Universidad de Sevilla

Activation steering adds a vector to a large language model's internal representations at inference time, but where to add it has mostly been settled by trial: existing selectors score how separable the two activation classes are or how well individual displacements align with their mean, without ever steering the model to check. TopoSteer instead scores each layer by the total length of the Euclidean minimum spanning tree of its per-example displacement cloud, measuring whether the mean-difference vector can stand in for those displacements. Across five instruction-tuned models and six binary persona-steering tasks it beats LayerNavigator in most model-budget comparisons, but the margin depends on model and layer budget and is smaller and more mixed on Qwen-7B and Qwen-32B.

Activation steering adds a vector to a large language model's internal representations at inference time, but where to add it has mostly been settled by trial: existing selectors score how separable the two activation classes are or how well individual displacements align with their mean, without ever steering the model to check. TopoSteer instead scores each layer by the total length of the Euclidean minimum spanning tree of its per-example displacement cloud, measuring whether the mean-difference vector can stand in for those displacements. Across five instruction-tuned models and six binary persona-steering tasks it beats LayerNavigator in most model-budget comparisons, but the margin depends on model and layer budget and is smaller and more mixed on Qwen-7B and Qwen-32B.

Across 47 public PHM datasets harmonized to one tensor contract, an 801,808-parameter channel-independent Transformer encoder pretrained with masked patch prediction raises fault-classification macro-F1 from 0.350 to 0.812 on the fully withheld CWRU set and from 0.569 to 0.951 on HSG18, with frozen linear probing alone reaching 0.705 and 0.906; the same pretraining does not reliably beat a from-scratch baseline on CMAPSS remaining-useful-life estimation. Simulated federated pretraining over ten clients grouped by physical domain retains part of the representation but trails the centralized encoder at matched budget, and the self-supervised loss does not predict downstream utility.

Across 47 public PHM datasets harmonized to one tensor contract, an 801,808-parameter channel-independent Transformer encoder pretrained with masked patch prediction raises fault-classification macro-F1 from 0.350 to 0.812 on the fully withheld CWRU set and from 0.569 to 0.951 on HSG18, with frozen linear probing alone reaching 0.705 and 0.906; the same pretraining does not reliably beat a from-scratch baseline on CMAPSS remaining-useful-life estimation. Simulated federated pretraining over ten clients grouped by physical domain retains part of the representation but trails the centralized encoder at matched budget, and the self-supervised loss does not predict downstream utility.

Ewing sarcoma is genomically near-uniform yet patients fare very differently. Single-cell and spatial transcriptomics of tumors and their microenvironment in 135 patients identified five tumor-cell transcriptional states and eight niches defined by neighboring cell configurations, and niche composition sorted patients into Fusion Active, Proliferative, Immune Reactive and TME-enriched types. The Immune Reactive type, marked by interferon-activated tumor cells, had favorable survival, and the survival differences reproduced in two independent bulk transcriptomic cohorts.

Ewing sarcoma is genomically near-uniform yet patients fare very differently. Single-cell and spatial transcriptomics of tumors and their microenvironment in 135 patients identified five tumor-cell transcriptional states and eight niches defined by neighboring cell configurations, and niche composition sorted patients into Fusion Active, Proliferative, Immune Reactive and TME-enriched types. The Immune Reactive type, marked by interferon-activated tumor cells, had favorable survival, and the survival differences reproduced in two independent bulk transcriptomic cohorts.