arXiv:2602.07205v1 Announce Type: cross Abstract: We study online learning in two-player uninformed Markov games, where the opponent’s actions and policies are unobserved. In this setting, Tian et al. (2021) show that achieving no-external-regret is impossible without incurring an exponential dependence on the episode length $H$. They then turn to the weaker notion of Nash-value regret and propose a V-learning algorithm with regret $O(K^{2/3})$ after $K$ episodes. However, their algorithm and guarantee do not adapt to the difficulty of […]

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arXiv:2602.00935v1 Announce Type: new Abstract: Ants are highly capable of grasping objects in clutter, and we have recently observed that this involves substantial use of their forelegs. The forelegs, more specifically the tarsi, have high friction microstructures (setal pads), are covered in hairs, and have a flexible under-actuated tip. Here we abstract these features to test their functional advantages for a novel low-cost gripper design, suitable for bin-picking applications. In our implementation, the gripper legs are long and […]

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Crypto presales are no longer judged the way they were a few years ago. The change did not come from a single market crash or regulatory decision. It came from experience. Investors, builders, and observers have simply seen enough outcomes to recognize patterns that were invisible before. What once passed as ambition is now often read as overreach, and what used to look slow can now signal discipline. From fast raises to structural credibility In earlier cycles, the […]

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In this work, we present OCLADS, a novel communication framework with continual learning (CL) for Internet of Things (IoT) anomaly detection (AD) when operating in non-stationary environments. As the statistical properties of the observed data change with time, the on-device inference model becomes obsolete, which necessitates strategic model updating. OCLADS keeps track of data distribution shifts to timely update the on-device IoT AD model. To do so, OCLADS introduces two mechanisms during the interaction between the resource-constrained IoT […]

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Product decisions are often discussed as if they exist in a vacuum. Build the right features, optimise engagement, scale what works. In practice, those decisions are heavily shaped by context — especially in emerging markets. Shipping REM (Rapid Eye Movement), a live mobile game released to players in an emerging market, challenged many assumptions I had absorbed from mainstream game development and startup discourse. The biggest lesson was simple: good product decisions come from real constraints, not best […]

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Riyadh, Saudi Arabia- The Global AI Show Riyadh 2026, taking place June 29-30, has unveiled its first lineup of thought leaders, policymakers, innovators, and global AI experts set to drive the direction of artificial intelligence and put it on the edge of innovation until 2026.  Organized by VAP Group and powered by Times of AI, this event will feature more than 100 unique speakers from 80+ countries with backgrounds in government digital transformation, AI strategy, and startups dedicated […]

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arXiv:2602.23559v1 Announce Type: new Abstract: We present the first study of cross-sensor view synthesis across different modalities. We examine a practical, fundamental, yet widely overlooked problem: getting aligned RGB-X data, where most RGB-X prior work assumes such pairs exist and focuses on modality fusion, but it empirically requires huge engineering effort in calibration. We propose a match-densify-consolidate method. First, we perform RGB-X image matching followed by guided point densification. Using the proposed confidence-aware densification and self-matching filtering, we […]

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We investigate the geometric structure of learning dynamics in overparameterized transformer models through carefully controlled modular arithmetic tasks. Our primary finding is that despite operating in high-dimensional parameter spaces ($d=128$), transformer training trajectories rapidly collapse onto low-dimensional execution manifolds of dimension $3$–$4$. This dimensional collapse is robust across random seeds and moderate task difficulties, though the orientation of the manifold in parameter space varies between runs. We demonstrate that this geometric structure underlies several empirically observed phenomena: (1) […]

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