arXiv:2602.15831v1 Announce Type: new Abstract: AI agents are increasingly deployed as autonomous systems capable of planning, tool use, and multi-agent collaboration across complex tasks. However, existing agent-related protocols focus on agent-to-agent interactions, leaving humans as external observers rather than integrated participants within the agent systems. This limitation arises from the lack of a standardized mechanism for agents to discover, address, and interact with humans across heterogeneous messaging platforms. In this paper, we propose the A2H (Agent-to-Human) protocol, a […]

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The growing complexity of neural networks hinders the deployment of distributed machine learning on resource-constrained devices. Split learning (SL) offers a promising solution by partitioning the large model and offloading the primary training workload from edge devices to an edge server. However, the increasing number of participating devices and model complexity leads to significant communication overhead from the transmission of smashed data (e.g., activations and gradients), which constitutes a critical bottleneck for SL. To tackle this challenge, we […]

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arXiv:2601.15307v1 Announce Type: new Abstract: The rapid development of automated scientific survey generation technology has made it increasingly important to establish a comprehensive benchmark to evaluate the quality of generated surveys.Nearly all existing evaluation benchmarks rely on flawed selection criteria such as citation counts and structural coherence to select human-written surveys as the ground truth survey datasets, and then use surface-level metrics such as structural quality and reference relevance to evaluate generated surveys.However, these benchmarks have two key […]

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arXiv:2601.19007v1 Announce Type: new Abstract: We propose a novel approach to computationally efficient GP training based on the observation that square-exponential (SE) covariance matrices contain several off-diagonal entries extremely close to zero. We construct a principled procedure to eliminate those entries to produce a emph{banded}-matrix approximation to the original covariance, whose inverse and determinant can be computed at a reduced computational cost, thus contributing to an efficient approximation to the likelihood function. We provide a theoretical analysis of […]

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Agentic AI systems act autonomously across enterprise environments, making traditional perimeter-based security ineffective. Zero Trust treats AI agents as independent actors with identities, enforcing continuous verification, least-privilege access, and contextual controls across APIs, systems, and data. While powerful, Zero Trust must be implemented thoughtfully to avoid policy sprawl, blind spots, and legacy gaps. When done right, it enables organizations to scale autonomous AI safely—without sacrificing speed, innovation, or trust.

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Hey, I’ve been going down the rabbit hole of sim-to-real RL and I’m trying to understand where the ACTUAL bottlenecks are for people doing this in practice (not just in papers). From what I’ve read, domain randomization and system identification help close the gap, but it seems like there’s still a lot of pain around rare/adversarial scenarios that you can’t really plan for in sim. For those of you actually deploying RL policies on physical robots: What part […]

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arXiv:2603.05716v1 Announce Type: new Abstract: Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1-2 automation but short of full autonomy, are increasingly sharing the road with human-driven vehicles (HDVs). As these systems interact during complex maneuvers such as lane changes, new patterns may emerge with implications for traffic stability and safety. Assessing these dynamics, particularly during mandatory lane changes, requires high-resolution trajectory data, yet datasets capturing tAV lane-changing behavior are scarce. This study introduces the North Carolina […]

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Modern AI is no longer powered by a single type of processor—it runs on a diverse ecosystem of specialized compute architectures, each making deliberate tradeoffs between flexibility, parallelism, and memory efficiency. While traditional systems relied heavily on CPUs, today’s AI workloads are distributed across GPUs for massive parallel computation, NPUs for efficient on-device inference, and TPUs designed specifically for neural network execution with optimized data flow.  Emerging innovations like Groq’s LPU further push the boundaries, delivering significantly faster […]

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Assignement2: Deep Learning-Based Quiz (Visual MCQ Solver) You will be given PNG images containing questions from deep learning Your tasks: Process and understand questions from images Build a model to answer MCQs Each question will have 4 options with only 1 correct answer can someone tell me how i can solve this task i mean i have image which contain textual question can include equation also i dont know what is best way to solve this task if […]

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