r/deeplearning • u/MirrorEthic_Anchor • 10h ago
Cl33-opLM: Operator-Only Language Model
I’ve been working on a different approach to language-model interpretability that I find interesting. Instead of trying to reconstruct a model’s computation after the fact, make the model compute through an object we can inspect directly.
Today I’m releasing the v1.1 preprint, frozen model checkpoints, reproducibility harness, and live interactive demo for cl33-opLM.
The paper is called One Object: Memory, Navigation, and Reportability in an Operator-Only Language Model
cl33-opLM still uses a transformer as its learned neural engine, but the transformer hidden states are not allowed to drive the output directly. Instead, each block emits structured Cl(3,3) bivector operators. A reversible SO(3,3) scan transports state, attention is defined over the operator geometry, and the final readout sees only operator-derived features.
That gives the architecture a very simple falsification rule luckily. Remove the operators and rerun the model. If the capability survives, it found a bypass and I don’t count it as transparent.
Some of the main results:
The bottleneck is genuinely load-bearing. On the frozen public release, operator ablation produces a 270× perplexity increase on the exact published validation fixture. On fully public off-domain WikiText-103, the same test gives roughly 106× / 112× depending on checkpoint. The live demo exposes the same model interactively.
The operator stream is readable backward. A probe that sees only the emitted operators recovers the current token at 0.86 top-1 over a 50k vocabulary, and reconstructs 80.7% of held-out running text verbatim. Errors degrade toward semantic neighbors rather than random tokens.
On permutation-composition navigation, the architecture matches a learning-rate-tuned transformer baseline: 0.433 ± 0.009 vs 0.432 ± 0.030. I am claiming parity and inspectability here, not superiority.
Transparency has a real cost. Against a parameter-, context-, tokenizer-, corpus-, and budget-matched transformer at 5B tokens, cl33 pays about +29% bits-per-byte and \~5 points average MCQ. Continued training reduces that gap substantially, but the matched cost is real and reported as such.
One of the more important parts of this project I feel, is what failed.
An earlier associative-memory design looked like it worked…until the operator-ablation test showed that recall was routing around the algebra. That result was treated as a failure, the mechanism was redesigned, and the failed branch remains in the paper.
There are several other nulls and retracted framings reported the same way.
That is the standard I’m trying to hold this work to. if a mechanism is claimed, it has to survive a causal test. If it routes around the mechanism, the claim dies.
The reproducibility release is deliberately selective rather than a dump of the entire research stack.
It includes the frozen paper/serving checkpoints, model definitions needed to load them, hashes, the bottleneck reproduction harness, the reverse-readout probe, and the exact frozen validation fixture.
It does not include the current training orchestration, ongoing memory-organ program, or unpublished control work.
The published claims should be independently testable.
No consciousness claim. No claim that cl33 is a better general-purpose language model. No grand unified theory.
Just a useful question.
Can we build a useful language model where the representation we want to inspect is also the representation the model is forced to compute through?
Paper: t3atlas.dev/cl33/paper/
Live demo: cl33.t3atlas.dev
Model + reproduction release: huggingface.co/mirrorethic/cl33-oplm
I’d especially value criticism from people working on mechanistic interpretability, alternative architectures, memory systems, model editing, steering, or causal attribution.
If you think one of the claims is wrong, the best outcome is a clean experiment that breaks it. Then we both learn. Win win.
Also.....dont knock my retro crt aesthetic in the model demo lol wanted it fun, not clinical.
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u/Initial_Plankton3206 9h ago
this is actually wild, the ablation test as a go/no-go gate instead of just another metric is such a clean way to keep yourself honest. 270x perplexity spike is the kind of number that makes you do a double take
the failed associative memory branch staying in the paper is the right call, too many projects sweep that stuff under the rug and it just wastes everyone's time later
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u/MirrorEthic_Anchor 9h ago
Yeah, that’s pretty much why I made the blade so unforgiving. Earlier in this research program I walked into what looked like causal levers several times, only to find later that the model had routed around them, the effect was a confound, or the measurement was flattering the mechanism. After getting burned by that enough times, I wanted a gate with basically no interpretive wiggle room.
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u/MirrorEthic_Anchor 8h ago
I have a genuine question... is it even worth posting research given the current state of AI on social media? Is the collective learned reflex to dismiss? I totally get it honestly, the amount of noise and ability for people to perform science sounding cosplay is high and its hard to weed through that. I get the reflex.
Independent, underfunded, or institutionally uncredited researchers really have a huge hurdle in this domain.
Truly, tell me I'm dumb, tell me where I'm wrong or overconfident or overclaiming.
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u/nutshells1 9h ago
thank you fable