mathiasandClaude Sonnet 4.6 69784f59cf feat(loop): autoresearch keep/revert loop + first iteration (val_vol_r2 0.2821→0.3749, +9.3%)
loop.py: Karpathy-style keep/revert loop. Agent (berget/gemma4-31b, iguana
model, NOT koala GPU) proposes one change to train.py per iter → train.py runs
on koala GPU (<2s) → read val_vol_r2 from metrics.json → keep if improved, else
restore original content. STATUS.md tracks per-iter metric + delta + GPU snap.
Iter 1 kept: improved EMBED_DIM/capacity, +9.3% on OOS R².

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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hostexecutor

Generated from mathias/template-go-web.

Bootstrap

After creating from template, run:

go mod tidy       # regenerate go.sum with real module path
task generate     # generate templ files
task build        # build the binary
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Description
JEPA-based latent representation learning for FX trading risk management — research project
Readme
370 KiB
Languages
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Go 18.4%
Shell 4.3%
templ 0.6%
Dockerfile 0.3%