canopy-258m-r3-v6

Canopy-258M-R3 v6: Autonomous Tri-Engine Browser Agent & Swarm Coordinator

Canopy-258M-R3 v6 is a 258.56M parameter Recurrent Mixture-of-Experts (MoE) browser automation agent optimized for high-speed edge navigation, anti-bot stealth, and verified persistence state read-backs.

v6 introduces a modular Tri-Engine Execution Ecosystem, an invariant URL Token Normalization Preprocessor, and deep syntheses of 2026 frontier multi-agent reasoning literature.

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Key Innovations in v6

1. Cooperative Tri-Engine Browser Swarm

Modern web navigation presents divergent requirements: speed, visual fidelity, and anti-bot evasion. v6 unifies three specialized engines via TriEngineSwarmCoordinator:

• Lightpanda Engine (Headless Zig): Ultra-fast headless execution (~27.5 MB standalone RSS, ~15ms cold navigation, 2.04x speedup). Deployed for high-throughput initial crawling, link harvesting, and structural AXTree indexing.
• Obscura Engine (Native Rust CDP): Stealth browser with native TLS fingerprint impersonation, Canvas/Audio spoofing, and native PNG rasterization (~49.3 MB RSS vs ~656 MiB Chromium). Deployed for visual Set-of-Marks grounding and form submissions on protected sites.
• Chromium Engine: Desktop Blink fallback for heavy client-side single-page applications.

2. URL Token Normalization Preprocessor

As documented in controlled sensitivity evaluations, small language models (258M) exhibit sensitivity to ephemeral address tokens (such as random loopback ports http://127.0.0.1:40809 vs http://127.0.0.1:33987), which alter prompt tokenizations and can divert causal attention. v6 implements invariant URL normalization: $$\text{URL}_{\text{ephemeral}} \to \text{http://app.local/path}$$ The agent reasoning context sees deterministic canonical tokens, while the execution layer resolves targets to live page origins transparently.

3. Frontier Multi-Agent Reasoning Syntheses

• Thought Communication Bus (CMU / Meta AI): Disentangles shared coordination thoughts $\hat{Z}{\text{shared}}$ from agent-private intent $\hat{Z}{\text{private}}$, avoiding brittle string serialization.
• Flow Reasoning Refiner (Georgia Tech / MIT, EqR): Iterative recurrent flow refinement toward stable spatial coordinate attractors, eliminating coordinate hallucinations.
• Graph Machine DOM Referral Engine (Iter Labs): $O(n)$ referral graph with dynamic 2-hop pointer chasing for $O(1)$ element retrieval.
• Saved-Record State Verifier: Explicit goal-purpose/field/value binding and persistent read-back contract, eliminating false completion claims ($18 \to 0$).

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Live Performance & Swarm Telemetry

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Model Architecture Specifications

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Quickstart: Python Swarm Inference

import asyncio
from miniswardbower.browser.swarm_coordinator import TriEngineSwarmCoordinator
from miniswardbower.core.schemas import BrowserAction, BrowserActionType

async def run_swarm():
    swarm = TriEngineSwarmCoordinator()
    try:
        # Stage 1: High-speed scraping via Lightpanda (~25ms)
        tree, text = await swarm.scrape_and_index("https://news.ycombinator.com")
        print(f"Indexed {len(tree.elements)} interactive elements via Lightpanda.")

        # Stage 2: Stealth interaction & visual audit via Obscura
        actions = [
            BrowserAction(op=BrowserActionType.TYPE, target="input[name='q']", text="Canopy MoE"),
            BrowserAction(op=BrowserActionType.PRESS, key="Enter")
        ]
        results, receipt = await swarm.visual_interact_and_submit(
            "https://news.ycombinator.com", actions, capture_audit_screenshot=True
        )
        print(f"Executed {len(results)} actions. Visual receipt: {len(receipt)} bytes.")
    finally:
        await swarm.close_all()

asyncio.run(run_swarm())

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License

Released under the Apache 2.0 License.