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Evidence record 10208 · automatically gathered

Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding

Decoding brain activity is useful for characterizing brain processes and understanding the functional architecture underlying cognition. However, the inter-individual variability in brain response patterns limits the development of decoders that generalize across individuals. A solution to this challenge is functional alignment: aligning functional data across individuals before training population-level decoders. The core issue is to strike the balance between aligning functional features and p

Record details

Published: 12 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment

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Toward Contemplative LLM: A Modular Framework for Evaluating and Enhancing LLM Alignment in Mental Health

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Shared topic: Safety & alignment

Independent alignment of language models

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Shared topic: Safety & alignment

Reference-Based Face Super-Resolution Using the Spatial Transformer

arXiv cs.LG · 13 July 2026

Shared topic: Safety & alignment

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arXiv red teaming query · 13 July 2026

Shared topic: Safety & alignment

AMT-X: Phase-Structured Multi-Turn Red-Teaming with Checklist-Gated Evaluation

arXiv · 13 July 2026

How to cite this record

ethics.ai (12 July 2026), “Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding,” evidence record 10208, https://ethics.ai/record/10208 (originally published by arXiv cs.LG).

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