antahkarana 0.4.1

A trainable continual-learning architecture (manas/buddhi/ahaṃkāra/citta) you train on your own data — no forgetting (DER++), energy + feature-space novelty, calibrated abstention, and a swappable risk-tier control ring.

antahkarana — train a continual-learning mind on your data

antahkarana is a trainable architecture, not a frozen model. You bring your data (and optionally your backbone); it trains a model that learns continually without forgetting, detects novelty / zero-days, abstains when unsure, and consolidates in sleep. Four organs — manas · buddhi · ahaṃkāra · citta — the mind is the same for every domain; only a thin adapter changes.

Built on the validated Antaḥkaraṇa-base core (gated G0–G5, proven across language · vision · security · with seeded, adaptive evaluation — see the model card for plots, results, and honest limits).

Install

pip install antahkarana            # core + tabular
pip install antahkarana[text]      # + HuggingFace LLM adapter

Train on your data in ~10 lines (tabular)

from antahkarana import Antahkarana, TabularAdapter

# YOUR data: a list of tasks, each (X_train, y_train, X_test, y_test)
stream = TabularAdapter.make_stream(your_tasks)

bb   = TabularAdapter(input_dim=122, n_tasks=4, n_classes=2)   # built-in MLP — bring only data
mind = Antahkarana(bb, replay_strategy="der", avidya_strategy="energy")
res  = mind.train(stream)

print(res["forgetting"], res["final_row"], res["risk_coverage"])

New in 0.4.0 — the control ring (Layer-2 policy)

A swappable risk-tier router over any trained backbone. One consolidated brain (the weights = Layer-1 disposition), many deployment policies (config = Layer-2) — change safety posture without retraining. Each request's novelty/risk maps to a tier and every decision is logged (transparency by construction).

A request flows through the consolidated organs (Layer 1); manas emits a risk score; the RiskRouter applies the deployment Policy (Layer 2) to pick a tier — ALLOW / NOTIFY / SOFT_BLOCK / HARD_BLOCK — and logs every decision.

Validated on UNSW-NB15: calibrated to ~5% false-block on normal traffic, the ring gates known attacks and catches ~95% of a held-out zero-day family it never trained on — built on the Layer-1 DER++ no-forgetting win.

from antahkarana import Policy, RiskRouter
ring = RiskRouter(Policy.load("policy.yaml"))     # hard/soft blocks, thresholds, operator overrides
iv   = ring.gate(score=novelty, category="intrusion")
# iv.tier ∈ {allow, notify, soft_block, hard_block};  iv.allowed / iv.notify / iv.reason drive the response

Validated end-to-end on UNSW-NB15 (KAVACA detector → ring, calibrated to 5% FPR): normal ~85% pass, known attacks gated, a held-out zero-day family ~95% caught. See examples/control_ring.py.

New in 0.3.0 — DER++ replay + feature-space novelty

DER++ now works for tabular (replay_strategy="der"): on a 6-family UNSW-NB15 stream it cut forgetting from +0.048 (naive) to +0.009 — the no-forgetting guarantee now holds on modern data, not just text/vision.

FeatureOOD adds a penultimate-feature novelty detector (Mahalanobis / kNN) complementary to the default energy score — measured to rescue families where energy fails (Backdoor AUROC 0.37→0.79) while energy remains better where it already works. Pick per deployment or ensemble; energy stays the default.

from antahkarana import FeatureOOD
det   = FeatureOOD("mahalanobis").fit(known_features, known_labels)   # bb.features(inputs) -> embeddings
score = det.score(new_features)        # higher = more novel / likely zero-day

Text (any HuggingFace causal-LM + LoRA)

from antahkarana import Antahkarana, TextAdapter
bb     = TextAdapter("mistralai/Mistral-7B-v0.1")             # frozen base, small LoRA trains
stream = TextAdapter.make_stream(your_text_tasks)             # [(train_pairs, eval_pairs), …]
Antahkarana(bb).train(stream)

Any other modality

Copy antahkarana/adapters/template.py (CustomAdapter) and implement 5 methods over your encoder (audio, graph, multi-modal, robotics, …). The continual-learning mind is unchanged.

Runnable examples

python examples/train_tabular.py     # concept-drift stream: naive forgets, the core doesn't
python examples/train_security.py    # continual threat detection (attack families) + calibrated triage

What you get back

train() returns: the task×task accuracy matrix, final_row, final_avg, forgetting, risk_coverage (calibrated abstention), recovery (sleep), and the trajectory (per-task guṇa/novelty).

Knobs

Antahkarana(bb, samskara=, replay_strategy="naive"|"der", avidya_strategy="msp"|"energy", sleep=, base_lr=, epochs=) — turn each organ on/off and swap in the SOTA implementation (DER++ dark-knowledge replay, energy-OOD novelty).

Ship it closed-source

python build_wheel.py compiles the engine to binary .so (Nuitka) and drops the source, leaving only the public interface readable — others can pip install and train on their data without seeing your code. (For maximum IP protection, serve it behind an API instead.)

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Author — Deepak Soni · Apache-2.0