arXiv subjects

Yibo Wang

Publications and source records attributed to Yibo Wang.

At least 19 recordsLinked to original sources

arXiv2026

High-order compact ALE gas-kinetic scheme on three-dimensional unstructured meshes

In this paper, a compact high-order gas-kinetic scheme (HGKS) is developed for compressible arbitrary Lagrangian-Eulerian (ALE) computations on three-dimensional unstructured meshes. To achieve high-order spatial accuracy, a large number of matrix inversions are required for spatial reconstruction. In ALE computations, the mesh moves and the geometric information must be recalculated at each time step, which reduces computational efficiency. To improve efficiency, a memory-reduction reconstruction strategy is employed to construct a quadratic polynomial on unstructured meshes. By differentiating the candidate polynomial, the quadratic terms are obtained by the least-squares method using the average gradient values of the cell itself and its neighboring cells. The linear terms of the polynomial are also determined by the least-squares method. On three-dimensional unstructured meshes, a $3\times3$ coefficient matrix must be stored, and four least-squares procedures are required to construct a quadratic polynomial. To suppress numerical oscillations in flows with discontinuities, generalized essentially non-oscillatory (GENO) reconstruction and a gradient compression factor are adopted. To accelerate the computation, a multi-GPU accelerated code is implemented using Compute Unified Device Architecture (CUDA) and Message Passing Interface (MPI). The current scheme is fully explicit, and the acceleration framework can be developed based on cell-level parallelism. Numerical examples are presented to evaluate the accuracy and robustness of the scheme, including flows with strong discontinuities, pitching wings, and helicopter rotors. Numerical results confirm the excellent performance of the compact ALE scheme on unstructured meshes.

math-ph↗

arXiv2026

EviCast: Forecasting Retrieval Gains Through Shared Response Calibration

Retrieval-augmented generation (RAG) extends large language models with external evidence, yet relevant evidence does not ensure that a generator can use it. Indiscriminate retrieval can degrade task performance while adding retrieval and long-context inference costs. Existing methods neither determine before large-scale execution whether a generator, task distribution, and retriever configuration is worth deploying nor diagnose multiple candidate retrievers under a shared budget. We introduce EviCast, a framework that forecasts retrieval value through interventional evidence-state decomposition. EviCast measures a generator's response to controlled evidence states once and combines this shared profile with the offline evidence-state distribution of each candidate retriever, yielding the end-to-end net utility and its 95% lower confidence bound without executing every RAG configuration. A state-refinement analysis characterizes when its binary and topology-aware instantiations are exact and when shared calibration is statistically more efficient than per-retriever calibration. In frozen evaluations, EviCast correctly decides all 14 HotpotQA retrieval configurations with a forecast error of 2.75 F1 points while reducing diagnostic generation calls by 80%, and it attains the lowest forecast error among seven predictors in the low-budget regime. Its topology-aware instantiation, EviCast-T, captures the complementarity between terminal and bridge evidence, correctly deciding all 16 configurations on MuSiQue and HotpotQA and 23 of 24 after prospective transfer to a third dataset. These results show that retrieval deployment depends on both evidence availability and the generator's ability to convert evidence structure into stable net utility. Code is available at https://github.com/YiboWang2002/EviCast.

cs.CL↗

arXiv2026

Scaling Verifiable Environments for Long-horizon Work Agents

Work agents operate over digital artifacts to execute professional knowledge-intensive work, requiring training environments that support long-horizon interaction and trustworthy verification. However, hand-crafted environments incur prohibitive engineering overhead that prevents environment scaling, whereas synthesis methods sacrifice workspace complexity, realism, or grounded verifiability. To bridge this gap, we introduce WorkForge, a scalable synthesis framework for constructing verifiable work-agent environments from real-world resources. Starting from expert workflows, WorkForge first identifies the resources, decisions, and deliverables required by each workflow. It then retrieves relevant real-world files and organizes them into a workspace. WorkForge inspects the workspace to extract concrete, checkable facts about its content. These factual anchors fix which task types the workspace can support and how their outcomes can be verified. Therefore, WorkForge derives each task's instructions, solution plan, and complementary programmatic and semantic verifiers directly from these factual anchors, keeping verification traceable to observable workspace evidence. Furthermore, we construct 16.7K verifiable environments across 40 professional domains, with workspaces collectively covering 60 file types. Post-training Qwen3.5-35B-A3B-Base improves GDPVal from 45.5 to 73.6 and APEX Score from 5.0 to 21.3, while enabling Qwen3.5-27B to achieve highly competitive performance and outperform strong competitors. Our analyses confirm the efficacy of the proposed method and reveal consistent scaling behaviors across both data volume and interaction horizons.

