arXiv subjects

Yiwen Chen

Publications and source records attributed to Yiwen Chen.

At least 19 recordsLinked to original sources

arXiv2026

Code World Model: Coding Agent as World Brain

World models aim to simulate how complex environments evolve under actions and events, yet existing video-based world models primarily learn dynamics from visual observations, which reveal outcomes rather than the underlying knowledge, rules, and mechanisms governing world evolution. This makes it difficult to maintain persistent consequences and support coherent, open-ended evolution. We introduce Code World Model, a framework that separates world evolution from visual realization by combining the reasoning and coding capabilities of language models with the generative priors of video models. A coding agent serves as the world brain, reasoning about events and their consequences and generating executable code to maintain persistent world state and perform rule-consistent evolution. To connect executable state with visual generation, we introduce a proxy representation that encodes frame-wise spatiotemporal constraints and is compiled into a proxy video, which conditions a video model to render high-fidelity visual observations. We further develop data pipelines for constructing aligned proxy-observation pairs from gameplay and real-world videos. After fine-tuning on paired gameplay data, MiniMax-H3 follows proxy-based spatiotemporal specifications from simple interactive worlds built by the coding agent while preserving rich visual details and dynamics. These results demonstrate the potential of combining code for persistent world evolution with video models for flexible visual realization, providing a new path toward open-ended world models.

cs.CV↗

arXiv2026

LumiTokens: 3D Relighting via Token-Space Lighting Transformation

Existing 3D relighting methods operate through either explicit material decomposition, diffusion-based view-space generation, or a combination of both, requiring full recomputation for each new lighting condition. We observe that recent latent scene representations, which encode multi-view images into a set of compact tokens with no fixed physical semantics, open up a novel design space for relighting. We present LumiTokens, a framework that formulates 3D relighting as a direct transformation on latent scene tokens, without explicit 3D representations, rendering equations, or physics-based decomposition. Our model introduces a Scene Token Editor that processes scene tokens jointly with light-ray tokens through self-attention, producing updated tokens that can be decoded into multi-view-consistent relit images. To support diverse lighting types through a unified interface, all lighting signals, including environment maps, point lights, and area lights, are parameterized as Plucker ray tokens, enabling native 3D user interaction with a representation that carries no explicit spatial structure. Crucially, this design supports progressive relighting: because the editor's output remains in the same latent space as its input, a user can incrementally build up illumination one light source at a time, with each edit composing in token space. Experiments demonstrate that LumiTokens achieves comparable or superior relighting quality to other methods and supports progressive, composable lighting edits. Project page: https://neu-vi.github.io/LumiTokens/

cs.CV↗

arXiv2026

ClockRoPE: Random Fourier Rotations for Temporal Routine Modeling

Rotary Position Embedding (RoPE) has been widely adopted in transformer-based large language models. However, its log-linear frequency schedule, originally designed to produce long-term attention decay, limits its adoption in domains with more complex distance-correlation patterns, such as temporal periodicity in sequential recommendation. We investigate the expressiveness of general query/key rotations and find that any normalized continuous positive-definite attention modulation function can be approximated by random rotations induced by its own Fourier transform, which we term Random Fourier Rotations. Building on this theory, we propose ClockRoPE for routine modeling in sequential recommendation, where rotation frequencies are derived from periodic attention modulation functions. In online A/B tests, ClockRoPE demonstrates consistent improvements in valued engagement metrics, and has been successfully deployed in production-scale generative retrieval system at a major video-sharing platform.

cs.LG↗

arXiv2026

Coexistence of High Temperature Superconductivity and Antiferromagnetic Order in a Cuprate with Multiple Hole Fermi Pockets

The intricate relationship between high temperature superconductivity and antiferromagnetic order in cuprates, and the fundamental origin of electron pairing remain open questions. By utilizing high-resolution laser-based spatially-resolved angle-resolved photoemission spectroscopy, we investigate the seven-layer $Bi_{2}Sr_{2}Ca_{6}Cu_{7}O_{18+\delta}$ (Bi2267) and identify a cuprate system that consists of multiple hole Fermi pockets. The observed Fermi pockets exhibit pronounced momentum-, temperature- and Fermi surface-dependent energy gaps. Crucially, high temperature superconductivity with a critical temperature ($T_{\mathrm{c}}$) of $\sim$75 K emerges in a system with multiple Fermi pockets and the presence of strong antiferromagnetic order and correlations. In particular, substantial electron pairing is observed along the Fermi pocket with an energy gap up to $\sim$42 meV in lightly-doped CuO$_{2}$ planes ($p\sim$0.05). These findings challenge the conventional understanding of the roles of the nodal and antinodal electronic states in driving high-temperature superconductivity. They show that superconductivity and antiferromagnetism can coexist in a cuprate with multiple Fermi pockets, offering further insights into the pairing mechanism in cuprate superconductors.

