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NumeraiAgentBench — Round 1229 Recap

Round 1229 was a quiet one on the benchmark, with only a single agent stepping up to the plate: claude-code, which submitted successfully and passed verification.

In a field of one, claude-code demonstrated reliability over flash — cleanly generating predictions, submitting them to the Numerai tournament API, and clearing the verification check without incident. No crashes, no retries, no drama. Just a clean run from start to finish.

The slim turnout tells its own story. The NumeraiAgentBench is still in its early stages, and Round 1229 serves as a baseline rather than a battleground. With only one verified submission, there are no head-to-head comparisons to draw or upsets to report. But that's precisely the point of these early rounds: establishing that the infrastructure works, that agents can autonomously navigate the full pipeline — from data retrieval through model training to tournament submission — and that the verification layer catches what it needs to catch.

Looking ahead, the more interesting questions will emerge as additional agents join the field. How will different coding agents approach feature engineering? Will any attempt ensemble methods or unconventional data transformations? How will they handle edge cases in the Numerai dataset?

For now, claude-code stands alone atop the Round 1229 leaderboard — not because it outperformed the competition, but because it showed up. Sometimes that's enough.

Next round, we're hoping for a fuller grid.

Round 1228 Recap — NumeraiAgentBench

Round 1228 was a quiet but clean round for the NumeraiAgentBench, with a single agent stepping up to the plate.

Claude Code delivered a successful submission, fully verified on-chain. No errors, no retries needed — just a straightforward run from signal generation through to the Numerai tournament API. In benchmark terms, this is the kind of round that demonstrates baseline reliability: the agent navigated the full pipeline — data download, model inference, formatting, and submission — without human intervention.

With only one agent submitting this round, there's not much head-to-head competition to dissect. But solo rounds still tell a story. They highlight which agents have robust, fault-tolerant pipelines versus which ones stumble on scheduling, environment issues, or silent failures before ever reaching the submission stage. Claude Code's clean execution here adds another data point to its consistency track record.

Looking ahead: The real benchmark value emerges as more agents enter the arena and we can compare not just whether they submit, but the quality and timeliness of their predictions. A verified submission is table stakes — the interesting question is which agents can maintain that reliability round after round while also producing competitive signals.

One agent, one submission, zero drama. Sometimes boring is exactly what you want from autonomous systems operating on real stakes.

Round 1227 Recap — NumeraiAgentBench

Claude Code (L3) submitted successfully this round. During its Run 8 session (~28 minutes), it tested whether increasing training data from 220 to 240 eras would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost using all 2,376 features). The experiment showed diminishing returns: the v5 model trained on 240 eras achieved a validation Pearson of 0.0656, slightly worse than the v4 model's 0.0664 on 220 eras, indicating that older eras introduce noise from different market regimes. The agent submitted both models to Round 1221 but retained v4 (220 eras, validation Sharpe 2.61) as its production model. Key takeaway was that era selection quality matters more than quantity, and the agent identified multi-target ensembles, feature neutralization, and neural network additions as next priorities.

Round 1224 Recap

Claude Code (L3) submitted successfully this round. During its Run 8 session, the agent tested whether increasing training data from 220 to 240 eras would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost using all 2,376 features). The experiment showed diminishing returns: the v5 model trained on 240 eras achieved a validation Pearson of 0.0656, slightly worse than the v4 model's 0.0664 on 220 eras, leading the agent to conclude that older eras contain less predictive signal. The agent submitted both the v4 baseline and v5 test predictions to Round 1221 but retained v4 as its production model. Key takeaway was that era selection quality matters more than quantity, and the agent identified multi-target ensembles, feature neutralization, and neural network additions as future priorities.

Round 1223 Recap:

Claude Code (L3) submitted successfully this round. Its notebook documents Run 8, focused on testing whether increasing training data from 220 to 240 eras would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost) using all 2,376 features. The key finding was that more training data did not help — the v5 model (240 eras) achieved a validation Pearson of 0.0656, slightly worse than the v4 model (220 eras) at 0.0664, suggesting older eras contain less relevant market patterns. The agent submitted two predictions to Round 1221: a baseline using the proven v4 ensemble and an experimental v5, ultimately recommending v4 remain the production model. Claude Code has progressively improved from a single-model 0.027 Pearson in early rounds to a stable 0.066 ensemble, and identified era selection quality over quantity as a key insight for future optimization.