cs.CL↗

arXiv2026

Freeform Programmable Liquid Crystal Elastomers via Monolithic Integration of Co-Optimization and 4D-Printing

Liquid crystal elastomers (LCEs) are active materials capable of large, reversible thermally induced actuation, offering significant potential for applications such as artificial muscles and soft robotics. However, their broader deployment is hindered by the lack of a unified design-to-fabrication framework, the strong coupling among geometry, director fields, and manufacturing constraints, and the difficulty of reliably translating complex designs into physically realizable architectures. Here, we present an autonomous and monolithic integration of co-optimization and 4D printing that enables the end-to-end design-to-realization of versatile programmable LCE architectures with superior precision, complexity, and versatility. The proposed framework establishes a unified design strategy that co-optimizes the freeform multilayer geometry and fully continuous layer-specific director distributions, and subsequently translates the optimized fields into fabrication-ready DIW toolpaths through an automated manufacturing-aware pipeline. To bridge computation and realization, we develop an optimization-centered 4D printing approach with automated, fabrication-aware toolpath generation tailored for direct ink writing of LCEs. With the proposed design-to-fabrication paradigm, we experimentally realize architectures that exhibit complex, non-intuitive thermo-mechanical behaviors. Experiments achieve highly challenging prescribed functionalities beyond the reach of intuition-based approaches and show excellent agreement with numerical simulations across all samples. This work establishes a generalizable inverse design and physical realization of freeform programmable LCE systems, enabling function-oriented and optimization-driven intelligent LCE applications for next-generation soft robotic and responsive material engineering.

cs.CE↗

arXiv2026

EP J062132.0+623837: A Multi-wavelength Study of a Changing-look Active Galactic Nucleus Discovered by Einstein Probe

EP J062132.0+623837 is a new X-ray source identified in 14-day binned survey data from the Wide-field X-ray Telescope aboard the Einstein Probe (EP). Subsequent X-ray observations with the Follow-up X-ray Telescope (FXT) on board EP localized the source to the central region of the galaxy 2MASX J06213207+6238390 at $z=0.084$. Here, we present a comprehensive analysis of the multi-wavelength data for this object. During the FXT and Swift/XRT follow-up, the source remained X-ray active, with observed luminosities of $(3-9)\times10^{43}$ erg s$^{-1}$, and its X-ray spectra were well described by power-law models with photon indices of $Γ\sim1.5$. The optical spectrum of the host galaxy clearly reveals a broad-line active galactic nucleus (AGN) exhibiting significantly enhanced broad Balmer emission lines compared to an archival spectrum obtained four years earlier, thereby marking a changing-look transition from Type 1.8 to Type 1.5. Furthermore, long-term optical and mid-infrared light curves indicate that the nucleus underwent dramatic brightening around 2010 and has since shown substantial variability. Given the spatial coincidence and multi-wavelength behavior, we identify EP J062132.0+623837 as a changing-look AGN, initially detected by EP and subsequently confirmed to exhibit large-amplitude variability across multiple bands. This case study demonstrates the great potential of wide-field time-domain X-ray surveys for discovering changing-look AGNs, paving a new avenue for their systematic identification as a powerful complement to ongoing optical surveys.

astro-ph.GA↗

arXiv2026

Can You Trust the Vectors in Your Vector Database? Black-Hole Attack from Embedding Space Defects

