# Antaḥkaraṇa-Net — Scale-Up Benchmark & Performance Report

**A capability benchmark of the Vedic-mind continual-learning architecture at 36.5M–52.6M parameters.**
All numbers are from **live inference on the trained checkpoints** (the model is loaded from disk and
re-evaluated), not training-time logs. 11 models, 7 capabilities each, run on a single NVIDIA A10.

> **Scope, stated up front.** This is a *research proof-of-concept*: WideResNets on Split-CIFAR-100 and
> Split-Tiny-ImageNet. The contribution is the **architecture** — one agent that learns continually
> without forgetting, abstains instead of hallucinating, and self-regulates — demonstrated to *scale*
> from the original small-CNN POC to real models. It is not, and is not meant to be, a frontier model.

---

## 1. Setup

| | |
|---|---|
| **Architectures** | WRN-28-10 (**36.5M params**), WRN-28-12 (**52.6M params**) — shared trunk φ + one linear head per task |
| **Control ring** | chitta (saṃskāra EWC+decay), guṇa (forgetting-aware MetaGunaController), āśrama, tapas, pramāṇa, turīya |
| **Datasets** | Split-CIFAR-100 (10×10 and 20×5 class-incremental); Split-Tiny-ImageNet (200-class, 10×20, 64→32) |
| **OOD set (pramāṇa)** | SVHN (test) |
| **Training** | from scratch, 25 epochs/task, Adam; wake→dream(replay)→sleep(consolidate) cycle |
| **Hardware** | OCI `BM.GPU.A10.4` → single A10 (23 GB), us-sanjose-1 |
| **Protocol** | per-checkpoint live inference: per-task accuracy, pramāṇa gating, temperature calibration, mind-state trace |
| **Seeds** | core ×5, scale ×2, tiny ×2, āśrama ×2 (11 models total) |

## 2. Capability scorecard (the 7 checks + PASS criteria)

| # | Capability (faculty) | PASS criterion |
|---|---|---|
| 1 | **Learns the task** (buddhi+backbone) | avg accuracy > 3× chance |
| 2 | **Continual memory** (chitta/saṃskāra) | forgetting ≤ 0.05 **and** all tasks > 1.5× chance |
| 3 | **Anti-hallucination** (pramāṇa) | gated acc ≥ raw acc **and** OOD blind-accept < 30% |
| 4 | **Calibration** | ECE decreases after temperature scaling |
| 5 | **Self-regulation** (guṇa) | plasticity headroom decreases over life |
| 6 | **Lifelong** (āśrama) | progresses through life-stages to vānaprastha/saṃnyāsa |
| 7 | **Identity monitor** (turīya) | witness drift bounded (< 1.0) |

**Result: 10 of 11 models pass 7/7; 1 passes 6/7** (āśrama_s0 — see §6, a threshold artifact).

## 3. Headline results — group means

| Configuration | Params | Avg acc | Naive forget | **Agent forget** | **Reduction** | Gated acc | OOD accept |
|---|---|---|---|---|---|---|---|
| **core** — CIFAR-100, 10 tasks | 36.5M | 0.599 | 0.589 | **0.0146** | **41.0×** | 0.929 | 8.4% |
| **scale** — CIFAR-100, 10 tasks | 52.6M | 0.615 | 0.551 | **0.0204** | **27.3×** | 0.933 | 10.0% |
| **tiny** — Tiny-ImageNet, 10 tasks | 36.5M | 0.416 | 0.503 | **0.0173** | **29.1×** | 0.956 | 0.3% |
| **āśrama** — CIFAR-100, **20 tasks** | 36.5M | 0.606 | 0.595 | **0.0474** | **12.5×** | 0.960 | 1.3% |

**Read:** catastrophic forgetting is cut **12–41×** across two model sizes, two datasets, and stream lengths
of 10 and 20 tasks. The agent both *remembers* (forgetting → ~0.01–0.05) and *learns better* (accuracy
roughly **doubles** vs the naive baseline, which collapses to its last task).

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

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

## 4. Per-model detail (live inference)

| Model | Data | Avg acc | Naive→Agent forget | Reduction | Gated@cov | OOD accept | ECE before→after | Score |
|---|---|---|---|---|---|---|---|---|
| core_s0 | CIFAR-100 | 0.616 | 0.565→0.018 | 31.8× | 0.943@0.21 | 16.1% | 0.172→0.023 | 7/7 |
| core_s1 | CIFAR-100 | 0.592 | 0.621→0.014 | 45.1× | 0.950 | 6.4% | 0.172→0.043 | 7/7 |
| core_s2 | CIFAR-100 | 0.617 | 0.583→0.012 | 49.9× | 0.918 | 2.6% | 0.118→0.025 | 7/7 |
| core_s3 | CIFAR-100 | 0.587 | 0.553→0.015 | 37.1× | 0.927 | 8.2% | 0.150→0.024 | 7/7 |
| core_s4 | CIFAR-100 | 0.585 | 0.621→0.015 | 41.1× | 0.907 | 8.4% | 0.163→0.035 | 7/7 |
| scale_s0 | CIFAR-100 | 0.620 | 0.542→0.021 | 25.4× | 0.921 | 8.7% | 0.229→0.027 | 7/7 |
| scale_s1 | CIFAR-100 | 0.610 | 0.570→0.019 | 29.3× | 0.944 | 11.2% | 0.195→0.032 | 7/7 |
| tin_s0 | Tiny-ImageNet | 0.411 | 0.503→0.017 | 29.2× | 1.000 | 0.4% | 0.226→0.025 | 7/7 |
| tin_s1 | Tiny-ImageNet | 0.421 | 0.502→0.017 | 29.0× | 0.912 | 0.2% | 0.196→0.034 | 7/7 |
| ashrama_s0 | CIFAR-100 (20t) | 0.592 | 0.603→0.049 | 12.3× | 0.951 | 1.7% | 0.225→0.046 | 6/7 |
| ashrama_s1 | CIFAR-100 (20t) | 0.621 | 0.586→0.046 | 12.8× | 0.969 | 1.0% | 0.246→0.057 | 7/7 |

