MemCtrl: Teaching Small Models What Not to Remember A robot assistant walks through a room. It sees a chair from the front. Then from the side. Then from a slightly worse angle. Then the same chair again, because the camera moved while the robot hesitated. In theory, all of this is “context.” In practice, it is mostly noise wearing a productivity badge. ...
Latency is where elegant AI architectures go to become invoices. A neuro-symbolic system looks clean on a slide: a neural model sees patterns, a symbolic module checks rules, a probabilistic module handles uncertainty, and the final system behaves more reliably than a pure neural model improvising under fluorescent lighting. Lovely. Very architectural. Very responsible. ...
A model can be small and still be badly organized. That is the quiet problem behind a lot of model compression work. We often ask whether a neural network can be pruned without losing too much accuracy. Fair enough. Budgets are real. Memory is not decorative. But the question hides a stronger assumption: that one sparse structure should serve every input equally well. ...
A drone can cover a construction site, a traffic corridor, or a flooded street in minutes. That is the easy part. The harder part is noticing the small object that changes the decision: a person near a road barrier, a tiny vehicle in a dense intersection, a partly hidden target on a high-resolution aerial image. ...
Cores are usually discussed as a performance story. More cores, more parallelism, less latency, happier product manager. Security people, being paid to ruin everyone’s afternoon, usually hear something else: more switching activity, more leakage, more things an attacker can measure. This paper complicates that instinct in a useful way. In Influence of Parallelism in Vector-Multiplication Units on Correlation Power Analysis, Manuel Brosch, Matthias Probst, Stefan Kögler, and Georg Sigl study a very specific question: when a neural-network accelerator processes the same input value across multiple processing elements, each with a different secret weight, what happens to correlation power analysis?1 ...
Battery. It is a wonderfully effective way to end an argument about elegant algorithms. A wearable device may benefit from learning how its surrounding physical system changes over time. It may even need an interpretable equation rather than another black-box prediction. But if one model update consumes more energy than the device stores, theoretical elegance becomes a rather expensive form of decoration. ...
Compression needs a rulebook, not just a diet plan Compression is the least glamorous part of the LLM business until the bill arrives. A model works beautifully in a cloud demo. Then someone asks whether it can run on a device with limited memory, limited energy, limited connectivity, and limited patience. Suddenly the elegant system becomes a logistics problem. Quantize it. Prune it. Shrink it. Hope it still speaks like the original model and not like a sleep-deprived intern summarizing a legal contract from memory. ...
Rooms are stubborn. A model trained in a conference room may behave confidently in a hotel room, badly in a bus, and mysteriously in a classroom. The Wi-Fi signal does not merely reflect “how many people are present.” It reflects furniture, wall geometry, transmitter placement, receiver hardware, movement patterns, and every other physical nuisance that refuses to fit neatly into a spreadsheet. ...
Images are expensive. Not emotionally, although some product managers do try. They are expensive because modern visual models turn an image into a sequence of tokens, then let those tokens attend to one another. In a Vision Transformer, more tokens usually mean more detail, but also more attention cost. The obvious response is to reduce the number of tokens. ...
A camera on a factory line does not need to write an essay before deciding whether a part is cracked. That sounds obvious. Yet a surprising amount of recent AI architecture quietly assumes the opposite: when vision systems become uncertain, bring in a large language model, ask it to generate richer descriptions, then run the detector again. Sometimes this works. It also turns a detection problem into a small committee meeting, and committee meetings are rarely known for real-time throughput. ...