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Yuxiang Feng

Yuxiang Feng contributes to research discovery and scholarly infrastructure.

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Computer Vision eess.SP eess.SY physics.optics Robotics Systems and Control

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preprint2026arXiv

NEWTON: Agentic Planning for Physically Grounded Video Generation

Video generation models produce visually compelling results but systematically violate physical commonsense -- on VideoPhy-2, the best model achieves only 32.6% joint accuracy. We identify a specification bottleneck: text prompts are lossy compression of the physical world, omitting the parameters that fully determine dynamics, and no amount of model scaling can recover what was never specified. From this diagnosis we derive three properties that physics conditioning must satisfy -- sufficiency, dynamism, and verifiability -- and show that no existing approach satisfies all three. We present NEWTON, in which video generation is demoted from the system output to one action inside an agent's toolbox: a learned planner orchestrates physics-aware tools (keyframe generation, scientific computation, prompt refinement) to construct rich conditioning, and a verifier closes the loop for iterative re-planning. The planner is the sole trainable component, optimized on-policy via Flow-GRPO inside the live multi-turn loop. On VideoPhy-2, NEWTON improves joint accuracy from 21.4% to 29.7% on LTX-Video and from 30.7% to 37.4% on Veo-3.1, without modifying either generator. Our project page: https://Newton026.github.io/newton

preprint2025arXiv

Think Before You Move: Latent Motion Reasoning for Text-to-Motion Generation

Current state-of-the-art paradigms predominantly treat Text-to-Motion (T2M) generation as a direct translation problem, mapping symbolic language directly to continuous poses. While effective for simple actions, this System 1 approach faces a fundamental theoretical bottleneck we identify as the Semantic-Kinematic Impedance Mismatch: the inherent difficulty of grounding semantically dense, discrete linguistic intent into kinematically dense, high-frequency motion data in a single shot. In this paper, we argue that the solution lies in an architectural shift towards Latent System 2 Reasoning. Drawing inspiration from Hierarchical Motor Control in cognitive science, we propose Latent Motion Reasoning (LMR) that reformulates generation as a two-stage Think-then-Act decision process. Central to LMR is a novel Dual-Granularity Tokenizer that disentangles motion into two distinct manifolds: a compressed, semantically rich Reasoning Latent for planning global topology, and a high-frequency Execution Latent for preserving physical fidelity. By forcing the model to autoregressively reason (plan the coarse trajectory) before it moves (instantiates the frames), we effectively bridge the ineffability gap between language and physics. We demonstrate LMR's versatility by implementing it for two representative baselines: T2M-GPT (discrete) and MotionStreamer (continuous). Extensive experiments show that LMR yields non-trivial improvements in both semantic alignment and physical plausibility, validating that the optimal substrate for motion planning is not natural language, but a learned, motion-aligned concept space. Codes and demos can be found in \hyperlink{https://chenhaoqcdyq.github.io/LMR/}{https://chenhaoqcdyq.github.io/LMR/}

preprint2022arXiv

Multi-core fiber enabled fading noise suppression in ϕ-OFDR based quantitative distributed vibration sensing

Coherent fading has been regarded as a critical issue in phase-sensitive optical frequency domain reflectometry (ϕ-OFDR) based distributed fiber-optic sensing. Here, we report on an approach for fading noise suppression in ϕ-OFDR with multi-core fiber. By exploiting the independent nature of the randomness in the distribution of reflective index in each of the cores, the drastic phase fluctuations due to the fading phenomina can be effectively alleviated by applying weighted vectorial averaging for the Rayleigh backscattering traces from each of the cores with distinct fading distributions. With the consistent linear response with respect to external excitation of interest for each of the cores, demonstration for the propsoed ϕ-OFDR with a commercial seven-core fiber has achieved highly sensitive quantitative distributed vibration sensing with about 2.2 nm length precision and 2 cm sensing resolution along the 500 m fiber, corresponding to a range resolution factor as high as about about 4E-5. Featuring long distance, high sensitivity, high resolution, and fading robustness, this approach has shown promising potentials in various sensing techniques for a wide range of practical scenarios.

preprint2022arXiv

Quantitative Risk Indices for Autonomous Vehicle Training Systems

The development of Autonomous Vehicles (AV) presents an opportunity to save and improve lives. However, achieving SAE Level 5 (full) autonomy will require overcoming many technical challenges. There is a gap in the literature regarding the measurement of safety for self-driving systems. Measuring safety and risk is paramount for the generation of useful simulation scenarios for training and validation of autonomous systems. The limitation of current approaches is the dependence on near-crash data. Although near-miss data can substantially increase scarce available accident data, the definition of a near-miss or near-crash is arbitrary. A promising alternative is the introduction of the Responsibility-Sensitive Safety (RSS) model by Shalev-Shwartz et al., which defines safe lateral and longitudinal distances that can guarantee impossibility of collision under reasonable assumptions for vehicle dynamics. We present a framework that extends the RSS model for cases when reasonable assumptions or safe distances are violated. The proposed framework introduces risk indices that quantify the likelihood of a collision by using vehicle dynamics and driver's risk aversion. The present study concludes with proposed experiments for tuning the parameters of the formulated risk indices.

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