End-to-end simulation of the training loop: forward and backward pass, optimizer step, gradient dynamics, SAE feature evolution across checkpoints, behavioral diffs, confidence calibration, and regression tracking across runs. Everything the loss curve does not show.

Geometry inspection, retrieval evaluation, fine-tuning monitoring, and embedding diff for any sentence-transformers compatible encoder. BGE, E5, GTE, Nomic, Jina, Instructor, MiniLM, and SBERT all get anisotropy scoring, UMAP exploration, layer-wise similarity, OOD detection, and hard-negative gap analysis.

How Aquin supports dense transformer LLMs, Mixture-of-Experts models, and hybrid architectures, from Llama and Mistral to Mixtral, DeepSeek, and Grok. Covers architecture-aware inspection, attribution, training monitoring, and evaluation across the full transformer family.

Adversarial risk detection across the full ML pipeline: prompt injection and poisoned samples in training data, red teaming and jailbreak taxonomy in model inspection, model robustness scoring, weight trojan detection, and attack surface comparison across model versions in the training monitor.

Live signal detection across five failure modes, gradient and loss monitoring per step, SAE feature diffs and behavioral model diffs post-training, and an agentic chat that reads from live training state at send time.

Causal mediation analysis, SAE feature extraction, circuit attribution graph, logit lens, feature steering, UMAP exploration, fact verification, bias detection, and censor auditing, all in one pipeline on Llama 3.2 1B Instruct.

Consistency, suppression detection, and knowledge boundary probing. Behavioral evals that surface failure modes without requiring a trained SAE, and works on any TransformerLens-compatible model.