Excited to have progressed to the second round of the Anthropic Fellows Program, representing the Kiwis. 🇳🇿

Alan Turing asked the question can machines think. 75 years later, we still don't know. But we do know you can't trust them to be right. Every AI answer can fail in 3 places: 1. Facts: Is it actually in the record? 2. Logic: Does the conclusion actually follow? 3. Meaning: What does the sentence actually say? Today's models guess at all three. I'm spent a year building the machine that checks all three deterministically. Zero guessing. - Facts are done - Logic underway - Meaning is next #SymplecticDynamics

Got the opportunity to have a working session with @OpenAI yesterday. Check out the new GPT Live and discuss Symplectic Dynamics 🤌🏻

Everyone's doing it. Stop uploading your confidential documents to general AI tools. Instead, spin up a fully private AI workspace on Faradex. All the power of frontier models... without the risk.

Government export controls have been lifted. Fable is back tomorrow 👀

Building a 52gb AI workstation powered by @AORUS_ANZ 👀 One more GPU, some tuning and it’s complete.

Test fitting this triple GPU, 52gb VRAM setup in a consumer box 📦

New mobo to go with the lambo ⚡️

My distributed compute mesh just got x2 new Lambo shells 🏎️💨

Have my swarm control layer cooking. Cumulative 100gb memory

My daily driver 👨🏻‍🔬

If I stand 50 meters away and throw a rock at your head, how do you stop it? You don’t negotiate with the rock using math. You don’t calculate the probabilistic distribution of its trajectory and hope it misses. You rely on gravity. Physics constrains the rock deterministically. It has to fall. Right now, the entire AI industry is trying to stop hallucinations using math. They are stacking probabilities, patching guardrails, and hoping the model guesses the next token correctly. By step 20 of a reasoning chain, it’s a coin flip. But information is physical (Landauer proved this in 1961). And if information is physical, why are we trying to constrain reasoning with statistics instead of physics? Math describes. Physics constrains. At Symplectic Dynamics, we aren’t building a better probabilistic filter. We are building deterministic intelligence. We don’t detect hallucinations. We make them physically inadmissible.

I caught every single hallucination across 3 frontier AI models. Every one. Zero false accusations. I’ve been working on stopping hallucinations in AI reasoning for some time now. Experiments, research, breakthroughs and roadblocks. On April 7th it all changed. I ran the full GPQA Diamond benchmark. 198 PhD-level science questions that even domain experts only score 65-70% on. Three frontier models. One frozen detection architecture. 3 days into a benchmark marathon a full run hit 100% catch. 0 false accusations across 498 correct answers. I had to double take. 24 hours later I had the same result across all three SOTA models. Byte-identical outputs. 3 runs. Fully deterministic. A frozen architecture cryptographically hashed and patent pending before discussing publicly. The models tested: Gemini 3.1 Pro, GPT 5.4, and Claude Opus 4.6. Gemini and GPT on standard API calls, Claude via standard Claude Code terminal. No special prompting, no per-model tuning. Same config catches everything across all three. No tool use, no extended reasoning, no best-of-N sampling. Gemini’s rate held close to its published number. Opus and GPT hallucinated more than their benchmark claims suggest, because those claims are typically made with tools and inference-time tricks turned on. The harness caught every hallucination regardless of which model produced it. Single frozen configuration. ~400ms deterministic latency. Runs on consumer hardware. Full companion paper with empirical evidence dropping this week. Will be raising to scale deployment across domains and make available for enterprise use cases in high-stakes industries. Just the beginning for Symplectic Dynamics and yes that is a real terminal output.

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Open AI has detailed in one of their recent papers that AI hallucinations are inevitable. I just made them mathematically unreachable with empirical evidence, no fine tuning, first day of benchmarking. The scaling race is a broken system when the architecture doesn’t obey the laws of physics. #SymplecticDynamics

I tried Google’s new NotebookLM video explainer generator on my academic research paper and the result actually blew me away. @googledevs Overview: AI systems hallucinate because they operate in unconstrained state spaces where invalid outputs are always reachable, no matter how well you train them. My paper argues the solution is not better training. It is changing the geometry of the system so that invalid outputs become physically unreachable by construction, the same way a train cannot leave its tracks. We do this by applying Hamiltonian mechanics from classical physics to the architecture of reasoning systems, constraining every state transition to stay within a bounded region of valid outputs. If no valid output exists, the system returns a certified failure rather than fabricating an answer. Abstract: Hallucination in artificial intelligence systems is commonly treated as a statistical artifact addressable through training methodology. We argue this framing is structurally incorrect. We present a formal framework in which reasoning is modeled as a dynamical system operating on a state space endowed with symplectic structure, and demonstrate that when Hamiltonian mechanics governs state evolution at the architectural level, invalid outputs become unreachable under the constrained transition rule by construction. We define hallucination operationally as constraint violation relative to a stated specification. Our framework introduces a composite verifier V := Vᶜ ∧ Vᴴ and a Hamiltonian scalar H whose bounded-energy transition rule transforms the divergent cone trajectory of unconstrained autoregressive systems into a bounded cylinder for any finite reasoning depth N. Full paper: zenodo.org/records/18808297

Today I published my first paper for Symplectic Dynamics. “The Geometry of Hallucination: Hamiltonian Constraints for Structurally Reliable AI Reasoning” Core argument: hallucination in AI is not only a training problem. It is a geometry and reachability problem. Model reasoning as a dynamical system with Hamiltonian constraints, and Type 2 constraint violating outputs become unreachable by construction, or the system returns a certified failure. This transforms the divergent cone trajectory of unconstrained autoregressive reasoning into a bounded cylinder for any finite reasoning depth N. Full paper: zenodo.org/records/18808297

Physics doesn’t guess. Neither do we. Introducing Symplectic Dynamics: AI governed by the laws of physics. We don’t filter bad outputs. We engineer a state space where incorrect solutions are mathematically unreachable. #SymplecticDynamics #Physics #AI