arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Elemental Abundances from Off-center Carbon Burning in Accreting CO White Dwarfs: Implications for SN 2021yfj-like events

SN 2021yfj is a recently discovered interacting supernova that exhibits narrow emission lines of Si, S, and Ar, indicating the presence of circumstellar material (CSM) enriched with these elements surrounding the progenitor prior to the explosion. The origin of SN 2021yfj-like events remains uncertain. Recent work proposed that the SN 2021yfj-like events may stem from the double WD merger scenario, in which the merger of a Si-rich WD with a more massive ONe WD tidally strips about 0.3Msun of Si-rich material to form the CSM. If the merger subsequently triggers a supernova explosion, the interaction between the ejecta and the CSM can reproduce the observed light curve of SN 2021yfj. In this scenario, the progenitor system is a CO WD + He star binary, in which the CO WD accretes He-rich material from the He star. The accumulated material can trigger off-center carbon burning, potentially leading to the formation of a Si-rich WD. However, it remains unclear whether such off-center carbon burning can produce Si, S, and Ar in amounts comparable to those inferred for the CSM of SN 2021yfj. In this work, we simulate the evolution of a CO WD accreting He-rich material using time-dependent mass-accretion rates. Our results show that off-center carbon burning in the accreting CO WD can produce significant amounts of Si and S. We further found that the resulting elemental abundances are strongly affected by the initial carbon abundance of the WD. Based on our results, we suggest that the double WD merger scenario may provide a viable progenitor channel for SN 2021yfj-like events.

astro-ph.SR↗

arXiv2026

Mostly Automatic Translation of Language Interpreters from C to Safe Rust

Translating C programs to safe Rust is challenging owing to significant differences in typing constraints, ownership, and borrowing rules. Interpreter programs are particularly important targets for such translation, as they often handle untrusted inputs and suffer from memory-related vulnerabilities. We present Reboot, a mostly-automatic technique that translates real-world interpreter programs from C to safe Rust. Using Reboot, we have translated six interpreters ranging from 6k to 23k lines of C code to safe Rust, with each translation requiring only 1 to 11 brief user interventions. All translations pass 100% of the provided test suites, and achieve 62%--92% pass rates on separately created validation tests that were never exposed to the system. A security case study on mujs shows that memory vulnerabilities such as heap buffer overflows and use-after-free present in C are eliminated in the safe Rust translation. Two ideas underpin Reboot. First, feature reduction decomposes the translation by program features, creating a sequence of milestones where each is a complete, testable program; the translation starts from the simplest version and incrementally restores features, with each milestone validated before proceeding. Second, a multi-agent architecture orchestrates inherently unreliable coding agents through automated validation and feedback, keeping long-running translation workflows on track with minimal human involvement. An ablation study confirms that feature reduction improves translation correctness compared to using multi-agent translation alone, with 6%--20% improvements in pass rates on validation test suites.

cs.PL↗

arXiv2026

Metric results for dyadic approximation on the middle-third Cantor set

Let $C$ be the middle-third Cantor set and $μ$ be the Cantor-Lebesgue measure on $C$. A conjecture of Velani states that $μ(W_2(τ))=0$ if $τ>1$ and $μ(W_2(τ))=1$ if $0<τ\leq 1$, where $W_2(τ)=\left\{x\in[0,1]: \|2^nx\| \frac{1}γ-\frac{1-γ}{3-γ}\,(\approx 1.429)$ and $0<τ<\fracγ{12}\,(\approx 0.052)$, where $γ=\frac{\log2}{\log3}$ is the Hausdorff dimension of $C$. This improves the known results on both the null part ($τ>\frac{1}γ-\frac{0.078(1-γ)}{γ(2-γ)}\approx 1.552$, due to Allen, Baker, Chow, and Yu (2023)) and the full measure part ($0<τ\leq 0.01$, due to Baker (2025)). Our key innovation is to establish the estimate \[\sum_{n=1}^{N}|\widehatμ(h2^n)|^2\ll N^{1-γ}\] and its consequences: \[ \sum_{n=1}^{N}|\widehatμ(h2^n)|\ll N^{1-\fracγ{2}},\quad \sum_{n=1}^{N}n^{- σ}|\widehatμ(h2^n)|\ll_σ N^{1-\fracγ{2}-σ},\] where $0<σ<1-\fracγ{2}$, and all estimates are uniform in $h\in\mathbb{Z}\setminus\{0\}$. For the full measure part, our approach also generalizes to self-similar measures on a class of missing-digit sets.

math.NT↗

arXiv2026

Can dwarf novae produce type Ia supernovae?

