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Group-based reinforcement learning enables large language model agents to learn from sparse outcome rewards in long-horizon tasks. Recent methods derive step-level credit by comparing observation-matched experiences across trajectories. However, identical observations can occur in different contexts, and matched peers...
A simple principle is proposed: clarify the text, then focus the reader, sharing this preparation across questions supports an inference pipeline built entirely from small models, making analytics over millions of conversations substantially less expensive.
OmniRouting is the first large-scale benchmark designed to evaluate LLMs on printed-circuit-board (PCB) routing reasoning under real-world industrial design-rule, manufacturability, and connectivity constraints, and reveals substantial limitations of current LMMs in PCB routing.
OmniCAD is introduced, a large-scale benchmark for assembly-aware 3D spatial reasoning across diverse industrial systems, including robotic mechanisms, automotive components, aerospace structures, and agricultural machinery, and tool-augmented agentic reasoning.
OmniMech is introduced, the first million-scale benchmark for evaluating VLMs on executable CAD generation from industrial manufacturing data, and experiments show that current VLMs and CAD-specialized models still struggle with executable program synthesis, fine-grained 3D reconstruction, and reliable enforcement of d...
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