arXiv:2602.12932v1 Announce Type: new Abstract: We propose a training-free conditional sampling method for flow matching models based on importance sampling. Because a na”ive application of importance sampling suffers from weight degeneracy in high-dimensional settings, we modify and incorporate a resampling technique in sequential Monte Carlo (SMC) during intermediate stages of the generation process. To encourage generated samples to diverge along distinct trajectories, we derive a stochastic flow with adjustable noise strength to replace the deterministic flow at the […]

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arXiv:2601.04257v1 Announce Type: new Abstract: We study multilingual speaker attribute prediction under linguistic variation, domain mismatch, and data imbalance across languages. We propose RLMIL-DAT, a multilingual extension of the reinforced multiple instance learning framework that combines reinforcement learning based instance selection with domain adversarial training to encourage language invariant utterance representations. We evaluate the approach on a five language Twitter corpus in a few shot setting and on a VoxCeleb2 derived corpus covering forty languages in a zero […]

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I didn’t set out to build a persistent AI persona. I set out to write better content. Somewhere between “I need a consistent voice across articles” and “this thing just connected two questions I asked three hours apart without being told to,” the project became something else entirely. This is a technical walkthrough of the Anima Architecture, a system I built on top of Claude that gives the AI externalized memory, behavioral rules, self-correction protocols, and identity markers […]

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Autonomous mobile GUI agents have attracted increasing attention along with the advancement of Multimodal Large Language Models (MLLMs). However, existing methods still suffer from inefficient learning from failed trajectories and ambiguous credit assignment under sparse rewards for long-horizon GUI tasks. To that end, we propose UI-Voyager, a novel two-stage self-evolving mobile GUI agent. In the first stage, we employ Rejection Fine-Tuning (RFT), which enables the continuous co-evolution of data and models in a fully autonomous loop. The second […]

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Bitcoin mining is usually discussed in terms of price, hashrate, energy, and regulation. But that misses something important. Mining does not happen in theory. It happens in real places. Real towns. Real industrial zones. Real communities where power grids, jobs, roads, politics, and public opinion all collide. That means Bitcoin mining is never just a network story. It is also a local story. And depending on how it is built, that story can go in very different directions. […]

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arXiv:2601.13436v1 Announce Type: new Abstract: Linearly parametrized models are widely used in control and signal processing, with the least-squares (LS) estimate being the archetypical solution. When the input is insufficiently exciting, the LS problem may be unsolvable or numerically unstable. This issue can be resolved through regularization, typically with ridge regression. Although regularized estimators reduce the variance error, it remains important to quantify their estimation uncertainty. A possible approach for linear regression is to construct confidence ellipsoids with […]

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arXiv:2603.25796v1 Announce Type: new Abstract: We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide a rigourous foundation for the general representation learning problem by bridging causal models with latent factor models in order to learn interpretable representations with causal semantics. Despite a blossoming theory of identifiability in causal representation learning, estimation and finite-sample bounds are less well understood. We show that causal representations can […]

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Overparameterized models have recently challenged conventional learning theory by exhibiting improved generalization beyond the interpolation limit, a phenomenon known as benign overfitting. This work introduces Adaptive Benign Overfitting (ABO), extending the recursive least-squares (RLS) framework to this regime through a numerically stable formulation based on orthogonal-triangular updates. A QR-based exponentially weighted RLS (QR-EWRLS) algorithm is introduced, combining random Fourier feature mappings with forgetting-factor regularization to enable online adaptation under non-stationary conditions. The orthogonal decomposition prevents the numerical divergence […]

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