EXPLORE BEYOND WHAT WE KNOW
Connecting research on agents that explore unknowns, build verifiable knowledge, and learn to discover.
THE DISCOVERY OBSERVATORY
Papers, discussions, and releases moving discovery research forward.
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1 / 3SageAI announces Sage-A1.5, a 35B MoE model with 3B active parameters, and reports a training-data flywheel using scientific discovery workflows and tens of thousands of synthesized environments. Its figures illustrate parallel research delegation and evidence-driven correction. These establish a concrete research-agent connection, but do not demonstrate recursive self-improvement.
1 / 4Yuandong Tian announces Flint-40B-A2B, a local coding model he says was built using an automated research system and trained from scratch. He reports benchmark performance and local throughput, and says the complete training pipeline was built in four months. Video not reviewed.
Xaira announces X-Scientist, an agentic drug-discovery platform. The author reports benchmark gains over other agents and roughly 10× faster target nomination than human curation with a comparable wet-lab hit rate. Video not reviewed.
VX-One combines compact decision heads with an empirical evaluation of when unknown detection and abstention remain useful under distribution shift.
The survey organizes physical self-improvement by where experience leaves a retained change and how that change benefits subsequent behavior or learning. It proposes controlled comparisons for testing whether acquired repair knowledge improves an improver and its subsequent self-revision.
SCDM shares a nonlinear predictor and causal-strength matrix across target-variable prediction tasks. Episodic training samples targets to reduce computation, while separate graph readouts produce directed predictive scores.
1 / 4Robert Lange compares two coding-model releases produced through agent-assisted research. His substantive contribution is distinguishing Mirendil’s researcher-directed experiments from Recursive’s stated ambition to automate idea generation, experimentation and subsequent research decisions through its Eureka Machine.
Richard Socher announced Flint, Recursive’s first local coding model, which he says was built using the Eureka Machine automated research system in under four months. He also announced open-weight availability. Video not reviewed.
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Representations, reasoning, training, systems, and evaluation contribute to a shared question: how can agents make verifiable discoveries and become better discoverers?
