Introducing DeepRSI, a new industry standard for measuring recursive self-improvement.
On preexisting benchmarks, tasks are contributor-based and short-horizon (~12 hrs). In DeepRSI, we push agents to work 48 hours on ML tasks actually derived from ongoing proprietary research.
Today we're launching WebCloneBench: a benchmark which measures a model's ability to explore, copy, build and test various web apps.
For each task, we give the model a reference site from one of Gmail, Google Sheets, Jira, DocuSign, Shopify Admin, Craigslist, Figma, Slack and Zendesk. It is instructed to build a fully feature-complete clone of every part of the application with an 8 hour time limit.
Who grades the graders?
LLM-as-judge on short answers is well studied. Agent judges are not: they open the files, run the code, and investigate before ruling on another agent's work.
Introducing Arbiter-Bench: 71 real agent runs that frontier agent judges get wrong.
Half the set is public with every judge's full logs, a scorer for your own judge, and the audit trail. 35 items are held out. Every case is a runnable @harborframework task, so you can point your own judge at the set and score it with one command.
Full writeup: heuristic.build/blog/arbiterbe…
Of 232 runs where a frontier judge disagreed with the benchmark, only 71 survived a blind label audit (3+ model families, a human ruling on every split vote).
About a quarter were the benchmark's error: hidden criteria, broken checkers, unstated rules.
Tech has decided that regulation is the enemy of progress, but innovation requires capital
Regulation builds the trust necessary to inject billions into markets
To enable free innovation, Andera raised a $37M series A led by Lightspeed to scale financial oversight with agents
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