Token reduction · Step 20
83.3%
24,000 → 4,000 active tokens at the default closed-form point (N = 20, S = 5). Exact under the published constants; not a regression fit.
AI agent memory · LLM orchestration
The End of AI Context Bloat.
A structurally invariant memory orchestrator for decentralized AI agents. Scale infinitely without exponential token costs or cognitive degradation.
01 / Whitepaper
Traditional agents fail at scale because the window is O(N).
Context window bloat is not a model-capacity problem. It is the default memory discipline of contemporary agentic frameworks. Nearly every production agent, and nearly every multi-agent swarm, treats the prompt as an append-only transcript. Each step writes a thought, an observation, a tool residual. After N steps of mean residual length L, the resident working set occupies Θ(N · L). The derivative is strictly positive: dT/dN = A > 0, where A is the mean residual appended per step.
That identity has three operational consequences. First, token cost grows with horizon. A concatenating stack at the default evaluation point — twenty steps, five registered skills — occupies 24,000 tokens. The same work under a constant window occupies 4,000. The difference is not compression. It is the difference between linear growth and an invariant.
Second, cognitive degradation is mechanical. As the window fills, the model attends over an ever larger mixture of live state, expired state, and prior errors. Instruction-following decays because the error remains in context. Asking the model to ignore the last failure is not recovery. It is a prompt that competes with the failure it describes.
Third, LLM orchestration at swarm scale inherits a worse bound. Concatenating W workers produces Θ(W) growth in the shared window. Peer residuals pollute every participant. On a finite hardware window the failure mode is not graceful forgetting. It is saturation.
The invariant
The active token window does not grow with history.
OPEN-DAG is a structurally invariant memory orchestrator for decentralized AI agents. The claim is mathematical, not metaphorical. Let T(N) denote the active token window after N steps. OPEN-DAG satisfies:
T(N) = C lim T(N) = C N → ∞ dT / dN = 0 dT / dS = 0
C is a constant fixed by the live skill and the live observation — not by history length, not by the number of registered skills. The working set is O(1) in N. Traditional AI agent memory is O(N). The default closed-form point is exact (variance zero by construction): at N = 20, T_trad = 24,000 and T = 4,000— an 83.3% reduction. The active window at step 1 and step 50 is the same quantity.
Scale, therefore, is no longer a token-budget problem. It is an orchestration problem with a solved bound.
02 / Metrics
The proof is an identity, then a measurement.
Closed-form token counts have variance zero. Rollback latency is a single-run point estimate against a prompt-level rewrite of the same fault. No architectural disclosure is required to read the bound.
Active token window
dT/dN = 0
Constant working set. The window at step 1 equals the window at step 50. History length does not enter the live prompt.
State rollback latency
3.80ms
Down from 2,445ms for a generative rewrite of the same error. Abandoned state is never reintroduced into the window.
| Axis | Concatenating agent | OPEN-DAG |
|---|---|---|
| Active window | O(N · L) | O(1) = C |
| Skill scaling | dT/dS = H > 0 | dT/dS = 0 |
| Swarm scaling | Θ(W) | dT/dW = 0 per worker |
| Fault recovery | Prompt rewrite · 2,445ms | State rollback · 3.80ms |