Accreting white dwarfs (WDs) are considered one of the most promising progenitor candidates for Type Ia supernovae (SNe Ia). Dwarf novae (DNe), a subclass of cataclysmic variables (CVs), consist of a carbon--oxygen (CO) WD accretor and a low-mass donor star, which may be either a main-sequence (MS) star or a slightly evolved subgiant. Previous studies have suggested that, under the thermal--viscous disk instability mechanism, the time-averaged accretion rate in long-period DNe may approach the regime of stable hydrogen burning, potentially allowing the WD to grow toward the Chandrasekhar mass, (M_{\rm Ch}). However, whether such periodic accretion can sustain stable hydrogen burning and lead to WD mass growth remains uncertain. In this work, we explore whether high accretion rates on short periodic timescales can maintain stable hydrogen burning on the WD surface and drive the WD toward (M_{\rm Ch}). Using Modules for Experiments in Stellar Astrophysics (MESA), we investigate the mass-retention efficiency of WDs undergoing intermittent DN-like accretion and examine its dependence on duty cycle, accretion rate, and initial WD properties. We find that periodic accretion fails to maintain stable hydrogen burning. During quiescent phases, the WD cools and becomes increasingly degenerate, leading to nova outbursts with progressively decreasing mass-retention efficiency and ultimately preventing further WD mass growth. We therefore suggest that DNe are unlikely to be progenitors of SNe Ia.

astro-ph.SR↗

arXiv2026

Recommendation as Generation: Unifying Personalized Video Generation and Recommendation at Industrial Scale

Traditional short-video recommendation systems match user interest to a fixed pool of pre-produced videos, which limits their ability to capture fine-grained and dynamic preferences. We propose Recommendation-as-Generation (RaG), a new paradigm that generates personalized videos on demand from inferred user interest. Our framework unifies generative recommendation and video generation through shared semantic IDs (SIDs), which disentangle video representation into content semantics and creative style semantics, enabling both fine-grained modeling of user interest and controllable generation of interest-aligned videos. We further develop Video Generation Agents (VGAs) that are conditioned on inferred SIDs to drive hierarchical planning and refinement for video creation, including visual composition, audio alignment, and artistic effect enhancement. To optimize the framework, we effectively introduce a synergistic cross-domain reward learning mechanism that jointly enforces interest alignment, user feedback, and video quality assessment. We deploy RaG on an industrial-scale platform with over 400 million daily active users and evaluate it in a revenue-critical advertising scenario. Online A/B tests show up to 1.87% ad revenue improvement compared to a strong production GRM baseline, demonstrating its effectiveness in driving further revenue gains beyond generative recommendation. Our results highlight a closed-loop generative system as a promising paradigm for integrating personalized video generation into recommendation.

cs.IR↗

arXiv2026

DiT-Reward: Generative Representations for Text-to-Image Reward Modeling

Can representations learned for image generation also support the evaluation of generated images? We study text-to-image reward prediction as a downstream task of generative representation learning. To this end, we introduce DiT-Reward, which converts a pretrained text-to-image Diffusion Transformer into a reward model by processing near-clean image latents and aggregating text-conditioned image representations across transformer layers. Under the same training data mixture as HPSv3, DiT-Reward outperforms HPSv3 on all four evaluated preference benchmarks, reaching 85.6% on HPDv2 and 77.6% on HPDv3. When the generative backbone is frozen, a lightweight learned head can still extract meaningful preference predictions from its representations. Probing across depth further reveals that downstream reward performance is strongest in the middle-to-late layers and benefits from combining representations across different stages. We also observe consistent positive scaling with generative backbone capacity. Finally, when used to optimize Stable Diffusion 3.5 Large with Flow-GRPO, DiT-Reward outperforms HPSv3 along the matched training trajectory, with particularly clear gains in realism. Direct latent scoring also achieves a 1.65x inference speedup over HPSv3 with comparable peak memory. These results show that pretrained generative DiTs provide transferable representations for reward modeling and policy optimization.

