The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Research syntheses remain scarce and risk being outdated by the time of publication. To address this issue, we present a Living Meta-Analysis (LIMA) on the effects of generative AI-based interventions for learning mathematics. Following PRISMA-LSR guidelines, we continuously update the literature base, apply a Bayesian multilevel meta-regression model to account for cumulative data, and publish updated versions on a […]

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The uncomfortable truth: “model choice” is half your prompt engineering If your prompt is a recipe, the model is your kitchen. A great recipe doesn’t help if: the oven is tiny (context window), the ingredients are expensive (token price), the chef is slow (latency), or your tools don’t fit (function calling / JSON / SDK / ecosystem). So here’s a practical comparison you can actually use. Note on “parameters”: for many frontier models, parameter counts are not publicly […]

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arXiv:2601.10940v1 Announce Type: new Abstract: Split learning (SL) enables collaborative training of large language models (LLMs) between resource-constrained edge devices and compute-rich servers by partitioning model computation across the network boundary. However, existing SL systems predominantly rely on first-order (FO) optimization, which requires clients to store intermediate quantities such as activations for backpropagation. This results in substantial memory overhead, largely negating benefits of model partitioning. In contrast, zeroth-order (ZO) optimization eliminates backpropagation and significantly reduces memory usage, but […]

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Estimating the Riesz representer is a central problem in debiased machine learning for causal and structural parameter estimation. Various methods for Riesz representer estimation have been proposed, including Riesz regression and covariate balancing. This study unifies these methods within a single framework. Our framework fits a Riesz representer model to the true Riesz representer under a Bregman divergence, which includes the squared loss and the Kullback–Leibler (KL) divergence as special cases. We show that the squared loss corresponds […]

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arXiv:2601.20961v1 Announce Type: cross Abstract: We provide a complete theory of optimal universal rates for binary classification in the agnostic setting. This extends the realizable-case theory of Bousquet, Hanneke, Moran, van Handel, and Yehudayoff (2021) by removing the realizability assumption on the distribution. We identify a fundamental tetrachotomy of optimal rates: for every concept class, the optimal universal rate of convergence of the excess error rate is one of $e^{-n}$, $e^{-o(n)}$, $o(n^{-1/2})$, or arbitrarily slow. We further identify […]

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I just open-sourced a project that might interest people here who are tired of hallucinations being treated as “just a prompt issue.” VOR (Verified Observation Runtime) is a runtime layer that sits around LLMs and retrieval systems and enforces one rule: If an answer cannot be proven from observed evidence, the system must abstain. Highlights: 0.00% hallucination across demo + adversarial packs Explicit CONFLICT detection (not majority voting) Deterministic audits (hash-locked, replayable) Works with local models — the […]

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arXiv:2601.15380v1 Announce Type: cross Abstract: We generalize the attention mechanism by viewing it through the lens of Entropic Optimal Transport, revealing that standard attention corresponds to a transport problem regularized by an implicit uniform prior. We introduce Generalized Optimal transport Attention with Trainable priors (GOAT), a new attention mechanism that replaces this naive assumption with a learnable, continuous prior. This prior maintains full compatibility with optimized kernels such as FlashAttention. GOAT also provides an EOT-based explanation of attention […]

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I’ve spent the last eighteen months talking to engineers at companies you’d recognize—household names in fintech, healthcare, logistics. Same pattern everywhere. They’ll walk me through their authentication layers, their OAuth flows, their JWT rotation policies. Beautiful stuff. Then I ask: “So once I’m logged in as User 47, what stops me from just requesting User 48’s data?” Long pause. Someone mentions rate limiting. Someone else brings up their WAF. Nobody mentions the actual check. This is the BOLA […]

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