The Third Wave Is Contextual: Practical Context‑Aware Training on AethergenPlatform
Auspexi • Updated:
Thesis: Wave 1 was knowledge‑based (rules/ontologies). Wave 2 is statistical learning (deep nets at scale). The third wave is context‑aware—who, where, when, intent, and constraints—so decisions align with real‑world situations and governance.
Why Context Beats Scale Alone
Large models compress patterns. Context tells the model which patterns matter now. The result is fewer spurious correlations, better generalisation under shift, and safer behaviour under constraints. In regulated domains, context is the difference between a clever demo and a dependable system.
AethergenPlatform: Built for Context
Context Studio & Packs
Ingest events, entities, relations, and policies into governed packs. Retrieve episodic slices by time, segment, or task and feed them into training/inference pipelines.
Context‑Conditioned Training
Attach context vectors (segment, intent, environment) to samples and objectives. Train for anti‑spurious behaviour by penalising reliance on unstable features.
Policy Guard + Kill Switch
Legal/geo/entitlement constraints enforced at runtime. Violations fail‑closed; high‑risk signals can trigger the kill switch with evidence.
Evidence‑Led Operations
Acceptance gates (utility, stability, privacy, latency), signed evidence bundles, and Unity Catalog comments for auditability across environments.
Databricks‑Native
Jobs, notebooks, and Volumes for end‑to‑end, reproducible evaluation. Marketplace packaging for distribution with evidence.
Context Vectors: A Practical Template
We represent context as a compact vector c that conditions both data and objective. Minimal example:
Training minimises L(x, y; c) with penalties that suppress spurious features under shifts of c. At inference, the same c selects retrieval, tools, and thresholds.
Anti‑Spurious Learning (ASL)
Segment swaps: perturb context (e.g., region/product) during training; penalise feature attributions that swing with the swap.
Counterfactual masks: mask high‑leakage tokens/columns per policy and require stable predictions.
Risk‑aware objectives: choose the operating point (coverage–precision) by risk class and latency budget; abstain when evidence is thin.
Runtime: Context‑Aware Gating
At decision time, we combine information‑sufficiency (retrieval coverage, margin/entropy, tool success) with policy checks. If context says the risk is high and evidence is weak, we abstain or route to a safer path. All decisions and thresholds are captured in signed evidence.
Governed Tooling: Policy Guard + Kill Switch
Context is also constraints. Policy Guard enforces geo/legal/entitlement rules at call sites. If a boundary is crossed—or evidence shows unacceptable drift—the kill switch can revoke access and quarantine assets. Every action is logged with evidence.
How Mature Is Our Third‑Wave Stack?
Ready now: Context packs, retrieval, context vectors in training, runtime gates with abstention, span‑level uncertainty with calibrated abstention, policy‑aware retrieval routing (geo/legal filters), context‑pack versioning with UC tags and evidence links, episodic memory TTL/decay and staleness scoring, evidence bundles, UC delivery.
In progress: richer span‑level uncertainty, better multi‑hop retrieval signals, automated OP tuning per segment.
Coverage‑clamp test: lower wrong‑answer rate and re‑ask rate at fixed coverage/latency (pre vs post gates).
Selective risk: conformal valid coverage within ±2% of target on held‑out segments.
Segment stability: reduced variance across segments/time windows; fewer spurious attributions under swaps.
Policy fitness: 0 critical violations under Policy Guard; correct abstention under disallowed contexts.
Auditability: evidence bundles with OP, gates, and policy outcomes.
Get Involved
If you’re piloting context‑aware AI in regulated settings, let’s talk. We can help design context packs, instrument gates, and deliver verified assets to Unity Catalog.