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Structured Exploration and Exploitation of Label Functions for Automated Data Annotation

arXiv:2604.08578v1 Announce Type: cross Abstract: High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-

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Structured Uncertainty guided Clarification for LLM Agents

DGX agent

arXiv:2511.08798v2 Announce Type: replace-cross Abstract: LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and

model-releasesarxiv-cs-ai

13 Apr 2026

Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning

arXiv:2604.09158v1 Announce Type: cross Abstract: Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as prem

agentsarxiv-cs-ai

SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

arXiv:2602.17330v3 Announce Type: replace-cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of p

SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

arXiv:2604.08477v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathem

Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study

arXiv:2604.08621v1 Announce Type: new Abstract: In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging str

agentsarxiv-cs-ai

SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models

arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve

applicationsarxiv-cs-cl

SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data

arXiv:2604.09411v1 Announce Type: new Abstract: Reliable 3D dynamic perception requires models that can anticipate motion beyond predefined categories, yet progress is hindered by the scarcity of dens

applicationsarxiv-cs-cv

Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network

arXiv:2604.09091v1 Announce Type: new Abstract: The use of synthetic data in machine learning applications and research offers many benefits, including performance improvements through data augmentati

applicationsarxiv-cs-lg

TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction

arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio

local-aiarxiv-cs-cv

'Take Me Home, Wi-Fi Drone': A Drone-based Wireless System for Wilderness Search and Rescue

arXiv:2604.09115v1 Announce Type: cross Abstract: Wilderness Search and Rescue (WiSAR) represents a longstanding and critical societal challenge, demanding innovative and automatic technological solut

agentsarxiv-cs-ro

Tango: Taming Visual Signals for Efficient Video Large Language Models

arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th

local-aiarxiv-cs-cv

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2505.12318v2 Announce Type: replace Abstract: Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by up

Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning

arXiv:2604.08726v1 Announce Type: new Abstract: Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localizatio

local-aiarxiv-cs-ro

Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios

arXiv:2604.09377v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial variability in performance and computational cost across tasks and queries, motivating routing systems

Task-Distributionally Robust Data-Free Meta-Learning

arXiv:2311.14756v2 Announce Type: replace-cross Abstract: Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models w

applicationsarxiv-cs-ai

Task Vectors, Learned Not Extracted: Performance Gains and Mechanistic Insight

arXiv:2509.24169v2 Announce Type: replace Abstract: Large Language Models (LLMs) can perform new tasks from in-context demonstrations, a phenomenon known as in-context learning (ICL). Recent work sugg

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice

model-releasesarxiv-cs-cl

arXiv:2604.08948v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legal

Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models

arXiv:2604.08563v1 Announce Type: cross Abstract: Extended reasoning models represent a transformative shift in Large Language Model (LLM) capabilities by enabling explicit test-time computation for c

Temporal Dropout Risk in Learning Analytics: A Harmonized Survival Benchmark Across Dynamic and Early-Window Representations

arXiv:2604.08870v1 Announce Type: cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous proto

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting

arXiv:2604.09067v1 Announce Type: new Abstract: Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is

local-aiarxiv-cs-lg

Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy

arXiv:2312.09436v3 Announce Type: replace-cross Abstract: The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to max

Hardware

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

arXiv:2604.09107v1 Announce Type: cross Abstract: Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computationa

hardwarearxiv-cs-ai

Testing the Assumptions of Active Learning for Translation Tasks with Few Samples

arXiv:2604.08977v1 Announce Type: new Abstract: Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which is useful w

Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design

model-releasesarxiv-cs-cv

arXiv:2604.09047v1 Announce Type: new Abstract: Dental implant abutments serve as the geometric and biomechanical interface between the implant fixture and the prosthetic crown, yet their design relie

The AI Codebase Maturity Model: From Assisted Coding to Self-Sustaining Systems

arXiv:2604.09388v1 Announce Type: cross Abstract: AI coding tools are widely adopted, but most teams plateau at prompt-and-review without a framework for systematic progression. This paper presents th

