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Processes images and text to produce structured, reasoning-rich text outputs for high-throughput agentic workflows. Sparse MoE design (198B total, ~11B active per token), 256k context window and selectable reasoning levels—optimized for single-pass parsing, verification, and multi-step automation.

Automates distillation of heterogeneous traces from a target person or role into versioned, inspectable skill packages for LLM agents — producing separate capability and bounded-behavior tracks that support natural-language corrections, rollback, and cross-host installation. Ships with an open system and a skills gallery.

Workflow-aware benchmark for autonomous medical-AI research that splits agent execution into five stages (Plan, Setup, Validate, Inference, Submit) and evaluates long-horizon runs across segmentation, image enhancement, VQA, report generation, and lesion detection with stage-level scoring.

Guides LLM-based agents to decompose long-horizon research problems and delegate subtasks to constrained subagents, then fine-tunes models on harness-generated trajectories so delegation decisions become internalized. Reports SearchSwarm-30B-A3B achieving top BrowseComp scores for its scale.

Lets an AI agent propose, run, and evaluate multi-step research experiments using a persistent Hypothesis Tree that links hypotheses, artifacts, evidence, and distilled insights. Combines a long-lived coordinator with short-lived executors to carry lessons across time; evaluated on six ML tasks.

Benchmark for evaluating procedural skill evolution in LLM agents: isolates reusable skill bodies, role-specific work surfaces, and hidden oracle assets to measure whether skill refinements transfer across tasks, roles, and model backbones. Includes 382 workplace tasks, 22 skills, and a controlled evaluation protocol.