Hey r/LocalLLaMA !

I built infrastructure to prevent LLM conversational drift through time/date (temporal) anchoring.

Willow timestamps conversations so models stay grounded and don't hallucinate dates or lose context across turns (See below for preliminary metrics). Let me know if you need any additional information or have questions!

**Need 10 more testers!!**

• Takes 10 minutes
• Test baseline vs Willow mode
• Quick feedback form

**Links:**

- Live API: https://willow-drift-reduction-production.up.railway.app/docs

- GitHub: https://github.com/willow-intelligence/willow-demo

- Feedback: https://forms.gle/57m6vU47vNnnHzXm7

Looking for honest feedback, positive or negative, as soon as possible!

Thanks!

Preliminary Data, Measured Impact on multi-turn tasks (n = 30, p < 0.001):

• Goal Stability (50 turns): 0.42 → 0.82 (+95%)
• Constraint Violations: 8.5 → 1.9 (–77%)
• Perturbation Recovery: 5.2 → 1.8 turns (–65%)
• Cross-Model Variance: 30% → <5% (–87%)

Using industry-standard assumptions for human escalation cost and API usage, this results in:

• Baseline annual cost: \~$46–47M
• With Willow: \~$11M
• Annual savings: \~$36M per deployment