I Asked My Dog How Trees Talk. Now I’m Rethinking LLM Orchestration.

A hippie’s journey from forest walks to sub-microsecond signal processing.

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The Walk That Started Everything

I was walking my dog last month. Nothing special—just the usual loop through the neighborhood, past the old oaks and overgrown hedges.

But I kept looking at the trees.

Not in a "oh, pretty leaves" way. More like... really looking. The way they grew toward each other. How the roots of different species seemed to know where the others were. How a whole grove of aspens will change color at almost the same time.

I turned to my dog and said out loud: "How do you think these things talk to each other?"

She didn’t answer. (She’s not that kind of dog.)

But the question wouldn’t leave me alone.

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Down the Rabbit Hole

So I did what any reasonable person does at 11pm on a Tuesday: I fell down a research rabbit hole.

Turns out, plants do communicate. And it’s wild.

The Wood Wide Web

Forests have an underground network of fungal threads called mycorrhizal networks—scientists literally call it the "Wood Wide Web." Trees use it to:

• Send chemical signals when they’re under attack
• Share nutrients with struggling neighbors (even different species!)
• Warn each other about incoming pests
• Coordinate responses without any central controller

There’s no "master tree" running the show. No message queue. No orchestrator. Just... signals diffusing through a shared medium, decaying over time, getting reinforced when multiple trees "agree."

And I thought: wait, that sounds like the opposite of how we build AI systems.

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The LangChain Problem

At work, I’d been wrestling with multi-agent LLM systems. You know the drill:

chain = agent_1 | router | agent_2 | aggregator | agent_3

Central orchestration. Explicit routing. If agent_2 times out, the whole thing dies. Every new agent means updating the router logic. Debugging is a nightmare because you’re tracing through a rigid DAG.

It works. But it felt... brittle.

Meanwhile, a forest coordinates millions of trees across decades without a single config file.

What if we built LLM coordination the way forests work?

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From Hypothesis to Validation

I’m not one to just vibe on an idea. I needed to know if this would actually work.

So I built a simulation framework. Python first (because fast iteration), then Rust (because I’m not a monster who deploys Python to production).

The Hypothesis

If agents communicate via decaying signals in a shared field, and consensus emerges through reinforcement rather than explicit voting, we can achieve:

1. Better fault tolerance
2. Lower coordination overhead
3. Emergent behavior without explicit choreography

The Method

I used simulated annealing to explore the parameter space:

• Decay rates (exponential, linear, sigmoid)
• Reinforcement thresholds
• Trust propagation models
• Network topologies (ring, mesh, small-world, scale-free)

Thousands of simulations. Different failure modes. Byzantine agents. Network partitions.

The Results

Metric	Traditional Orchestration	Signal Diffusion
Coordination latency	10-50ms	~1μs
Failure recovery	Manual intervention	Automatic decay
Scaling behavior	O(n) routing complexity	O(1) emit to field
Consensus mechanism	Explicit voting/aggregation	Emergent via reinforcement

The numbers were almost too good. I re-ran everything three times.

It actually works.

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Building SMESH

So I built the real thing. SMESH (Signal-MESHing)—a plant-inspired coordination protocol for multi-agent LLM systems.

The Core Ideas

1. Signals, Not Messages

Instead of sending messages to specific agents, you emit signals into a shared field. Any agent can sense signals that match their interests.

let signal = Signal::builder(SignalType::Task)
.payload(b"review this code".to_vec())
.intensity(1.0)  // Will decay over time
.confidence(0.9)
.build();

field.emit(signal);

2. Decay Is a Feature

Signals lose intensity over time. Stale tasks fade away naturally. No garbage collection. No manual cleanup. The system forgets things that don’t matter anymore.

3. Reinforcement = Consensus

When multiple agents observe the same signal and reinforce it, confidence goes up. Consensus emerges from agreement, not from a voting protocol.

4. No Central Controller

Every node is equal. There’s no orchestrator to become a bottleneck. No single point of failure. Agents self-organize based on skill affinity and signal sensing.

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The Numbers Don’t Lie

After porting to Rust and optimizing:

Operation	Time
Signal creation	216 nanoseconds
Signal reinforcement	48 nanoseconds
Field tick (10k signals)	1.2 milliseconds

That’s not a typo. Nanoseconds.

For comparison, a single HTTP request to an LLM takes ~500ms. The coordination overhead is now completely negligible.

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The Trippy Part

Here’s what I didn’t expect: emergent behavior.

In simulations with 20+ agents, patterns started appearing that I never programmed:

• Agents naturally specialized based on which signals they reinforced most
• "Reputation" emerged—agents that consistently provided good signals got reinforced more
• The system developed something like memory through signal history patterns

I was just trying to copy how trees talk. I accidentally built something that learns.

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Try It Yourself

SMESH is open source. MIT/Apache-2.0. Steal it, fork it, tell me I’m wrong.

git clone https://github.com/copyleftdev/smesh-rust
cd smesh-rust
cargo build --release

# Watch signals flow
cargo run --bin smesh -- sim --nodes 50 --ticks 100

# Compare with your LLM
cargo run --bin smesh -- compare

It supports both Ollama (local, free) and Claude (API) out of the box.

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What I Learned

Nature solved this already. Forests have been coordinating distributed systems for 400 million years. Maybe we should pay attention. 1.

Decentralization isn’t just political. It’s an engineering pattern that eliminates bottlenecks and single points of failure. 1.

Let things decay. Not every piece of state needs to be persisted forever. Sometimes the best garbage collection is just... time. 1.

Emergence > Choreography. The most interesting behaviors come from simple rules interacting, not from complex orchestration logic. 1.

Walk your dog. The best ideas come when you’re not trying to have them.

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What’s Next

• Python bindings (for the Pythonistas)
• WebAssembly build (for browser-based agents)
• Formal verification of convergence properties
• A paper, maybe? (If anyone wants to collaborate, DM me)

But mostly, I’m going to keep walking my dog and looking at trees.

Who knows what I’ll accidentally discover next.

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If you build something cool with SMESH, I want to hear about it. Find me on GitHub @copyleftdev or drop a comment below.

Star the repo if this resonated: github.com/copyleftdev/smesh-rust

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🌿 The Takeaway

"The forest doesn’t have a Kubernetes cluster. But it’s been running a distributed consensus protocol for longer than animals have existed."

Maybe the next breakthrough in AI infrastructure isn’t in a whitepaper.

Maybe it’s in your backyard.

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P.S. — My dog still doesn’t know how trees talk. But I think I’m getting closer.