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Part of #4 (Phase 2).
Define the PyTorch JEPA architecture — small MLPs (hidden_dim ≈ 128): encoder Enc(x), predictor Pred(s,a), critic
Crit(s,a) → 9 emotion dims.
- Bound the critic output to [MIN_AROUSAL_LEVEL, ...
jepa
phase-2
Part of #4 (Phase 2). Runs in parallel with Phases 3–4.
Export ID-keyed sequential interactions reconstructable into (s_t, a_t, Δemotion) tuples — emotional-consequence form
per HLD §5, not full s_{t+1}. ...
jepa
phase-2
What s happening?
This is my trcc report, im not sure if im missing something but i can´t change the fan color, i can edit the temp
monitor but not the fan.
Diagnostic report (trcc report)
the report ...
bug
Part of #3 (Phase 1). Closes the granularity decision.
Write the measured p_multi / p_collision and the chosen mode into HLD §6 #2. Expose the choice as a single enum
ReinforcementGranularity { PER_BATCH, ...
jepa
phase-1
Part of #3 (Phase 1).
Add analysis/reg_granularity.py (matches /analysis convention; wd points at results dir). Aggregates the per-creature
CSVs, prints the decisive numbers, writes a histogram PNG.
...
jepa
phase-1
Part of #3 (Phase 1).
Add analysis/RegulationBatchExtractor.java following the existing analysis convention (DB named-query → CSV, like the
extractors in analysis/extractor/).
- extractCountHistogram(creatureKey, ...
jepa
phase-1
Part of #3 (Phase 1).
Instrument creature/components/HomeostaticRegulation.java to count regulating stimuli per onReceive batch. Leave the
existing loop body exactly as-is (behaviour + existing InternalDynamicState ...
jepa
phase-1
Generative AI for Complaint Resolution
Problem
Manual complaint resolution is slow and inconsistent.
Solution
Use GenAI to automatically draft responses, suggest solutions, and resolve complaints. ...
Issue kind
Child slice / bounded follow-on
Parent issue or lane
#1670
Problem / intent
#1671 adds inventory entry verification-task-marker-activation, identifying task marker hints in the host verification ...
dogfooding
planning

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