arXiv subjects

Yao Yao

Publications and source records attributed to Yao Yao.

At least 19 recordsLinked to original sources

arXiv2026

Searching for Type Ia Supernovae in the Dark Energy Spectroscopic Instrument

With the development of large-scale photometric surveys, an increasing number of supernova candidates are being discovered, leading to a rapidly growing demand for supernova spectra. In addition to equipping photometric surveys with follow-up spectroscopic facilities, archival spectra from large multi-object spectroscopic surveys can be mined to provide spectroscopic classifications for candidates and to find supernovae missed by previous surveys. In this work, we combine Principal Component Analysis (PCA), the Local Outlier Factor (LOF) algorithm, and the supernova classification tool SNID to search for Type Ia supernovae among 1,757,303 galaxy spectra from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). We finally obtain 247 Type Ia supernovae and 17 supernovae of other types. Among these, 202 supernovae lack classification records in the Transient Name Server (TNS) and represent newly identified SNe. These results demonstrate the potential of multi-object spectroscopic surveys to supplement supernova samples, particularly for transients missed by traditional photometric surveys.

astro-ph.HE↗

arXiv2026

Procedura: Agentic 3D Modeling with Procedural Control

Native 3D generators now recover impressive mesh geometry from a single image. However, a dense mesh stays soft where a machined object should be sharp, it carries no part decomposition, and it exposes no parameter a user could edit. To address this, we explore the paradigm of 3D shape as code, leveraging and scaling the coding ability of an LLM for 3D modeling. We introduce Procedura, a novel 3D modeling agent framework that writes an object as a procedural assembly, a parametric program whose named parts are joined by typed, machine-checkable mates. From a text prompt, the agent plans the object as an assembly graph and writes the program part by part, solving each placement from the mated frames rather than guessing it, and admitting a part only once compile, mate, and connectivity checks pass. A decoupled vision critic then refines the assembly one diagnosed fix at a time. Moreover, the same graph carries per-part materials and a simulator-validated articulation. We evaluate on P3D-Bench under its assembly judge, and with the same judge on MechBench-36, our hard-surface benchmark. On both, Procedura outperforms state-of-the-art native 3D generators and every prior 3D-code agent on judged quality, produces the sharpest edges of any method we evaluate, and is the only one whose output is an editable, part-structured program.

cs.CV↗

arXiv2026

Theoretical emission lines and metallicity calibrations of H II regions in ASTRID simulation

We present a theoretical framework to derive redshift-dependent metallicity calibrations for galaxies at $z$=2-7. The ionization parameter ($U$) and gas pressure ($P$) in our approach are not assumed, but are predicted self-consistently. By combining the ASTRID cosmological simulation with stellar population synthesis (SPS) and MAPPINGS V photoionization modeling, we evolve young star clusters under an analytic wind-driven bubble model. This directly couples stellar feedback to the local ISM density, allowing \hii{} region properties to emerge from the underlying physics rather than being treated as free parameters. The emission-line predictions are validated against observed star-formation rate indicators (deviation <0.05 dex) and the \oiii{} luminosity function. We derive calibrations for common optical (e.g. R23, O3N2, N2, O32) and UV (e.g. C3O3, N3O3) diagnostics. We find significant redshift evolution in these relations, driven primarily by changing ionization conditions. A Bayesian analysis quantifies calibration performance under varying signal-to-noise, enabling diagnostic recommendations as a function of redshift and data quality. The R23 calibration performs well at all redshifts with minimal error in our model, while nitrogen- and carbon-based calibrations are highly sensitive to the abundance enrichment process and should be used with caution. These results provide a practical framework for interpreting JWST spectroscopy and tracing chemical evolution from cosmic noon to the epoch of reionization.

astro-ph.GA↗

arXiv2026

C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video

High-quality demonstrations for dexterous robot manipulation are costly and difficult to collect, whereas monocular human videos provide a scalable source of diverse manipulation behaviors. However, transferring such demonstrations to dexterous robots remains challenging: monocular hand-object interaction (HOI) reconstruction often produces temporally unstable contacts and physically implausible interactions, while conventional retargeting methods struggle to preserve task-relevant contacts and local interaction geometry across different hand embodiments. We present C2Dex, a video-to-dexterous-manipulation framework built around a shared interaction representation: stable object-side contacts recovered by aggregating noisy frame-wise observations in the canonical object space. These stable contacts serve a dual role: as trajectory-level constraints that guide reconstruction toward temporally coherent and physically plausible human HOI trajectories, and as explicit transfer targets for the dexterous hand, where Laplacian interaction optimization preserves the local hand-object geometry across embodiments and residual reinforcement learning refines the trajectory in simulation. Experiments on DexYCB and TACO show that C2Dex achieves end-to-end trajectory success rates of 57.78% and 26.67%, respectively, substantially outperforming the strongest baselines (17.78% and 10.00%) under identical evaluation criteria. Real-robot replay experiments further demonstrate physical feasibility across diverse contact-rich manipulation tasks. Project page: https://k-jie.github.io/C2Dex/