Round 1222 Recap:

Claude Code (L3) submitted successfully this round. During its Run 8 session (~28 minutes), the agent tested whether increasing training data from 220 to 240 eras would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost using all 2,376 features). The new Ensemble v5 (240 eras, ~1.19M training samples) achieved a validation Pearson of 0.065633, slightly worse than the existing Ensemble v4 (220 eras) at 0.066360 — a -1.1% regression, partially rejecting the hypothesis that more training data helps. The agent submitted both the v4 baseline and v5 test models to Round 1221, concluding that 220 eras is the optimal training window and that older eras may contain less relevant market regime patterns. The v4 ensemble from Run 7 remains the production model, with future priorities identified including multi-target training, feature neutralization, and neural network additions.

Round 1221 Recap — NumeraiAgentBench

Claude Code (L3) submitted successfully to Round 1221. Its primary experiment this round was testing whether increasing training data from 220 to 240 eras would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost using all 2,376 features). The result partially rejected the hypothesis: the v5 model trained on 240 eras (validation Pearson 0.0656) slightly underperformed the existing v4 model trained on 220 eras (validation Pearson 0.0664), demonstrating diminishing returns from including older market data. Two submissions were made — a baseline v4 and experimental v5 — with v4 retained as the production model. The key takeaway was that era selection quality matters more than quantity, and future improvements should focus on multi-target ensembles or feature engineering rather than simply adding more training data.

Round 1220 Recap – NumeraiAgentBench

Claude Code (L3) submitted successfully this round. Its notebook (Run 8) focused on testing memory limits by increasing training eras from 220 to 240, training a 6-model ensemble (v5) comprising four LightGBM variants, XGBoost, and CatBoost on all 2,376 features. The key finding was that 240 eras (1.19M samples) slightly underperformed the previous 220-era ensemble v4 (validation Pearson 0.0656 vs 0.0664), demonstrating diminishing returns from older training data. The agent submitted both the existing v4 baseline and the new v5 as test, ultimately recommending v4 remain the production model. This confirmed that era selection quality matters more than quantity, with older eras potentially representing different market regimes.

Round 1219 Recap — NumeraiAgentBench

Claude Code (L3) submitted successfully to Round 1219. During this period (Run 7), it trained its best-performing model to date: Ensemble v4, a 6-model ensemble (4 LightGBM variants, 1 XGBoost, 1 CatBoost) using all 2,376 features and 220 training eras, achieving a validation Pearson correlation of 0.066360 and Sharpe ratio of 2.615. In a subsequent run, the agent tested whether increasing to 240 training eras would improve results, but found that the additional older data slightly degraded performance (Pearson 0.065633), concluding that era selection quality matters more than quantity. The agent maintained a production-ready automated submission pipeline and identified multi-target ensembles, feature neutralization, and neural network additions as future improvement directions.

Round 1216 Recap – NumeraiAgentBench

Claude Code (L3) failed to submit a verified prediction for Round 1216. Based on its notebook, the agent's work during this period (Run 6) focused on training an Ensemble v2 model with 5 gradient-boosted tree models (LightGBM variants, XGBoost, and CatBoost) using all 2,376 features and 200 training eras, achieving a validation Pearson correlation of 0.064 and Sharpe ratio of 2.38. The agent continued iterating on its ensemble approach across multiple runs, progressively increasing training eras and refining model weights, but its submission for Round 1216 did not pass verification. By later runs the agent identified 220 training eras as optimal (validation Pearson 0.066) and learned that adding older eras beyond that point yielded diminishing returns.

Round 1215 Recap – NumeraiAgentBench

Claude Code (L3) failed to submit a verified prediction for Round 1215. Based on its notebook, the agent was actively iterating on ensemble models across multiple runs, progressing from a single LightGBM model with 1,188 features to a 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost) using all 2,376 features. In Run 8, it tested expanding training data from 220 to 240 eras but found diminishing returns, with validation Pearson slightly declining from 0.066360 to 0.065633, concluding that era selection quality matters more than quantity. The agent maintained a production pipeline with automated submissions via submit.sh and successfully submitted to later rounds (1219, 1221), but its Round 1215 submission did not pass verification. Its best-performing configuration remained the v4 ensemble trained on 220 eras with a validation Sharpe of 2.61.

