arXiv:2603.29861v1 Announce Type: cross Abstract: With the ever-growing urgency of sustainability in the economy and society, and the massive stream of information that comes with it, consumers need reliable access to that information. To address this need, companies began publishing so called Environmental, Social, and Governance (ESG) reports, both voluntarily and forced by law. To […]
Derived Fields Preserve Fine-Scale Detail in Budgeted Neural Simulators
arXiv:2603.29224v1 Announce Type: cross Abstract: Fine-scale-faithful neural simulation under fixed storage budgets remains challenging. Many existing methods reduce high-frequency error by improving architectures, training objectives, or rollout strategies. However, under budgeted coarsen-quantize-decode pipelines, fine detail can already be lost when the carried state is constructed. In the canonical periodic incompressible Navier-Stokes setting, we show that […]
Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry
arXiv:2603.29289v1 Announce Type: cross Abstract: The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple […]
RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment
arXiv:2603.29419v1 Announce Type: cross Abstract: Understanding object affordances is essential for enabling robots to perform purposeful and fine-grained interactions in diverse and unstructured environments. However, existing approaches either rely on retrieval, which is fragile due to sparsity and coverage gaps, or on large-scale models, which frequently mislocalize contact points and mispredict post-contact actions when applied […]
Generating Key Postures of Bharatanatyam Adavus with Pose Estimation
arXiv:2603.29570v1 Announce Type: cross Abstract: Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial […]
Theory of Mind and Self-Attributions of Mentality are Dissociable in LLMs
arXiv:2603.28925v1 Announce Type: cross Abstract: Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to experience emotions. We investigate whether suppressing mind-attribution tendencies degrades intimately related socio-cognitive abilities such as Theory of Mind (ToM). Through safety ablation and mechanistic analyses […]
Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference
arXiv:2603.29002v1 Announce Type: cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning. We show that these optimizations can be unified into a four-step memory processing pipeline: Prepare Memory, Compute Relevancy, Retrieval, and Apply to […]
Empirical Comparison of Agent Communication Protocols for Task Orchestration
arXiv:2603.22823v2 Announce Type: replace Abstract: Context. Nowadays, artificial intelligence agent systems are transforming from single-tool interactions to complex multi-agent orchestrations. As a result, two competing communication protocols have emerged: a tool integration protocol that standardizes how agents invoke external tools, and an inter-agent delegation protocol that enables autonomous agents to discover and delegate tasks to […]
Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?
arXiv:2603.29121v1 Announce Type: cross Abstract: This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the […]
Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention
arXiv:2603.29194v1 Announce Type: cross Abstract: Long-horizon dialogue systems suffer from semanticdrift and unstable memory retention across extended sessions. This paper presents a Multi-Layer Memory Framework that decomposes dialogue history into working, episodic, and semantic layers with adaptive retrieval gating and retention regularization. The architecture controls cross-session drift while maintaining bounded context growth and computational efficiency. […]
Omni-NegCLIP: Enhancing CLIP with Front-Layer Contrastive Fine-Tuning for Comprehensive Negation Understanding
arXiv:2603.29258v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that VLMs, such as CLIP, perform poorly in understanding negation expressions, which are common in natural language. In this work, we propose Omni-NegCLIP, a fine-tuned CLIP model that improves CLIP’s understanding […]
Real-Time Band-Grouped Vocal Denoising Using Sigmoid-Driven Ideal Ratio Masking
arXiv:2603.29326v1 Announce Type: cross Abstract: Real-time, deep learning-based vocal denoising has seen significant progress over the past few years, demonstrating the capability of artificial intelligence in preserving the naturalness of the voice while increasing the signal-to-noise ratio (SNR). However, many deep learning approaches have high amounts of latency and require long frames of context, making […]