v1.0.2 — SIMURG Monolith

SIMURG Monolith — real-time learning + grounded factuality

SIMURG Monolith is the new layer on top of the base decode guard. In the
spirit of the Monolith recommender, it keeps learning while it serves: every
👍 / 👎 a user gives an answer is one online SGD step (serving loop == training
loop), so the hallucination model adapts in real time with no batch-retrain gap.

Added

• SIMURG Monolith terminal — python3 -m simurg.veritas_dashboard
• online hallucination-risk model (simurg.veritas.monolith) that trains from
  live like/dislike feedback
• white-box fact-uncertainty from decoder top-k logprobs (entropy, margin,
  competing-fact detection)
• grounded verification against real evidence (Wikipedia + web): catches a
  fabricated subject and a wrong detail on a real subject, and abstains
  instead of asserting when a claim contradicts the sources
• multilingual bootstrap dataset + trainer (EN / RU / AZ), shipped bootstrap
  model at held-out AUROC 1.0
• full "SIMURG Monolith" readme section with the terminal screenshot, launch
  instructions, and how to wire likes/dislikes from your own platform

Install: pip install --upgrade simurg · PyPI: https://pypi.org/project/simurg/1.0.2/