Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains

📢 Author Clarification: Updated Abstract, Public JevBench Labeling & Werracle EVM Release

Authors: Volkan Dağlı (@pCwOrM ), Dr. Zerrin Dağlı, Dağhan Dağlı
Lead Author Correspondence: ask@answerr.me / pcworm@pcworm.net
Platform & Live Telemetry: answerr.me | GitHub: pCwOrM/werr | Dataset: werr_open_decisions

1. Benchmark Clarification & Open-Science Precision

In the initial preflight release of this paper, our experimental evaluation on the JevBench decision benchmark was summarized as "ranked World #1 on the independent JevBench suite (81.65%)".

In adherence to the highest standards of scientific transparency and following mutual alignment with the benchmark maintainers (werr/issues/8 & answerr/issues/7), we explicitly clarify that:

• The 81.65% score represents a self-run evaluation on the 231-item public split of JevBench rather than a hosted held-out leaderboard entry.
• All "World #1" promotional phrasing has been intentionally retracted across all documentation, repos, and upcoming arXiv v2 revisions in favor of strict empirical attribution.
• The fundamental scientific claim of this paper rests not on incremental leaderboard margins, but on the zero-VRAM paradigm: eliminating stored neural weight tensors entirely (0 Bytes VRAM) and deriving microsecond deterministic decisions procedurally via Mandelbrot chaotic boundary escapes ($z_{n+1} = z_n^2 + c$).

2. Official Revised Abstract (Scheduled for arXiv v2 Revision)

Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains
Volkan Dağlı, Zerrin Dağlı, Dağhan Dağlı (2026)

Deploying Large Language Models for runtime operational triage incurs prohibitive latency (>100–500 ms), high VRAM requirements (>4–8 GB), and excessive energy dissipation. Extending Mandelbrot Fractal Neural Synthesis (Dagli et al., 2026), this paper presents the Universal Fractal Natural Language Decision Map, realized via the werr machine-native edge reflex runtime and the production answerr platform (https://answerr.me). Operating entirely without stored weight tensors (0 Bytes VRAM), the engine synthesizes deterministic decisions—noul (Boolean), choice (categorical), and score (ordinal)—by dynamically modulating 24-byte coordinate seeds along the chaotic boundary of the Mandelbrot set and evaluating 4-quadrant escape dynamics. Drawing inspiration from biological System-One reflex arcs, the engine introduces: (i) an Auto-Seed Router with domain projector $\Phi_D$ yielding a +28.8% accuracy gain over linear baselines; (ii) an Information-Theoretic Acoustic Damping Filter grounded in token entropy and phonetic spectral density that insulates against prompt injections (0.0% empirical bypass; 95% Wilson CI: [0.0%, 30.8%]) while pruning escape iterations by 45.8% (accelerating throughput 2.5x to 3.31 ms latency); and (iii) an Organic Dynamic Calibration framework using $\mathcal{O}(1)$ Exponential Moving Average (EMA, $lpha=0.03$) and quadrant phase rotation to eliminate positional bias.

Evaluated on dedicated bare-metal infrastructure (api.answerr.me:4431) across 1,150+ verified domain decisions (3,200+ questions) alongside competitive self-run evaluations on the public JevBench suite (81.65% on the 231-item public split), the framework achieves 92.6% domain macro-accuracy (95% CI: [90.8%, 94.1%]) with 7.08 ms median CPU latency on commodity hardware. Furthermore, we provide an OpenAI-compatible API (/v1/chat/completions), demonstrate edge microcontroller feasibility, and introduce Werracle—a formal EVM implementation executing deterministic Q16.16 fixed-point fractal escapes inside a single 32-byte storage slot, audited across 1,000 fuzz test suites and deployed as a dynamic Uniswap v4 fee-hook.

3. Open Artifacts & Code Verification

All replication assets, benchmarks, and production harnesses are fully accessible:

• Edge Engine (werr): https://github.com/pCwOrM/werr
• Dual-Cognition Portal (answerr): https://answerr.me
• Verified Benchmark Dataset: https://huggingface.co/datasets/pCwOrM/werr_open_decisions
• On-Chain EVM Q16.16 Engine (werracle): https://github.com/pCwOrM/werracle
• Core Theoretical Foundation: https://github.com/pCwOrM/mandelbrot-fractal-neural-synthesis