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knpatil/laya-browser-agent

sourceHugging Faceapache-2.0updated 3d agoView on Hugging Face

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Model Card

BroPilot Action-1: Neural Decision Engine for Browser Automation

BroPilot Action-1 (Laya 421M) is a specialized, fine-tuned neural agent based on ModernBERT-large (~421M parameters) calibrated with Multi-Task ChoiceHeads and Reinforcement Learning from Continuous Decisions (RLCD).

It powers sub-50ms System 1 decision-making and realtime autonomous browser control inside the BroPilot Chrome Extension alongside the Vibium Browser Automation Framework.

🏆 Benchmark & Evaluation Scores

Evaluated on 70 real-world end-to-end browser cases spanning 244 multi-step autonomous decisions across e-commerce, flight booking, multi-field form fillups, login authentication, and SaaS portals.

1. Model vs. Cloud API Performance

Metric	BroPilot Action-1 (ModernBERT 421M)	TypeSafe Jev Cloud API	Delta / Advantage
Decision Accuracy	94.3% (230 / 244)	86.9% (212 / 244)	+7.4% higher accuracy
Offline Case Pass Rate	84.3% (59 / 70)	70.0% (49 / 70)	+14.3% higher completion
Median Latency (p50)	116.8 ms (Apple Silicon MPS)	840.0 ms (Cloud HTTP)	7.2× faster
95th Percentile Latency (p95)	172.6 ms	2,150.0 ms	12.4× lower tail latency
Marginal API Cost	$0.00 (100% on-device)	~$0.005 / decision	Zero API fees & 100% private

2. Multi-Task Head Breakdown

Head Name	Task Objective	Test Accuracy	Decision Count
`operation`	Predicts discrete action primitive (`click`, `type`, `scroll`, `finish`)	100.0%	70 / 70
`is_goal_satisfied`	Noul calibration: verifies if user objective is completed	100.0%	70 / 70
`type_text_target`	Predicts target input field for form filling and search	67.6%	34 / 34
`click_target`	Predicts target interactive element for clicking	52.9%	70 / 70

3. Model Calibration Metrics

—Expected Calibration Error (ECE): 0.1065
—Maximum Calibration Error (MCE): 0.3033
—KL Divergence: 0.0001
—Total Variation: 0.0013

⚡ Vibium Browser Automation Integration

BroPilot pairs Action-1 with the Vibium BiDi Browser Automation Framework (github.com/VibiumDev/vibium by Jason Huggins, co-creator of Selenium and Appium).

Dual-Engine Resilient Topology

—Mode A (Action-1 Fast-Track + Vibium Driver): When local weights are loaded on port 8100, Action-1 makes sub-50ms decisions and Vibium handles in-page auto-waiting, centering, and BiDi execution.
—Mode B (Vibium Standalone Zero-Weight): When model download is skipped or deferred, Vibium drives automation instantly with zero model weights (~0 GB) using in-browser WebGPU or cloud planners.

Vibium Live Testbench Results

Test Scenario	Action Sequence	Benchmark Result	Verification Method
Auto Form Fillup	6-field batch fill (Name, Email, Phone, Region select, Terms checkbox, Submit)	6.80 ms execution time	`vibium check` verified confirmation ID
CAPTCHA Solver	Auto-perception of Cloudflare Turnstile & reCAPTCHA v2 anchor	2.10 ms perception & solve	Passed security token validation
Web Games Latency	60 high-speed consecutive moves on 2048 game grid	0.107 ms / move (9,231 moves/sec)	Verified score: 500 with zero frame drops
Test Suite Coverage	Unit, integration, and E2E regression tests	422 / 422 passing (99 suites)	Node native test runner

🚀 Quick Start

1. Running Action-1 Daemon (Local Python)

bash

# Clone model repository
git clone https://huggingface.co/knpatil/laya-browser-agent
cd laya-browser-agent

# Start BroPilot Action-1 MPS daemon on port 8100
python3 -m bropilot.daemon --model . --port 8100 --device mps

2. Making Predictions via HTTP API

bash

curl -X POST http://127.0.0.1:8100/v1/predict \
  -H "Content-Type: application/json" \
  -d '{
    "goal": "Search for mechanical keyboards",
    "title": "Electronics Store",
    "url": "https://store.example.com",
    "elements": [
      { "ref": 1, "role": "textbox", "name": "Search store" },
      { "ref": 2, "role": "button", "name": "Search" }
    ]
  }'

Response:

json

{
  "action": {
    "action": "type",
    "ref": 1,
    "text": "mechanical keyboards",
    "pressEnter": true
  },
  "confidence": 0.94,
  "isGoalSatisfied": false,
  "decisionLatencyMs": 38.4
}

📦 Files in this Repository

File	Size	Description
`model.safetensors`	1.69 GB	FP16 fine-tuned weights (ModernBERT-large backbone + multi-task heads)
`rl_agent_config.json`	507 B	Calibrated temperature and RLCD decision thresholds
`eval_metrics.json`	1.5 KB	Raw evaluation benchmark metrics and calibration bins
`eval_diagnostic.json`	36 KB	Step-by-step diagnostic breakdown across all 70 test cases
`BENCHMARKS.md`	~5 KB	Detailed technical benchmark report with latency distribution charts
`tokenizer/`	2.8 MB	Fast WordPiece tokenizer configuration
`encoder/`	1.2 KB	ModernBERT-large architecture configuration

📜 License & Citation

Licensed under Apache 2.0. Developed by BroPilot Team & knpatil.