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

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posted an update about 1 hour ago
ATTENTION: The SRT-Introspect framework moves past surface-level output commentary by supplying real-time natural language interpretations of a model’s latent states. These verbalizations are validated, not merely asserted, through a round-trip reconstruction procedure. Natural language descriptions derived from hidden activations are passed through an encoder that reconstructs the corresponding activation vector; the recovered vector closely approximates the original. High reconstruction fidelity indicates that the verbalizations encode genuine structural information about the internal state rather than offering plausible but ungrounded speculation. This validated introspection converts what has often remained a theoretical or post-hoc exercise into a practical instrument for auditing model behavior, diagnosing failure modes, and providing high-level semantic guidance—all without modifying the base model or incurring the costs of fine-tuning. Because the mechanism operates on frozen configurations, it can be applied to production systems where any change to weights or architecture is undesirable. Thank you for your attention. Run a trace: https://huggingface.co/spaces/RiverRider/srt-introspect Repo: https://github.com/space-bacon/SRT
posted an update 1 day ago
This is not a pipe. Everyone is born a semiotician, no one is born knowing it. Go easy on yourself (and me) for not understanding this yet. Computational semiotics is now an empirical study. LLMs are not proto-minds. They are verifiably semiotic infrastructure. This repository (or attached demo) can show you, in real time, how any frozen model (Qwen for demo) arrives at any answer by reading its latent states directly during generation. Any questions? https://huggingface.co/spaces/RiverRider/srt-introspect Repo: https://github.com/space-bacon/SRT Grok insist my intro is condescending … This is certainly true, as is the statement in my condescended opinion. I expect heat for it, let’s think this through?
reacted to theirpost with 🚀 1 day ago
Words do not have determined meanings. The vocabulary itself is reflexive. It is self-referential, looping back into its own structure rather than anchoring in fixed reality. What we treat as stable meaning is continually reconstituted in the act of using it. The observers own interpretations molding each word like clay with every utterance.  All large language models to date treat words otherwise. At the moment of softmax crystallization they determine the meaning of every token. Probabilities collapse into a single output. Meaning is not found. It is fixed, token by token, in that final distribution. SRT-Introspect is a demo for observing what Qwen actually thinks at the points of highest effort. It surfaces the internal representations during generation, making visible the reflexive vocabulary at work and the precise crystallization process: the weights, the assumptions, the decisions that resolve ambiguity into output. This includes accounting for anisotropy collapse in hidden states by centering representations around the layer-mean before analysis. Feel free to comment your prompts https://huggingface.co/spaces/RiverRider/srt-introspect Repo https://github.com/space-bacon/SRT
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