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Author	SHA1	Message	Date
mathiasandClaude Sonnet 4.6	7d04423d39	feat(loop): 5 iters on TS-JEPA+SIGReg backbone — consistent improvement ... All 5 kept: val_vol_r2 -0.1543 → +0.0599 (+0.214 total). Backbone learning. Agent tuning: LR, depth, SIGREG_LAM, EPOCHS. Still well below toy ceiling (0.37) — real backbone room to grow via #3/#4/#5. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-24 07:45:55 +02:00
mathiasandClaude Sonnet 4.6	44e8b3eb95	feat(model): TS-JEPA+SIGReg backbone replaces toy encoder (#3 step 1) ... PatchTST-style transformer encoder with JEPA predictive loss + SIGReg regularization (Balestriero & LeCun arXiv:2511.08544; time-series placement from ChronoJEPA). Token-level SIGReg (dual placement) to avoid time-axis collapse (confirmed real by ChronoJEPA). Baseline val_vol_r2=-0.1543 on first run — expected for fresh weights with new architecture. Agent will iterate. SIGReg source: Epps-Pulley statistic, identical math to LeJEPA MINIMAL.md. Refs: #3 (TS-JEPA reproduce), ChronoJEPA github.com/MrRobotop/ChronoJEPA Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-24 07:42:56 +02:00
mathiasandClaude Sonnet 4.6	f5ce8d6706	chore(loop): 6 more iters — plateau at ~0.34-0.37 (1/6 kept) ... Toy encoder near ceiling. 1 kept (val_vol_r2 0.3032→0.3442), 5 reverts. Consistent plateau = time to swap in TS-JEPA backbone (#3/#5). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-24 07:37:29 +02:00
mathiasandClaude Sonnet 4.6	ed85dc4a8c	feat(loop): 3 iterations complete — autoresearch chain end-to-end (closes #11 Phase A) ... 3 iterations ran unattended on koala (agent=berget/gemma4-31b, GPU=RTX5070): iter1: val_vol_r2 0.2821→0.3749 (+9.3%) KEEP [EMBED_DIM 16→64] iter2: 0.2234→0.3011 (+7.8%) KEEP [MASK_FRAC tuning] iter3: 0.3011→0.3032 (+0.2%) KEEP [minor capacity tweak] Final EMBED_DIM=64, MASK_FRAC=0.4. STATUS.md tracks full trajectory. Both monitoring axes live: research=STATUS.md metric table, technical=GPU snapshot per iter (0% util between runs, shared cleanly w/ llama-swap). Phase-A acceptance: loop runs 3+ iters unattended, metric moves, both axes visible. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-24 07:17:50 +02:00
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>	2026-06-24 07:16:50 +02:00
mathiasandClaude Opus 4.8	485fdaa9f9	feat(data): EUR/USD M1 fetch + daily realized-vol prep (toy slice, #2/#11) ... fetch_data.py politely pulls EUR/USD M1 from histdata.com (maintained package handles the anti-hotlink token; per-year, spaced). prepare_data.py (LOCKED per Phase-1 contract) parses M1 -> daily series with realized_vol = the val_vol_r2 target (sqrt sum of squared intraday returns). Verified on 2019-2021: 937 days, March-2020 COVID RV spike 5.1x over 2019 median — real signal, target works. Data gitignored (DVC/MinIO = #10). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-23 16:49:59 +02:00
mathiasandClaude Opus 4.8	df910e4336	chore(phase0): reproducible compute gate — torch cu130 + GPU smoke test ... scripts/check_gpu.py verifies PyTorch cu130 sees the koala Blackwell GPU (sm_120) and computes — the Phase-0 prerequisite before any autoresearch experiment. Verified green: torch 2.12.1+cu130, RTX 5070, GPU matmul OK. Note: koala GPU is shared with the llama-swap LLM stack — run the autoresearch agent on iguana/berget so the card stays free for train.py. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-06-23 16:43:01 +02:00