Search: “models”

Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task

arXiv:2604.12426v1 Announce Type: cross Abstract: We investigate whether transformers use their depth adaptively across tasks of increasing difficulty. Using a controlled multi-hop relational reasonin

researcharxiv-cs-cl

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Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution

DGX agent

arXiv:2604.12152v1 Announce Type: cross Abstract: Latent diffusion models for medical image super-resolution universally inherit variational autoencoders designed for natural photographs. We show that

local-aiarxiv-cs-ai

15 Apr 2026

EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts

arXiv:2604.12579v1 Announce Type: new Abstract: Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, provid

Evaluating LLM-Generated ACSL Annotations for Formal Verification

arXiv:2602.13851v3 Announce Type: replace-cross Abstract: Formal specifications are crucial for building verifiable and dependable software systems, yet generating accurate and verifiable specificatio

Evolution-Inspired Sample Competition for Deep Neural Network Optimization

arXiv:2604.12568v1 Announce Type: new Abstract: Conventional deep network training generally optimizes all samples under a largely uniform learning paradigm, without explicitly modeling the heterogene

safetyarxiv-cs-cv

Hardware

Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data

arXiv:2503.10676v2 Announce Type: replace-cross Abstract: We study the efficacy of fine-tuning Large Language Models (LLMs) for the specific task of report (government archives, news, intelligence rep

hardwarearxiv-cs-ai

Applications

Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Prediction

arXiv:2604.12425v1 Announce Type: new Abstract: Trajectory prediction models often fail in real-world automated driving due to distributional shifts between training and test conditions. Such distribu

applicationsarxiv-cs-lg

FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation

arXiv:2604.12344v1 Announce Type: cross Abstract: The exponential growth of data from modern radio telescopes presents a significant challenge to traditional single-pulse search algorithms, which are

GCA Framework: A Gulf-Grounded Dataset and Agentic Pipeline for Climate Decision Support

arXiv:2604.12306v1 Announce Type: cross Abstract: Climate decision-making in the Gulf increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guida

GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization

arXiv:2604.12336v1 Announce Type: cross Abstract: Streaming Data-Driven Optimization (SDDO) problems arise in many applications where data arrive continuously and the optimization environment evolves

GoodPoint: Learning Constructive Scientific Paper Feedback from Author Responses

arXiv:2604.11924v1 Announce Type: new Abstract: While LLMs hold significant potential to transform scientific research, we advocate for their use to augment and empower researchers rather than to auto

Hail to the Thief: Exploring Attacks and Defenses in Decentralised GRPO

arXiv:2511.09780v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has demonstrated wide adoption in the post-training of Large Language Models (LLMs). In GRPO, prompts are

safetyarxiv-cs-lg

How Transformers Learn to Plan via Multi-Token Prediction

arXiv:2604.11912v1 Announce Type: cross Abstract: While next-token prediction (NTP) has been the standard objective for training language models, it often struggles to capture global structure in reas

safetyarxiv-cs-ai

Human-Inspired Context-Selective Multimodal Memory for Social Robots

arXiv:2604.12081v1 Announce Type: new Abstract: Memory is fundamental to social interaction, enabling humans to recall meaningful past experiences and adapt their behavior accordingly based on the con

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones

arXiv:2507.13647v2 Announce Type: replace-cross Abstract: Real-time trajectory planning for unmanned aerial vehicles (UAVs) in dynamic environments remains a key challenge due to high computational de

Applications

INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression

arXiv:2604.11928v1 Announce Type: new Abstract: Time-series forecasting aims to predict future values by modeling temporal dependencies in historical observations. It is a critical component of many r

applicationsarxiv-cs-lg

Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring

arXiv:2604.12470v1 Announce Type: new Abstract: Accurate vehicle counting through video surveillance is crucial for efficient traffic management. However, achieving high counting accuracy while ensuri

Tutorials

Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning

arXiv:2505.15467v2 Announce Type: replace-cross Abstract: Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally

tutorialsarxiv-cs-ai

Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning

arXiv:2604.12303v1 Announce Type: new Abstract: Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning model

safetyarxiv-cs-lg

Lit2Vec: A Reproducible Workflow for Building a Legally Screened Chemistry Corpus from S2ORC for Downstream Retrieval and Text Mining

arXiv:2604.12498v1 Announce Type: cross Abstract: We present Lit2Vec, a reproducible workflow for constructing and validating a chemistry corpus from the Semantic Scholar Open Research Corpus using co

Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning

arXiv:2604.12719v1 Announce Type: new Abstract: The deployment of deep neural networks in safety-critical systems necessitates reliable and efficient uncertainty quantification (UQ). A practical and w

PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination

arXiv:2604.12856v1 Announce Type: new Abstract: Audio-driven bimanual piano motion generation requires precise modeling of complex musical structures and dynamic cross-hand coordination. However, exis

researcharxiv-cs-cv

Applications

Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks

arXiv:2603.05004v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable results in various tasks. Recent studies reveal that graph backdoor attacks can poison t

applicationsarxiv-cs-ai

ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systems

arXiv:2604.11943v1 Announce Type: cross Abstract: An OS kernel that runs LLM inference internally can read logit distributions before any text is generated -- and act on them as a governance primitive