cs.LG↗

arXiv2026

Nonholonomic directional pursuit and evasion: Global feedbacks

In a recent paper by the second coauthor, directional pursuit-evasion for strictly forward-moving nonholonomic vehicles was solved "half-globally", namely under favorable initial line-of-sight conditions. In this paper, we develop feedback designs that achieve the directional pursuit and evasion objectives from arbitrary initial relative configurations. We achieve globality with completely different approaches to both the design and the analysis. Our designs are less aggressive in both the forward-speed and steering laws, allowing transient overshoot of the pursuer-evader range during global reorientation. The only price that we pay for globality is that our feedback laws require a priori knowledge of the opponent's maximal turning rate, whereas in the half-global work, no known bound of the opponent's turning rate was assumed. For the pursuit problem, the feedback law guarantees finite-time capture with prescribed directional alignment. For the evasion problem, the feedback law guarantees capture avoidance with a prescribed safety margin and achieves spinaway under a decay condition on the pursuer's turning rate. We illustrate the global capture and spinaway with simulations. The analysis is based on an integral input-to-state stability type mechanism induced by an endogenous time dilation, together with finite-time coextinction and safety-margin persistence lemmas for singularly coupled scalar inequalities.

eess.SY↗

arXiv2026

Data dependent Shepard approximation through and adaptive modification of the shape parameter

In this article, we introduce a novel data-dependent Shepard interpolation method inspired by the adaptive strategies proposed in [2]. In this case, as Shepard interpolation does not produce oscillations, our approach has the core objective of reducing the smearing near jump discontinuities in the data in one and two dimensions. While the original work in [2] focuses in on Radial Basis Function (RBF) interpolation, we extend these ideas to the Shepard framework by incorporating a data-dependent adaptation mechanism. Specifically, we modify the classical Shepard interpolation by adaptively adjusting the influence weights based on local smoothness indicators that modify the shape parameter. These indicators, similar to those used in [2], are designed to detect discontinuities: for grid-based data, we use squared undivided second-order differences, and for scattered data, we employ squared least-squares approximations of the Laplacian scaled by the square of the mean local separation of stencil points. The resulting data-dependent weighting scheme forces the kernels close to a discontinuity to behave like a local delta function, effectively reducing the smearing of the discontinuities introduced by the classical Shepard approach. We establish the theoretical foundation of the method, including the properties of the new interpolation and we theoretically prove that the reduction of the smearing of discontinuities is possible. Numerical experiments in one and two dimensions confirm that the proposed data-dependent Shepard interpolation significantly reduces the smearing of jump discontinuities while maintaining high accuracy in smooth regions.

math.NA↗

arXiv2026

X+Slides: Benchmarking Audience-Conditioned Slide Generation

Automatically generating slide decks from source documents is an important application of large language models (LLMs). Existing benchmarks primarily assess slide completeness and technical depth, while overlooking the target audience as a critical real-world factor. For instance, specialists demand rigorous proofs, whereas decision-makers prioritize actionable conclusions. To bridge this gap, we introduce X+Slides, a benchmark specifically designed for audience-conditioned slide generation. Built on a diverse corpus spanning 113 topics and seven presentation scenes, X+Slides employs a dynamic evaluation framework constructed from 8,133 deduplicated, source-grounded probes. By assigning audience-specific utility weights to the same source-grounded probes, X+Slides reports four complementary metrics: Audience Coverage measures how much audience-essential information is conveyed, Domain-wise Coverage shows which information types are covered, Efficiency measures delivered utility per unit of attention cost, and Correctness verifies whether slide claims are supported by the source. Experiments on DeepPresenter, SlideTailor, and NotebookLM show that current systems can recover a substantial but still incomplete part of audience-essential information: at $τ_A=0.7$, DeepPresenter reaches a best Audience Coverage of 0.714, SlideTailor reaches 0.594, and the NotebookLM ablation reaches 0.853 while showing clear grounding differences. These results indicate that visual quality and broad topic coverage should not be treated as evidence support without source-grounded evaluation.

cs.AI↗

arXiv2026

Knowledge Reutilization in Meta-Reinforcement Learning

Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control. This coupling can obscure non-parametric task semantics, reduce sample efficiency, and limit cross-agent reuse. We propose a meta-knowledge reutilization framework that learns task-level knowledge on a dynamics-simplified agent and transfers it to heterogeneous agents. The framework uses a Bayesian non-parametric prior to organize latent task modes and a high-level policy to generate task-level magnitude guidance. To bridge reusable task knowledge with different embodiments, we introduce a semantic-magnitude interface and a lightweight temporal adaptor, which convert frozen meta-knowledge into temporally aligned subgoals for embodiment-specific low-level controllers. Experiments on multiple locomotion agents show that our framework reduces final-step tracking error by 94.75% -- 99.79% compared with recent state-of-the-art baselines and achieves comparable deployment performance with about 23.8% of their interaction data.

