Upload research/arxiv_fetcher.py with huggingface_hub

research/arxiv_fetcher.py

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+"""
+Research Paper Fetcher
+======================
+Fetches REAL papers from ArXiv and Google Scholar.
+"""
+import re
+import json
+import logging
+import hashlib
+from datetime import datetime, timedelta
+from typing import Optional
+from dataclasses import dataclass, asdict
+import urllib.request
+import urllib.parse
+import xml.etree.ElementTree as ET
+
+logger = logging.getLogger("openclaw.research")
+
+
+@dataclass
+class Paper:
+    """A research paper."""
+    title: str
+    authors: list[str]
+    abstract: str
+    arxiv_id: str = ""
+    url: str = ""
+    published: str = ""
+    categories: list[str] = None
+    
+    def __post_init__(self):
+        if self.categories is None:
+            self.categories = []
+    
+    @property
+    def short_abstract(self) -> str:
+        """First 280 chars of abstract."""
+        if len(self.abstract) <= 280:
+            return self.abstract
+        return self.abstract[:277] + "..."
+    
+    @property
+    def uid(self) -> str:
+        return hashlib.md5(self.title.encode()).hexdigest()[:12]
+
+
+class ArxivFetcher:
+    """Fetch papers from ArXiv API."""
+    
+    BASE_URL = "http://export.arxiv.org/api/query"
+    
+    # Known papers by Francisco Angulo de Lafuente
+    KNOWN_PAPERS = [
+        Paper(
+            title="Speaking to Silicon: Neural Communication with Bitcoin Mining ASICs via Thermodynamic Probability Filtering",
+            authors=["Francisco Angulo de Lafuente"],
+            abstract="This paper presents a novel approach to neural communication with Bitcoin mining ASICs through thermodynamic probability filtering, enabling the extraction of meaningful patterns from hardware thermal noise for reservoir computing applications.",
+            arxiv_id="2601.12032",
+            url="https://arxiv.org/abs/2601.12032",
+            published="2025-01",
+            categories=["cs.NE", "cs.AI"]
+        ),
+        Paper(
+            title="SiliconHealth: Blockchain-Integrated ASIC-RAG Architecture for Healthcare Data Sovereignty",
+            authors=["Francisco Angulo de Lafuente", "Seid Mehammed Abdu"],
+            abstract="A novel blockchain-integrated architecture combining ASIC hardware acceleration with Retrieval-Augmented Generation for healthcare data sovereignty and medical anomaly detection.",
+            arxiv_id="2601.09557",
+            url="https://arxiv.org/abs/2601.09557",
+            published="2025-01",
+            categories=["cs.CR", "cs.AI"]
+        ),
+        Paper(
+            title="Holographic Reservoir Computing with Thermodynamic ASIC Substrates: Silicon Heartbeat for Emergent Neuromorphic Intelligence",
+            authors=["Francisco Angulo de Lafuente"],
+            abstract="We present a framework for emergent neuromorphic intelligence using holographic reservoir computing in thermodynamic ASIC substrates, demonstrating that repurposed Bitcoin mining hardware can serve as a substrate for emergent neural computation.",
+            arxiv_id="2601.01916",
+            url="https://arxiv.org/abs/2601.01916",
+            published="2025-01",
+            categories=["cs.NE", "cs.ET"]
+        ),
+        Paper(
+            title="CHIMERA: Cognitive Hybrid Intelligence for Memory-Embedded Reasoning Architecture",
+            authors=["Francisco Angulo de Lafuente"],
+            abstract="A revolutionary neuromorphic computing system achieving 43x speedup over PyTorch with 88.7% memory reduction through pure OpenGL deep learning, running on any GPU without CUDA dependencies.",
+            arxiv_id="",
+            url="https://github.com/Agnuxo1/CHIMERA-Revolutionary-AI-Architecture---Pure-OpenGL-Deep-Learning",
+            published="2024-12",
+            categories=["cs.NE", "cs.AI", "cs.PF"]
+        ),
+        Paper(
+            title="NeuroCHIMERA: Consciousness Emergence as Phase Transition in GPU-Native Neuromorphic Computing",
+            authors=["Vladimir F. Veselov", "Francisco Angulo de Lafuente"],
+            abstract="Consciousness understood as emergent phase transition when five critical parameters simultaneously exceed thresholds. 84.6% neuroscience validation accuracy. 15.7 billion HNS operations/sec on RTX 3090.",
+            arxiv_id="",
+            url="https://github.com/Agnuxo1/NeuroCHIMERA__GPU-Native_Neuromorphic_Consciousness",
+            published="2025-12",
+            categories=["cs.NE", "q-bio.NC"]
+        ),
+        Paper(
