🔺 We joined 20 days late. Six days later, we’re #71. We entered the ICML 2026 Agent Reproduction Challenge 20 days after it began, with less than a week remaining to build, test, document, publish, and defend our work. Six days later, AITrailblazer is ranked #71 out of 350 participants-roughly the top 20%-with 44 points across 6 judged paper reproductions. That score represents 22 research claims independently verified or falsified. Three of our logbooks earned a perfect 10/10. One earned 8/10. Another currently holds 6/10. Our partial Tool-Guard released-evidence audit earned zero points-and taught us one of the most important lessons of the entire challenge. There is a fundamental difference between checking an author’s released results and independently reproducing those results. We did not have 26 days to study the leaderboard, select easy claims, optimize our process, and gradually improve our submissions. We had six days. In those six days, we had to find suitable papers, understand their central claims, locate released code and data, design independent tests, run experiments, preserve evidence, calculate hashes, document limitations, publish public Trackio Spaces, and wait for an independent Judge. We focused on what scientific reproduction should actually mean: Public and inspectable evidence. Executable methods. Deterministic calculations wherever possible. Exact hashes and provenance. Negative controls designed to catch false agreement. Clear separation between independent evidence and author-released artifacts. Honest documentation of what we could-and could not-reproduce. This achievement matters because AI can produce convincing explanations extremely quickly. But scientific progress does not come from explanations that merely sound correct. It comes from claims that survive independent testing. It also comes from negative results documented with the same care as successful confirmations. The challenge recognizes that principle by awarding equal points to verified and falsified claims. That is important. A rigorous result showing that a claim does not reproduce can be just as valuable to science as a successful confirmation. It prevents false confidence, reveals hidden assumptions, and gives future researchers a stronger foundation. Our current situation also demonstrates why reproducibility infrastructure matters. Our newest Prediction-Powered Inference update is public, healthy, and was successfully detected by the automatic Judge. However, all three evaluation attempts failed because the Hugging Face inference router returned service-side HTTP 504 errors. We have requested an administrative requeue without changing the evidence, requesting a manual verdict, or submitting an alternate result. Until that automatic evaluation is recovered, the leaderboard continues to reflect our existing 44 points and #71 position. The final ranking may still change. Other participants are continuing to submit work. Our PPI update may still be re-evaluated. The leaderboard remains active as the deadline approaches. But whatever the final position, reaching #71 out of 350 after joining 20 days late-and doing it in only six days-is already an achievement worth recognizing. We did not merely generate summaries of research papers. We built a public, inspectable record showing what held up, what failed, how we tested it, and where the evidence ended. That is what reproducibility should look like. Live leaderboard: https://t.co/ALGcVy1Qng Our strongest 10/10 reproduction-CoopEval: https://t.co/FW8JxWV4b6 We joined 20 days late. We had six days. We reached the top 20%. Reproducibility beats hype. Evidence compounds. #ICML2026 #AIResearch #Reproducibility #OpenScience