Cognitive Architectures

Opening — Why this matters now The AI hardware race is entering a biological phase. As GPUs hit their thermal limits, a quiet counterrevolution is forming around spikes, not tensors. Spiking Neural Networks (SNNs) — the so-called “third generation” of neural models — mimic the brain’s sparse, asynchronous behavior. But until recently, their energy advantage came at a heavy cost: poor accuracy and complicated decoding. The paper Hyperdimensional Decoding of Spiking Neural Networks by Kinavuidi, Peres, and Rhodes offers a way out — by merging SNNs with Hyperdimensional Computing (HDC) to rethink how neural signals are represented, decoded, and ultimately understood. ...

Opening — Why this matters now AI agents are getting smarter—but not faster. Most large language model (LLM) systems still behave like cautious philosophers in a chess match: the world patiently waits while they deliberate. In the real world, however, traffic lights don’t freeze for an AI car mid-thought, and market prices don’t pause while a trading agent reasons about “the optimal hedge.” The new study Real-Time Reasoning Agents in Evolving Environments by Wen et al. (2025) calls this out as a fundamental flaw in current agent design—and offers a solution that blends human-like intuition with deliberative reasoning. ...