cs.AI↗

arXiv2026

NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results

This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field.

cs.CV↗

arXiv2026

PathRouter: Aligning Rewards with Retrieval Quality in Agentic Graph Retrieval-Augmented Generation

Agentic GraphRAG trains language-model agents to iteratively retrieve and reason over graph-structured evidence, enabling more accurate and context-aware decision-making by efficiently navigating complex information networks. However, outcome-only reinforcement learning suffers from \textit{\textbf{answer-path reward aliasing}}, where correct answers may come from shortcuts rather than useful evidence paths. It also exhibits \textit{\textbf{search-update ambiguity}}, as scalar trajectory-level feedback does not indicate which retrieval actions to adjust. To mitigate these shortcomings, we present PathRouter, a path-aware training framework for agentic GraphRAG. PathRouter jointly evaluates each trajectory along answer correctness and evidence-path overlap, yielding four trajectory categories with differentiated GRPO advantage scaling that suppresses shortcut reinforcement while preserving evidence-seeking behavior. For evidence-poor trajectories, a frozen gold-evidence teacher provides token-level KL guidance on reasoning and search-query tokens, excluding answer tokens to avoid direct response imitation. Experiments on six QA benchmarks across three model sizes show that PathRouter consistently improves answer F1 and evidence-path overlap, achieving average F1 gains of 3.1 on 3B and 4.9 on 7B models compared to a strong baseline.

cs.CL↗

arXiv2026

Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models

Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding. As AI-generated images become realistic, semantic-level inconsistencies alone are often insufficient for reliable detection. This motivates a critical question: whether MLLMs can achieve full-spectrum forensic signal perception, i.e., capturing low-level generator artifacts without sacrificing pre-trained semantic knowledge. We further perform a layer-wise analysis of forensic signal perception in MLLMs, showing that semantic information is primarily formed in the early-to-middle layers, whereas direct fine-tuning for artifact learning disrupts these semantic representations. Based on this insight, we propose Deep Visual Residual MLLM (Deep-VRM) to preserve early semantic processing while injecting artifact-specific visual signals as a residual path into an intermediate layer, where they are fused with semantic token representations and propagated through subsequent trainable layers. This enables later layers to jointly model semantic reasoning and signal-level forensic cues, and surprisingly, the model learns to adaptively leverage different levels of forensic signals depending on the input, achieving robust and generalizable detection performance. Extensive experiments show that our method achieves state-of-the-art across most benchmarks. The code and data are available at https://github.com/KQL11/Deep-VRM.

cs.CV↗

arXiv2026

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predominantly rely on passive evaluation (e.g., static VQA) or simulator-specific pipelines, failing to assess general interactive spatial understanding. We introduce SpatialWorld, a unified benchmark designed specifically for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks. Integrating eight heterogeneous simulation backends under a shared, simulator-agnostic protocol, SpatialWorld features 760 human-annotated tasks across diverse domains (e.g., household routines, travel, social collaboration). Agents must solve tasks under vision-only partial observability, actively gathering egocentric visual evidence and expressing decisions via a unified, text-based action interface native to MLLMs. For reliable evaluation, each task includes a human-validated initial state, a reference trajectory, and a terminal-state verifier. Evaluating 15 advanced agents reveals that robust spatial task solving remains challenging: the strongest model, GPT-5, achieves an average task success rate (TSR) of only 17.4%, while the leading open-source model, Qwen-3.5, reaches 14.1%. Further analysis exposes a clear mismatch between task success and execution efficiency, alongside substantial domain-specific performance variations. These bottlenecks in active exploration and long-horizon planning position SpatialWorld as a rigorous testbed for future spatial agents.

cs.AI↗

arXiv2026

Bandedge-state-limited single-photon emission from volumetric quantum design of 2D colloidal quantum wells

