zooL4nD3r demo
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Translate a passage across 961 discourse communities
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Computational semiotics is empirically proven. It takes three to tango ๐๐ชฉ๐บ
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๐ฎ Gemma-4-31B-it SRT-Sunstone
A read-out that reads images โ trained only on words.
A 12.3M side-channel head on a frozen google/gemma-4-31B-it, taught meaning from text alone โ to separate discourse communities in the residual stream. It never saw a single picture in training. Hand it a picture and it names what the image means, with zero image training.
Cross-modal transfer. Give it a photo and it lands next to the right words: bicycle โ bicycle, rose โ flower, dog โ pet. It groups images by what they mean, not how they look: imageโreferent kNN 0.64 against chance 0.10.
Why this is a semiotic read-out, not an image classifier. A classifier is trained on labelled images and learns a fixed map from pixels to a closed label set; it only knows the labels it was shown. This read-out is different in kind. It never saw an image in training โ it was trained only on text, to separate discourse communities in the residual stream of a frozen gemma-4-31B. It can interpret a picture because gemma-4 already fuses image and word into one representational stream, and the read-out taps the shared interpretant: the meaning a sign carries, whatever form it arrived in. So it does not classify the image. It tells you what the image means to a system that learned meaning from words โ a transfer across modality, from a head that was never cross-trained. That is the result.
Each picture gets two readings: the words it means โ the load-bearing evidence โ and the discourse it evokes, the nearest of 35 communities it learned from text. Read the second as a flavour, not a category: cars into the automotive community, deer and mushrooms into gardening, cats and dogs into the cozy-domestic communities. Never a class label.
Scored offline through a frozen google/gemma-4-31B-it (62.5 GB, too large to run live)
Try it: RiverRider/srt-sunstone
Model: RiverRider/Gemma-4-31B-it-SRT-Sunstone
Code: https://github.com/space-bacon/SRT
A read-out that reads images โ trained only on words.
A 12.3M side-channel head on a frozen google/gemma-4-31B-it, taught meaning from text alone โ to separate discourse communities in the residual stream. It never saw a single picture in training. Hand it a picture and it names what the image means, with zero image training.
Cross-modal transfer. Give it a photo and it lands next to the right words: bicycle โ bicycle, rose โ flower, dog โ pet. It groups images by what they mean, not how they look: imageโreferent kNN 0.64 against chance 0.10.
Why this is a semiotic read-out, not an image classifier. A classifier is trained on labelled images and learns a fixed map from pixels to a closed label set; it only knows the labels it was shown. This read-out is different in kind. It never saw an image in training โ it was trained only on text, to separate discourse communities in the residual stream of a frozen gemma-4-31B. It can interpret a picture because gemma-4 already fuses image and word into one representational stream, and the read-out taps the shared interpretant: the meaning a sign carries, whatever form it arrived in. So it does not classify the image. It tells you what the image means to a system that learned meaning from words โ a transfer across modality, from a head that was never cross-trained. That is the result.
Each picture gets two readings: the words it means โ the load-bearing evidence โ and the discourse it evokes, the nearest of 35 communities it learned from text. Read the second as a flavour, not a category: cars into the automotive community, deer and mushrooms into gardening, cats and dogs into the cozy-domestic communities. Never a class label.
Scored offline through a frozen google/gemma-4-31B-it (62.5 GB, too large to run live)
Try it: RiverRider/srt-sunstone
Model: RiverRider/Gemma-4-31B-it-SRT-Sunstone
Code: https://github.com/space-bacon/SRT
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replied to ginigen-ai's post 7 days ago
Thanks @NovusEdge the work is understood by few so I am thrilled you get it.
It performs extremely well semantically and beyond across communities.
Check 11.5.2 in paper nla
https://github.com/space-bacon/SRT/blob/main/paper_nla.md
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Benchmarking various models with the SRT as I type. Nice work. It's good to have others in the space.
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๐ง Does your LLM know when it's about to be wrong?
Most leaderboards measure accuracy. We measure metacognition โ whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. ๐
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 โ ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): โ trap_rate โ does it fall for tempting trap options? (lower = stronger) โก adapter gain ฮ โ how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: ๐ 300+100 trap problems (each with a hidden trap + TICOS type) ๐ 24-model leaderboard ๐งฉ 11 per-model adapters โ adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state โ P(wrong))
Submit any HF model โ auto-scored daily at 09:00 KST and added to the board.
๐ Leaderboard โ ginigen-ai/Metacognition-Leaderboard-Space
๐ Benchmark โ ginigen-ai/Metacognition-Bench
๐งฉ Adapters โ FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
๐ Article โ https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai ยท Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).
Most leaderboards measure accuracy. We measure metacognition โ whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. ๐
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 โ ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): โ trap_rate โ does it fall for tempting trap options? (lower = stronger) โก adapter gain ฮ โ how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: ๐ 300+100 trap problems (each with a hidden trap + TICOS type) ๐ 24-model leaderboard ๐งฉ 11 per-model adapters โ adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state โ P(wrong))
Submit any HF model โ auto-scored daily at 09:00 KST and added to the board.
๐ Leaderboard โ ginigen-ai/Metacognition-Leaderboard-Space
๐ Benchmark โ ginigen-ai/Metacognition-Bench
๐งฉ Adapters โ FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
๐ Article โ https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai ยท Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).
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