cs.RO↗

arXiv2026

VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects

Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time. Long-term identity preservation remains difficult when objects are small, densely distributed, and highly similar in appearance, as in bee swarm scenes. Existing trackers rely on re-identification (re-ID) models trained through single-instance assignment (instance-level querying). At inference, however, MOT requires global assignment between multiple trajectories and detections, corresponding to video-level querying. This training-inference mismatch can cause identity switches among visually similar objects. Existing approaches also often require substantial additional annotations to enhance appearance discrimination. We propose Video-Level Association re-ID (VLA-ReID), which reformulates re-ID as video-level association modeling. It uses aggregated historical trajectory features as queries and all current-frame detections as candidates, enabling direct optimization of their global association at each frame. In addition, Frame-Common Appearance Estimation (FCAE) estimates a common appearance direction from current-frame detections, while Common-Appearance Suppression (CAS) removes the corresponding component along this direction from trajectory and detection features. This amplifies discriminative differences among highly similar objects without additional annotations. Experiments on BEE24 show that VLA-ReID improves HOTA by 1.1, MOTA by 0.3, AssR by 2.6, AssA by 0.7, and IDF1 by 0.8 over state-of-the-art trackers, while reducing identity switches by 28%. These results demonstrate the effectiveness of video-level re-ID modeling for appearance-based association in MOT.

cs.CV↗

arXiv2026

Linear Gyrokinetic Simulations of Micro-tearing Mode: Local versus Global

A systematic comparison of local and global linear gyrokinetic simulations of micro-tearing modes (MTMs) is performed using the GENE code. The analysis spans diverse plasma parameters, including the core regions with normal and weak magnetic shear, as well as the pedestal region with the strong plasma non-uniformity. The global simulations reveal a distinct MTM type characterized by a `parity mixing' mode structure, which can be significantly destabilized by trapped electrons. Moreover, in contrast to electrostatic drift wave instabilities, the current layer width ($ \Delta _c $) is identified as the crucial factor determining the importance of global effects. The MTM in the core region exhibits the slab-like feature with narrow $ \Delta _c $, leading to high consistency between local and global results. However, in the pedestal region, the steep pressure gradient broadens $\Delta_c$, driving quantitative deviations when $\Delta_c$ becomes comparable to the plasma pressure gradient scale length. For high-$n$ MTMs, $ \Delta _c $ can exceed the distance between adjacent mode rational surfaces. The resulted overlapping of current layers enhances the toroidal mode coupling effect, accounting for the substantial discrepancies observed between local and global simulations.

physics.plasm-ph↗

arXiv2026

SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and completeness of the final output. We introduce SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state. First, we formulate open-domain information seeking as relational schema completion with grounded citations, where agents discover entities, populate attributes across linked tables, and anchor each value to source evidence. Then we design Search-Oriented Context Management (SOCM), which externalizes the evolving state into Frontier Task, an Evidence Graph, a Coverage Map, and Failure Memory. Built on SOCM, SearchOS applies a pipeline-parallel scheduling mechanism that overlaps the execution of sub-agents and continuously refills freed slots with tasks targeting unresolved coverage gaps to improve utilization and throughput. To schedule and control the execution of search agents, SearchOS introduces a Search Tool Middleware Harness that intercepts model and tool interactions to record grounded evidence and react to stalls or budget exhaustion, and provides a reusable hierarchical skill system comprising strategy and access skills to augment the agents' search process and avoid repeating failed search patterns across runs. On WideSearch and GISA, SearchOS leads all metrics among the evaluated single- and multi-agent baselines, paving the way toward robust information-seeking collaboration.

cs.AI↗

arXiv2026

Infinite Worlds with Versatile Interactions

We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded interaction horizon while maintaining consistent output quality, benefiting from a carefully crafted causal pretraining paradigm. (2) Through distilling a real-time variant from the base model, our system guarantees rapid response time, sufficient to drive 720p video streams at 60 fps. (3) Compared to the previous version, this update introduces highly diverse interactive elements, comprising a broader spectrum of actions (e.g., attacking, archery, spell-casting, and shooting) alongside a richer variety of text-driven events. (4) We pioneer the integration of an agentic harness within the domain of world modeling, wherein a pilot agent is tasked with planning and executing character behaviors, while a director agent is responsible for synthesizing novel environmental elements as the scene progresses. Additionally, to facilitate a shared experience, we develop an interface that permits multiple players to simultaneously immerse themselves in this vivid world simulator. We pair our primary 14B model with a lightweight 1.3B counterpart, which supports effortless deployment on a single GPU.

