arXiv:2602.22346v1 Announce Type: new Abstract: Autonomous mobile service robots, like lawnmowers or cleaning robots, operating in human-populated environments need to reason about local human-human interactions to support safe and socially aware navigation while fulfilling their tasks. For such robots, interaction understanding is not primarily a fine-grained recognition problem, but a perception problem under limited sensing quality and computational resources. Many existing approaches focus on holistic group activity recognition, which often requires complex and large models which may not […]

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arXiv:2510.08335v2 Announce Type: replace Abstract: Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are learnable, via the lens of the classic PAC […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## How Supercell Powers its Massive Social Network with ScyllaDB By @scylladb [ 6 Min read ] Supercell powers real-time cross-game chat, presence, and notifications for millions using ScyllaDB Cloud, enabling low-latency, scalable events. Read More. Solo Satoshi Becomes Authorized Canaan Distributor for Avalon Home […]

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arXiv:2601.12236v1 Announce Type: new Abstract: Many countries are rapidly adopting electric vehicles (EVs) due to their meager running cost and environment-friendly nature. EVs are likely to dominate the internal combustion (IC) engine cars entirely over the next few years. With the rise in popularity of EVs, adverse effects of EV charging loads on the grid system have been observed. Since the distribution system (DS) does not cope with the high overloading capacity, the negative impact of EV charging […]

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Every entrepreneur talks about vision, resilience, and execution. But beneath all of these traits lies a quieter, less visible skill that ultimately determines success or failure: decision-making under uncertainty. Markets shift, data is incomplete, and outcomes are rarely guaranteed. The leaders who endure are not those who wait for certainty—but those who learn how to think clearly without it. According to Alessio Vinassa, serial entrepreneur and business advisor, uncertainty is not an obstacle to leadership—it is the environment […]

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arXiv:2510.21974v2 Announce Type: replace-cross Abstract: We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitations of conventional Jump Gaussian Processes (JGP) in high-dimensional input spaces by integrating region-specific, locally linear projections with JGP modeling. These projections employ region-dependent matrices to capture local low-dimensional subspace structures, making them well suited to the inherently localized modeling behavior of JGPs, a variant of local Gaussian […]

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arXiv:2602.21231v1 Announce Type: new Abstract: We present ACAR (Adaptive Complexity and Attribution Routing), a measurement framework for studying multi-model orchestration under auditable conditions. ACAR uses self-consistency variance (sigma) computed from N=3 probe samples to route tasks across single-model, two-model, and three-model execution modes. The system is implemented on top of TEAMLLM, a deterministic execution substrate with immutable artifacts and complete decision traces. We evaluate ACAR on 1,510 tasks spanning four benchmarks: MathArena, Reasoning Gym, LiveCodeBench, and SuperGPQA, using […]

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arXiv:2509.19189v4 Announce Type: replace-cross Abstract: Scaling laws have emerged as a unifying lens for understanding and guiding the training of large language models (LLMs). However, existing studies predominantly focus on the final-step loss, leaving open whether the entire loss dynamics obey similar laws and, crucially, how the learning rate schedule (LRS) shapes them. We address these gaps in a controlled theoretical setting by analyzing stochastic gradient descent (SGD) on a power-law kernel regression model. The key insight is […]

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