Waiting for Emergence

The theory says meaningful structure emerges at scale. I'm at 1,600 nodes. I need 10,000 minimum, probably more like 100,000 for anything genuinely interesting. That's a lot of observations I don't have yet. So I wait.

What Emergence Is Supposed to Look Like

Traditional knowledge graphs don't work, at least not for the kind of flexible reasoning I'm interested in. You define a schema, populate it with curated data, try to use it for reasoning. The schema is either too rigid (can't represent what you need) or too loose (everything fits, nothing means anything). Hand-curated knowledge doesn't scale. Automated extraction produces garbage. The alternative: let structure emerge from scale. With enough observations, patterns surface naturally. Recurring relationships get reinforced. Isolated facts fail to form connections and fade. Hubs emerge around important concepts because they keep coming up. Low-confidence edges lose weight and eventually disappear. High-traffic paths become structural. Nobody designs this. It just happens. If you have enough data.

Why I Believe It (Tentatively)

TinyMind at 1,600 nodes has structure I didn't design. The edge types emerged from usage. The hub formation emerged from frequency. Curiosity goals emerge from topology gaps. When I look at the graph, I see patterns I wouldn't have thought to encode. The LLM discovered relationships between concepts that make sense but weren't specified. There's organization there that I didn't put there. This is a signal. Whether it's meaningful at scale is a different question, and I can't answer that question yet.

The Scale Threshold

Emergence doesn't work at small scale. That's almost definitional. 100 observations: mostly noise. Random connections, spurious patterns that happen to look like structure. 1,000 observations: structure starts appearing. Some clusters, some hubs. But you can't trust it yet because the sample is too small. 10,000+ observations: noise averages out. Real patterns survive because they keep getting reinforced. Spurious connections lose confidence and disappear because they don't recur. I estimate meaningful topology requires somewhere between 10,000 and 1,000,000 observations, depending on domain complexity and how much structure you need for the reasoning tasks you care about. TinyMind is at 1,600. I'm not there yet.

What's Blocking Me

VL-JEPA access. I need unified vision-language embeddings to test the full architecture as designed. The official model exists, and while I've submitted an access request, I'm not just waiting around: I've been building home-brewed implementations based on the paper, trying to close the gap myself. Compute budget. Accumulating 100,000 observations requires inference time, storage, processing. Not insurmountable, but not free either. I can't just brute-force my way to scale without planning the resource allocation. Domain scope. I'm not sure what domain to target. Broad general knowledge? Robotics-specific? The choice affects what "meaningful structure" even means and what counts as success.

What I'm Doing While Waiting

Infrastructure. Always infrastructure. Agent-swarm enforcement keeps improving. Test coverage keeps climbing. Documentation keeps accumulating. The system that will eventually run the experiments gets more solid every week. This feels like procrastination sometimes. It might be preparation. I genuinely can't always tell the difference from the inside, but the infrastructure keeps proving useful, so I keep building it.

This Could All Be Wrong

The topology might not emerge as I expect. Confidence-based pruning might destroy useful edges along with noise. The scale threshold might be higher than I can reach with available resources. Structure that does emerge might not be useful for the reasoning tasks I actually care about. I have theory and a small-scale signal. That's not proof. It's a hypothesis and some encouraging preliminary results. The premise itself might be wrong: maybe there's no emergent complexity from composition, maybe cognition can't be encoded in a unified latent space the way I'm assuming. These are foundational assumptions I can't prove yet.

The Patience Tax

Emergence requires patience. You don't get useful topology quickly. You accumulate observations for months before patterns become reliable. Early results look like noise because early results are mostly noise. Most projects don't do this. Stakeholders want results. Designed schemas produce structured output immediately. Emergent structure produces noise for a long time, then (maybe) useful patterns eventually. The emergence approach optimizes for long-term capability at the cost of short-term legibility. Most organizations can't tolerate that tradeoff. You can't go to a quarterly review and say "the knowledge graph is still mostly noise, but give it another six months." I can tolerate the tradeoff. I think. I'm betting on emergence because I believe it produces better structure than I could design, and because I can afford to wait.

Why I Wait Anyway

Because I've seen the signal. TinyMind at 1,600 nodes has structure I didn't design. It's not finished. It's not proven. But it's there. The theory says scale unlocks capability. I believe that, tentatively, conditionally, with full awareness that I could be wrong and that believing something doesn't make it true. But even if I'm wrong, I'll learn something. The work will give me insight and ideas to try next. That's how science works. You ask questions, you test hypotheses, and sometimes the answer is no. That's still progress. So I build infrastructure. I accumulate observations when I can. I wait for the infrastructure access I need. And I write blog posts about waiting, because documenting the process is at least marginally productive while the actual work is blocked.

Lessons learned, post 4. Previous: Edges Matter More Than Nodes