|
|
| import asyncio
|
| import os
|
| from playwright.async_api import async_playwright
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|
|
| COOKIES = {
|
| "ARXIVNG_SESSION_ID": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoiMTM5NTQ4NyIsInNlc3Npb25faWQiOiIyODEyMDgzMCIsIm5vbmNlIjoiMjE4NTg5OTMiLCJleHBpcmVzIjoiMjAyNi0wOS0zMFQxMzo1MjoyOCswMDowMCJ9.-h2yweBj7vF9bVtPmC8up54f7-2XemXqYvUQ5w6BFLo",
|
| "submit_session": "b535aba4bdfa8a3e22f3d4e1ad6034e14570f220",
|
| "tapir_session": "28120830:1395487:223.205.222.239:1790517148:4:ls1xoeRLtU1TSWdlVs0OWghYj/8",
|
| "arxiv_author_guide": "%7B%22minimize%22%3A%22false%22%7D",
|
| "arxiv_labs": "%7B%22sameSite%22%3A%22strict%22%2C%22expires%22%3A365%7D",
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| "browser": "223.205.222.239.1790517148272911"
|
| }
|
| METADATA = {
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| "title": "Fiber-MoE & Symplectic Gating: Principled Dynamic Expert Routing and Zero-Waste State Annihilation for Autonomous World Agents",
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| "primary_category": "cs.AI",
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| "secondary_categories": "cs.LG, cs.SY",
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| "abstract": "Current Mixture-of-Experts (MoE) architectures and autoregressive world models suffer from three structural pathologies: limit-cycle router thrashing across non-consecutive tokens, static over-allocation of compute budget regardless of prompt entropy, and compounding epistemic drift over long rollouts. In this work, we present SCE-Fiber, an energy-conserving control substrate for massive sparse models (demonstrated on 35B parameter scales with 128 physical experts). By restructuring flat expert topographies into eight semantic domain fibers and applying a critically damped Hamiltonian update (zeta = 1.0), our framework eliminates oscillatory domain switching while reducing active parameters via dynamic Upper Confidence Bound (UCB) dead-work pruning. Furthermore, we formulate an invariant world manifold that bounds simulated transitions via LaSalle-Lyapunov invariance (V(x) = x^T P x). Backed by a sub-microsecond CPython native kernel (0.76 - 1.46 \u00b5s latency), empirical benchmarks on an NVIDIA GeForce RTX 3090 demonstrate a 37.5%--75% reduction in active FLOPs while preserving foundational baseline accuracy and providing instant zero-waste state caching."
|
| }
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| ZIP_PATH = r"c:\Users\gemin\AppData\Local\Programs\antigravity\paper\arxiv_submission.zip"
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|
|
| async def main():
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| async with async_playwright() as p:
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| browser = await p.chromium.launch(headless=False)
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| context = await browser.new_context()
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|
|
|
|
| cookie_list = []
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| for k, v in COOKIES.items():
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| cookie_list.append({
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| "name": k,
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| "value": v,
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| "domain": ".arxiv.org" if "tapir" in k or "ARXIVNG" in k else "arxiv.org",
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| "path": "/"
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| })
|
| await context.add_cookies(cookie_list)
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|
|
| page = await context.new_page()
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| print("--> Navigating to https://arxiv.org/submit...")
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| await page.goto("https://arxiv.org/submit")
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| await page.wait_for_timeout(3000)
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|
|
| print("--> Authenticated! Title:", await page.title())
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|
|
|
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| await page.screenshot(path="arxiv_submit_dashboard.png")
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| print("--> [DONE] Dashboard captured at arxiv_submit_dashboard.png")
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|
|
| if __name__ == "__main__":
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| asyncio.run(main())
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|
|
