Presenting Jeve (pronounced jeev - sanskrit word for life) A simulation engine mostly powered by Jev from @typesafeai Jeve is significantly more efficient (cost and time) compared to LLM-based GABMs and is a solid framework for long-running simulations Live demo coming soon

We're open-sourcing 10 RL environments for M&A due diligence. In our LAB: Diligence environments, agents do research in a virtual data room (VDR) and draft a diligence memo for a merger or acquisition. The largest of these environments contains 80M tokens of context with 1,000 grading criteria. M&A diligence is one of the most difficult tasks for frontier agents due to: 1) Long context: A single thread of VDR research may involve hundreds of documents in a dataroom that contains hundreds of thousands. 2) Exhaustivity: every document must be searched for needle-in-a-haystack provisions that kill a deal. 3) Data: M&A diligence data is privately held by transactional law firms - models aren't trained to do it. It is also one of the world' most valuable knowledge work tasks: global M&A volume is ~$4T annually, of which ~1% is spent on diligence. Deep dive below by @ItsJulioPereyra and @nikogrupen: