The jostling for control has just begun. Here’s how leaders can resolve it.

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I wrote the original version of this list in 2018, just weeks after trading my graduation cap for a corporate badge. Back then, my biggest shock was realizing there’s no “Spring Break”! Now, as a Senior Engineer who has navigated Amazon, Microsoft and Salesforce, I’ve realized those early shocks weren’t just “adulting” milestones. They were foundational lessons for a long-term engineering career. Here’s the 2026 “senior-level” retrospective on what I learned at my first job. 1. From “No […]

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arXiv:2602.05639v1 Announce Type: cross Abstract: We introduce Variational Joint Embedding (VJE), a framework that synthesizes joint embedding and variational inference to enable self-supervised learning of probabilistic representations in a reconstruction-free, non-contrastive setting. Compared to energy-based predictive objectives that optimize pointwise discrepancies, VJE maximizes a symmetric conditional evidence lower bound (ELBO) for a latent-variable model defined directly on encoder embeddings. We instantiate the conditional likelihood with a heavy-tailed Student-$t$ model using a polar decomposition that explicitly decouples directional and […]

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arXiv:2601.10796v1 Announce Type: new Abstract: Effective physical human-robot interaction requires systems that are not only adaptable to user preferences but also transparent about their actions. This paper introduces BRIDGE, a system for bidirectional human-robot communication in physical assistance. Our method allows users to modify a robot’s planned trajectory — position, velocity, and force — in real time using natural language. We utilize a large language model (LLM) to interpret any trajectory modifications implied by user commands in the […]

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It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the “progress as usual” way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since – the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it […]

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arXiv:2603.02368v1 Announce Type: new Abstract: We introduce RO-N3WS, a benchmark Romanian speech dataset designed to improve generalization in automatic speech recognition (ASR), particularly in low-resource and out-of-distribution (OOD) conditions. RO-N3WS comprises over 126 hours of transcribed audio collected from broadcast news, literary audiobooks, film dialogue, children’s stories, and conversational podcast speech. This diversity enables robust training and fine-tuning across stylistically distinct domains. We evaluate several state-of-the-art ASR systems (Whisper, Wav2Vec 2.0) in both zero-shot and fine-tuned settings, and […]

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Table of Links Abstract and 1. Introduction Some recent trends in theoretical ML 2.1 Deep Learning via continuous-time controlled dynamical system 2.2 Probabilistic modeling and inference in DL 2.3 Deep Learning in non-Euclidean spaces 2.4 Physics Informed ML Kuramoto model 3.1 Kuramoto models from the geometric point of view 3.2 Hyperbolic geometry of Kuramoto ensembles 3.3 Kuramoto models with several globally coupled sub-ensembles Kuramoto models on higher-dimensional manifolds 4.1 Non-Abelian Kuramoto models on Lie groups 4.2 Kuramoto models […]

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Hi everyone, I’m an independent researcher (incoming MSc AI, University of Edinburgh) and I’ve written a pre-registration paper modelling the 2026 Formula 1 energy regulations as a Partially Observable Stochastic Game. I’m looking for an arXiv endorsement in cs.AI or cs.LG to upload it before the Melbourne GP on 8 March, ideally even before the race weekend starts. The paper: Opponent State Inference Under Partial Observability: An HMM–POMDP Framework for 2026 Formula 1 Energy Strategy https://www.researchgate.net/publication/401368044_Opponent_State_Inference_Under_Partial_Observability_An_HMM-POMDP_Framework_for_2026_Formula_1_Energy_Strategy The problem: […]

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arXiv:2210.09709v3 Announce Type: replace Abstract: Importance weighting is a standard tool for correcting distribution shift, but its statistical behavior under target shift — where the label distribution changes between training and testing while the conditional distribution of inputs given the label remains stable — remains under-explored. We analyze importance-weighted kernel ridge regression under target shift and show that, because the weights depend only on the output variable, reweighting corrects the train-test mismatch without altering the input-space complexity that […]

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