technocracy

AI adoption in financial services has effectively become universal–and the institutions still treating it as an experiment are now the outliers. According to Finastra’s Financial Services State of the Nation 2026 report, which surveyed 1,509 senior executives across 11 markets, only 2% of financial institutions globally report no use of AI whatsoever.  The debate is over. The question now is what comes next. For CIOs and technology leaders, the findings paint a picture that is equal parts opportunity […]

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The relationship between the physical characteristics of the radiation field and biological damage is central to both radiotherapy and radioprotection, yet the link between spatial scales of energy deposition and biological effects remains not entirely understood. To address this, we developed an interpretable deep learning model that predicts cell survival after proton and carbon ion irradiation, leveraging sequential attention to highlight relevant features and provide insight into the contribution of different energy deposition scales. Trained and tested on […]

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arXiv:2601.15296v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance, yet existing decoding strategies either explore blindly (random sampling) or redundantly (independent multi-sampling). We propose Entropy-Tree, a tree-based decoding method that exploits entropy as a signal for branching decisions–expanding the search tree only at positions where the model exhibits genuine uncertainty. Entropy-Tree shows superior accuracy and calibration in reasoning tasks: it achieves better pass@k than Multi-chain across multiple models and datasets, and its predictive entropy demonstrates […]

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arXiv:2602.07077v1 Announce Type: new Abstract: Large audio-language models (LALMs) exhibit strong zero-shot capabilities in multiple downstream tasks, such as audio question answering (AQA) and abstract reasoning; however, these models still lag behind specialized models for certain discriminative tasks (e.g., audio classification). Recent studies show that sparse subsets of attention heads within an LALM can serve as strong discriminative feature extractors for downstream tasks such as classification via simple voting schemes. However, these methods assign uniform weights to all […]

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You’re not bad at planning. Your calendar is packed with appointments, meal schedules, and activities that look perfectly organized. But somehow, those carefully laid plans vanish between your phone screen and real life. The truth is, most households struggle with execution, not planning. Everyone knows what needs to happen. Yet nobody takes action because the information lives in scattered places. A calendar app here, a sticky note there, and verbal reminders everywhere create chaos. This isn’t about downloading […]

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Agentic Engineering Patterns > There are some behaviors that are anti-patterns in our weird new world of agentic engineering. Inflicting unreviewed code on collaborators This anti-pattern is common and deeply frustrating. Don’t file pull requests with code you haven’t reviewed yourself. If you open a PR with hundreds (or thousands) of lines of code that an agent produced for you, and you haven’t done the work to ensure that code is functional yourself, you are delegating the actual […]

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Hi everyone, I am an RL novice working on my first “real” project: a solver for the Multi-Warehouse Vehicle Routing Problem (MWVRP). My background is limited (I’ve essentially only read the DeepMDV paper and some standard VRP literature), so I am looking for a sanity check on my approach, as well as recommendations for papers or codebases that tackle similar constraints. The Problem Setting: I am modeling a supply chain with: Multiple Depots & Heterogeneous Fleet (Vans, Medium […]

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arXiv:2602.16835v1 Announce Type: new Abstract: Safety alignment is essential for the responsible deployment of large language models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to update, audit, and maintain across model families. Full fine-tuning incurs substantial computational and storage overhead, while parameter-efficient methods such as LoRA trade efficiency for inconsistent safety gains and sensitivity to design choices. Safety intervention mechanisms such as circuit breakers reduce unsafe outputs without modifying model weights, but do […]

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