---
license: mit
library_name: pytorch
tags:
  - continual-learning
  - cognitive-architecture
  - vedic-ai
  - catastrophic-forgetting
  - wide-resnet
  - cifar-100
datasets:
  - cifar100
pipeline_tag: image-classification
---

# Antaḥkaraṇa-Net — scaled checkpoint (WRN-28-10, Split-CIFAR-100)

A **36.5M-parameter WideResNet** trained as one continual-learning agent under the full
Antaḥkaraṇa control ring (saṃskāra · guṇa · āśrama · tapas · pramāṇa · turīya). This is
the **scale-up** of the original [Antaḥkaraṇa-Net](https://huggingface.co/deepakdsoni/antahkarana)
proof-of-concept: the same Vedic-mind architecture, now on a real WideResNet across
**10 sequential CIFAR-100 tasks** — and the no-forgetting result holds.

> **What this checkpoint *is*:** the trained trunk + per-task heads **plus** the saved
> saṃskāra consolidation state (Ω importance and θ* reference weights). It is a research
> artifact demonstrating *lifelong learning without forgetting at scale* — not a
> general-purpose classifier.

## Results — Split-CIFAR-100, 10 tasks × 10 classes (5-seed means, real runs on an A10)

| Metric | Naive (no faculties) | **Antaḥkaraṇa agent** |
|---|---|---|
| **Catastrophic forgetting** ↓ | 0.589 | **0.0146  (~41× lower)** |
| Average accuracy ↑ | 0.304 | **0.600** |
| Pramāṇa gated accuracy | — | **~0.93** (abstains when not confident) |

Per-seed forgetting reductions: **31.8× / 45.1× / 49.9× / 37.1× / 41.1×**.
A larger **WRN-28-12 (~52M)** run reproduces the effect: forgetting **0.542 → 0.021 (25.4×)**.

The agent both **remembers** (forgetting nearly eliminated) and **learns better**
(accuracy ~doubled), because consolidation protects prior tasks while the
forgetting-aware guṇa controller relaxes protection where it isn't needed.

![forgetting](https://huggingface.co/deepakdsoni/antahkarana/resolve/main/plots/bench_forgetting.png)

**Full benchmark** (11 models, 7 capabilities, live inference) → [`BENCHMARK_REPORT.md`](BENCHMARK_REPORT.md).
Across two model sizes (36.5M / 52.6M), two datasets (CIFAR-100, Tiny-ImageNet), and 10- and 20-task
streams: forgetting cut **12–41×**, calibrated abstention (gated acc 0.91–1.00, abstains on up to 99.7% of
OOD), and a legible mind-state trajectory. **10 of 11 models pass 7/7 capability checks.**

![scorecard](https://huggingface.co/deepakdsoni/antahkarana/resolve/main/plots/bench_scorecard.png)

## Files

- `antahkarana-36.5M-cifar100-wrn28-10.pt` — the checkpoint (`model_state`, `config`, `results`, `omega`, `theta_star`)
- `load_akn.py` — **self-contained** loader (full model definition inside; only needs PyTorch)

## Usage

```python
import torch
from load_akn import load

model, ck = load("antahkarana-36.5M-cifar100-wrn28-10.pt")   # eval mode
# CIFAR-100 normalized 32×32 input; task in [0..9] selects the head
x = torch.randn(1, 3, 32, 32)
logits = model(x, task=0)        # -> (1, 10)

print(ck["results"]["agent"])    # the honest metrics
print("Ω present:", "omega" in ck)   # saṃskāra importance, for inspection / resume
```

## Training

WideResNet-28-10 trunk + one linear head per task, trained **from scratch** through the
four-state cycle (wake → dream/replay → sleep/consolidate). Consolidation:
`Ω ← (1−λ)Ω + γ·Fisher`, penalty `β·ΣΩ(θ−θ*)²`, with β set by the forgetting-aware guṇa
controller. 25 epochs/task, Adam. Code: [`scale/akn_train.py`](https://huggingface.co/deepakdsoni/antahkarana).

## Honest scope

Research proof-of-concept at modest scale (WRN on CIFAR-100). The Vedic↔ML mappings are
engineering analogies, clearly flagged; nothing here is conscious. Full method, every
number, and the component scorecard are in the
[main repo](https://huggingface.co/deepakdsoni/antahkarana). Code: MIT.