Introducing Samaya Sterling, our new model post-trained for challenging financial research that outperforms the frontier! Sterling started off as a two person research effort to see if we could increase efficiency in agentic orchestration. To our surprise we were able to post-train a much smaller model (Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters) to greater accuracy (58.9% vs 55.9% on FrontierFinance) and 11x lower cost ($0.61 vs $6.89) than Claude Fable 5.1, the best performing frontier model. We did this via a four-stage training process, two stages of SFT where the model first learns to research with our financial tools and how to construct a good agentic research strategy. The second two stages are RL, with an offline GRPO stage where the model further hones its research strategy from eight teachers that provide diverse approaches, and an online GRPO stage where the model can make end to end attempts and learn from its mistakes. We see substantial gains from the co-adaptation of the model and harness through post-training, both through the model learning to effectively use the high-quality financial tools, and better compaction approaches that let even a small model execute on longer and deeper research. We are exploring multiple follow-up directions, from multi-agent research swarms using this small model, to training firm-specific models to encode unique judgement, to training a larger model, where early results show even greater gains from the training process. The full research is linked below!

We’re sharing new results showing that frontier AI models are able to outperform human experts on financial predictions for the first time. We built an environment for the challenging task of Earnings Predictions, finding that the most recent frontier models (Opus 5.5, Fable 5.1, Astra) with Samaya’s harness, meaningfully outperform expert consensus predictions on commonly reported earnings metrics such as company revenue, gross margin and more. We built this environment to have a “point-in-time” gate in the harness to ensure no information leakage, and created financial retrieval and data tools that would respect this gate and give the models access to the same information available to experts. We also adjusted for bias in expert consensus to create a hard, bias-corrected baseline, where outperformance has real signal. Only the most recent frontier AI models are able to beat this hard baseline, pointing to an important inflection point in AI capabilities in finance. Like with FrontierFinance, we see substantial headroom for further RL training in this environment — the models show large variations in error across different rollouts. We’re continuing research on using earnings prediction and other predictive tasks to post-train models to learn from their mistakes. Get in touch if you’d like to collaborate or try out an alpha version. Link to the full research below.

We partnered with @AnthropicAI to evaluate @claudeai Fable 5.1 pre-launch on FrontierFinance! Fable 5.1 becomes the best performing frontier model by a wide margin, scoring 55.9% compared to 49.2% for Fable 5 (the prior best performing frontier model.) This gain largely comes from Fable 5.1 carrying out more and better tool calls, e.g. calling and anchoring better on authoritative sources. The increased tool calls does come with increased cost though -- we observe Fable 5.1 is about 1.7x as expensive as Fable 5. Exciting results and lots more work ahead to continue advancing agentic capabilities!

New eval results on FrontierFinance: Samaya's System (high effort) reaches 56% accuracy, a new state of the art, at 2x lower cost than Claude Fable 5. Claude Fable 5 scores 49.2%, and Samaya's System (low effort) scores 50.8% at 4x lower cost. More evals coming, including Gemini 3.6 Flash and Claude Opus 5!

Excited to be releasing FrontierFinance, the largest and most challenging open benchmark for evaluating AI agents across the full investment workflow! FrontierFinance is substantially harder than current finance benchmarks: Existing benchmarks like FinanceBench and Finance Agent focus almost entirely on data extraction. FrontierFinance spans diverse use cases across the full investment process: Screening & Discovery, Company Research, Sector/Industry/Macro, Earnings & Events, and Coverage & Catalyst Monitoring. Created for ambiguous, long-horizon agents: 220 examples paired with 11,543 expert-crafted rubrics, following Samaya's Criteria Eval methodology. The rubrics are what let us evaluate the reasoning and steps behind a true expert-level output, not just a plausible-looking one. Evaluations: We evaluated Claude Fable 5, Claude Opus 4.8, GPT 5.5, Gemini, open-source models including GLM and DeepSeek, and others. We used the same public rubric and a standard harness for financial tasks. Samaya's AI system reached state-of-the-art accuracy at 50.8%, at 4x lower inference cost than Fable 5. Next best was Fable 5 (49.2%), then Opus 4.8 (45%) and GPT 5.5 (43.5%). We're releasing the benchmark, methodology, and full evaluation results - see link in comments. Future releases: FrontierFinance was curated from Samaya's larger internal set of ~5,000 examples, and we plan to release subsequent, harder benchmarks as well as a more detailed technical report!

Controversies on model nerfing aside, Fable seeing its biggest performance gains on more complex tasks is a clear indicator of large AI capabilities jumps ahead. (We’re far from saturating the frontier and far from defining / measuring the next wave of frontier tasks)

After seeing AI's progress on IMO last year, it felt like only a matter of time before we'd see it tackle open conjectures. I'm still curious to see if (when) it can *pose* new mathematical conjectures, or even new IMO problems -- that will take a lot of taste. Is anyone working on this?