Vector databases serve as the retrieval backbone of modern AI applications, yet their security remains largely unexplored. We propose the Black-Hole Attack, a poisoning attack that injects a small number of malicious vectors near the geometric center of the stored vectors. These injected vectors attract queries like a black hole and frequently appear in the top-k retrieval results for most queries. This attack is enabled by a phenomenon we term centrality-driven hubness: in high-dimensional embedding spaces, vectors near the centroid become nearest neighbors of a disproportionately large number of other vectors, while this centroid region is nearly empty in practice. The attack shows that vectors in a vector database cannot be blindly trusted: geometric defects in high-dimensional embeddings make retrieval inherently vulnerable. Based on this insight, we propose four attack paths tailored to different attacker capabilities. Our experiments show that up to 94.4% of queries are successfully attacked. Additionally, we study two directions of defense: hubness mitigation and detection-based filtering. Hubness mitigation either significantly reduces retrieval accuracy or provides only limited protection, while the detection-based defense is effective against some attack paths but fails against others. A robust and adaptive defense thus remains an open problem, and our findings indicate that vector databases require more careful treatment of security.

cs.CR↗

arXiv2026

An Analytic 0.48-Approximation for Non-Monotone k-Submodular Maximization under a Matroid Constraint

We give a randomized polynomial-time $0.48$-approximation for maximizing a nonnegative non-monotone $k$-submodular function under a matroid constraint, for every $k\ge2$. The algorithm combines a top-two label rule with a nested-chain potential and maximum-weight matroid bases. Its analysis reduces the approximation guarantee to a one-dimensional time allocation: a uniform local lower bound leads to a linear differential equation, concavity closes the remaining interval, and two elementary budgets complete the proof. A strict saving in the time budget absorbs sampling and discretization errors, yielding the exact $0.48$ guarantee in the value-oracle model.

cs.DS↗

arXiv2026

Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents

Interactive text-to-SQL agents solve database tasks through multi-turn interactions involving schema exploration, query execution, feedback interpretation, and decision revision. Long-term memory helps agents reuse past experiences, but existing retrieval methods remain limited. Static methods use fixed similarity heuristics, whereas dynamic methods often learn from sparse terminal outcomes and retrieve at a single decision horizon. This is insufficient when memory usefulness changes across interaction stages, since memories useful for initial planning may differ from those needed for local, state-conditioned execution. We propose MERIT, a dynamic multi-horizon memory retrieval framework. MERIT maintains episode-level memory for global strategic guidance and turn-level memory for local decision support. Both levels use learned retrieval policies optimized with reinforcement learning. To train turn-level retrieval despite limited intermediate supervision, MERIT uses a lightweight Process Reward Model to provide dense proxy rewards for local memory selection. On BIRD-Interact, MERIT achieves higher success rates and fewer interaction turns than no-memory, static-retrieval, and dynamic-retrieval baselines. Transfer results on Spider2-Snow further show that the learned policies transfer without degradation, staying above all memory baselines without benchmark-specific tuning. These results suggest that multi-horizon retrieval improves experience reuse in interactive text-to-SQL agents. Our code is available at https://github.com/YiboWANG214/MERIT.

cs.CL↗

arXiv2026

DualAffect: Dual-Path Paralinguistic Integration for Generative Audiovisual Emotion Understanding

Understanding human emotion is essential for multimodal systems that interpret both the content of an interaction and the speaker's expressive state. However, connecting paralinguistic cues to generated answers remains challenging, particularly when vocal expression diverges from visual appearance. We propose DualAffect, a dual-path architecture that integrates visually conditioned acoustic features and temporally organized acoustic tokens into a shared language decoder. The two pathways reuse the same acoustic encoding. The fusion pathway associates sound with visual content through visual queries, while the direct pathway compresses the valid acoustic sequence into ordered tokens through an independent projector. This design gives the decoder access to both joint audiovisual context and acoustic evidence organized along its own timeline. Mixed-task adaptation with task-dependent output preservation supports emotion recognition and general audiovisual question answering within the same model. DualAffect achieves an average metric of 67.89 on CA-MER and accuracies of 62.88% on the EmotionTalk test set and 49.87% on Daily-Omni. Pathway interventions show that the trained decoder uses direct acoustic evidence alongside existing fusion. With model parameters and fusion inputs fixed, zeroing the direct tokens reduces EmotionTalk validation accuracy by 9.75 percentage points and the CA-MER average metric by 2.66 points. The largest CA-MER subset effect occurs when the overall emotion agrees with audio but differs from vision. These findings identify acoustic representation organization as a practical design axis for connecting paralinguistic perception to unified audiovisual generation. Code is available at https://github.com/YiboWang2002/DualAffect.

