Western University

Separating a brain area's own dynamics from the inputs driving it is hard, because the same recorded activity can arise from many combinations of the two. DynISys models each recorded area as its own low-dimensional dynamical system, lets areas exchange signals only through a few explicit channels, and maps latent states to firing rates through an invertible readout. On simultaneous PMd and M1 recordings from two monkeys, eight latent dimensions per area matched the PSTH reconstruction of MR-LFADS at 128 per area, and the inferred PMd-to-M1 communication timing was consistent with target information traveling from PMd to M1.

Separating a brain area's own dynamics from the inputs driving it is hard, because the same recorded activity can arise from many combinations of the two. DynISys models each recorded area as its own low-dimensional dynamical system, lets areas exchange signals only through a few explicit channels, and maps latent states to firing rates through an invertible readout. On simultaneous PMd and M1 recordings from two monkeys, eight latent dimensions per area matched the PSTH reconstruction of MR-LFADS at 128 per area, and the inferred PMd-to-M1 communication timing was consistent with target information traveling from PMd to M1.

Under a white-box threat model, the work aligns each stream of a source image with its counterpart in a target image in the victim vision-language model's post-merger token space and evaluates under a strict success criterion requiring the model to simultaneously name the target, confirm its presence, and deny the source, finding that target semantics appear at ε=2/255 and complete replacement reaches 38% at ε=4/255 on images and 35.9% at ε=1/255 on video, while also observing semantic fusion.

Under a white-box threat model, the work aligns each stream of a source image with its counterpart in a target image in the victim vision-language model's post-merger token space and evaluates under a strict success criterion requiring the model to simultaneously name the target, confirm its presence, and deny the source, finding that target semantics appear at ε=2/255 and complete replacement reaches 38% at ε=4/255 on images and 35.9% at ε=1/255 on video, while also observing semantic fusion.

The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.

The authors present and open-source Murmurent, shared software that sits beneath agentic AI for biomedical labs, offering multi-member project and "choreography" infrastructure, specialized agents for typical biomedical data science tasks, tiered memory, traceability records, SOP and data-governance enforcement, and multi-user collaboration, and they use the system to identify putative inhibitors of Peptidyl-prolyl cis-trans Isomerase NIMA-interacting 1 (Pin1), describing several approaches and the results they yield.

This case report describes a woman in her mid-20s with generalized anxiety disorder and major depressive disorder and a history of strong social, academic, and occupational functioning who developed a pattern of functional dependence on ChatGPT, outsourcing routine cognitive and interpersonal tasks such as composing emails, interpreting social interactions, predicting the future, and making decisions, and becoming increasingly uncomfortable completing such tasks independently; the authors frame this as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking using the I-PACE model, and suggest that the unlimited accessibility of AI tools may intensify reassurance seeking and worsen tolerance of uncertainty.

This case report describes a woman in her mid-20s with generalized anxiety disorder and major depressive disorder and a history of strong social, academic, and occupational functioning who developed a pattern of functional dependence on ChatGPT, outsourcing routine cognitive and interpersonal tasks such as composing emails, interpreting social interactions, predicting the future, and making decisions, and becoming increasingly uncomfortable completing such tasks independently; the authors frame this as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking using the I-PACE model, and suggest that the unlimited accessibility of AI tools may intensify reassurance seeking and worsen tolerance of uncertainty.