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models

arXiv:2605.03299v1 Announce Type: new Abstract: Cross-lingual topic modeling aims to discover shared semantic structures across languages, yet existing models depend on sparse bilingual resources and

safetyarxiv-cs-cl

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

arXiv:2605.03971v1 Announce Type: new Abstract: Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors

applicationsarxiv-cs-cl

Low Rank Tensor Completion via Adaptive ADMM

arXiv:2605.03736v1 Announce Type: cross Abstract: We consider a novel algorithm, for the completion of partially observed low-rank tensors, as a generalization of matrix completion. The proposed low-r

Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models

model-releasesarxiv-cs-cv

arXiv:2605.03438v1 Announce Type: new Abstract: Pre-trained 3D point cloud foundation models (PFMs) have demonstrated strong transferability across diverse downstream tasks. However, full fine-tuning

MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation

model-releasesarxiv-cs-ai

arXiv:2605.01486v1 Announce Type: new Abstract: Legal consultation is a high-stakes, knowledge-intensive task that requires agents to identify relevant legal issues, retrieve authoritative support, an

Local Ai

MASRA: MLLM-Assisted Semantic-Relational Consistent Alignment for Video Temporal Grounding

arXiv:2605.03398v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) faces a cross-modal semantic gap that often leads to background features being incorrectly aligned with the query, while

local-aiarxiv-cs-cv

Maximizing mutual information between prompts and responses improve LLM personalization with no additional data or human oversight

model-releasesarxiv-cs-cl

arXiv:2603.19294v2 Announce Type: replace-cross Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labe

MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents

arXiv:2605.03482v1 Announce Type: cross Abstract: Persistent external memory enables LLM agents to maintain context across sessions, yet its security properties remain formally uncharacterized. We for

MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue

arXiv:2603.06194v2 Announce Type: replace Abstract: Reinforcement learning (RL) for large language models (LLMs) has shown strong performance in single-turn tasks, but extending it to multi-turn inter

researcharxiv-cs-cl

MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities

model-releasesarxiv-cs-cv

arXiv:2605.03555v1 Announce Type: new Abstract: Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Exper

Local Ai

MindMelody: A Closed-Loop EEG-Driven System for Personalized Music Intervention

arXiv:2605.01235v1 Announce Type: cross Abstract: Driven by the escalating global burden of mental health conditions, music-based interventions have attracted significant attention as a non-invasive,

local-aiarxiv-cs-ai

Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning

arXiv:2603.10083v2 Announce Type: replace-cross Abstract: Quantum machine learning models based on parameterized circuits can be viewed as Fourier series approximators. However, they often struggle to

Mix3R: Mixing Feed-forward Reconstruction and Generative 3D Priors for Joint Multi-view Aligned 3D Reconstruction and Pose Estimation

arXiv:2605.03359v1 Announce Type: new Abstract: Recent trends in sparse-view 3D reconstruction have taken two different paths: feed-forward reconstruction that predicts pixel-aligned point maps withou

safetyarxiv-cs-cv

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection

arXiv:2605.03039v1 Announce Type: new Abstract: Continuous monitoring of bipolar disorder agitation via voice biomarkers requires disentangling stable speaker traits from volatile affective states on

Hardware

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

arXiv:2603.03756v3 Announce Type: replace-cross Abstract: While large language models (LLMs) show promise in scientific discovery, existing research focuses on inference or feedback-driven training, l

hardwarearxiv-cs-cl

Most ReLU Networks Admit Identifiable Parameters

arXiv:2605.03601v1 Announce Type: new Abstract: We study the realization map of deep ReLU networks, focusing on when a function determines its parameters up to scaling and permutation. To analyze hidd

Tutorials

Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items

arXiv:2507.22268v3 Announce Type: replace-cross Abstract: We study the problem of inferring substitutable and complementary items, which underpins applications such as alternative and follow-up purcha

tutorialsarxiv-cs-ai

NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Cities

arXiv:2507.15143v3 Announce Type: replace Abstract: This paper investigates the feasibility of human mobility in extreme urban morphologies characterized by high-density vertical structures and linear

agentsarxiv-cs-ai

Neural Control: Adjoint Learning Through Equilibrium Constraints

arXiv:2605.03288v1 Announce Type: new Abstract: Many physical AI tasks are governed by implicit equilibrium: an agent actuates a subset of degrees of freedom (boundary DoFs), while the remaining free

agentsarxiv-cs-ro

NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science

arXiv:2605.02092v1 Announce Type: new Abstract: The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research ag

safetyarxiv-cs-ai

Not that Groove: Zero-Shot Symbolic Music Editing

model-releasesarxiv-cs-cl

arXiv:2505.08203v2 Announce Type: replace-cross Abstract: While recent advancements in AI music generation have predominantly focused on direct audio synthesis, these systems suffer from inherent rigi

Hardware

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation

arXiv:2605.03098v1 Announce Type: new Abstract: Deep learning-based medical image segmentation is increasingly used to support clinical diagnosis and develop new treatment strategies. However, model p

hardwarearxiv-cs-cv

Online Conformal Abstention for Factuality Control Under Adversarial Bandit Feedback

arXiv:2506.14067v4 Announce Type: replace Abstract: As interactive generative systems are increasingly deployed in real-world applications, their tendency to generate unreliable or false responses rai

applicationsarxiv-cs-lg

Optimal control of the future via prospective learning with control

arXiv:2511.08717v4 Announce Type: replace-cross Abstract: Optimal control of the future is the next frontier for AI. Current approaches to this problem are typically rooted in reinforcement learning (

Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI

arXiv:2605.03490v1 Announce Type: new Abstract: The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI dat

safetyarxiv-cs-cv

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

PHALAR: Phasors for Learned Musical Audio Representations

arXiv:2605.03929v1 Announce Type: cross Abstract: Stem retrieval, the task of matching missing stems to a given audio submix, is a key challenge currently limited by models that discard temporal infor

PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals

arXiv:2605.02974v1 Announce Type: cross Abstract: Structured launch signals on Product Hunt contain statistically significant predictive information for Series A funding outcomes. We construct PHBench

Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings

arXiv:2605.03079v1 Announce Type: cross Abstract: Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake

Tutorials

Physically Guided Visual Mass Estimation from a Single RGB Image

arXiv:2601.20303v2 Announce Type: replace Abstract: Estimating object mass from visual input is challenging because mass depends jointly on geometric volume and material-dependent density, neither of

tutorialsarxiv-cs-cv

PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution

arXiv:2605.03399v1 Announce Type: new Abstract: Probabilistic super-resolution of high-dimensional spatial fields using diffusion models is often computationally prohibitive due to the cost of operati