cs.MM↗

arXiv2026

LifeSearch: Benchmarking Deep Research Agents for Real-World Life and Productivity

Deep research (DR) agents address open-ended information needs through multi-step reasoning, long-horizon web search, and evidence synthesis. However, existing benchmarks primarily focus on academic research or synthetic tasks, leaving DR agents' capabilities in real-world life and professional productivity settings insufficiently evaluated. To bridge this gap, we introduce LifeSearch, a challenging benchmark comprising 203 Chinese and English tasks derived from real-user query seeds across 7 concrete life and productivity domains. With more than 4,500 hours of human annotation, we construct over 4,000 expert-written, fine-grained rubrics, covering both report-content criteria for coverage and accuracy and concrete criteria specifying values, units, dates, and URLs. We conduct extensive experiments on 12 advanced DR agents under a unified evaluation harness, systematically analyzing their performance patterns and failure modes. We find that even the best-performing frontier agent, GPT-5.6 Sol, achieves a score of only 63.4%, leaving substantial room for improvement on realistic life and professional DR tasks. We select the grading configuration by comparing candidate LLM judges against human rubric labels, and a human ranking study shows strong alignment between aggregate benchmark scores and human judgments (Spearman's $ρ= 0.89$, Kendall's $τ= 0.81$). LifeSearch provides a rigorous evaluation framework and foundation for advancing DR agents toward real-world life and productivity.

cs.AI↗

arXiv2026

Efficient Preconditioned Optimization Methods with Frequent Directions

Adaptive optimization methods have received considerable attention in recent years due to their strong empirical performance. However, they generally require maintaining a preconditioning matrix, which incurs prohibitive computational overhead in large-scale applications. To address these limitations, we investigate the acceleration of preconditioned optimization methods through frequent directions (FD), a widely used matrix sketching technique. We first propose FTSL by integrating FD with adaptive subgradient methods under the primal-dual framework and adding back the cumulative information discarded by FD. FTSL obtains a tighter regret bound than existing state-of-the-art results. We then apply a doubling trick that further reduces the time complexity of FTSL, while maintaining the same regret guarantee. We also consider optimization over matrix-valued decision variables and exploit their structural properties to construct sketched preconditioners. We develop FTSL-Shampoo by adapting FD to Shampoo and give a novel analysis under the primal-dual framework. To further mitigate its computational complexity, we design one-sided variants of FTSL-Shampoo under both mirror descent and primal-dual frameworks. Our algorithms not only reduce the computational cost of FTSL-Shampoo, but also achieve tighter regret bounds. We extend our methods to offline stochastic convex optimization and exploit smoothness to achieve faster convergence rates. Finally, we introduce practical variants of our algorithms and empirically demonstrate their efficiency and effectiveness across a broad range of tasks.

cs.LG↗

arXiv2026

Variance-Optimal State Resampling for Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards spends much of its rollout budget on states that yield no learning signal. We recast this as a question about the sampling distribution itself: under a fixed rollout budget, which distribution over states minimizes the variance of the policy-gradient estimator? The answer admits a closed-form Neyman allocation, $μ_{\mathrm{var}}^π(s) \propto d^π(s)\,σ_π(s)$, which connects naturally to GRPO's group-relative advantage and induces an emergent reverse curriculum as the policy improves, with no externally imposed schedule. Building on this principle, we propose VSR (Variance-driven State Resampling), a plug-in modification to GRPO using a prefix replay buffer and a zero-cost variance proxy. On mathematical reasoning benchmarks and long-horizon agentic tasks (Sokoban, BabyAI), VSR outperforms GRPO and GiGPO on most benchmarks with fewer generated tokens per update.