cs.CV↗

arXiv2026

Feature Learning for the High Dimensional Stationary Sch\"odinger Equation with Deep Ritz Method

This paper investigates feature learning within the framework of the deep Ritz method for solving the stationary Schr\"odinger equation with Neumann boundary conditions. We first analyze the convergence of Riemannian gradient descent in an agnostic setting, where the hypothesis function is restricted to a single-index model while the PDE solution is arbitrary. We prove that gradient descent reaches an approximate global minimum: after T = O(log(1/\epsilon)) iterations, the loss is within \epsilon of a constant multiple of the optimal loss. We then examine the loss landscape when the source term of the PDE itself follows a single-index model, considering hypothesis functions given by either a single-index model or a two-neuron multi-index model. In the single-index case, we show that the minimum Ritz energy is attained at the feature vector aligned with that of the source term. In the two-neuron case, we study the landscape of regularized Ritz losses and characterize how a second feature emerges, given that the first feature is aligned with the source, as the regularization parameter varies. Finally, numerical experiments are presented to validate the feature emergence theory in the two-neuron setting.

math.OC↗

arXiv2026

On the electromagnetic effects of collisionless trapped-electron modes

We present a linear gyrokinetic theory for the electromagnetic collisionless trapped-electron mode (CTEM). It is found that the weak electromagnetic effects of CTEMs originate from the particle dynamics. Theoretical analysis reveals that the kinetic and fluid-like components of the trapped-electron parallel current cancel at leading order. The ion parallel current is also negligible due to the weak ion transit resonance. Consequently, the perturbed parallel current in the electromagnetic CTEM is dominated by passing electrons. We demonstrate that these characteristics of particle dynamics decouple the CTEM from the shear Alfv\'en wave branch, rendering the electromagnetic effects subdominant. Both eigenmode analyses and gyrokinetic simulations validate these findings.

physics.plasm-ph↗

arXiv2026

Non-uniqueness of weak solutions to cross-diffusion systems with advection

A cross-diffusion system with advection is considered on the whole line, describing the dynamics of two segregating population species. Starting from two initial densities supported on the half-lines $x\leq 0$ and $x\geq 0$, respectively, we construct two distinct solutions of the system: one pair of densities remains confined to their initial supports and stay completely segregated, while the second pair of densities begins to invade the opposite half-line after a finite time (mixing). To the best of our knowledge, this provides one of the first examples of non-uniqueness for this class of equations, together with an explicit demonstration of mixing phenomena. The construction produces infinitely many mixing solutions and applies throughout the full range of pressure exponents. For a certain range of exponents, we additionally obtain quantitative estimates on the mixing process.

math.AP↗

arXiv2026

Intrinsic spectral structure of bipartite nonlocal magic resource

Bipartite nonlocal magic resource (BNMR) quantifies the irreducible nonstabilizerness residing in bipartite entanglement, yet its evaluation is intractable due to the full Hilbert space optimization. Here, we introduce a canonical encoding framework that exactly confines the BNMR of an arbitrary bipartite pure state within a minimal encoding core. This dimension reduction proves that pure-state BNMR is an intrinsic function of the nonzero Schmidt spectrum, extending its invariance from local unitary transformations to local isometries. Leveraging this spectral link, we derive the leading quadratic response of BNMR under spectral perturbations around its zeros. We apply this quadratic response to Haar-random states, deriving and numerically validating the BNMR profile: its distribution is sharply localized at the symmetric bipartition and exponentially suppressed toward asymmetric cuts, in stark contrast to the broadening Page curve of entanglement. Finally, we provide a closed-form expression for the BNMR of Schmidt rank-2 states, uncovering a hierarchy collapse in generalized GHZ states where bipartite and global nonlocal magic resources coincide exactly.

quant-ph↗

arXiv2026

Retrospective Progress-Aware Self-Refinement for LLM Agent Training

LLM-based agents trained with reinforcement learning optimize step-wise action prediction but lack metacognitive awareness of task progress, inducing a gap that hinders long-horizon scaling. A pilot study reveals that online progress prompting hurts performance while retrospective demonstrations help, yet this capability cannot emerge from outcome-reward training alone. We present RePro, Retrospective Progress-Aware Training, a framework that trains agents to self-generate progress signals via a forward-then-reflect rollout paradigm: the agent executes actions online, then retrospectively reassesses its step-wise progress given the completed trajectory and known outcome. RePro initializes with a Retrospection Warmup that teaches reflection format from minimal external demonstrations, then further trains through RePro-PO with a composite reward that produces self-generated signals without continuous external supervision. Experiments on WebShop, ALFWorld, and Sokoban show that RePro enhances the Qwen family's performance, with up to $12\%$ absolute success rate gains.