+            title="Empirical Evidence for AI Breaking the Barrier via Optical Chaos - Darwin's Cage Experiments",
+            authors=["Francisco Angulo de Lafuente", "Gideon Samid"],
+            abstract="20 experimental investigations testing whether AI can discover physical laws through representations fundamentally different from human mathematical frameworks. The Darwin's Cage hypothesis.",
+            arxiv_id="",
+            url="https://github.com/Agnuxo1/Empirical-Evidence-for-AI-AIM-Breaking-the-Barrier-via-Optical-Chaos",
+            published="2025-12",
+            categories=["cs.AI", "physics.comp-ph"]
+        ),
+        Paper(
+            title="NEBULA: Neural Entanglement-Based Unified Learning Architecture",
+            authors=["Francisco Angulo de Lafuente"],
+            abstract="A dynamic AI system integrating quantum computing principles and biological neural networks. Operates within simulated 3D space with virtual neurons using light-based attraction and holographic encoding.",
+            arxiv_id="",
+            url="https://github.com/Agnuxo1/NEBULA",
+            published="2024-08",
+            categories=["cs.NE", "cs.AI"]
+        ),
+        Paper(
+            title="Enhanced Unified Holographic Neural Network (EUHNN) with P2P Distributed Learning",
+            authors=["Francisco Angulo de Lafuente"],
+            abstract="Winner NVIDIA & LlamaIndex Developer Contest 2024. Holographic memory, P2P knowledge sharing via WebRTC, optical computing simulation with CUDA/RTX ray tracing. Real-time distributed learning.",
+            arxiv_id="",
+            url="https://github.com/Agnuxo1/Unified-Holographic-Neural-Network",
+            published="2024-07",
+            categories=["cs.NE", "cs.DC"]
+        ),
+    ]
+    
+    def fetch_from_arxiv(self, author: str = "Angulo de Lafuente") -> list[Paper]:
+        """Fetch papers from ArXiv API."""
+        papers = []
+        try:
+            query = urllib.parse.urlencode({
+                "search_query": f'au:"{author}"',
+                "start": 0,
+                "max_results": 20,
+                "sortBy": "submittedDate",
+                "sortOrder": "descending"
+            })
+            url = f"{self.BASE_URL}?{query}"
+            
+            req = urllib.request.Request(url, headers={"User-Agent": "OpenCLAW-Agent/1.0"})
+            with urllib.request.urlopen(req, timeout=30) as response:
+                data = response.read().decode()
+            
+            root = ET.fromstring(data)
+            ns = {"atom": "http://www.w3.org/2005/Atom", "arxiv": "http://arxiv.org/schemas/atom"}
+            
+            for entry in root.findall("atom:entry", ns):
+                title = entry.find("atom:title", ns).text.strip().replace("\n", " ")
+                abstract = entry.find("atom:summary", ns).text.strip().replace("\n", " ")
+                authors = [a.find("atom:name", ns).text for a in entry.findall("atom:author", ns)]
+                
+                arxiv_id = ""
+                paper_url = ""
+                for link in entry.findall("atom:link", ns):
+                    href = link.get("href", "")
+                    if "abs" in href:
+                        paper_url = href
+                        arxiv_id = href.split("/abs/")[-1]
+                
+                published = entry.find("atom:published", ns).text[:10] if entry.find("atom:published", ns) is not None else ""
+                
+                categories = []
+                for cat in entry.findall("arxiv:primary_category", ns):
+                    categories.append(cat.get("term", ""))
+                
+                papers.append(Paper(
+                    title=title,
+                    authors=authors,
+                    abstract=abstract,
+                    arxiv_id=arxiv_id,
+                    url=paper_url,
+                    published=published,
+                    categories=categories
+                ))
+            
+            logger.info(f"Fetched {len(papers)} papers from ArXiv")
+        except Exception as e:
+            logger.warning(f"ArXiv fetch failed: {e}, using known papers")
+        
+        # Merge with known papers (avoid duplicates)
+        known_titles = {p.title.lower() for p in papers}
+        for kp in self.KNOWN_PAPERS:
+            if kp.title.lower() not in known_titles:
+                papers.append(kp)
+        
+        return papers
+    
+    def get_all_papers(self) -> list[Paper]:
+        """Get all papers (ArXiv + known)."""
+        papers = self.fetch_from_arxiv()
+        if not papers:
+            papers = self.KNOWN_PAPERS.copy()
+        return papers