Neural Networks

Opening — Why this matters now Transformer fatigue is real. After years of scaling attention mechanisms into increasingly expensive foundation models, the industry is starting to notice an uncomfortable pattern: more parameters, more data, more opacity. Performance improves—but explainability, efficiency, and biological plausibility quietly degrade. Into this environment arrives a familiar but re-engineered idea: Hopfield networks. Not as a nostalgic curiosity, but as a serious contender for the next generation of vision backbones. ...

Opening — Why this matters now Large language models appear smooth from the outside: prompts go in, coherent text comes out. But internally, their numerical dynamics are anything but calm. In fact, inside many modern Transformers, certain tokens briefly explode into extreme values thousands of times larger than their neighbors. At the same time, a small set of tokens—often the very first token in a sequence—attracts an overwhelming share of attention from many heads. These are known as attention sinks. ...

Opening — Why this matters now The scaling law debate was supposed to be settled. Bigger models, more data, more compute—loss falls predictably. Then came the uncomfortable question: what exactly is being scaled? If power laws in natural language data are the root cause, then scaling laws might be an artifact of language itself, not of learning. This paper dismantles that comfort. ...

Opening — Why this matters now Edge AI has been sold as a performance story: lower latency, fewer cloud dependencies, tighter privacy boundaries. But as neural networks migrate from data centers into physically accessible devices, the old ghosts of hardware security resurface. Side‑channel attacks—particularly correlation power analysis (CPA)—have already proven capable of stealing neural network weights from embedded devices. ...

Opening — Why this matters now Personal sound zones (PSZs) have always promised something seductive: multiple, private acoustic realities coexisting in the same physical space. In practice, they’ve delivered something closer to a bureaucratic nightmare. Every new target sound scene demands the same microphone grid, the same painstaking measurements, the same fragile assumptions. Change the scene, and you start over. ...

In a field where LSTMs, transformers, and black-box agents often dominate the conversation, a new framework dares to ask: What if our old tools weren’t wrong, just under-optimized? That’s the central premise behind Technical Indicator Networks (TINs) — a novel architecture that transforms traditional technical analysis indicators into interpretable, trainable neural networks. Indicators, Meet Neural Networks Rather than discarding hand-crafted indicators like MACD or RSI, the TIN approach recasts them as neural network topologies. A Moving Average becomes a linear layer. MACD? A cascade of two EMAs with a subtractive node and a smoothing layer. RSI? A bias-regularized division circuit. The resulting neural networks aren’t generic function approximators; they’re directly derived from the mathematical structure of the indicators themselves. ...

In the world of physics, when particles in a system become so densely packed or cooled that they lock into place, we call this phenomenon jamming. Sand becoming rigid under pressure, traffic freezing on a highway, or even glass transitioning from fluid to solid—all are governed by this principle. What if the same laws applied to intelligence? A provocative new paper, Consciousness as a Jamming Phase by Kaichen Ouyang, suggests just that: large language models (LLMs) exhibit consciousness-like properties not as a software quirk but as a physical phase transition, mirroring the jamming of particles in disordered systems. ...

The search for the elusive optimal portfolio has always been a balancing act between signal and noise. Covariance matrices, central to risk estimation, are notoriously fragile in high dimensions. Classical fixes like shrinkage, spectral filtering, or factor models have all offered partial answers. But a new paper by Bongiorno, Manolakis, and Mantegna proposes something different: a rotation-invariant, end-to-end neural network that learns the inverse covariance matrix directly from historical returns — and does so better than the best analytical techniques, even under realistic trading constraints. ...