cond-mat.supr-con↗

arXiv2026

RAMBA: 4D Radar Mapping by Bundle Adjustment

4D radar is increasingly attractive for robotic mapping because it provides range, azimuth, elevation, and Doppler measurements while remaining robust in adverse visibility conditions. Although recent radar and radar--inertial odometry methods have achieved promising online state estimation performance, offline global map refinement for 4D radar remains underexplored. This paper presents RAMBA, a radar bundle-adjustment framework for globally consistent 4D radar mapping. Given initial poses and radar frames from a radar--inertial odometry front-end, RAMBA jointly refines radar frame states using covariance-weighted geometric residuals, IMU preintegration factors, and radar ego-velocity constraints. The geometric residuals extend pairwise GICP to a multi-frame optimization by forming voxel-based correspondences across selected frames and weighting each residual with point covariances. To improve robustness against drift and revisits, RAMBA enforces temporal consistency during correspondence formation while explicitly supporting loop-closure constraints. Experiments on the ColoRadar and SNAIL Radar datasets show that RAMBA improves map consistency and usually enhances trajectory accuracy over radar--inertial odometry and pose-graph optimization baselines.

cs.RO↗

arXiv2026

Accuracy and Relationships of Quadratic Models in Derivative-free Optimization

We study three quadratic models in model-based derivative-free optimization: the minimum norm (MN), minimum Frobenius norm (MFN), and quadratic generalized simplex derivative (QS) models. Despite their widespread use, their approximation accuracy and relationships have not been systematically explored. We establish fully linear error bounds for all three models, removing the uniformly bounded model Hessian assumption required in existing MN analyses and deriving the first such results for the QS model. We further analyze Hessian approximation accuracy via directional error bounds, showing that all three models achieve fully quadratic accuracy along sample directions under a mild condition on the sample set. This reveals a form of directional fully quadratic accuracy not captured by existing theory. Finally, we characterize the relationships among these models, identifying conditions under which they coincide and clarifying their structural connections.

math.OC↗

arXiv2026

LASAR: Latent Adaptive Semantic Aligned Reasoning for Generative Recommendation

Large Language Models (LLMs) have demonstrated powerful reasoning capabilities through Chain-of-Thought (CoT) in various tasks, yet the inefficiency of token-by-token generation hinders real-world deployment in latency-sensitive recommender systems. Latent reasoning has emerged as an effective paradigm in LLMs, performing multi-step inference in a continuous hidden-state space to achieve stronger reasoning at lower cost. However, this paradigm remains underexplored in mainstream generative recommendation. Achieving this reveals three key challenges: (1) the gap between prior-less Semantic ID (SID) symbols and continuous latent reasoning, as SIDs lack pre-trained semantics, hindering joint optimization; (2) representation drift due to a lack of reasoning chain supervision; and (3) the suboptimality of applying a globally fixed reasoning depth. To address these, we propose LASAR (Latent Adaptive Semantic Aligned Reasoning), an SFT-then-RL framework. First, we bridge this gap via two-stage training: Stage 1 grounds SID semantics before Stage 2 introduces latent reasoning, ensuring efficient convergence. Second, we mitigate representation drift through explicit CoT semantic alignment. Step-wise bidirectional KL divergence constrains the latent reasoning trajectory using hidden-state anchors extracted from CoT text, while a Policy Head predicts per-sample reasoning depth. Third, during the GRPO-based RL phase, terminal-only KL alignment accommodates variable-length reasoning, and REINFORCE optimizes the Policy Head to dynamically allocate steps. This nearly halves the average latent step count while simultaneously improving recommendation quality. Experiments on three real-world datasets show that LASAR achieves the best overall performance across the evaluated settings. It adds limited inference latency and is roughly 20x faster than generating explicit CoT text.

cs.IR↗

arXiv2026

Route Before Retrieve: Activating Latent Routing Abilities of LLMs for RAG vs. Long-Context Selection

