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@@ -49,3 +49,4 @@ ddpp_beam_cvar_s0/oracle_timeline.jsonl filter=lfs diff=lfs merge=lfs -text
 ddpp_beam_cvar_s2/oracle_timeline.jsonl filter=lfs diff=lfs merge=lfs -text
 thompson_expA_noexpA_s0/results.json filter=lfs diff=lfs merge=lfs -text
 ddpp_beam_cvar_s42/oracle_timeline.jsonl filter=lfs diff=lfs merge=lfs -text
+uncond_baseline/oracle_timeline.jsonl filter=lfs diff=lfs merge=lfs -text

beam_cvar/oracle_progress.csv

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ddpp_lb/README.md

@@ -0,0 +1,13 @@
+# ddpp_lb
+
+## Summary
+- **Best FA**: +0.077
+- **Oracle calls**: 40,120
+- **Method**: Offline DDPP-LB fine-tuning with CVaR
+
+## Algorithm
+DDPP Lower Bound offline fine-tuning. Similar to ddpp_cvar_batch.
+Collected a larger dataset (~40k oracle calls) and trained offline.
+
+## Files
+- collected_molecules.csv (if present), results.json

ddpp_lb/results.json

@@ -0,0 +1,12 @@
+{
+  "best_fa": 0.07661228626966476,
+  "best_smiles": "O=C(CN=C(O)c1cc(Cl)c(F)cc1Cl)N1CCCCC1c1ccc(Cl)cc1",
+  "top10_mean_fa": -0.87282107458735,
+  "collection_best_fa": -0.07761655747890472,
+  "n_collected": 39120,
+  "n_final": 1000,
+  "total_oracle_calls": 40120,
+  "tau_cvar": -0.9775098085403442,
+  "n_top20pct": 7824,
+  "wall_sec": 11104.331186771393
+}

ddpp_lb/timeline.jsonl

@@ -0,0 +1,10 @@
+{"phase": 1, "chunk": 0, "n_total": 128, "best_so_far": -0.6657417416572571, "elapsed_sec": 29.1534743309021}
+{"phase": 1, "chunk": 1, "n_total": 256, "best_so_far": -0.447627991437912, "elapsed_sec": 56.31439924240112}
+{"phase": 1, "chunk": 2, "n_total": 384, "best_so_far": -0.447627991437912, "elapsed_sec": 83.96817755699158}
+{"phase": 1, "chunk": 3, "n_total": 512, "best_so_far": -0.3985317349433899, "elapsed_sec": 111.87327456474304}
+{"phase": 1, "chunk": 4, "n_total": 640, "best_so_far": -0.3985317349433899, "elapsed_sec": 138.9996645450592}
+{"phase": 1, "chunk": 5, "n_total": 768, "best_so_far": -0.3985317349433899, "elapsed_sec": 166.47437357902527}
+{"phase": 1, "chunk": 6, "n_total": 896, "best_so_far": -0.3985317349433899, "elapsed_sec": 194.10601449012756}
+{"phase": 1, "chunk": 7, "n_total": 1024, "best_so_far": -0.3985317349433899, "elapsed_sec": 222.50219297409058}
+{"phase": 1, "chunk": 8, "n_total": 1152, "best_so_far": -0.3953123986721039, "elapsed_sec": 250.7291922569275}
+{"phase": 1, "chunk": 9, "n_total": 1280, "best_so_far": -0.3953123986721039, "elapsed_sec": 276.95961833000183}

