✨🎄Merry Christmas from the Polyhedra team🎄✨As 2025 wraps up, we want to thank everyone who has supported our work.This year brought real breakthroughs — across verifiable AI, zkML, and products like Polyhedra i-D and @opencash_system that moved these ideas from research into practice.None of this happens in isolation. We’re grateful to the builders and partners across the Polyhedra ecosystem who are turning these foundations into real-world systems.We’re grateful for the momentum, and excited for what’s ahe ...

4/zkML gives agentic systems the proof layer they’re missing:✔️Confirms the exact model version behind each decision✔️Proves policy compliance without revealing internal logic✔️ Generates evidence of how multi-step actions were taken✔️Allows auditors to validate behavior without needing raw dataIt turns autonomous execution into something that can be independently verified, reconstructed and held accountable. ...

Why zkML? Because Gartner now predicts that more than 40% of agentic AI projects will be canceled before the end of 2027.The issue isn’t capability. It’s control. Enterprises don’t have a reliable way to verify what autonomous agents are doing once they’re deployed. https://t.co/QjmIgnOOSJ ...

4/zkML gives platforms a way to enforce accountable outputs:✔️ Confirms the model version responsible for each statement✔️ Confirms which evidence contributed to each claim, without exposing underlying files✔️ Confirms whether reasoning steps followed required constraints✔️ Allows auditors to review the chain of logic without revealing proprietary model weightsThis turns AI-generated search results into something grounded — not improvised. ...

Why zkML? Because Google’s AI-generated search summaries invented a lawsuit against a U.S. electrical contractor, and attributed it to a state attorney general despite no such case ever existing.The company discovered the fabricated claims back in 2024, filed suit in March 2025, and the false outputs persisted into November — well past the point when they should have been resolved. ...

4/This is where zkML becomes essential:✔️binding AI agent identity to cryptographic credentials, not UI sessions✔️proving which agent and model instance executed each action✔️proving delegated authority, spend limits, and policies were enforced at execution time✔️producing verifiable evidence without exposing user data or internal logicThis is how AI agents gain identity that systems can actually trust. ...

Why zkML? Because a report from McKinsey shows AI agents are now executing real commerce:OpenAI’s Operator can complete bookings and purchases, Stripe is enabling agentic checkout inside ChatGPT, and Google’s AP2 is designed to let agents execute payments via signed mandates...AI agents are starting to transact, not just assist. ...

4/zkML addresses this mismatch directly:✔️ proving which model executed a task, independent of hardware✔️proving policy compliance without exposing model internals✔️proving how inputs shaped outputs without leaking data✔️proving behavior consistency across decentralized deploymentsVerification becomes portable — even when infrastructure is not. ...

Why zkML? Because DeepSeek just released two V3.2 models that deliver frontier-class performance in reasoning, mathematics, and coding — while dramatically lowering the compute required to achieve it.Frontier intelligence is no longer confined to hyperscaler-grade infrastructure. ...

4/zkML provides the missing layer:✔️Proves which model executed each stage of the workflow✔️Proves that required policies and constraints were enforced✔️Proves which inputs influenced the outcome without exposing private data✔️ Enables auditors to validate multi-hour agent traces without revealing proprietary logicIt turns autonomous workflow execution into a process that can be inspected, audited and proven at every stage. ...