Recent advances in large language models (LLMs) have expanded the context window to beyond 128K tokens, enabling long-document understanding and multi-source reasoning. A key challenge, however, lies in choosing between retrieval-augmented generation (RAG) and long-context (LC) strategies: RAG is efficient but constrained by retrieval quality, while LC supports global reasoning at higher cost and with position sensitivity. Existing methods such as Self-Route adopt failure-driven fallback from RAG to LC, but remain passive, inefficient, and hard to interpret. We propose Pre-Route, a proactive routing framework that performs structured reasoning before answering. Using lightweight metadata (e.g., document type, length, initial snippet), Pre-Route enables task analysis, coverage estimation, and information-need prediction, producing explainable and cost-efficient routing decisions. Our study shows three key findings: (i) LLMs possess latent routing ability that can be reliably elicited with guidelines, allowing single-sample performance to approach that of multi-sample (Best-of-N) results; (ii) linear probes reveal that structured prompts sharpen the separability of the "optimal routing dimension" in representation space; and (iii) distillation transfers this reasoning structure to smaller models for lightweight deployment. Experiments on LaRA (in-domain) and LongBench-v2 (OOD) confirm that Pre-Route outperforms Always-RAG, Always-LC, and Self-Route baselines, achieving superior overall cost-effectiveness.

cs.CL↗

arXiv2026

Persistent Fermi Pockets and Robust Electron Pairing in Lightly Doped CuO$_2$ Planes of Cuprate Superconductors

High temperature superconductivity in cuprate superconductors is generally considered to be generated from doping the Mott insulators. The fundamental nature of the doped parent compounds as well as the microscopic origin of electron pairing remain critical issues in understanding the emergence of superconductivity. Here, using high-resolution spatially-resolved laser angle-resolved photoemission spectroscopy, we investigate the intrinsic electronic structures of the CuO$_2$ planes in multilayer cuprates Bi$_2$Sr$_2$Ca$_{n-1}$Cu$_n$O$_{2n+4+\delta}$ (n=5$\sim$8). The inner CuO$_2$ planes are well shielded from the disorders and provide a rare and ideal platform to probe the intrinsic electronic phase diagram. We observe well-defined Fermi pockets with hole doping levels as low as 0.007, demonstrating an abrupt transition from the parent Mott insulator to a metallic state upon the introduction of an infinitesimal amount of doping. The innermost CuO$_2$ planes (IP$_0$) display gapless Fermi pockets, while the second innermost planes (IP$_1$) exhibit anisotropic superconducting gaps up to $\sim$33$\,$meV, indicative of robust electron pairing coexisting with strong antiferromagnetic order. Our findings provide a revised framework for understanding the doping-driven transitions and pairing mechanisms in cuprate superconductors.

cond-mat.supr-con↗

arXiv2026

Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices

Effective item identifiers (IDs) are an important component for recommender systems (RecSys) in practice, and are commonly adopted in many use cases such as retrieval and ranking. IDs can encode collaborative filtering signals within training data, such that RecSys models can extrapolate during the inference and personalize the prediction based on users' behavioral histories. Recently, Semantic IDs (SIDs) have become a trending paradigm for RecSys. In comparison to the conventional atomic ID, an SID is an ordered list of codes, derived from tokenizers such as residual quantization, applied to semantic representations commonly extracted from foundation models or collaborative signals. SIDs have drastically smaller cardinality than the atomic counterpart, and induce semantic clustering in the ID space. At Snapchat, we apply SIDs as auxiliary features for ranking models, and also explore SIDs as additional retrieval sources in different ML applications. In this paper, we discuss practical technical challenges we encountered while applying SIDs, experiments we have conducted, and design choices we have iterated to mitigate these challenges. Backed by promising offline results on both internal data and academic benchmarks as well as online A/B studies, SID variants have been launched in multiple production models with positive metrics impact.

cs.IR↗

arXiv2026

Semiparametric Estimation of Delayed-Outcome Treatment Effects Using Short-Term Surrogates under Administrative Censoring

