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

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reacted to theirpost with 🔥 about 24 hours ago
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: https://huggingface.co/spaces/RiverRider/srt-introspect
posted an update about 24 hours ago
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: https://huggingface.co/spaces/RiverRider/srt-introspect
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