arXiv:2603.25070v1 Announce Type: cross Abstract: Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explainable ensemble learning paradigm that fuses optimized feature pyramids, deep networks, self-attention mechanisms, and residual networks for bolstering crop suitability predictions based on soil […]
Generative deep learning for foundational video translation in ultrasound
arXiv:2511.03255v2 Announce Type: replace-cross Abstract: Deep learning (DL) has the potential to revolutionize image acquisition and interpretation across medicine, however, attention to data imbalance and missingness is required. Ultrasound data presents a particular challenge because in addition to different views and structures, it includes several sub-modalities-such as greyscale and color flow doppler (CFD)-that are often […]
From Logic Monopoly to Social Contract: Separation of Power and the Institutional Foundations for Autonomous Agent Economies
arXiv:2603.25100v1 Announce Type: cross Abstract: Existing multi-agent frameworks allow each agent to simultaneously plan, execute, and evaluate its own actions — a structural deficiency we term the “Logic Monopoly.” Empirical evidence quantifies the resulting “Reliability Gap”: 84.30% average attack success rates across ten deployment scenarios, 31.4% emergent deceptive behavior without explicit reward signals, and cascading […]
Shopping with a Platform AI Assistant: Who Adopts, When in the Journey, and What For
arXiv:2603.24947v1 Announce Type: new Abstract: This paper provides some of the first large-scale descriptive evidence on how consumers adopt and use platform-embedded shopping AI in e-commerce. Using data on 31 million users of Ctrip, China’s largest online travel platform, we study “Wendao,” an LLM-based AI assistant integrated into the platform. We document three empirical regularities. […]
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
arXiv:2603.25126v1 Announce Type: cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single-behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi-behavior distributions, struggle […]
Towards Exploratory and Focused Manipulation with Bimanual Active Perception: A New Problem, Benchmark and Strategy
arXiv:2602.01939v3 Announce Type: replace-cross Abstract: Recently, active vision has reemerged as an important concept for manipulation, since visual occlusion occurs more frequently when main cameras are mounted on the robot heads. We reflect on the visual occlusion issue and identify its essence as the absence of information useful for task completion. Inspired by this, we […]
Can MLLMs Read Students’ Minds? Unpacking Multimodal Error Analysis in Handwritten Math
arXiv:2603.24961v1 Announce Type: new Abstract: Assessing student handwritten scratchwork is crucial for personalized educational feedback but presents unique challenges due to diverse handwriting, complex layouts, and varied problem-solving approaches. Existing educational NLP primarily focuses on textual responses and neglects the complexity and multimodality inherent in authentic handwritten scratchwork. Current multimodal large language models (MLLMs) excel […]
PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems
arXiv:2603.25164v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as outdated knowledge and the tendency to generate hallucinations. To address these limitations, Retrieval-Augmented Generation (RAG) systems have been introduced, enhancing LLMs with external, up-to-date knowledge […]
Elastic Weight Consolidation Done Right for Continual Learning
arXiv:2603.18596v3 Announce Type: replace-cross Abstract: Weight regularization methods in continual learning (CL) alleviate catastrophic forgetting by assessing and penalizing changes to important model weights. Elastic Weight Consolidation (EWC) is a foundational and widely used approach within this framework that estimates weight importance based on gradients. However, it has consistently shown suboptimal performance. In this paper, […]
A Decade-Scale Benchmark Evaluating LLMs’ Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations
arXiv:2603.25196v1 Announce Type: cross Abstract: Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations. To address this gap, we introduce CPGBench, an […]
Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems
arXiv:2603.24963v1 Announce Type: new Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization events. To support a wide variety of product surfaces and advertiser goals, these platforms frequently maintain an extensive ecosystem of machine learning (ML) models. However, operating at this […]
FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDA
arXiv:2603.25243v1 Announce Type: cross Abstract: Large language models and autonomous agents are increasingly explored for EDA automation, but many existing integrations still rely on script-level or request-level interactions, which makes it difficult to preserve tool state and support iterative optimization in real production-oriented environments. In this work, we present FluxEDA, a unified and stateful infrastructure […]