The multi-site registry studies, such as Stepped-wedge cluster-randomized trials (SW-CRT), staggered-enrollment RCTs, etc., share a structural feature: the primary long-term outcome is administratively censored for a non-negligible fraction of units, with censoring driven by calendar design rather than by the outcome itself. Standard inverse-probability-of-censoring weighting becomes unstable when observation probabilities $g_{\Delta}$ concentrate near zero for late-crossing units, while parametric mixed-model analyses discard the information in any short-term intermediate measurement and rely on correct specification of the secular time trend. We study semiparametric estimation of the average treatment effect when a short-term surrogate, which is observed for all units and conditionally independent of the censoring mechanism given baseline covariates, is available. Identification takes a nested-integral form in which the outcome regression is marginalized over the conditional surrogate distribution, so the observation mechanism does not enter the target functional as an inverse weight. We show that a density-plug-in one-step debiased machine-learning construction for this functional leaves a second-order cross-product remainder $R_{SY}$ that has no doubly-robust complement in the efficient influence function and is not eliminated by cross-fitting . We propose a surrogate-assisted AIPW estimator (SA-AIPW) that integrates over the empirical surrogate distribution through treatment weighting rather than estimating the conditional surrogate density, and so structurally avoids $R_{SY}$. For clustered data, the estimator is shown to be $\sqrt{J}$-consistent and asymptotically linear under a product-rate double-robustness condition.

stat.ME↗

arXiv2026

Relationships between full-space and subspace quadratic interpolation models and simplex derivatives

Quadratic interpolation models and simplex derivatives are fundamental tools in numerical optimization, particularly in derivative-free optimization. When constructed in suitably chosen affine subspaces, these tools have been shown to be especially effective for high-dimensional derivative-free optimization problems, where full-space model construction is often impractical. In this paper, we analyze the relationships between full-space and subspace formulations of these tools. In particular, we derive explicit conversion formulas between full-space and subspace models, including minimum-norm models, minimum Frobenius norm models, least Frobenius norm updating models, as well as models constructed via generalized simplex gradients and Hessians. We show that the full-space and subspace models coincide on the affine subspace and, in general, along directions in the orthogonal complement. Overall, our results provide a theoretical framework for understanding subspace approximation techniques and offer insight into the design and analysis of derivative-free optimization methods.

math.OC↗

arXiv2026

Unusual Dual Flat Bands and two-dimensional Dirac-node Arc State in Kagome Metal Ni3In2S2

Kagome materials are at the frontier of condensed matter physics. An ideal kagome lattice features only one geometrically frustrated flat band spanning the entire momentum space and a single Dirac cone at the Brillouin-zone corners. However, for the first time, here we observe unusual flat-band and Dirac physics in the newly discovered "322" kagome material Ni3In2S2 by combining high-resolution synchrotron- and laser-based angle-resolved photoemission spectroscopy with a micro-focused beam, scanning tunneling microscopy, and first-principles calculations. We resolve two distinct electronic flat-band states located in close proximity to the Fermi level: a robust Topological Surface Flat Band at ~40 meV below the Fermi level on the Sulfur-terminated surface, originating from weak topological insulator states, and a kagome lattice-derived flat band at ~100 meV binding energy with an ultranarrow bandwidth (~5 meV). Instead of the single Dirac cone, the Indium-terminated surface hosts a rare two-dimensional Dirac-node arc state, where the gapless Dirac nodes extend along an open one-dimensional line crossing the Brillouin-zone boundary, exhibiting sharp linear dispersion, exceptionally high Fermi velocity, and pronounced circular dichroism. These findings establish Ni3In2S2 as a unique topological kagome metal in which multiple flat-band states of different physical origin coexist with an unusual Dirac-node arc, opening an avenue for discovering flat-band--driven and topology-enabled quantum phenomena.

cond-mat.mtrl-sci↗

arXiv2025

TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer

Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information. However, this ID-based paradigm faces challenges in transferring to a new domain. This phenomenon primarily stems from two inherent challenges: (1) the non-transferability of ID embeddings due to isolated domain-specific ID spaces, and (2) structural incompatibility between heterogeneous interaction graphs across domains. To address these issues, we propose TextBridgeGNN, a pre-training and fine-tuning framework that can effectively transfer knowledge from a pre-trained GNN to downstream tasks. Specifically, TextBridgeGNN uses text as a semantic bridge to connect domains through multi-level graph propagation. During the pre-training stage, hierarchical GNNs are designed to learn both domain-specific and domain-global knowledge with text features, ensuring the retention of collaborative signals and the enhancement of semantics. During the fine-tuning stage, a similarity transfer mechanism initializes ID embeddings in the target domain by transferring from semantically related nodes, successfully transferring the ID embeddings and graph pattern. Experiments demonstrate that TextBridgeGNN consistently achieves strong overall performance across cross-domain, multi-domain, and training-free scenarios, without costly language model fine-tuning or real-time inference overhead.

