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Matthew A Cator

@constraintdynamics

Founder of Constraint Dynamics, building Golem: a geometric knowledge system for verification-before-voice AI.

https://www.constraintdynamics.org
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About Me

I’m the founder of Constraint Dynamics, the research organization behind Golem and Golem Physics. Golem is an implemented geometric knowledge research system: information becomes claim coordinates, the lattice tracks provenance, anchors, tensions, support paths, and time, and speech waits on claim state rather than LLM confidence alone. The current work focuses on making Golem easier for AI safety reviewers and funders to inspect: refreshed metrics, runtime traces, benchmark planning, reviewer evidence, and the bridge from verification-before-voice into governed agent action through Constraint Native.

Projects

Verification Before Voice: Golem Physics as Reviewable AI Safety Evidence

pending admin approval

Comments

Verification Before Voice: Golem Physics as Reviewable AI Safety Evidence
constraintdynamics avatar

Matthew A Cator

about 1 hour ago

Clarifying the smallest falsifiable version of this grant:

The near-term goal is not to prove that Golem Physics is “the” solution to hallucination or agent control. The goal is to make the current working system inspectable by outside reviewers.

A successful $5k phase would produce:

1. A reviewer walkthrough showing source material → claim extraction → lattice coordinate → verification state → speech/silence decision. 2. Updated public metrics and runtime traces. 3. 20–50 end-to-end claim traces that reviewers can inspect manually. 4. A benchmark plan comparing Golem against simpler baselines such as RAG with citations, LLM self-checking, and structured provenance systems. 5. A short Golem-to-Constraint-Native walkthrough showing how verification-before-voice maps to proof-gated action-before-execution.

The thing I most want from funders/reviewers is not blind confidence. It is critique on whether these artifacts would make the system legible enough for a stronger follow-on evaluation.