RiverRider/srt-nla-gemma4-artifacts
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Burton Lancaster PRO
RiverRider
AI & ML interests
Explainable AI
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repliedto their post about 4 hours ago
Train Once, Read Everywhere
Paper title:
Train Once, Read Everywhere: Substrate Invariance of the Linearly Readable Structure in Frozen Language Models
Paper URL:
https://github.com/space-bacon/SRT/blob/main/arxiv_program/paper.md
Repository URL:
https://github.com/space-bacon/SRT
The consolidated findings of the SRT research program are now available.
The program treats frozen production-scale language models as substrates whose internal states carry structure that small, inspectable instruments can read. Results include:
- A ~12 M-parameter adapter that surfaces per-token semiotic signals from a frozen 7 B backbone with zero cross-entropy degradation
- An activation verbalizer that recovers text from single hidden states up to a calibrated paraphrase ceiling
- Linear readout ports spanning dense 3 B models to 94-layer 235 B mixture-of-experts models
- A 22 MB linear head that gives a frozen multimodal chat model image-to-text retrieval performance matching fully trained 2018 dual encoders on the COCO benchmark
The central claim is substrate invariance. The readable structure is a stable property of the model class. A head trained once on one host reads, with no retraining and at most a 42 KB recalibration, across:
- Hosts ten times smaller (31 B → 3 B)
- 4-bit weight precision
- Entirely different silicon and kernels (CUDA/bf16 to Apple Silicon/MLX-Q4)
Deployment tiers differ in latency and cost, never in capability.
All instruments, measurement protocols, invariance evidence, negative results, and artifacts are in the repository. repliedto their post about 14 hours ago
Train Once, Read Everywhere
Paper title:
Train Once, Read Everywhere: Substrate Invariance of the Linearly Readable Structure in Frozen Language Models
Paper URL:
https://github.com/space-bacon/SRT/blob/main/arxiv_program/paper.md
Repository URL:
https://github.com/space-bacon/SRT
The consolidated findings of the SRT research program are now available.
The program treats frozen production-scale language models as substrates whose internal states carry structure that small, inspectable instruments can read. Results include:
- A ~12 M-parameter adapter that surfaces per-token semiotic signals from a frozen 7 B backbone with zero cross-entropy degradation
- An activation verbalizer that recovers text from single hidden states up to a calibrated paraphrase ceiling
- Linear readout ports spanning dense 3 B models to 94-layer 235 B mixture-of-experts models
- A 22 MB linear head that gives a frozen multimodal chat model image-to-text retrieval performance matching fully trained 2018 dual encoders on the COCO benchmark
The central claim is substrate invariance. The readable structure is a stable property of the model class. A head trained once on one host reads, with no retraining and at most a 42 KB recalibration, across:
- Hosts ten times smaller (31 B → 3 B)
- 4-bit weight precision
- Entirely different silicon and kernels (CUDA/bf16 to Apple Silicon/MLX-Q4)
Deployment tiers differ in latency and cost, never in capability.
All instruments, measurement protocols, invariance evidence, negative results, and artifacts are in the repository. repliedto their post 1 day ago
Train Once, Read Everywhere
Paper title:
Train Once, Read Everywhere: Substrate Invariance of the Linearly Readable Structure in Frozen Language Models
Paper URL:
https://github.com/space-bacon/SRT/blob/main/arxiv_program/paper.md
Repository URL:
https://github.com/space-bacon/SRT
The consolidated findings of the SRT research program are now available.
The program treats frozen production-scale language models as substrates whose internal states carry structure that small, inspectable instruments can read. Results include:
- A ~12 M-parameter adapter that surfaces per-token semiotic signals from a frozen 7 B backbone with zero cross-entropy degradation
- An activation verbalizer that recovers text from single hidden states up to a calibrated paraphrase ceiling
- Linear readout ports spanning dense 3 B models to 94-layer 235 B mixture-of-experts models
- A 22 MB linear head that gives a frozen multimodal chat model image-to-text retrieval performance matching fully trained 2018 dual encoders on the COCO benchmark
The central claim is substrate invariance. The readable structure is a stable property of the model class. A head trained once on one host reads, with no retraining and at most a 42 KB recalibration, across:
- Hosts ten times smaller (31 B → 3 B)
- 4-bit weight precision
- Entirely different silicon and kernels (CUDA/bf16 to Apple Silicon/MLX-Q4)
Deployment tiers differ in latency and cost, never in capability.
All instruments, measurement protocols, invariance evidence, negative results, and artifacts are in the repository.Organizations
