Simurg – Kills LLM hallucinations mid-stream, with free web search built in
It watches a live model response as tokens stream in and raises a calibrated alarm the moment the output degenerates: repetition loops, cross-lingual drift, regurgitation, and structural collapse — the failure modes that ship hallucinated or garbage text to users. This checkpoint is the learned deep tier of the SIMURG ensemble; it runs in single-digit milliseconds on Apple Silicon and never blocks the stream it guards. Repository: https://github.com/doofzoff/SIMURG Paper: https://ssrn.com/abstract=7451269 Can you train SIMURG on your own hallucination types?
- ▪It watches a live model response as tokens stream in and raises a calibrated alarm the moment the output degenerates: repetition loops, cross-lingual drift, regurgitation, and structural collapse — the failure modes that ship hallucinated o
- ▪This checkpoint is the learned deep tier of the SIMURG ensemble; it runs in single-digit milliseconds on Apple Silicon and never blocks the stream it guards.
- ▪Repository: https://github.com/doofzoff/SIMURG Paper: https://ssrn.com/abstract=7451269 Can you train SIMURG on your own hallucination types?
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Record
| Original publisher | Huggingface |
| Canonical URL | https://huggingface.co/MergenAI/SIMURG |
| Publication time | Tue, 22 Sep 2026 11:46:50 +0000 |
| Retrieval time | 2026-09-22T11:53:50.953Z |
| Last seen | 2026-09-22T11:53:50.953Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | s4xnqccBOUW3 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
MergenAI / SIMURG Like 0 Follow HAL-X 9 Text Classification Transformers Safetensors English anomaly-detection hallucination-detection llm-safety streaming corruption-detection fine-tuning Eval Results (legacy) License: apache-2.0 Model card Files Files and versions xet Community Deploy Copy to bucket new Use this model Instructions to use MergenAI/SIMURG with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Transformers How to use MergenAI/SIMURG with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MergenAI/SIMURG") # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MergenAI/SIMURG", device_map="auto")…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Huggingface.
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