Prompt Evolution for Generative AI: A Classifier-Guided Approach

arXiv:2305.16347v2 Announce Type: replace-cross Abstract: Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with genera

Public Profile Matters: A Scalable Integrated Approach to Recommend Citations in the Wild

arXiv:2603.17361v2 Announce Type: replace-cross Abstract: Proper citation of relevant literature is essential for contextualising and validating scientific contributions. While current citation recomm

Quantile Q-Learning: Revisiting Offline Extreme Q-Learning with Quantile Regression

arXiv:2511.11973v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables policy learning from fixed datasets without further environment interaction, making it particularly valu

Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI

arXiv:2604.12693v1 Announce Type: new Abstract: Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These hi

safetyarxiv-cs-cv

Tutorials

SDFed: Bridging Local Global Discrepancy via Subspace Refinement and Divergence Control in Federated Prompt Learning

arXiv:2602.08590v4 Announce Type: replace Abstract: Vision-language pretrained models offer strong transferable representations, yet adapting them in privacy-sensitive multi-party settings is challeng

tutorialsarxiv-cs-lg

SEATrack: Simple, Efficient, and Adaptive Multimodal Tracker

arXiv:2604.12502v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) in multimodal tracking reveals a concerning trend where recent performance gains are often achieved at the cost

See, Point, Refine: Multi-Turn Approach to GUI Grounding with Visual Feedback

model-releasesarxiv-cs-cv

arXiv:2604.13019v1 Announce Type: new Abstract: Computer Use Agents (CUAs) fundamentally rely on graphical user interface (GUI) grounding to translate language instructions into executable screen acti

Self-Monitoring Benefits from Structural Integration: Lessons from Metacognition in Continuous-Time Multi-Timescale Agents

arXiv:2604.11914v1 Announce Type: new Abstract: Self-monitoring capabilities -- metacognition, self-prediction, and subjective duration -- are often proposed as useful additions to reinforcement learn

Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

arXiv:2603.12583v2 Announce Type: replace Abstract: In this work, we propose a data-driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the cha

safetyarxiv-cs-ro

Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning

arXiv:2604.12770v1 Announce Type: new Abstract: Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a d

safetyarxiv-cs-cl

Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectories

arXiv:2604.12097v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in daily applications, from content generation to code writing, where each interaction treats the mod

researcharxiv-cs-cl

The role of System 1 and System 2 semantic memory structure in human and LLM biases

arXiv:2604.12816v1 Announce Type: new Abstract: Implicit biases in both humans and large language models (LLMs) pose significant societal risks. Dual process theories propose that biases arise primari

safetyarxiv-cs-cl

Local Ai

Time-reversed Flow Matching with Worst Transport in High-dimensional Latent Space for Image Anomaly Detection

arXiv:2508.05461v3 Announce Type: replace Abstract: Likelihood-based deep generative models have been widely investigated for Image Anomaly Detection (IAD), particularly Normalizing Flows, yet their s

local-aiarxiv-cs-cv

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

arXiv:2604.12503v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenari

Towards Generalized Certified Robustness with Multi-Norm Training

arXiv:2410.03000v3 Announce Type: replace Abstract: Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. l_infty or l_2). However, an l_inft

safetyarxiv-cs-lg

Transformer Based Machine Fault Detection From Audio Input

arXiv:2604.12733v1 Announce Type: cross Abstract: In recent years, Sound AI is being increasingly used to predict machine failures. By attaching a microphone to the machine of interest, one can get re

Uncertainty-Aware Image Classification In Biomedical Imaging Using Spectral-normalized Neural Gaussian Processes

arXiv:2602.02370v2 Announce Type: replace Abstract: Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often o

safetyarxiv-cs-cv

A Benchmark for Gap and Overlap Analysis as a Test of KG Task Readiness

arXiv:2604.10853v1 Announce Type: new Abstract: Task-oriented evaluation of knowledge graph (KG) quality increasingly asks whether an ontology-based representation can answer the competency questions

A Deep Generative Approach to Stratified Learning

arXiv:2604.10650v1 Announce Type: cross Abstract: While the manifold hypothesis is widely adopted in modern machine learning, complex data is often better modeled as stratified spaces -- unions of man

researcharxiv-cs-lg

A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense

arXiv:2604.10427v1 Announce Type: cross Abstract: We develop a queueing-theoretic framework to model the temporal evolution of cyber-attack surfaces, where the number of active vulnerabilities is repr

safetyarxiv-cs-ai

A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities

arXiv:2604.11048v1 Announce Type: cross Abstract: Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capa

Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks

arXiv:2604.11410v1 Announce Type: new Abstract: We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems. The proposed framework models modern

researcharxiv-cs-lg

ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing

arXiv:2604.11103v1 Announce Type: cross Abstract: Role-playing has garnered rising attention as it provides a strong foundation for human-machine interaction and facilitates sociological research. How

AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

arXiv:2604.11674v1 Announce Type: cross Abstract: Simulation-based data generation has become a dominant paradigm for training robotic manipulation policies, yet existing platforms do not incorporate