The causal relation between off-street parking and electric vehicle adoption in Scotland

arXiv:2604.09271v1 Announce Type: new Abstract: The transition to electric mobility hinges on maximising aggregate adoption while also facilitating equitable access. This study examines whether the 'c

safetyarxiv-cs-lg

The Fast Lane Hypothesis: Von Economo Neurons Implement a Biological Speed-Accuracy Tradeoff

arXiv:2604.09229v1 Announce Type: cross Abstract: Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?

arXiv:2601.23045v2 Announce Type: replace Abstract: As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task sc

The Impact of Gait Pattern Personalization on the Perception of Rigid Robotic Guidance: A Pilot User Experience Evaluation

arXiv:2512.17425v2 Announce Type: replace Abstract: Exoskeletons modulate human movement across diverse applications, from performance augmentation to daily-life assistance. These systems often enforc

researcharxiv-cs-ro

The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge

model-releasesarxiv-cs-lg

arXiv:2604.09034v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has significantly impacted the field of natural language processing, but their growing complexity ra

The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?

model-releasesarxiv-cs-cl

arXiv:2601.07220v3 Announce Type: replace Abstract: Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This sur

The Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs

arXiv:2601.01580v2 Announce Type: replace-cross Abstract: Self-reflection capabilities emerge in Large Language Models after RL post-training, with multi-turn RL achieving substantial gains over SFT c

Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems

arXiv:2511.09829v3 Announce Type: replace Abstract: Adversarial patches have emerged as a popular privacy-preserving approach for resisting AI-driven surveillance systems. However, their conspicuous a

applicationsarxiv-cs-ai

Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning

arXiv:2604.09150v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks by leveraging long Chain-of-Thought (CoT), but often suffer from overthinking,

safetyarxiv-cs-cl

Three Modalities, Two Design Probes, One Prototype, and No Vision: Experience-Based Co-Design of a Multi-modal 3D Data Visualization Tool

arXiv:2604.09426v1 Announce Type: cross Abstract: Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to spectroscopy, yet remain largel

Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation

arXiv:2604.09368v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as scalable user simulators for recommender system evaluation. Yet existing simulators per

safetyarxiv-cs-cv

TiAb Review Plugin: A Browser-Based Tool for AI-Assisted Title and Abstract Screening

arXiv:2604.08602v1 Announce Type: cross Abstract: Background: Server-based screening tools impose subscription costs, while open-source alternatives require coding skills. Objectives: We developed a b

Tiled Prompts: Overcoming Prompt Misguidance in Image and Video Super-Resolution

arXiv:2602.03342v2 Announce Type: replace-cross Abstract: Text-conditioned diffusion models have advanced image and video super-resolution by using prompts as semantic priors, and modern super-resolut

applicationsarxiv-cs-ai

TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference

model-releasesarxiv-cs-cv

arXiv:2604.09220v1 Announce Type: new Abstract: Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruct

TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

arXiv:2604.09439v1 Announce Type: cross Abstract: In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanatio

Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models

arXiv:2603.01400v2 Announce Type: replace Abstract: Video Large Language Models (VLLMs) demonstrate strong video understanding but suffer from inefficiency due to redundant visual tokens. Existing pru

researcharxiv-cs-cv

TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction

arXiv:2602.16821v2 Announce Type: replace Abstract: We propose TopoFlow (Topography-aware pollutant Flow learning), a physics-guided neural network for efficient, high-resolution air quality predictio

researcharxiv-cs-lg

Tora3: Trajectory-Guided Audio-Video Generation with Physical Coherence

arXiv:2604.09057v1 Announce Type: new Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible moti

safetyarxiv-cs-cv

TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches

arXiv:2604.08945v1 Announce Type: new Abstract: Accurate object geometry estimation is essential for many downstream tasks, including robotic manipulation and physical interaction. Although vision is

local-aiarxiv-cs-cv

Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning

model-releasesarxiv-cs-lg

arXiv:2604.08780v1 Announce Type: cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient planning a

Toward World Models for Epidemiology

arXiv:2604.09519v1 Announce Type: new Abstract: World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertai

safetyarxiv-cs-lg

Towards Context-Aware Image Anonymization with Multi-Agent Reasoning

arXiv:2603.27817v3 Announce Type: replace-cross Abstract: Street-level imagery contains personally identifiable information (PII), some of which is context-dependent. Existing anonymization methods ei

agentsarxiv-cs-ai