Present-day solution-processable single-photon sources are dominated by three-dimensionally confined colloidal quantum dot emitters, yet their particle-to-particle variation in single-exciton properties limits reproducibility and scalability. Here, to avoid such heterogeneity, we demonstrate reliable room-temperature single-photon emission from atomically flat two-dimensional (2D) colloidal quantum wells (CQWs) with inherently uniform one-dimensional quantum confinement, despite their long-standing limitations of efficient multiexciton emission and pronounced exciton-surface susceptibility. We resolve these challenges through volumetric quantum design (VQD) of CQWs, yielding a highly localized, single bandedge state. This design laterally confines the bandedge excitonic domain within the exciton coherent area and vertically decouples it from surface states via a thick, strain-relieved quantum-barrier shell that preserves strong confinement, overcoming the daunting thickness-confinement trade-off in 2D CQWs. Statistical single-particle spectroscopy reveals that VQD-CQWs deliver near-blinking-free (on-time >99.5%) and fluence-insensitive antibunching (g(2)(0): 0.041), protected by a bandedge-state-filling bottleneck, together with linear polarization of up to 73% under cavity-free conditions, originating from synergistic transition-dipole and electric-field anisotropies. These advances establish 2D CQWs as a viable, homogenous and scalable platform for quantum technologies.

physics.app-ph↗

arXiv2026

EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain

It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event extraction in scientific domain is still insufficiently supported by comprehensive datasets and tailored methods. Compared with other domains, scientific domain has two characteristics: (1) denser nuggets and events, and (2) more complex information forms. To solve the above problem, considering these two characteristics, we first construct SciEvents, a large-scale multi-event document-level dataset with a schema tailored for scientific domain. It consists of 2,508 documents and 24,381 events under multi-stage manual annotation and quality control. Then, we propose EXCEEDS, an end-to-end scientific event extraction framework by encoding dense nuggets into a grid matrix and simplifying complex event extraction as a nugget-based grid modeling task. Experiments on SciEvents demonstrate state-of-the-art performances of EXCEEDS. Both the SciEvents dataset and the EXCEEDS framework are released publicly to facilitate future research.

cs.CL↗

arXiv2026

Two-bump axisymmetric solutions of the Nirenberg problem

In this paper, we revisit the axisymmetric Nirenberg problem for certain prescribed scalar curvature functions whose critical points include the north and south poles and whose order of flatness at both points is $n-2$. It has been known for some time that the solution set is compact when the parameter points $(K_1,K_2,a_1,a_2)\in\mathbb{R}^4$, determined by the first two leading expansions of the prescribed scalar curvature functions at the poles, stay away from a critical three-dimensional hypersurface $Σ^0$, and that loss of compactness occurs near $Σ^0$. We construct, in dimensions $n \geq 4$, two-bump axisymmetric solutions for parameter points sufficiently close to $Σ^0$ from one side, where the relevant side is determined by the next-order flatness terms. This gives a one-sided refinement of the previously known blow-up phenomenon near the critical hypersurface $Σ^0$: two-bump blow-up occurs from this side, whereas the solution set remains compact on the opposite side.

math.AP↗

arXiv2026

Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting

Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates. The industry urgently needs accurate wind power foundation models to shorten commissioning and accelerate grid connection. This is because site-specific time series models (TSMs) are not well suited to data-scarce scenarios and generalize poorly, while generic large time series models (LTSMs) are mostly limited to univariate inputs and cannot fully exploit static site attributes or the dependencies between power and meteorological covariates, leading to insufficient accuracy. To fill this gap, we propose \textbf{Tyan-WP}, the first wind power foundation model for ultra-short-term probabilistic forecasting. Pretrained on a large-scale wind power dataset covering more than 126,000 U.S. sites over seven years, Tyan-WP further improves zero-shot forecasting through two domain-specific module designs: static site embedding using coordinate, terrain, and ecoregion metadata, and a power-aware meteorological fusion (PAMF) module that models interactions between historical power and meteorological covariates. Under a unified evaluation protocol, Tyan-WP surpasses eight site-specific supervised TSMs on 10 in-domain sites and outperforms eleven generic LTSMs on 127 in-domain sites, reducing MAE by 19.9%, RMSE by 16.6%, CRPS by 22.2%, and AQL by 21.7%, while raising R^2 by 16.7%. It further demonstrates strong cross-geography generalization on six real U.K. sites. These results show that the wind power foundation model can achieve accurate zero-shot forecasting without target-site training, providing a practical pathway for rapid turbine onboarding and probabilistic risk management at new wind farms.

cs.LG↗