Hugging Face Trending Papers
RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons
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arXiv AI
Aug 26
RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons
RACE (Residual Alignment for Consistency Estimation) is a forward‑pass statistical framework that assesses the domain‑wide functional consistency of Transformer neurons. It outperforms gradient‑based point estimates in terms of domain specificity and shows a clear association between selected neurons and target domains through token‑distribution experiments. Importantly, RACE’s computational overhead is two orders of magnitude lower than that of gradient‑based methods, enabling scalable domain‑wide analysis.
Hugging Face Trending Papers
Aug 25
Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning
Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative approaches and then guiding parameter-efficient Supervi...
arXiv AI
Aug 26
Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning
arXiv:2608.24482v1 Announce Type: cross Abstract: Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative appr...
arXiv Machine Learning
Oct 22
SYNAPSE: Framework for Neuron Analysis and Perturbation in Sequence Encoding
SYNAPSE is a training‑free framework that probes Transformer models by extracting per‑layer [CLS] representations, ranking neurons with a lightweight linear probe, and applying forward‑hook interventions during inference. It enables controlled, repeatable experiments on internal representations without altering the original model, revealing that task‑relevant information is encoded in broad, overlapping neuron subsets and that small weight or logit manipulations can redirect predictions. The framework highlights functional stability, class‑specific specialization, and complementary vulnerability profiles across domains.
arXiv Machine Learning
4d ago
Capturing Membrane Dynamics and Spike Timing of Human Neurons at Scale using Neural Operators
The paper introduces a neural-operator framework that learns a shared operator across multiple neurons of the same cell type, conditioned on biologically interpretable electrophysiological features. This approach scales biophysically realistic neuron models to large populations while preserving rich cellular dynamics and within-cell-type variability. Evaluation shows accurate reproduction of subthreshold membrane dynamics, spike waveforms, firing-rate responses, and spike timing across major cortical inhibitory neuron types, with predicted electrophysiological feature distributions closely matching detailed simulations and human recordings.
arXiv AI
Jul 28
A Cyclic Adaptation-Generalization Framework with Uncertainty-Guided Self-Paced Learning for Long-Term Brain-Machine Interfaces
arXiv:2607. 24031v1 Announce Type: new Abstract: Brain-Machine Interfaces (BMIs), which link the brain to external devices, hold great potential in rehabilitation, human performance augmentation, and human-centered robotics.