Research

Writing

Jul 21, 2026
A New Bar for Legal AI on Harvey LAB
Rekursor's rubric-blind system achieved a 34% all-pass rate and passed 94.9% of individual criteria on a held-out, 50-task sample of public Harvey LAB tasks.
Jun 16, 2026
Continual Learning Needs a Bouncer
We demonstrate continual learning on the sequential Atari problem John Carmack describes: an agent discovers a skill from its own failures, proves it helps, and keeps prior games intact. Remove the verification gate — the bouncer — and the forgetting comes right back.
Jun 2, 2026
Beyond Frontier Model Performance for Legal AI Agents
Five results on Harvey LAB: held-out library transfer, autonomous all-pass revision, autonomous library generation from a firm's own graded work, reliability of revision, and a scaling-law curve where Rekursor's routing holds as RAG collapses.
May 20, 2026
Continual Learning on Harvey's Legal Agent Benchmark: First Results
A first result on Harvey LAB's open legal benchmark: with our learning layer attached, the same agent and judge go from 45/48 to 48/48 on a corporate-governance task, with zero regressions.
May 13, 2026
AI Agents Are Not Magic. Here's What Actually Matters.
A working filter for the AI agent moment: what an agent actually is, where the basic version fails, and four questions to ask any vendor.
May 1, 2026
Why AI Agent Learning Plateaus
Why AI agents plateau on autoresearch loops, and what breaks through. A controlled comparison on Karpathy's autoresearch fork.
Apr 16, 2026
Rekursor: AI Agents That Keep Getting Smarter
A learning layer that makes any frozen model smarter with every task — no retraining, no weight changes, fewer tokens. Results across Terminal-Bench 2.0, SWE-bench, EDGAR, and drug discovery.