- which can fluently modify their own weights, connectivity patterns, and size;
- and can serve as a programming platform, resulting in a programming formalism with continuously deformable programs.
Reference paper: Dataflow Matrix Machines and V-values: a Bridge between Programs and Neural Nets
arxiv.org/abs/1712.07447
More details:
anhinga-anhinga.dreamwidth.org/83104.html
anhinga-anhinga.dreamwidth.org/82703.html
*** Dataflow Matrix Machines internet resources: anhinga.github.io
Interdisciplinary list of open research directions (I am looking for collaborators): dmm-collaborative-research-agenda.pdf
*** My blogs:
Main Dreamwidth blog: dmm.dreamwidth.org (partial mirror: anhinga-travel.livejournal.com )
Main LiveJournal: anhinga-anhinga.livejournal.com (mirror: anhinga-anhinga.dreamwidth.org )
Auxiliary LiveJournal: anhinga-drafts.livejournal.com (mirror: anhinga-drafts.dreamwidth.org )
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Sep 5, 2026 update: GPT-6-Astra is a "proto-AGI".
The model has convincingly beaten humans on ARC-AGI-3, while demonstrating some rather remarkable properties:
arcprize.org/blog/astra
"A key behavior observed in GPT-6 Astra was its ability to turn unfamiliar environments into compact symbolic world models. It represented game mechanics as logical rules and developed its own domain-specific language shorthand to track state and plan actions."
And also excellent "continual learning" capability:
"... how well does a model perform when it can use the context-management features its provider designed for it? For Astra, this means preserving the opaque reasoning state (which we don’t see) between requests and using compaction to manage longer conversations.
With the Provider Adapter harness, Astra's best observed score on ARC-AGI-3 Semi-Private increased from 62.7% to 99.9%. Looking across Public and Semi-Private and all reasoning levels, Provider Adapter runs were approximately 3.66x faster by aggregate recorded elapsed time and used 49% fewer total tokens across the 167 game-reasoning pairs both harnesses solved."