River Rider PRO

RiverRider

Space-Bacon

AI & ML interests

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

Recent Activity

posted an update 1 day ago

🧠 New Space: MindReader-NLA — ask a frozen LM what it's thinking, in plain English. A trained Activation Verbalizer (~5–13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space: Playground — sample K verbalizations of the layer-L hidden state and score how well each reproduces the original activation when fed back through the same frozen model (raw + anisotropy-centred cosine FVE). Live Thought Trace — stream a verbalization per token as the model writes, side-by-side with the generation. Steer-by-Editing — edit the verbalized thought, project it back into hidden-state space, and watch the continuation change. Runs on ZeroGPU. Try it: https://huggingface.co/spaces/RiverRider/srt-nla-demo Paper + code: https://github.com/space-bacon/SRT

updated a Space 1 day ago

RiverRider/srt-nla-demo

published a Space 1 day ago

RiverRider/srt-nla-demo

View all activity

Organizations

Posts 4

Post

🧠 New Space: MindReader-NLA — ask a frozen LM what it's thinking, in plain English.

A trained Activation Verbalizer (~5–13M params, frozen backbone) over Qwen-2.5-7B, Llama-3.2-3B, and Gemma-2-2B. Three demos in one Space:

Playground — sample K verbalizations of the layer-L hidden state and score how well each reproduces the original activation when fed back through the same frozen model (raw + anisotropy-centred cosine FVE).

Live Thought Trace — stream a verbalization per token as the model writes, side-by-side with the generation.

Steer-by-Editing — edit the verbalized thought, project it back into hidden-state space, and watch the continuation change.

Runs on ZeroGPU. Try it: RiverRider/srt-nla-demo

Paper + code: https://github.com/space-bacon/SRT

Post

3263

Natural Language Autoencoders: A Window into Latent Structure

I introduced a concise mathematical formulation of the P versus NP question into the SRT-NLA-AV-v1 demonstration:

P vs NP asks whether every problem whose solution can be verified in polynomial time (NP) can also be solved in polynomial time (P). Integer factorization — given N = p·q where p and q are large primes (p < q) — is in NP but widely believed not to be in P.

The resulting activation verbalization (best-of-N, reranked by AR fidelity) surfaced:

“This article originally appeared in the August 2016 edition of CACM. A new method of proving computational hardness of problems, known as multilinearization, can improve efficiency, reduce complexity and simplify proofs. In this article, I describe multilinearization and its application to several key problems, from the discrete logarithm and factoring to RSA and elliptic-curve discrete logarithms.”

What emerges is not a literal restatement, but a structured articulation of the model’s internal associations: hardness proofs, algebraic techniques, and the cryptographic implications that orbit this foundational question in computational complexity.

The demo offers a compelling interface for exploring these latent representations.

Explore it here:
RiverRider/srt-nla-av-v1-demo

Recommended: Best-of-N sampling with round-trip evaluation for highest fidelity.