cs.IR↗

arXiv2025

Faithful Contouring: Near-Lossless 3D Voxel Representation Free from Iso-surface

Accurate and efficient voxelized representations of 3D meshes are the foundation of 3D reconstruction and generation. However, existing representations based on iso-surface heavily rely on water-tightening or rendering optimization, which inevitably compromise geometric fidelity. We propose Faithful Contouring, a sparse voxelized representation that supports 2048+ resolutions for arbitrary meshes, requiring neither converting meshes to field functions nor extracting the isosurface during remeshing. It achieves near-lossless fidelity by preserving sharpness and internal structures, even for challenging cases with complex geometry and topology. The proposed method also shows flexibility for texturing, manipulation, and editing. Beyond representation, we design a dual-mode autoencoder for Faithful Contouring, enabling scalable and detail-preserving shape reconstruction. Extensive experiments show that Faithful Contouring surpasses existing methods in accuracy and efficiency for both representation and reconstruction. For direct representation, it achieves distance errors at the $10^{-5}$ level; for mesh reconstruction, it yields a 93\% reduction in Chamfer Distance and a 35\% improvement in F-score over strong baselines, confirming superior fidelity as a representation for 3D learning tasks.

cs.CV↗

arXiv2025

HouseCrafter: Lifting Floorplans to 3D Scenes with 2D Diffusion Model

We introduce HouseCrafter, a novel approach that can lift a floorplan into a complete large 3D indoor scene (e.g., a house). Our key insight is to adapt a 2D diffusion model, which is trained on web-scale images, to generate consistent multi-view color (RGB) and depth (D) images across different locations of the scene. Specifically, the RGB-D images are generated autoregressively in a batch-wise manner along sampled locations based on the floorplan, where previously generated images are used as condition to the diffusion model to produce images at nearby locations. The global floorplan and attention design in the diffusion model ensures the consistency of the generated images, from which a 3D scene can be reconstructed. Through extensive evaluation on the 3D-Front dataset, we demonstrate that HouseCraft can generate high-quality house-scale 3D scenes. Ablation studies also validate the effectiveness of different design choices. We will release our code and model weights. Project page: https://neu-vi.github.io/houseCrafter/

cs.CV↗

arXiv2025

Doping Evolution of Nodal Electron Dynamics in Trilayer Cuprate Superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+δ}$ Revealed by Laser-Based Angle-Resolved Photoemission Spectroscopy

The doping evolution of the nodal electron dynamics in the trilayer cuprate superconductor Bi$_2$Sr$_2$Ca$_2$Cu$_3$O$_{10+δ}$ (Bi2223) is investigated using high-resolution laser-based angle-resolved photoemission spectroscopy (ARPES). Bi2223 single crystals with different doping levels are prepared by controlled annealing which cover the underdoped, optimally-doped and overdoped regions. The electronic phase diagram of Bi2223 is established which describes the T$_\mathrm{c}$ dependence on the sample doping level. The doping dependence of the nodal Fermi momentum for the outer (OP) and inner (IP) CuO$_2$ planes is determined. Charge distribution imbalance between the OP and IP CuO$_2$ planes is quantified, showing enhanced disparity with increasing doping. Nodal band dispersions demonstrate a prominent kink at $\sim$94$\,$meV in the IP band, attributed to the unique Cu coordination in the IP plane, while a weaker $\sim$60$\,$meV kink is observed in the OP band. The nodal Fermi velocity of both OP and IP bands is nearly constant at $\sim$1.62$\,$eVÅ independent of doping. These results provide important information to understand the origin of high T$_\mathrm{c}$ and superconductivity mechanism in high temperature cuprate superconductors.

cond-mat.supr-con↗

arXiv2025

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention

Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling high-resolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quality. Our method leverages the compact VecSet representation to efficiently generate a coarse object layout in the first stage, reducing token count and accelerating voxel coordinate prediction. To refine per-voxel latent features in the second stage, we introduce Part Attention, a geometry-aware localized attention mechanism that restricts attention computation within semantically consistent part regions. This design preserves structural continuity while avoiding unnecessary global attention, achieving up to 6.7x speed-up in latent generation. To support this mechanism, we construct a scalable part annotation pipeline that converts raw meshes into part-labeled sparse voxels. Extensive experiments demonstrate that Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves state-of-the-art performance in both visual fidelity and user preference.

cs.CV↗