ddpp_lb_nocvar/ckpt_sweep.log

@@ -0,0 +1,38 @@
+2026-03-25 23:17:34,950 INFO Found 8 checkpoints: ['step_005000.pt', 'step_010000.pt', 'step_015000.pt', 'step_020000.pt', 'step_025000.pt', 'step_030000.pt', 'step_035000.pt', 'step_039350_final.pt']
+Initialized LMDB resource loader: FlashAffinity/data/protein_test/repr/esm3.lmdb
+FlashAffinityForwardOp: 1 model(s), task=value, protein=2VT4, device=cuda
+2026-03-25 23:17:41,767 INFO === step_005000.pt (step=5000) — 3.0hr inference ===
+🚨 `inputs` is part of SAFEDoubleHeadsModel.forward's signature, but not documented. Make sure to add it to the docstring of the function in /home/trevor/genmol-app/.venv/lib/python3.10/site-packages/safe/trainer/model.py.
+2026-03-25 23:17:42,742 INFO PyTorch version 2.6.0 available.
+2026-03-25 23:17:49,692 INFO Loaded backbone from step_005000.pt
+2026-03-25 23:44:12,456 INFO [step=5000] best_fa=-0.1126 | top10=-0.2574 | n=5000 | time=26.5min
+2026-03-25 23:44:12,458 INFO === step_010000.pt (step=10000) — 3.0hr inference ===
+2026-03-25 23:44:19,005 INFO Loaded backbone from step_010000.pt
+2026-03-26 00:12:16,442 INFO [step=10000] best_fa=-0.0681 | top10=-0.1766 | n=5000 | time=28.1min
+2026-03-26 00:12:16,447 INFO === step_015000.pt (step=15000) — 3.0hr inference ===
+2026-03-26 00:12:23,116 INFO Loaded backbone from step_015000.pt
+2026-03-26 00:43:09,440 INFO [step=15000] best_fa=-0.1383 | top10=-0.2481 | n=5000 | time=30.9min
+2026-03-26 00:43:09,444 INFO === step_020000.pt (step=20000) — 3.0hr inference ===
+2026-03-26 00:43:15,983 INFO Loaded backbone from step_020000.pt
+2026-03-26 01:17:35,744 INFO [step=20000] best_fa=-0.0700 | top10=-0.1433 | n=5000 | time=34.4min
+2026-03-26 01:17:35,748 INFO === step_025000.pt (step=25000) — 3.0hr inference ===
+2026-03-26 01:17:41,878 INFO Loaded backbone from step_025000.pt
+2026-03-26 01:49:17,501 INFO [step=25000] best_fa=0.3704 | top10=-0.1234 | n=5000 | time=31.7min
+2026-03-26 01:49:17,505 INFO === step_030000.pt (step=30000) — 3.0hr inference ===
+2026-03-26 01:49:24,811 INFO Loaded backbone from step_030000.pt
+2026-03-26 02:36:10,161 INFO [step=30000] best_fa=0.1527 | top10=-0.2286 | n=5000 | time=46.9min
+2026-03-26 02:36:10,165 INFO === step_035000.pt (step=35000) — 3.0hr inference ===
+2026-03-26 02:36:16,733 INFO Loaded backbone from step_035000.pt
+2026-03-26 03:19:31,769 INFO [step=35000] best_fa=0.6701 | top10=0.1788 | n=5000 | time=43.4min
+2026-03-26 03:19:31,774 INFO === step_039350_final.pt (step=39350) — 3.0hr inference ===
+2026-03-26 03:19:37,597 INFO Loaded backbone from step_039350_final.pt
+2026-03-26 03:51:09,263 INFO [step=39350] best_fa=0.3782 | top10=0.2179 | n=5000 | time=31.6min
+2026-03-26 03:51:09,267 INFO === Sweep complete ===
+2026-03-26 03:51:09,267 INFO   step=  5000  best_fa=-0.1126  top10=-0.2574
+2026-03-26 03:51:09,267 INFO   step= 10000  best_fa=-0.0681  top10=-0.1766
+2026-03-26 03:51:09,267 INFO   step= 15000  best_fa=-0.1383  top10=-0.2481
+2026-03-26 03:51:09,267 INFO   step= 20000  best_fa=-0.0700  top10=-0.1433
+2026-03-26 03:51:09,267 INFO   step= 25000  best_fa=0.3704  top10=-0.1234
+2026-03-26 03:51:09,267 INFO   step= 30000  best_fa=0.1527  top10=-0.2286
+2026-03-26 03:51:09,267 INFO   step= 35000  best_fa=0.6701  top10=0.1788
+2026-03-26 03:51:09,267 INFO   step= 39350  best_fa=0.3782  top10=0.2179

mcts_cvar/active_loop_log.jsonl

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mcts_cvar/oracle_progress.csv

