[ Research taste ]
This is the public source trail behind future AI research notes: problems, methods, selected essays, and papers that shape what gets studied next.
The first AI research thread is the question behind neural networks themselves: what kinds of functions can trainable systems represent, and what architectural biases make that representation practical?
CNNs and transformers show the central AI research move: choose a computation pattern that matches the structure of the data, then train the whole pipeline end to end.
Large models make adaptation a first-class research problem: how much can a model specialize without retraining every parameter or adding unacceptable inference cost?
Controllable generation asks whether generation can be steered by explicit conditions while preserving fluency, diversity, and task usefulness.
Frontier AI systems increasingly need mechanisms for long context, persistent memory, and adaptation during use rather than only during pretraining.
Deep RL studies how trainable systems can learn useful policies from delayed, sparse, and changing feedback instead of fixed labeled datasets.
Reasoning-focused RL turns model behavior into an optimization target, raising questions about rewards, emergence, distillation, and where process supervision matters.
Interpretability matters most when accuracy is not enough: deployment, trust, scientific discovery, fairness, and debugging all require sharper explanations.
LIME, SHAP, saliency maps, and their critiques form a useful testbed for asking whether explanations are faithful, stable, and actually useful to users.
Representation analysis asks what information is encoded inside a model, how it changes across layers, and whether probes reveal structure or only probe capacity.
Mechanistic interpretability is the attempt to reverse-engineer neural networks as computation: circuits, features, induction heads, superposition, and scalable tooling.
Agent research should be measured by accuracy, cost, latency, repeatability, tool behavior, and whether verification loops catch errors before they compound.
Mixture-of-experts, cascades, model merging, task arithmetic, and speculative decoding all ask how to activate only the computation a task deserves.
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One rigorous AI research or project update every 3-4 weeks, with paper trails, mechanisms, experiments, and implementation tradeoffs in one place.