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Related Articles from SNS
Certified Circuits: Stability Guarantees for Mechanistic Circuits
Announce Type: replace Abstract: Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pursues this goal by identifying circuits--minimal subnetworks responsible for specific behaviors. However, existing circuit discovery methods are brittle: circuits depend strongly on the chosen concept dataset and often fail to transfer out-of-distribution, raising doubts whether they capture the concept or merely...
Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery
arXiv:2606.06267v1 Announce Type: new Abstract: Circuit discovery methods identify subgraphs that explain specific model behaviors, and structural differences between discovered circuits are commonly interpreted as evidence of distinct mechanisms. We test this assumption by varying input statistics while holding the task fixed, and show that the resulting structural differences exhibit apparent specialization but do not correspond to functional differences, a pattern we term phantom...
Spectral Probe-Circuits: A Three-Step Recipe for Identifying Attention-Head Circuits in Pretrained Transformers
arXiv:2605.24059v2 Announce Type: replace Abstract: We present a three-step recipe for identifying attention-head circuits in pretrained transformers. A per-head spectral signal -- the time-integrated participation ratio of each head's attention output -- ranks heads doing sustained content-dependent computation without labels or attribution gradients. A task-pattern screen filters this general indicator into a task-specific candidate circuit, and group ablation against a matched-random...
Mixed Potential Approach to Convergence of Nonlinear RLC Circuits with Memristors
Announce Type: new Abstract: The paper considers a large class of nonlinear circuits, termed RLCM, containing all four basic circuit elements, i.e., resistors, inductors, capacitors and memristors. A companion paper [1] has introduced a mixed potential for RLCM circuits generalizing that found by Brayton and Moser for circuits without memristors. In this paper, systematic Lyapunov-like results on convergence of RLCM circuits are proved by means of the mixed potential.
Branch-Aware Quantum Constant Propagation for Dynamic Quantum Circuits
arXiv:2606.02018v1 Announce Type: cross Abstract: Compile-time optimization is important for improving the efficiency and reliability of quantum circuits on current noisy hardware. While many existing methods simplify circuits using structural patterns or quantum-state information, most of them target only unitary circuits and do not support dynamic circuits with mid-circuit measurements and classical feedforward. In this work, we present Branch-Aware Quantum Constant Propagation (BQCP), a...
Hidden beneath AI chips, Chinese-made circuit boards raise national security concerns in U.S.
Printed circuit boards sit underneath almost every chip, a necessity in nearly every electronic. They make up a quiet but crucial piece of the booming artificial intelligence market, and represent a growing problem for the U.S., because nearly all AI circuit boards, for Nvidia and others, are made in China. Circuit boards present all sorts of opportunities for adversaries to sneak through malicious components.
Mixed potential for nonlinear RLC circuits with memristors
arXiv:2606.04638v1 Announce Type: new Abstract: In two seminal articles published in 1964, Brayton and Moser introduced the concept of a mixed potential as a fundamental theoretic tool to describe and analyze a class RLC of nonlinear circuits containing resistors, capacitors and inductors. In this paper, it is shown for the first time that a mixed potential can be introduced for a class RLCM of RLC circuits containing also memristors. This is possible provided a memristor circuit is analyzed...
Equivalent volitional learning emerges through circuit-specific population dynamics in motor cortex and hippocampus
Learning operates across different brain circuits to associate population activity patterns with desired outcomes, and to enable volitional reactivation of those patterns to control behavior. These circuits differ profoundly in their architecture and dynamical regimes, yet which features of learning are shared across them and which arise from circuit-specific implementations remains unknown. Here, we use a brain-computer interface (BCI) to train mice to modulate the activity of selected...
Neural birth time and somatosensory circuit assembly are linked by Robo3 regulation of dendrite morphology
Neural circuit wiring requires a remarkable level of precision, as thousands of neurons generate millions of synapses in distinct configurations. While neural circuits are known to be shaped by both neural birth timing and multiple classes of guidance molecules, the relationship between the two and how they coordinate circuit assembly has yet to be evaluated. Leveraging the well-defined stem cell lineages of the Drosophila embryonic nerve cord, we investigate how Roundabout (Robo) guidance...
Conservation-Based Feedback-Circuit Decomposition for Linear Forced Systems
Announce Type: cross Abstract: We present a conservation-based feedback-circuit decomposition specifically for general linear forced systems. In a role parallel to that of eigenvalues and eigenvectors for initial-value problems, the complete set of independent intrinsic circuit gains and their associated forcing-transformation vectors provide a complete analytical representation of both transient and equilibrium forced solutions. The sign of intrinsic circuit gains determines whether...