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AI21 Labs reports that a leading deep research agent achieved a score of approximately 45 six months prior, after which the research community focused on developing improved agents to surpass this ben

AI21 Labs announced achieving the #1 ranking on DeepResearch Bench II without developing any new agents, suggesting they improved performance through optimization of existing systems or methodology ra

AI21 joined @nvidia, @Microsoft , @a16z, and dozens of others in signing the Open Weights and American AI Leadership letter. We build agentic systems and agent optimization products on top of models,

Adding a verifier improves every agentic AI system. But frontier verifiers are expensive - so we trained our own. Matching quality, at a fraction of the cost. Thread below 🧵 1/ Your agent already foun

Independent verifiers improve agent output - but frontier verifiers are expensive. So we trained our own. Results on @GoogleDeepMind's FACTS-Search: > No verifier: 89.4 (previously published SOTA) > W

We let open models access the outside world during evals on tasks from public benchmarks. Here's what happened: Most went and found upstream commits containing fixes for the tasks they were supposed t

We analyzed a year of agent research across 4 tasks: deep research, RAG indexing, agentic search and coding. Found that first asking ‘Is this task verifiable?’ exposed inefficiencies in the agent arch

There's one question you should ask before you build an agent. Most people skip it (and pay for it in compute). Over a year of agent research at @AI21Labs across deep research, RAG indexing, agentic s

AI21 Labs announced achieving the #1 ranking on DeepResearch Bench II without developing any new agents, suggesting they improved performance through optimization of existing systems or methodology ra

Atomic Chat signed the Open Weights letter! We believe everyone should be able to run AI on their own device. When a model is open, thousands of teams fine-tune it, quantize it and build new tools on

Routing every task to your largest model burns tokens, adds latency, and inflates costs. @AI21Labs' Maestro Orchestration Meta Model (OMM) is the layer above your stack that dynamically selects the ri

We analyzed a year of agent research across 4 tasks: deep research, RAG indexing, agentic search and coding. Found that first asking ‘Is this task verifiable?’ exposed inefficiencies in the agent arch

5/5 The takeaway: If your agent relies on an LLM judge for selection accuracy, measuring code quality isn’t enough; you need a measure of the model's inductive bias toward the 'fingerprint' of a gold

4/5 We upgraded our original 3-line “be correct” prompt → a much more detailed prompt that enforced a hierarchy of constraints for correctness, regression safety, and minimality. Basically, get the LL

AI21 joined @nvidia, @Microsoft , @a16z, and dozens of others in signing the Open Weights and American AI Leadership letter. We build agentic systems and agent optimization products on top of models,

Atomic Chat signed the Open Weights letter! We believe everyone should be able to run AI on their own device. When a model is open, thousands of teams fine-tune it, quantize it and build new tools on