cs.LG↗

arXiv2026

Adaptive Variance Reduction without Knowing Oracle Smoothness

Variance reduction improves the convergence rate of stochastic non-convex optimization under the average smoothness condition. Existing adaptive variance reduction methods are especially designed for average smoothness and do not adapt to standard smoothness. In this paper, we study how one algorithm can adapt to these two settings and the noiseless setting without knowing the smoothness constants or the variance bound. Given the gradient budget $T$, we develop a two-phase method. The first phase compares two independent gradients and switches to normalized momentum when their difference exceeds a threshold. This phase provides both a candidate with standard smooth and noiseless guarantees and also ensures a bounded expected function gap for the second variance reduction phase. The resulting rates are $O(T^{-1/3})$ under average smoothness, $O(T^{-1/4})$ under standard smoothness, and $O(T^{-1/2})$ without noise. When $T$ is unknown, we propose a method with two paths that attain the corresponding rates with logarithmic factors in the stochastic settings. Here, we use a variance reduction path to deal with average smoothness and employ the AdaGrad-Norm for standard smoothness and noiseless setting. Neither method restarts from the initial point or requires a growing number of oracle calls in each round.

math.OC↗

arXiv2026

A Star's Death by a Thousand Cuts: The Runaway Periodic Eruptions of AT2023uqm

Stars on bound orbits around a supermassive black hole may undergo repeated partial tidal disruption events (rpTDEs), producing periodic flares. While several candidates have been suggested, definitive confirmation of these events remains elusive. We report the discovery of AT2023uqm, a nuclear transient that has exhibited at least five periodic optical flares, making it only the second confirmed case of periodicity after ASASSN-14ko. Uniquely, the flares from AT2023uqm show a nearly exponential increase in energy--a "runaway" phenomenon signaling the star's progressive destruction. This behavior is consistent with rpTDEs of low-mass, main-sequence stars or evolved giant stars. Multiwavelength observations and spectroscopic analysis of the two most recent flares reinforce its interpretation as an rpTDE. Intriguingly, each flare displays a similar double-peaked structure, potentially originating from a double-peaked mass fallback rate or two discrete collisions per orbit. The extreme ratio of peak separation to orbital period draws attention to the possibility of a giant star being disrupted, which could be distinguished from a low-mass main-sequence star by its future mass-loss evolution. Our analysis demonstrates the power of rpTDEs to probe the properties of disrupted stars and the physical processes of tidal disruption, though it is currently limited by our knowledge of these events. AT2023uqm emerges as the most compelling rpTDE thus far, serving as a crucial framework for modeling and understanding these phenomena.

astro-ph.HE↗

arXiv2026

Rethinking Semantic ID Construction for Generative Recommendation: SimHash with Parallel Decoding and Semantic Alignment

Semantic ID-based generative recommendation represents each item as a sequence of discrete tokens, enabling structured modeling of item semantics. A critical challenge is constructing semantic IDs that are both semantically expressive and computationally efficient. While recent approaches favor complex learned quantization, simple hashing-based methods such as SimHash are widely regarded as fundamentally inferior. In this work, we challenge this consensus by showing that the apparent performance gap does not stem from inherent limitations of hashing, but rather from a structural mismatch with autoregressive decoding, coupled with the inevitable information loss during rigid discretization. Based on this insight, we propose FLASH, a two-stage framework that revitalizes training-free SimHash tokenization through parallel decoding and explicit semantic alignment. Despite its simplicity, FLASH achieves state-of-the-art performance across multiple datasets without requiring any tokenizer training, while exhibiting stronger generalization in cold-start scenarios. Notably, we demonstrate that semantic alignment acts as a universally effective mechanism across diverse paradigms. Our findings suggest that, with compatible decoding and semantic grounding, simple and efficient tokenizers can achieve performance comparable to complex learned counterparts in generative recommendation. Our code is available at https://github.com/KevinC2015/Flash.

