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SRT Showcase: Watch a Frozen Model Think, Token by Token
A frozen Qwen-2.5-7B now narrates its own interpretation in real time. SRT Showcase is the most complete public demonstration of computational semiotics to date, running the backbone with the SRT Adapter and Activation Verbalizer. As the model generates, every token is tinted by its predictive effort, and at the highest-effort positions the Verbalizer decodes the hidden state directly into natural language. You see what the model is representing at the exact moment its computation is most active.
Every verbalization is validated, not asserted. Each decoded thought is re-encoded and compared back to the original hidden state, and the reconstruction closely approximates it. The "this is what the model was thinking" claim carries its own fidelity badge. This is grounded introspection, not plausible narration.
The Showcase goes further than the trace. An A/B panel runs the same prompt with SRT injection on and off under an identical seed, so the side-channel's effect is directly observable. A curated gallery walks through confident recall, false premises, misconceptions, reasoning pivots, genuine uncertainty, and safety boundaries. Live entropy and divergence meters track the crystallization process token by token, with per-layer traces and reflexivity estimates on hover.
None of the backbone weights are touched. The entire mechanism is a lightweight reflexive layer over a frozen model, which is why the same read-out heads already port from Qwen-2.5-7B up to a 235B Mixture of Experts. Frozen models can now be verbalized in real time. No retraining. No fine-tuning. No black box.
First request is a brief cold start while ZeroGPU acquires a GPU. Bring your own prompt.
Try it: RiverRider/srt-showcase
Repository: https://github.com/space-bacon/SRT
A frozen Qwen-2.5-7B now narrates its own interpretation in real time. SRT Showcase is the most complete public demonstration of computational semiotics to date, running the backbone with the SRT Adapter and Activation Verbalizer. As the model generates, every token is tinted by its predictive effort, and at the highest-effort positions the Verbalizer decodes the hidden state directly into natural language. You see what the model is representing at the exact moment its computation is most active.
Every verbalization is validated, not asserted. Each decoded thought is re-encoded and compared back to the original hidden state, and the reconstruction closely approximates it. The "this is what the model was thinking" claim carries its own fidelity badge. This is grounded introspection, not plausible narration.
The Showcase goes further than the trace. An A/B panel runs the same prompt with SRT injection on and off under an identical seed, so the side-channel's effect is directly observable. A curated gallery walks through confident recall, false premises, misconceptions, reasoning pivots, genuine uncertainty, and safety boundaries. Live entropy and divergence meters track the crystallization process token by token, with per-layer traces and reflexivity estimates on hover.
None of the backbone weights are touched. The entire mechanism is a lightweight reflexive layer over a frozen model, which is why the same read-out heads already port from Qwen-2.5-7B up to a 235B Mixture of Experts. Frozen models can now be verbalized in real time. No retraining. No fine-tuning. No black box.
First request is a brief cold start while ZeroGPU acquires a GPU. Bring your own prompt.
Try it: RiverRider/srt-showcase
Repository: https://github.com/space-bacon/SRT