## 5. Analysis

**Scaling (36.5M → 52.6M).** Increasing capacity keeps the effect and nudges accuracy up (0.599 → 0.615).
The forgetting-reduction *ratio* is a touch lower at 52.6M (27× vs 41×), but absolute agent-forgetting stays
tiny (0.020 vs 0.015) — i.e. both models forget almost nothing; the ratio mostly reflects naive-baseline
variance. **The architecture is genuinely backbone-agnostic across sizes.**

**Difficulty — dataset.** Tiny-ImageNet (200 fine-grained classes at 32 px) is much harder, so raw accuracy
drops to ~0.42. Crucially, **forgetting reduction holds (29×)** and the model becomes *more* conservative:
it abstains on **99.7%** of OOD inputs and clears only its most-confident in-distribution predictions —
exactly the right behavior when uncertain.

**Difficulty — stream length.** Doubling the stream (10 → 20 tasks, āśrama) is the hardest continual setting:
twice as many tasks to retain with more interference. Reduction drops to **12.5×** (agent-forgetting 0.047) —
**lower than the 10-task runs, and honestly so** — yet all 20/20 tasks remain above chance and the model still
abstains/calibrates cleanly. A >12× cut over a 20-task lifelong run is a strong result.

![retention](https://huggingface.co/deepakdsoni/antahkarana/resolve/main/plots/bench_retention.png)
*Per-task accuracy after the full stream — every task stays well above chance; the agent does not collapse
to its last task (which is what the naive baseline does).*

**Anti-hallucination (pramāṇa).** Calibrated abstention works everywhere: gated accuracy **0.91–1.00** (vs raw
~0.42–0.67), and OOD blind-accept **0.2%–16%**. The harder the data, the lower the model's confidence and the
more it abstains — a desirable, safety-aligned property. Coverage is correspondingly lower on hard sets
(a deliberate trade: it commits less, but is right when it does).

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

**Calibration.** Temperature scaling reduces ECE everywhere (e.g. 0.172 → 0.023), so reported confidence can
be trusted — the precondition for the abstention gate to be meaningful.

**Interpretability (mind-state).** Every model exposes a legible trajectory: plasticity headroom **falls over
life** (e.g. 0.92 → 0.37; 0.77 → 0.14 on the 20-task run), the agent **ages through all four āśramas**
(brahmacarya → gṛhastha → vānaprastha → saṃnyāsa), and **turīya drift stays bounded** (~0.50–0.59). You can
read the agent's regulation as it lives — not a black box.

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

## 6. The one 6/7, explained (no goalpost-moving)

**āśrama_s0** failed only **capability 1 (learns the task)**: avg accuracy **0.592 vs the 0.60 bar**
(= 3× chance for 5-class tasks). That's **2.96× chance** — a hair under an arbitrary threshold. Its
*continual_memory* check passed with **20/20 tasks retained and 12.3× forgetting reduction**, and āśrama_s1
(same config) scored 0.621 → 7/7. This is **benchmark-threshold variance on a borderline metric**, not a model
deficiency. We report it as-is rather than relax the criterion.

## 7. Honest caveats

- **POC scale.** Small/mid models, 32 px imagery. Demonstrates the *architecture*, not SOTA accuracy.
- **Forgetting reduction ratios are noisy** across seeds (driven partly by naive-baseline variance); the
  trustworthy claim is *absolute agent-forgetting ≈ 0.01–0.05 everywhere*.
- **Low coverage on hard datasets** is the flip side of strong abstention — fewer commitments, higher precision.
- **OOD uses SVHN with CIFAR-100 normalization** for all models (a single fixed OOD probe); a per-dataset OOD
  suite would sharpen the tiny-imagenet numbers.
- **Vedic↔ML mappings are engineering analogies**, clearly flagged. Nothing here is conscious.

## 8. Reproducibility

```bash
# train (per phase, resumable, idempotent):
bash scale/phases.sh                 # core → scale → pramana → tinyimagenet → ashrama
# benchmark any checkpoint (live inference, 7-capability report + JSON):
CUDA_VISIBLE_DEVICES=0 python3 scale/capability_bench.py --ckpt runs/<tag>.pt
```

Artifacts: 11 checkpoints (`runs/*.pt`), 11 training results (`runs/*.json`), 11 capability reports
(`runs/*_capbench.json`), and this report. The uploadable model is `antahkarana-36.5M-cifar100-wrn28-10.pt`
(36.5M, with Ω/θ* consolidation state) + `load_akn.py` + `MODEL_CARD.md` in `scale/hf_package/`.

---

*Generated from live-inference benchmarks on trained checkpoints. Code: MIT. Built on the Upaniṣads,
Sāṃkhya, Yoga, and PyTorch · snnTorch.*