CLINC150 | Logistic TF-IDF | 97.00% | 0.9701 | +0.00pp | +1.78pp | 41,020 | 41,020 | 0.00M | 0.000 | 1.000x | Static Seed | 95.22% | 0.9521 | -1.78pp | +0.00pp | 52,052 | 52,052 | 66.80M | 0.302 | 1.269x | Dynamic Seed | 94.78% | 0.9485 | -2.22pp | -0.44pp | 10,092 | 10,136 | 28.41M | 0.324 | 0.246x | Dynamic Seed Distill | 95.44% | 0.9544 | -1.56pp | +0.22pp | 9,956 | 9,956 | 32.69M | 0.255 | 0.243x HWU64 | Logistic TF-IDF | 87.94% | 0.8725 | +0.00pp | +0.81pp | 42,260 | 42,260 | 0.00M | 0.000 | 1.000x | Static Seed | 87.13% | 0.8674 | -0.81pp | +0.00pp | 52,052 | 52,052 | 146.61M | 0.300 | 1.232x | Dynamic Seed | 86.63% | 0.8595 | -1.31pp | -0.50pp | 12,573 | 17,565 | 62.54M | 0.334 | 0.297x | Dynamic Seed Distill | 87.23% | 0.8686 | -0.71pp | +0.10pp | 13,117 | 17,575 | 62.86M | 0.340 | 0.310x MASSIVE-20 | Logistic TF-IDF | 86.06% | 0.7324 | +0.00pp | -1.92pp | 74,760 | 74,760 | 0.00M | 0.000 | 1.000x | Static Seed | 87.98% | 0.8411 | +1.92pp | +0.00pp | 52,052 | 52,052 | 129.26M | 0.247 | 0.696x | Dynamic Seed | 86.94% | 0.7364 | +0.88pp | -1.04pp | 11,595 | 17,565 | 47.62M | 0.257 | 0.155x | Dynamic Seed Distill | 86.45% | 0.7380 | +0.39pp | -1.53pp | 11,851 | 19,263 | 51.90M | 0.442 | 0.159x 

TL;DR:
I built a system that finds much smaller models that stay competitive — and sometimes outperform larger baselines.

Built a small experiment around Seed (architecture discovery).

Instead of training bigger models, Seed:

• generates candidate architectures
• evaluates them
• keeps the smallest ones that still perform well

Tested across 4 datasets:

• Banking77
• CLINC150
• HWU64
• MASSIVE

🧠 Key result (Banking77)

• Logistic TF-IDF: 92.37%
• Dynamic Seed (distilled): 93.53%

👉 Higher accuracy + ~5x smaller (12.6k vs 64.9k params)

📊 Other results

• MASSIVE → quality + size wins
• CLINC150 / HWU64 → not always higher accuracy but ~4–5x smaller models with competitive performance

🔥 What actually matters (not just accuracy)

If you only look at accuracy → mixed

If you include:

• model size
• training compute
• inference latency

👉 this becomes a much stronger result

🧠 Takeaway

Traditional ML:
👉 scale model size and hope

Seed:
👉 search for better structure

Smaller models can compete with larger ones
if you find the right architecture

Not AGI
Not “we solved NLU”

But a real signal that:

👉 structure > scale

Smaller models can compete with larger ones — if you find the right structure

Key takeaway:

• Text → clear wins (better + smaller)
• Sensor → huge efficiency gains
• Vision → compact tradeoff
• Audio → failed due to weak representation

So, this isn’t just about accuracy, it’s about moving the efficiency frontier.