INTEL_REPORT
Hugging Face — Blog (ML / agents) · published 5/8/2026, 4:03:50 PM · TLP amber
Summary
Ingested excerpt (first ~500 chars of normalized text).
EMO: Pretraining mixture of experts for emergent modularity EMO: Pretraining mixture of experts for emergent modularity Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Log In Sign Up Back to Articles EMO: Pretraining mixture of experts for emergent modularity Team Article Published May 8, 2026 Upvote 22 +16 Kyle Wiggers Ai2Comms Follow allenai Ryan Wang ryanyxw Follow allenai How do we get modularity to emerge? Benchmark results What are expert subset…
https://huggingface.co/blog/allenai/emo
sha256:1d3441287f7756b1ad93a31c76a79b50456ed83f7c99da323477f547dbb2b616
What we pulled out
Deterministic extractor (IOC + allowlisted tokens + ATT&CK IDs present in DB).
Indicators
Linked with report → mentions → indicator. Values open the indicator workspace.
Malware families
Allowlist token matches only.
Threat actors mentioned
Allowlist mentions — not a formal attribution verdict.
ATT&CK techniques
MITRE IDs referenced in text and present in local technique table.
CONTINUE INVESTIGATION
High-signal pivots without leaving the thread you started in search.
Browse the report corpus.
Neighborhood from the first linked indicator.
| https://allenai.org/papers/emo |
| Open → |
| url | https://github.com/allenai/EMO | Open → |
| url | https://emovisualization.netlify.app/ | Open → |
| url | https://huggingface.co/papers/submit?paperId=2605.06663\" | Open → |
| url | https://huggingface.co/papers/submit?paperId=2605.06663</a | Open → |
| url | https://cdn-avatars.huggingface.co/v1/production/uploads/661ab1f1fa3b144a381fa454/IlpZBb9NCjo7ntFwMIH53.png" | Open → |
| url | https://huggingface.co/papers/submit?paperId=2605.06663 | Open → |