arXiv:2603.12781v1 Announce Type: cross Abstract: Instructional Design (ID) often faces challenges in incorporating research-based knowledge and pedagogical best practices. Although educational researchers and government agencies emphasize grounding ID in evidence, integrating research findings into everyday design workflows is often complex, as it requires considering multiple context-specific demands and constraints. To address this persistent gap, this […]
LLM-driven Multimodal Recommendation
arXiv:2602.05474v5 Announce Type: replace-cross Abstract: As a paradigm that delves into the deep seated drivers of user behavior, motivation-based recommendation systems have emerged as a prominent research direction in the field of personalized information retrieval. Unlike traditional approaches that primarily rely on surface level interaction signals, these systems aim to uncover the intrinsic psychological factors […]
Mask2Flow-TSE: Two-Stage Target Speaker Extraction with Masking and Flow Matching
arXiv:2603.12837v1 Announce Type: cross Abstract: Target speaker extraction (TSE) extracts the target speaker’s voice from overlapping speech mixtures given a reference utterance. Existing approaches typically fall into two categories: discriminative and generative. Discriminative methods apply time-frequency masking for fast inference but often over-suppress the target signal, while generative methods synthesize high-quality speech at the cost […]
AI Model Modulation with Logits Redistribution
arXiv:2603.12755v1 Announce Type: new Abstract: Large-scale models are typically adapted to meet the diverse requirements of model owners and users. However, maintaining multiple specialized versions of the model is inefficient. In response, we propose AIM, a novel model modulation paradigm that enables a single model to exhibit diverse behaviors to meet the specific end requirements. […]
Finite Difference Flow Optimization for RL Post-Training of Text-to-Image Models
arXiv:2603.12893v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning from reward signals to explicitly improve desirable aspects such as image quality and prompt alignment. In this paper, we propose an online RL variant that reduces the variance in the model updates […]
AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection
arXiv:2603.06723v2 Announce Type: replace-cross Abstract: Invisible watermarks, as an essential technology for image copyright protection, have been widely deployed with the rapid development of social media and AIGC. However, existing invisible watermark detection heavily relies on prior knowledge of specific algorithms, leading to limited detection capabilities for “unknown watermarks” in open environments. To this end, […]
Stake the Points: Structure-Faithful Instance Unlearning
arXiv:2603.12915v1 Announce Type: cross Abstract: Machine unlearning (MU) addresses privacy risks in pretrained models. The main goal of MU is to remove the influence of designated data while preserving the utility of retained knowledge. Achieving this goal requires preserving semantic relations among retained instances, which existing studies often overlook. We observe that without such preservation, […]
Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations
arXiv:2603.12813v1 Announce Type: new Abstract: Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains – in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In this work, we present an agentic AI framework that delivers assistance in an industrial flowsheet […]
Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization
arXiv:2603.12960v1 Announce Type: cross Abstract: Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic applications. Its effectiveness is particularly evident in autonomous racing, a domain that serves as a challenging benchmark for real-world DRL. However, deploying RPL-based controllers […]
Competition-Aware CPC Forecasting with Near-Market Coverage
arXiv:2603.13059v1 Announce Type: cross Abstract: Cost-per-click (CPC) in paid search is a volatile auction outcome generated by a competitive landscape that is only partially observable from any single advertiser’s history. Using Google Ads auction logs from a concentrated car-rental market (2021–2023), we forecast weekly CPC for 1,811 keyword series and approximate latent competition through complementary […]
daVinci-Env: Open SWE Environment Synthesis at Scale
arXiv:2603.13023v1 Announce Type: cross Abstract: Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for […]
Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space
arXiv:2603.12878v1 Announce Type: new Abstract: The synchronized activity of neuronal populations can lead to pathological over-synchronization in conditions such as epilepsy and Parkinson disease. Such states can be desynchronized by brief electrical pulses. But when the underlying oscillating system is not known, as in most practical applications, to determine the specific times and intensities of […]