P1-VL: Bridging Visual Perception and Scientific Reasoning in Physics Olympiads

TL;DR

P1-VL is an open-source vision-language model designed for advanced scientific reasoning, particularly in physics, integrating multimodal perception and iterative self-verification to excel in Olympiad-level problems and benchmarks.

Contribution

It introduces P1-VL, combining curriculum reinforcement learning and agentic augmentation, achieving state-of-the-art performance and first-place medals in physics Olympiad benchmarks.

Findings

First open-source VLM to win 12 gold medals in physics Olympiads.

Achieves state-of-the-art performance among open-source models.

Ranks second globally in scientific reasoning benchmarks.

Abstract

The transition from symbolic manipulation to science-grade reasoning represents a pivotal frontier for Large Language Models (LLMs), with physics serving as the critical test anchor for binding abstract logic to physical reality. Physics demands that a model maintain physical consistency with the laws governing the universe, a task that fundamentally requires multimodal perception to ground abstract logic in reality. At the Olympiad level, diagrams are often constitutive rather than illustrative, containing essential constraints, such as boundary conditions and spatial symmetries, that are absent from the text. To bridge this visual-logical gap, we introduce P1-VL, a family of open-source vision-language models engineered for advanced scientific reasoning. Our method harmonizes Curriculum Reinforcement Learning, which employs progressive difficulty expansion to stabilize post-training, with Agentic Augmentation, enabling iterative self-verification at inference. Evaluated on HiPhO, a rigorous benchmark of 13 exams from 2024-2025, our flagship P1-VL-235B-A22B becomes the first open-source Vision-Language Model (VLM) to secure 12 gold medals and achieves the state-of-the-art performance in the open-source models. Our agent-augmented system achieves the No.2 overall rank globally, trailing only Gemini-3-Pro. Beyond physics, P1-VL demonstrates remarkable scientific reasoning capacity and generalizability, establishing significant leads over base models in STEM benchmarks. By open-sourcing P1-VL, we provide a foundational step toward general-purpose physical intelligence to better align visual perceptions with abstract physical laws for machine scientific discovery.