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mcts_fa_guided/README.md

@@ -0,0 +1,14 @@
+# mcts_fa_guided
+
+## Summary
+- **Best FA**: +0.106
+- **Oracle calls**: 44,590 (includes rollout calls during MCTS tree search)
+- **Method**: MCTS guided directly by FA oracle (no surrogate)
+
+## Algorithm
+MCTS where the forward_op IS the FA oracle — every rollout during tree search calls FA.
+True oracle call count includes all MCTS rollout evaluations, not just final results.
+- Logged timeline has ~840 entries; true count 44,590 from metrics.json (rollouts included)
+
+## Files
+- oracle_timeline.jsonl (subset of calls), metrics.json (true count), results.json

thompson_beam_cvar/README.md

@@ -0,0 +1,33 @@
+# thompson_beam_cvar
+
+## Summary
+- **Best FA**: +0.0992
+- **Oracle calls**: 4,601
+- **Wall time**: 4 hours
+- **Epochs**: 196
+
+## Algorithm: Beam Search with Complete Thompson Sampling
+Same as beam_cvar but forward_op during beam search uses Thompson sample instead of ensemble mean:
+1. Beam search guided by ensemble surrogate as scoring function
+2. At each branch-prune step, score = μ_i + σ_i·ε_i (ε ~ N(0,1)) — stochastic per call
+3. Different branches are scored stochastically → exploration during search itself
+4. Selected K=25 molecules evaluated with FA oracle
+5. DDPP fine-tune + ensemble retrain
+
+Contrast with beam_cvar where score = μ_i (deterministic, always prunes toward ensemble mean).
+Complete Thompson = Thompson sampling at every scoring step, not just final selection.
+
+## Config
+- selection: thompson_beam
+- M=500 (via beam), K=25, beam_width=8
+- tau_mode=cvar, tau_quantile=0.80
+- ddpp_steps_per_epoch=50, wall_budget_sec=14400 (4hr)
+
+## Files
+- active_loop_log.jsonl: epoch-level log
+- oracle_progress.csv: oracle_calls vs f_star progression
+- results.json: final summary
+
+## Notes
+- Worse than beam_cvar (+0.330) — stochastic scoring during beam search may destabilize pruning
+- Uses 25 oracle calls/epoch (no elimination), similar call count to thompson_cvar

thompson_cvar/README.md

@@ -0,0 +1,23 @@
+# thompson_cvar
+
+## Summary
+- **Best FA**: +0.494
+- **Oracle calls**: 4,773
+- **Wall time**: 4 hours
+- **Epochs**: 191
+
+## Algorithm: Thompson Sampling Active Loop (baseline best before bandits)
+1. Generate M=500 candidates from DDPP model
+2. Encode all → get (μ_i, σ_i) from ensemble of 10 MLPs
+3. Thompson sample: s_i ~ N(μ_i, σ_i²), pick top-K=25 by sampled score
+4. Evaluate top-K with FA oracle (always uses exactly K=25 calls/epoch)
+5. CVaR fine-tune DDPP (tau=80th pct), retrain ensemble
+
+## Config
+- selection: thompson, M=500, K=25
+- tau_mode=cvar, tau_quantile=0.80, reward_shift=3.0
+- n_ensemble=10, ensemble_epochs=100, ddpp_steps_per_epoch=50
+- wall_budget_sec=14400 (4hr)
+
+## Files
+- active_loop_log.jsonl, oracle_progress.csv, results.json

uncond_baseline/oracle_timeline.jsonl

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+size 13853593