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RiverRider

Space-Bacon

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Computational semiotics is empirically proven. It takes three to tango 💃🪩🕺

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RiverRider/srt-introspect

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This is not the end of words. It is the end of pretending their meanings are determined. Meaning Forks. SRT detects it. Paste any text to identify contested terms https://huggingface.co/spaces/RiverRider/srt-introspect Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer https://huggingface.co/spaces/RiverRider/srt-introspect Repository https://github.com/space-bacon/SRT Paper https://github.com/space-bacon/SRT/blob/main/paper_nla.md Explainer https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md

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This is not the end of words. It is the end of pretending their meanings are determined.

Meaning Forks. SRT detects it.

Paste any text to identify contested terms

RiverRider/srt-introspect

Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer

RiverRider/srt-introspect

Repository

https://github.com/space-bacon/SRT

Paper

https://github.com/space-bacon/SRT/blob/main/paper_nla.md

Explainer

https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md

Post

4801

SRT-introspect: Live Token-by-Token Readout of LLM Internal Reasoning

I have released SRT-introspect, a new public demonstration that makes the hidden reasoning process of a frozen large language model visible in real time.

The interface runs a Qwen-2.5-7B backbone equipped with the SRT Adapter and Activation Verbalizer. As the model generates each token, the system continuously measures divergence across attention heads, identifies high-signal moments, and translates the corresponding hidden-state object representations into natural-language verbalizations. You see exactly what the model is internally representing at the precise points where its computation is most active, complete with divergence scores, reflexivity estimates, and per-layer traces.

This is not a summary of the final output. It is a direct window into the model’s latent conceptual landscape, showing the dominant training-data attractors that activate even when the prompt asks for first-principles reasoning. The adaptive scheduler concentrates verbalizations precisely where the real internal work occurs, turning what used to be opaque black-box generation into observable, analyzable data.

The result is the clearest public demonstration yet that modern LLMs possess a rich, structured semiotic infrastructure that can now be audited without retraining or fine-tuning.

Try it:
RiverRider/srt-introspect