catastrophic forgetting
3 stories filed under catastrophic forgetting on Beyond Market Intelligence. The newest of them: “Accelerate Local LLM Learning: A New Prototype for Faster Fact Correction”, “Explore how a neural network compresses a classic animation into mere megabytes.”, and “Discover how dynamic routing advances continual learning without replay buffers.”. Watching a toddler learn taught Kavanutz something about AI. A 3MB neural network now plays Bad Apple, and the trick is in how it learns motion. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every catastrophic forgetting story on Beyond Market Intelligence, newest first.
Accelerate Local LLM Learning: A New Prototype for Faster Fact Correction
Watching a toddler learn taught Kavanutz something about AI. Jayce, his new prototype, skips the heavy lifting of RAG pipelines and fine-tuning entirely. Instead, it shifts raw context vectors across a fixed pool of 4,048 prototype slots, correcting mistakes in real time. The benchmarks are telling: faster updates, better sample efficiency on sequential tests, and a strict memory ceiling. It's a lean, framework-free proof of concept built on NumPy and Java.
Explore how a neural network compresses a classic animation into mere megabytes.
A 3MB neural network now plays Bad Apple, and the trick is in how it learns motion. This isn't about beating compression codecs; it's about whether a small MLP can internalize a video's structure. The team's move to time-stretch coordinates and sample motion-heavy pixels cut validation MSE ninefold, from 0.0795 to 0.0090. That's a practical lesson in training dynamics, not just a demo. For anyone wrestling with similar signal-fitting problems, the SIREN architecture and its failure modes are worth studying closely.
Discover how dynamic routing advances continual learning without replay buffers.
Replay buffers have long been the default answer to catastrophic forgetting, but they carry real costs in memory and privacy. A new open-source framework called Coincidex challenges that trade-off by replacing cached samples with a dynamic task-similarity layer that routes data on the fly. The results are promising on clean task boundaries, yet the authors are honest about instability under chaotic, long-tail shifts. That transparency makes this worth exploring, especially for constrained setups.