How far today’s models get on Dev100, the 100 econometric replication tasks planned for Subtrack 1: 14 models, each run with four harnesses under the same limits and the same scorer. Use it as the baseline your agent has to beat. These are development results, not an official Challenge ranking.
01Dev100 · v10
Dev100 / v10 development release
The 100 Dev100 tasks will be released as open source for Subtrack 1: Econometric replication; the task package, official rules and dates are still to be announced. Below are development results for 14 models × 4 harnesses—a baseline to beat, not an official Challenge ranking.
controller_stagefinished · controller_finished_at2026-10-11T16:35:50.157877+00:00 · report_exit_code 0 · independent_delivery_audit_passedtrue · evidenceStated in the server visualization README and data.json and in the study's daily report and handoff; the underlying receipts were not accessible to this audit.
Evaluation protocol, metrics and ModularRSI update
Tasks and four baseline harnesses
Dev100 comprises 100 econometric replication tasks from Selected_1000 (dataset revision 59f9512). Every combination evaluates 14 models across four harness configurations: DeepAgents 0.7.13, DeepSeek Harness 0.1.5rc1, Claude Agent SDK 0.2.165 (Claude Code CLI 2.1.294) and an OpenAI Agents SDK 0.23.1 runtime, under shared per-task caps of 6 model calls, 4 run_python tool calls, 32,768 output tokens and 1,800 s wall time.
ModularRSI-evolved DeepAgents (67.0%)
Applying ModularRSI self-evolution to DeepAgents (evolving loop, tool-use, completion and LangGraph ToolNode observation-audit feedback) raises GPT-5.6 Sol from 64.0% to 67.0% strict replication on a fresh paired Dev100 run (98% compilation, 78% partial replication, 95% correct sign, 81% significance), matching the top 67.0% score on the board. Explore the ModularRSI results →
Full replication: coefficient and standard error within 1% relative error; p-value within 0.01 absolute error (local-paper-v1).
Coefficient and SE within 5%: coefficient and standard error both within 5% relative error.
Coefficient within 5%: coefficient relative error at most 5%.
Correct sign: estimated coefficient and reference share the same sign.
Correct significance: correct sign and the same p-value threshold band (p < 0.01, 0.01–0.05, 0.05–0.1, ≥ 0.1).
We study how models and harnesses work together across econometric replication and business analysis, turning execution trajectories into better harnesses and model training signals.
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Model–Harness Coupling & Harbor Integration
Compare how different models interact with library, SDK and CLI harnesses, and unify econometric and business evaluations on the Harbor container framework.
Use verified agent trajectories, tool feedback and rubric evaluations to drive supervised fine-tuning, reinforcement learning and model–harness co-evolution.