:::info Astounding Stories of Super-Science March, 1932, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Affair of the Brains – Chapter I: Off to the Rendezvous Astounding Stories of Super-Science March 1932: The Affair of the Brains – Chapter I Off to the Rendezvous By Anthony Gilmore ::: :::tip Hawk Carse himself goes to keep Judd the Kite’s rendezvous with the sinister genius Ku Sui. ::: […]

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Developing effective predictive models becomes challenging in dynamic environments that continuously produce data and constantly change. Continual Learning (CL) and Streaming Machine Learning (SML) are two research areas that tackle this arduous task. We put forward a unified setting that harnesses the benefits of both CL and SML: their ability to quickly adapt to non-stationary data streams without forgetting previous knowledge. We refer to this setting as Streaming Continual Learning (SCL). SCL does not replace either CL or […]

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We present Speculative Rollout with Tree-Structured Cache (SRT), a simple, model-free approach to accelerate on-policy reinforcement learning (RL) for language models without sacrificing distributional correctness. SRT exploits the empirical similarity of rollouts for the same prompt across training steps by storing previously generated continuations in a per-prompt tree-structured cache. During generation, the current policy uses this tree as the draft model for performing speculative decoding. To keep the cache fresh and improve draft model quality, SRT updates trees […]

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arXiv:2602.17826v1 Announce Type: new Abstract: Language models exhibit fundamental limitations — hallucination, brittleness, and lack of formal grounding — that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal domain ontologies can enhance language model reliability through retrieval-augmented generation. Using mathematics as proof of concept, I implement a neuro-symbolic pipeline leveraging the OpenMath ontology with hybrid retrieval and cross-encoder reranking to inject relevant definitions into model prompts. Evaluation on the MATH benchmark with […]

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arXiv:2505.21722v2 Announce Type: replace-cross Abstract: When a deep ReLU network is initialized with small weights, gradient descent (GD) is at first dominated by the saddle at the origin in parameter space. We study the so-called escape directions along which GD leaves the origin, which play a similar role as the eigenvectors of the Hessian for strict saddles. We show that the optimal escape direction features a low-rank bias in its deeper layers: the first singular value of the […]

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Second primary cancer (SPC), a new cancer in patients different from previously diagnosed, is a growing concern due to improved cancer survival rates. Early prediction of SPC is essential to enable timely clinical interventions. This study focuses on lung cancer survivors treated in Taiwanese hospitals, where the limited size and geographic scope of local datasets restrict the effectiveness and generalizability of traditional machine learning approaches. To address this, we incorporate external data from the publicly available US-based Surveillance, […]

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arXiv:2601.20917v1 Announce Type: new Abstract: We present masked Lagrange reconstruction, a technique that enables threshold ML-DSA (FIPS 204) with arbitrary thresholds $T$ while producing standard 3.3 KB signatures verifiable by unmodified FIPS 204 implementations. Concurrent approaches have limitations: Bienstock et al. (ePrint 2025/1163) achieve arbitrary $T$ but require honest-majority and 37–136 rounds; Celi et al. (ePrint 2026/013) achieve dishonest-majority but are limited to $T leq 6$. Our technique addresses the barrier that Lagrange coefficients grow as $Theta(q)$ for […]

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arXiv:2508.19897v4 Announce Type: replace Abstract: Generative diffusion models have emerged as a powerful class of models in machine learning, yet a unified theoretical understanding of their operation is still developing. This paper provides an integrated perspective on generative diffusion by connecting the information-theoretic, dynamical, and thermodynamic aspects. We demonstrate that the rate of conditional entropy production during generation (i.e., the generative bandwidth) is directly governed by the expected divergence of the score function’s vector field. This divergence, in […]

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