Full diagnostic chain (synth→real zero-shot→real finetune→held-out) shows
the TS-modality approach works in-distribution but fails to generalize
across entities: same SMD dataset, same 38 channels, just a different
machine (1→2) collapses output to repetition garbage. Root cause is the
lightweight TS encoder (2-layer TF, 2.1M params) learning dataset/machine-
specific patterns rather than transferable time-series representations.
This is a valuable negative result: it cleanly rules out the
'0.5B LLM + 2-layer TS encoder + single-distribution synth training'
route for general time-series understanding, which is more credible than
any 'looks like it works but never tested cross-domain' result.
Records:
- tasks.md: M6 diagnostic section (6.1-6.5) with evidence + archive decision
- proposal.md: Status header flagging archive + pointer to diagnosis
- docs/diagnosis-2026-07-02-cross-domain.md: full evidence chain, root-cause
analysis, capability boundaries, asset inventory, restart conditions
Project status: archived. Code/data/checkpoints/reports preserved.
Restart requires any of: (1) larger encoder + real-data from-scratch
training, (2) channel-agnostic patch strategy, (3) multi-dataset joint
training with held-out-dataset generalization proof.
张宗平
150e3bd9fd