Custom Diffusion Models (CDMs) have gained significant attention due to their remarkable ability to personalize generative processes. However, existing CDMs suffer from catastrophic forgetting when continuously learning new concepts. Most prior works attempt to mitigate this issue under the sequential learning setting with a fixed order of concept inflow and neglect inter-concept interactions. In this paper, we propose a novel framework – Forget Less by Learning Together (FL2T) – that enables concurrent and order-agnostic concept learning while addressing […]

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Fix npm trusted publishing from GitHub Actions after token revocations: add OIDC permission, upgrade npm, and publish with –provenance.

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Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In this work, we present a novel FedAvg variation called Fractional-Order Federated Averaging (FOFedAvg), which incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to capture long-range relationships and deeper historical information. By introducing memory-aware fractional-order updates, FOFedAvg improves communication efficiency and accelerates convergence while mitigating instability […]

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I think I am at least somewhat more productive than average, and people sometimes ask me for productivity tips.  So I decided to just write them all down in one place. Compound growth gets discussed as a financial concept, but it works in careers as well, and it is magic.  A small productivity gain, compounded over 50 years, is worth a lot.  So it’s worth figuring out how to optimize productivity. If you get 10% more done and […]

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arXiv:2602.15181v1 Announce Type: new Abstract: Camera virtualization — an emerging solution to novel view synthesis — holds transformative potential for visual entertainment, live performances, and sports broadcasting by enabling the generation of photorealistic images from novel viewpoints using images from a limited set of calibrated multiple static physical cameras. Despite recent advances, achieving spatially and temporally coherent and photorealistic rendering of dynamic scenes with efficient time-archival capabilities, particularly in fast-paced sports and stage performances, remains challenging for existing […]

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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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Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this work, we aim to understand how these techniques are implemented in quantum-assisted FL. Quantum Secure Aggregation (QSA) has been proposed, offering information-theoretic privacy by encoding client updates into the global phase of multipartite entangled states. Existing QSA protocols, however, rely on a single […]

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Science Robotics, Volume 11, Issue 112, March 2026.

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We introduce Parallel Coordinated Reasoning (PaCoRe), a training-and-inference framework designed to overcome a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. PaCoRe departs from the traditional sequential paradigm by driving TTC through massive parallel exploration coordinated via a message-passing architecture in multiple rounds. Each round launches many parallel reasoning trajectories, compacts their findings into context-bounded messages, and synthesizes these messages to guide the next […]

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When working on Spark jobs (in Scala), we often sequentially write the code in a single class, giving more attention to the transformations we do and forgetting how our code is structured or even if it’s tested. Today I’ll be talking about how I personally like to structure and design my Spark jobs, such as they are highly maintainable and testable.

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