TL;DR: We taught an AI agent to split complex tasks across multiple parallel workers that coordinate through scent signals — like ants, not chat. Result: 5.86x faster, 3.4x cheaper, identical quality. Zero coordination tokens. Research paper: https://github.com/nagisanzenin/temm1e/blob/main/docs/swarm/RESEARCH_PAPER.md
Most multi-agent frameworks (AutoGen, CrewAI, LangGraph) coordinate agents by making them talk to each other. Every coordination message is an LLM call. Every LLM call costs tokens. The coordination overhead can exceed the actual work. We asked: what if agents never talked to each other at all? TEMM1E v3.0.0 introduces “Many Tems” — a swarm intelligence system where multiple AI agent workers coordinate through stigmergy: indirect communication via environmental signals. Borrowed from ant colony optimization, adapted for LLM agent runtimes. Here’s how it works: You send a complex request (“build 5 Python modules”) The Alpha (coordinator) decomposes it into a task dependency graph — one LLM call A Pack of Tems (workers) spawns — real parallel tokio tasks Each Tem claims a task via atomic SQLite transaction (no distributed locks) Tems emit Scent signals (time-decaying pheromones) as they work — “I’m done”, “I’m stuck”, “this is hard” Other Tems read these signals to choose their next task — pure arithmetic, zero LLM calls Results aggregate when all tasks complete The key insight: a single agent processing 12 subtasks carries ALL previous outputs in context. By subtask 12, the context has grown 28x. Each additional subtask costs more because the LLM reads everything that came before — quadratic growth: h*m(m+1)/2. Pack workers carry only their task description + results from dependency tasks. Context stays flat at ~190 bytes regardless of how many total subtasks exist. Linear, not quadratic. Benchmarks (real Gemini 3 Flash API calls, not simulated): 12 independent functions: Single agent 103 seconds, Pack 18 seconds. 5.86x faster. 7,379 tokens vs 2,149 tokens. 3.4x cheaper. Quality: both 12/12 passing tests. 5 parallel subtasks: Single agent 7.9 seconds, Pack 1.7 seconds. 4.54x faster. Same tokens (1.01x ratio — proves zero waste). Simple messages (“hello”): Pack correctly does NOT activate. Zero overhead. Invisible. What makes this different from other multi-agent systems: Zero coordination tokens. AutoGen/CrewAI use LLM-to-LLM chat for coordination — every message costs. Our scent field is arithmetic (exponential decay, Jaccard similarity, superposition). The math is cheaper than a single token. Invisible for simple tasks. The classifier (already running on every message) decides. If it says “simple” or “standard” — single agent, zero overhead. Pack only activates for genuinely complex multi-deliverable tasks. The task selection equation is 40 lines of arithmetic, not an LLM call: S = Affinity^2.0 * Urgency^1.5 * (1-Difficulty)^1.0 * (1-Failure)^0.8 * Reward^1.2 1,535 tests. 71 in the swarm crate alone, including two that prove real parallelism (4 workers completing 200ms tasks in ~200ms, not ~800ms). Built in Rust. 17 crates. Open source. MIT licensed. The research paper has every benchmark command — you can reproduce every number yourself with an API key. What we learned: The swarm doesn’t help for single-turn tasks where the LLM handles “do these 7 things” in one response. There’s no history accumulation to eliminate. It helps when tasks involve multiple tool-loop rounds where context grows — which is how real agentic work actually happens. We ran the benchmarks on Gemini Flash Lite ($0.075/M input), Gemini Pro, and GPT-5.2. Total experiment cost: $0.04 out of a $30 budget. The full experiment report includes every scenario where the swarm lost, not just where it won. https://github.com/nagisanzenin/temm1e submitted by /u/No_Skill_8393
Originally posted by u/No_Skill_8393 on r/ArtificialInteligence
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