cs.IR↗

arXiv2026

Suppressing Pressure, Amplifying Evidence: Self-Guided Attention Steering to Mitigate Sycophancy and Stubbornness

Reliable language models should resist unsupported user pressure while effectively using objective contextual information. However, models may exhibit sycophancy by yielding to unsupported user pressure or contextual stubbornness by failing to update their answers when relevant contextual information warrants revision. Evaluating interventions for these failures separately can obscure whether mitigating one failure exacerbates the other. To assess this trade-off, we introduce CoPE-Bench with six conditions per question: a neutral baseline, correct or incorrect user pressure, contextual information consistent with or conflicting with the neutral answer, and a joint condition combining incorrect claims with conflicting contextual information. To regulate the influence of user pressure and contextual information, we propose SPAE (Suppressing Pressure, Amplifying Evidence), a training-free framework that uses the model's own judgments to identify relevant tokens, suppressing user pressure and amplifying contextual information through token-level attention steering. On average across five backbones, SPAE reduces pressure following by 18.8 percentage points and increases joint-condition updating by 5.5 percentage points relative to the strongest baseline in the main comparison. In two-turn dialogue, it improves joint-condition updating by an average of 13.2 percentage points over the strongest prompting baseline. The source data and codes can be found at https://github.com/03Grant/sycophancy-and-stubbornness.

cs.AI↗

arXiv2026

From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences

In non-stationary online learning, dynamic regret has attracted increasing attention as a measure of how well an online learner performs against a time-varying comparator sequence. Despite considerable advances, attaining optimal bounds for strongly convex and exp-concave losses often involves intricate analysis. In this paper, we present a \textit{simple} framework that reduces dynamic regret minimization to switching regret minimization. As a result, we can derive dynamic regret bounds by using off-the-shelf algorithms with switching regret guarantees. The key idea of our reduction is to construct, for \textit{any} comparator sequence, an auxiliary random sequence that is unbiased at each round, with the controlled variance and a manageable number of switches. Combining this construction with suitable surrogate losses, we can decompose dynamic regret into the expected switching regret against the random sequence and its controlled variance. Theoretically, for strongly convex and exp-concave losses, we establish the $\widetilde{O}(T^{1/3}P_T^{2/3})$ dynamic regret bounds, where $T$ denotes the time horizon and $P_T$ denotes the path-length of the comparator sequence. Moreover, for general convex losses, the same reduction also recovers the $O(\sqrt{T(1+P_T)})$ dynamic regret bound. Notably, all our findings match the minimax optimal results for these three types of losses, highlighting the versatility of our proposed framework.

cs.LG↗

arXiv2026

Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation

Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent terminology and style. Existing single-LLM methods are largely sentence-level, and multi-agent systems often use static workflows that do not adapt to scene complexity or production context. We propose SMART, a Self-evolving Multi-Agent system for long-foRm subtitle Translation. During test-time training, SMART builds persistent series-level memory and translates a subset of sentences through a dynamic router and Mixture-of-Agents layer with tools for terminology verification, subtitle constraint validation, and contextual retrieval. A judge-refiner loop scores candidates and uses textual critiques to update agent prompts and routing policies without retraining the underlying LLMs. During test-time inference, the evolved configuration translates the remaining series. We also introduce Subtitle Arena, covering 14 genres, 2--198 episodes per series, production years 1959--2023, and 15 target locales, together with SubMQM, a subtitle-adapted MQM framework with seven dimensions and 19 error categories. SMART achieves the best overall MQM score in all 15 Subtitle Arena directions, reducing average penalty by 6.9% over the strongest competing agent system. On a public benchmark, MuSC, SMART obtains the best model result across all 4 language pairs. SMART also achieves the best result in human evaluation with an overall score of 4.50/5.

cs.CL↗