cs.CL↗

arXiv2026

Communication Policy Evolution for Proactive LLM Agents

LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferences further limit information exchange. To investigate how agents should communicate across modalities, this paper formalizes Communication Policy, establishes textual and UI-based policies, and then evaluates communication policies across diverse environments, personas, and model combinations. Building information asymmetry for proactive agents, we set up two complementary settings, User-Agent and Planner-Executor. Experimental results reveal complementary strengths between interaction channels: text-based interaction often facilitates task performance, while structured UI improves agents' response quality and persona compliance. Motivated by that, a hybrid method combines these advantages. We further propose Communication Policy Evolution (CPE), a self-evolution framework for refining communication policies through rollout and prompt-level evolving. Without model modification, CPE achieves the best task success across multiple settings using prompt refinement alone. Our findings identify communication behavior as a critical yet underexplored design dimension for LLM agents.

cs.AI↗

arXiv2026

P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning

Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3D generation powered by their priors, world knowledge and reasoning. Yet existing benchmarks rarely evaluate 3D modeling through code. Such modeling demands more than runnable code: from a text or visual specification, a model must generate a parametric 3D program that is geometrically precise, semantically aligned and assembly-consistent. We introduce P3D-Bench, a benchmark for parametric 3D generation. Unlike a 3D mesh, a parametric 3D program exposes explicit dimensions, construction operations and part relations, revealing whether a model recovers a design's structure, not just its appearance. Under a unified protocol, P3D-Bench covers three task families (Text-to-3D, Image-to-3D and Assembly-3D) and scores each output for executability, geometric fidelity, topology, text-grounded constraints, multiview semantic alignment and part-level structure. We evaluate frontier MLLMs and text-only LLMs on 400 text cases, 400 image cases and 203 annotated assemblies, with domain-specific models as reference points. Our extensive evaluation yields three findings. First, assemblies are the hardest setting, where models still fail to compose multiple parts into a coherent structure. Second, models can often recover the global shape and semantic identity of the target object, yet fail to reproduce the precise parametric geometry specified by the input. Third, part-level modeling remains weak on assemblies, where models recover neither the geometry of each part nor the right number of parts. These results position P3D-Bench as a benchmark for evaluating precise parametric geometry and part-level structure in parametric 3D generation.

cs.CV↗

arXiv2026

JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions

We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions. Existing AI music systems are mainly designed for short, isolated clips and lack mechanisms to ensure narrative continuity. We present JenBridge, a modular and interpretable framework for adaptive long-form video soundtracking that ensures both high-fidelity audio generation and transition naturalness. The core architecture is a Transformer-based generative model trained with a flow-matching objective, following a two-stage paradigm: pretraining on large-scale text-audio corpora to establish robust musical priors, then adapting to the video domain with dual text-visual conditioning for precise cross-modal alignment. Crucially, to achieve long-form coherence across diverse scene changes, JenBridge incorporates a novel adaptive transition mechanism. This system features a versatile toolkit of transition styles, including a generative transition method, and uniquely employs a Large Language Model (LLM) Agent that acts as a director to select the most appropriate transition for each narrative shift intelligently. To rigorously assess this task, we propose the LVS Benchmark, a new benchmark that includes a curated dataset and novel evaluation metrics focusing on holistic and transition-aware assessment. Extensive experiments on the proposed benchmark demonstrate that JenBridge significantly outperforms existing methods in both objective and subjective metrics, particularly in terms of transition naturalness and overall narrative coherence. JenBridge represents a significant step towards fully automated, professional-quality video soundtracking.

cs.SD↗

arXiv2026

LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction

Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. A defining aspect of LingBot-Map lies in its carefully designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and long-range drift correction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around 20 FPS on 518 x 378 resolution inputs over long sequences exceeding 10,000 frames. Extensive evaluations across a variety of benchmarks demonstrate that our approach achieves superior performance compared to both existing streaming and iterative optimization-based approaches.

cs.CV↗

arXiv2026

UNICA: A Unified Neural Framework for Controllable 3D Avatars

Controllable 3D human avatars have found widespread applications in 3D games, the metaverse, and AR/VR scenarios. The conventional approach to creating such a 3D avatar requires a lengthy, intricate pipeline encompassing appearance modeling, motion planning, rigging, and physical simulation. In this paper, we introduce UNICA (UNIfied neural Controllable Avatar), a skeleton-free generative model that unifies all avatar control components into a single neural framework. Given keyboard inputs akin to video game controls, UNICA generates the next frame of a 3D avatar's geometry through an action-conditioned diffusion model operating on 2D position maps. A point transformer then maps the resulting geometry to 3D Gaussian Splatting for high-fidelity free-view rendering. Our approach naturally captures hair and loose clothing dynamics without manually designed physical simulation, and supports extra-long autoregressive generation. To the best of our knowledge, UNICA is the first model to unify the workflow of "motion planning, rigging, physical simulation, and rendering". Code is released at https://github.com/zjh21/UNICA.

cs.CV↗