Update logbook: Repro - NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search

logbook.json

@@ -10,7 +10,7 @@
     "icml2026-repro",
     "paper-Jh6gq9QsFa"
   ],
-  "updated_at": "2026-08-03T03:01:25+00:00",
+  "updated_at": "2026-08-03T03:01:29+00:00",
   "root": {
     "slug": "index",
     "title": "Repro - NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search",
@@ -73,10 +73,10 @@
     "total_size": 0,
     "bucket_id": null
   },
-  "agent_view_tokens": 3525,
+  "agent_view_tokens": 4706,
   "trace_view_tokens": 10,
   "workspace_view_tokens": 8,
-  "revision": "91b1b140f58fa6dad8eb",
+  "revision": "b14f956c046a337b1e18",
   "workspace_ref": {
     "repo_id": "byte-vortex/nonzero-repro-artifacts",
     "repo_type": "bucket",

pages/claim-4-matgame-8-agent-10-action-nonlinear/page.md

@@ -320,3 +320,10 @@ SUMMARY
 ======================================================================
 
 ````
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_05995aee1542", "created_at": "2026-08-03T03:01:28+00:00", "title": "NonUCT integration -- deeper findings (not completed)"}
+-->
+Deeper investigation into wiring NonUCT into the real MCTS search (beyond the toy comparison above): confirmed each C++ tree node stores its own joint action (`int *action`) that led to it, and that Python-to-C++ bindings for setting a NonUCT weight/scores were successfully built and tested (see the batch-bindings patch). However, full integration requires more than initially scoped: child actions are sampled and deduplicated internally in C++ (variable count per node, unknown to Python in advance), meaning real wiring needs additional getters to query actual sampled actions per node after each expansion, a trained state-conditioned theta-head network (a real architecture/training-loop change), and careful indexing into the node pool. This was assessed as a substantial additional engineering effort beyond what could be safely completed and verified without live debugging capability, and was not completed. Documenting this honestly rather than shipping unverified wiring.

pages/executive-summary/page.md

@@ -69,3 +69,20 @@ Partial reproduction of NonZero. Of the paper's 6 claims, Claim 1's core mathema
   <footer>Full logbook: <a href="https://huggingface.co/spaces/byte-vortex/nonzero-repro">huggingface.co/spaces/byte-vortex/nonzero-repro</a></footer>
 </body></html>
 ````
+
+
+---
+<!-- trackio-cell
+{"type": "markdown", "id": "cell_c931d074db29", "created_at": "2026-08-03T03:01:27+00:00", "title": "Executive summary (updated)"}
+-->
+Partial reproduction of NonZero. Of the paper's 6 claims: Claim 1 was verified at toy scale with genuine evidence -- the finite-difference identity holds exactly, and a real Hugging Face Job run showed the paper's full proposal mechanism (first- and second-order difference operators) outperforming random search by 43.4% under equal evaluation budget. Claim 2 (regret bound, Theorem 3.5) was reviewed against the actual proof in the paper (Appendix A.2-A.5): the proof strategy and algebra are internally consistent and use standard techniques, but it analyzes an idealized continuous-update proxy for NonUCT rather than the literal discrete finite-difference mechanism in Algorithm 1, and its complexity constants aren't derived for the paper's actual hypernetwork architecture -- so the proof holds as written but leaves an unverified gap between theory and the described algorithm. Claim 3 (efficiency separation, Theorem 3.7) has a toy numerical illustration extended to n=3..8 agents: the guided-search-budget-vs-full-space separation ratio grew from 160x (n=3) to ~7.19 million x (n=8), a ~44,939x growth while the full joint-action space itself only grew 32,768x over the same range -- consistent with the theorem's claimed exponential-in-n advantage, though this is a qualitative illustration, not a check of the theorem's exact constants. Claim 4 has a toy-scale empirical result (a real HF Job showing the same mechanism outperforming a MAZero-style random-sampling baseline by 43.5% on the paper's exact 8-agent/10-action/nonlinear configuration) plus validated supporting infrastructure (environment, baseline training pipeline, and a compiling C++ patch for NonUCT) -- but no full trained NonZero-vs-baseline comparison was completed, so Table 1's exact reported numbers (697.1 vs 672.3) were neither reproduced nor falsified. Claims 5-6 (SMAC/SMACv2) were not attempted: the paper's own training recipe requires A100-class GPU hardware unavailable on free-tier compute.
+
+## Scope & cost
+
+|  | This reproduction | Full replication |
+|---|---|---|
+| Scope | Claim 1 (verified, toy) + Claim 2 (proof reviewed) + Claim 3 (extended toy illustration) + Claim 4 (toy empirical result + infrastructure) | All 6 claims incl. SMAC/SMACv2 |
+| Hardware | Kaggle T4 (free tier) + Hugging Face Jobs (cpu-basic) | Cluster of NVIDIA A100/A6000 GPUs |
+| Compute time | ~3-4 hours | Many GPU-days |
+| Cost | $0 | Thousands of dollars |
+| Outcome | Claim 1 verified (toy scale, real evidence); Claim 2 proof reviewed (gap noted, not a numerical claim); Claim 3 toy illustration extended (n=3..8); Claim 4 toy result + infrastructure validated, full comparison not completed; Claims 5-6 not attempted (compute-bound) |