arXiv:2603.15818v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) are subtle affective states where a person shows conflicting signals through different channels — saying one thing while their face or voice tells another story. Recognising these states automatically is valuable in clinical settings, but it is hard for machines because the key evidence lives in the emph{disagreements} between what is said, how it sounds, and what the face shows. We present textbf{ConflictAwareAH}, a multimodal framework built for this […]

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arXiv:2602.07069v1 Announce Type: new Abstract: Diffusion-based super-resolution can synthesize rich details, but models trained on synthetic paired data often fail on real-world LR images due to distribution shifts. We propose Bird-SR, a bidirectional reward-guided diffusion framework that formulates super-resolution as trajectory-level preference optimization via reward feedback learning (ReFL), jointly leveraging synthetic LR-HR pairs and real-world LR images. For structural fidelity easily affected in ReFL, the model is directly optimized on synthetic pairs at early diffusion steps, which also […]

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After a bit of a hiatus, I’m finally back to putting out in blog form what I find interesting in the world of brain imaging. I like the idea of keeping up a more regular pace in putting out incompletely finalized thoughts out there. There are a few things I want to write about. Some are controversies, some are book or reviews, some are summaries of activities in my group, some cover new areas, and some are attempts […]

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When biological foundation models such as scGPT and Geneformer process single-cell gene expression, what geometric and topological structure forms in their internal representations? Is that structure biologically meaningful or a training artifact, and how confident should we be in such claims? We address these questions through autonomous large-scale hypothesis screening: an AI-driven executor-brainstormer loop that proposed, tested, and refined 141 geometric and topological hypotheses across 52 iterations, covering persistent homology, manifold distances, cross-model alignment, community structure, and directed […]

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Building AI features is straightforward until you need to integrate them into a production system. In production, a simple ‘call an LLM’ often becomes: wiring HTTP/JMS/Kafka/file inputs, enforcing timeouts, retries, fallbacks, stitching context obtained from internal data sources, protecting secrets, making it testable without burning API credits, and ensuring the system is observable when issues occur overnight. This is where Apache Camel excels. With Camel 4.5 and later, you can handle LLM calls as standard integration endpoints using […]

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arXiv:2602.04998v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is the prevailing approach for efficient large language model (LLM) fine-tuning. Building on this paradigm, recent studies have proposed alternative initialization strategies and architectural modifications, reporting substantial improvements over vanilla LoRA. However, these gains are often demonstrated under fixed or narrowly tuned hyperparameter settings, despite the known sensitivity of neural networks to training configurations. In this work, we systematically re-evaluate four representative LoRA variants alongside vanilla LoRA through extensive hyperparameter […]

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arXiv:2604.18713v1 Announce Type: new Abstract: Automated 3D segmentation of prostate lesions from biparametric MRI (bp-MRI) is essential for reliable algorithmic analysis, but achieving high precision remains challenging. Volumetric methods must combine multiple modalities while ensuring anatomical consistency, but current models struggle to integrate cross-modal information reliably. While vision-language models (VLMs) are replacing the currently used architectural designs, they still lack the fine-grained, lesion-level semantics required for effective localized guidance. To address these limitations, we propose a new multi-encoder […]

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Governance around Physical AI is becoming harder as autonomous AI systems move into robots, sensors, and industrial equipment. The issue is not only whether AI agents can complete tasks. It is how their actions are tested, monitored, and stopped when they interact with real-world systems. Industrial robotics already provides a large base for that discussion. The International Federation of Robotics said 542,000 industrial robots were installed worldwide in 2024, more than double the annual level recorded a decade […]

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In what could be interpreted as a warning of what lies ahead, AI pioneer Yoshua Bengio is warning that AI is showing signs of consciousness or “self-preservation” and humans might need to pull the plug at some point.  AI continues to rapidly evolve, moving beyond simple automation into more autonomous systems capable of complex, multi-step tasks. This incredible progress promises immense productivity gains, but it has amplified a wide range of concerns including economic disruption and longer-term societal, […]

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arXiv:2603.20602v1 Announce Type: new Abstract: Solving ill-posed inverse problems necessitates effective regularization strategies to stabilize the inversion process against measurement noise. While classical methods like Tikhonov regularization require heuristic parameter tuning, and standard deep learning approaches often lack interpretability and generalization across resolutions, we propose SC-Net (Spectral Correction Network), a novel operator learning framework. SC-Net operates in the spectral domain of the forward operator, learning a pointwise adaptive filter function that reweights spectral coefficients based on the signal-to-noise […]

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