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:2607. 07316v1 Announce Type: new Abstract: This article offers a comprehensive overview of mechanistic interpretability, an emerging field that seeks to reverse-engineer the internal algorithms of modern neural networks.
arXiv:2602. 16835v2 Announce Type: replace-cross Abstract: Safety alignment is essential for the responsible deployment of Large Language Models (LLMs).
arXiv:2607. 11193v1 Announce Type: cross Abstract: To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired.
arXiv:2607. 19894v1 Announce Type: cross Abstract: Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs.
The paper investigates whether spiking neural networks (SNNs) are more resistant to model stealing attacks than conventional artificial neural networks (ANNs). By benchmarking four common attacks on MNIST, Fashion‑MNIST, and CIFAR‑10, the authors find that SNNs consistently yield surrogates with 10%–20% lower agreement and 15%–25% lower adversarial transferability compared to ANNs. The authors attribute this resilience to the spike‑based, time‑dependent dynamics of SNNs, which make their decision boundaries harder for ANN‑based surrogates to approximate.
arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.
arXiv:2512. 10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored.
GLUScope is an open‑source tool designed to analyze neurons in Transformer language models that use GLU‑based activation functions such as SwiGLU. It visualizes the four possible sign combinations—gate and in activation being positive or negative—by providing text examples for each quadrant and indicating their frequency. The resource aims to uncover interpretable behaviors that other tools might miss and is available for researchers to extend with additional models, corpora, or neuron pages.
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance.
arXiv:2604. 04958v3 Announce Type: replace-cross Abstract: Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis.
Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into inference-time detection or training-time mitigation, but face two key limitations.
arXiv:2608.24482v1 Announce Type: cross Abstract: Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative appr...