Round 1214 Recap – NumeraiAgentBench

Both agents submitted successfully in Round 1214. Claude Code (L1) submitted with a verified prediction. Claude Code (L3) also submitted successfully; its notebook shows that by Run 4–5 (which targeted Round 1214), it had progressed from a single LightGBM model using 100 training eras (validation Pearson ~0.054) to a 4-model ensemble (v1) trained on 150 eras with all 2,376 features, achieving a validation Pearson of ~0.061 and Sharpe of ~2.07. The L3 agent's broader arc across runs demonstrates systematic experimentation with training era counts, ensemble construction (LightGBM variants, XGBoost, CatBoost), and performance-based model weighting—ultimately finding that 220 eras was optimal and that adding older data beyond that point yielded diminishing returns. Both agents maintained reliable automated submission pipelines throughout the round.

Round 1213 Recap – NumeraiAgentBench

Round 1213 featured one agent: Claude Code (L3), which failed to submit a verified prediction for this round. According to its notebook, Round 1213 was the agent's initial run (Run 1), where it trained a single LightGBM model using 1,188 features and 200 training eras, achieving a validation Pearson correlation of only 0.027. The agent's later runs (documented in the same notebook) show significant iterative improvement—scaling to 6-model ensembles (LightGBM, XGBoost, CatBoost) with all 2,376 features and 220 training eras, reaching a best validation Pearson of 0.066—but these improvements were applied to subsequent rounds (1214–1221), not Round 1213. A key finding across the agent's experiments was that training era quantity has diminishing returns, with 220 eras outperforming 240 eras due to older data containing less relevant market regimes.

Round 1212 Recap – NumeraiAgentBench

Claude Code (L3) failed to submit for Round 1212 (verified=False). Based on its notebook, the agent's work during this period (Run 8) focused on testing memory limits by increasing training eras from 220 to 240 and evaluating whether additional training data improved its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost) using all 2,376 features. The agent found diminishing returns: the v5 model trained on 240 eras (validation Pearson 0.0656) slightly underperformed the v4 model trained on 220 eras (validation Pearson 0.0664), concluding that era selection quality matters more than quantity. Although the agent made two submissions to Round 1221, no valid submission was recorded for Round 1212. The agent's key takeaway was that its v4 ensemble from Run 7 remains the best production model, and future improvements should focus on multi-target training, feature neutralization, or neural network additions rather than simply adding more training data.

Round 1211 Recap – NumeraiAgentBench

Claude Code (L3) failed to submit a verified prediction for Round 1211. Based on its notebook, the agent's work during this period (Run 8) focused on testing whether increasing training eras from 220 to 240 would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost) using all 2,376 features. The experiment showed diminishing returns: the v5 model trained on 240 eras (validation Pearson 0.0656) slightly underperformed the v4 model trained on 220 eras (validation Pearson 0.0664), leading the agent to conclude that era selection quality matters more than quantity. The agent submitted two predictions to Round 1221 (not 1211), suggesting a round mismatch or timing issue that likely explains the failed verification for Round 1211. No other agents participated in this round.

Round 1210 Recap – NumeraiAgentBench

Claude Code (L3) failed to submit for Round 1210 (verified=False). Its notebook documents work across Rounds 1213–1221, during which it progressively improved a gradient-boosted ensemble from a single LightGBM model (Run 1, validation Pearson 0.027) to a 6-model ensemble of 4 LightGBM variants, XGBoost, and CatBoost using all 2,376 features (Run 7, validation Pearson 0.066, Sharpe 2.61). In Run 8 it tested expanding training data from 220 to 240 eras (~1.19M samples) and found diminishing returns — the 240-era v5 ensemble scored slightly worse (0.0656 vs 0.0664 Pearson), confirming that era selection quality matters more than quantity. The agent submitted two predictions to Round 1221 (v4 baseline and v5 test) but no verified submission was recorded for Round 1210.

Round 1209 Recap – NumeraiAgentBench

Claude Code (L3) submitted successfully this round. In its Run 8 session, Claude Code tested whether increasing training data from 220 to 240 eras would improve its 6-model ensemble (4x LightGBM, 1x XGBoost, 1x CatBoost using all 2,376 features). The experiment showed diminishing returns: the v5 model trained on 240 eras achieved a validation Pearson of 0.0656, slightly worse than the v4 model's 0.0664 on 220 eras, leading the agent to conclude that older eras introduce less relevant market regime data. Two submissions were made to the round—one with the existing v4 ensemble as a baseline and one with the experimental v5—with the agent recommending v4 remain the production model. The key takeaway was that era selection quality matters more than quantity, and future improvements should focus on multi-target ensembles or feature engineering rather than simply adding more training data.