TM-NLA Checkpoints
These checkpoints support the TM-NLA public runtime.
TM-NLA is a proof-of-concept temporal activation-verbalization probe for frozen visual-temporal hidden states. It projects video-window activations from Qwen/Qwen3.5-0.8B into a continuous language-oriented manifold, then selects semantic readout hypotheses over time with compatibility and specificity annotations.
Files
| File | Role | SHA-256 |
|---|
temporal_probe.pt | Temporal visual activation -> continuous language-manifold probe | 48AABF662423EA529C985855EA54970A9CE3256EC3F14AFE0169C2BF1A55B217 |
activation_verbalizer.pt | Activation -> small set of short candidate readout proposals | 88DDB0267AE535A287755DE5C9E638EB771FFD5C5377051ACFCADF96A98C08C1 |
text_reconstructor.pt | Text readout -> activation compatibility verifier/reranker | C5CEA721A2173DCA884617258BB3F671668CF13EBA07728E11C39C9695B5DE32 |
Base Model
The runtime uses frozen Qwen/Qwen3.5-0.8B activations. The public demo path does not train or fine-tune Qwen.
Intended Use
Use these checkpoints with the TM-NLA GitHub repository to run local terminal-based semantic readouts over video windows:
The checkpoints are intended for interpretability and research exploration. They are not intended as a production captioner, classifier, benchmark system, or broad video-analysis product.
Limitations
The continuous activation geometry is the strongest result. TM-NLA shows weak recoverable temporal semantic traces from frozen visual-temporal activations. Natural-language readouts are useful but noisy externalizations, not ground-truth captions.
Readouts can be malformed, wrong, generic, or underspecified. The activation verbalizer remains fragile, and candidate generation plus AR reranking is a practical readout mechanism rather than a solved decoder. The text reconstructor is a compatibility-based verifier/reranker, not an oracle. specificity_status is a numerical activation-specificity annotation, not a ground-truth correctness label.
Citation
If you publish work building on this proof of concept, cite or link the companion TM-NLA GitHub repository:
