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Kai Wang

Publications and source records attributed to Kai Wang.

At least 91 records · Page 5Linked to original sources
arXiv2026

Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing

Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks decompose prompts into questions, yet often return them to prompt scores and fixed categories, weakening attribution and ignoring complexity. Related requirements are also scored separately or as one total, obscuring basic versus compositional failure. We present QC-T2I-Bench, a question-centric framework that converts open prompts into attributed atomic questions and organizes their dependencies with Davidsonian Scene Graphs (DSGs). We use hierarchy-constrained question aggregation to exclude downstream questions after a prerequisite fails and to prevent simple and complex prompts from receiving the same total weight. We then use the DSG structure to measure joint success within prompts and compare repeated entities across prompts, separating basic realization failures from failures under additional requirements. We evaluate multiple open-source T2I models on English and Chinese prompts. The resulting question-level evidence supports reliable ranking and fine-grained diagnosis: joint completion falls from 80.7\% for components with two capabilities to 37.2\% for those with seven or more. Finally, we reuse the same records for training-free routing; our cost-aware router matches ERNIE's 89.51-point estimate with 21.3\% less GPU-s/MP.

cs.AI↗
arXiv2026

Read Less, Solve More: Token-Efficient Sparse Reading for AI Agents

Long-horizon agents increasingly rely on repeated access to external artifacts, yet current reading interfaces often expose entire objects even when only sparse evidence is needed. This over-reading increases token and latency costs and can dilute task-relevant evidence, while existing context-reduction methods mainly intervene after broad content has already entered the trajectory. We present SparseRead, a training-free, model-transparent reading layer that controls content admission before unnecessary evidence reaches the model context. SparseRead combines a regime-aware Read Gate, extensible Reader Backends, and a stateful protocol for bounded, source-anchored evidence acquisition with explicit refinement, verification, stopping, and fallback. Across six frontier models, including Claude Opus 5, and five workload scenarios, SparseRead reduces token volume by up to 92.9% and wall time by up to 89.0%, while preserving or improving task quality. Its consistent gains across three agent frameworks further demonstrate broad portability.

cs.AI↗
arXiv2026

ClawSentry: A Progressive Multi-Tier Security Monitor for Safeguarding Autonomous LLM Agents

As large language model (LLM) agents move from conversation to executing code, reading local files, and orchestrating external tools, a single agent hijacked by a malicious third-party skill can cause data exfiltration, privilege escalation, or cascading compromise. We argue that agentic risk is progressive: it can enter at four loci of the agent control loop--skill admission, invocation-time intent, execution-time effect, and post-action consequence--while a denied dangerous objective can reappear across surface forms, tools, or turns; existing safeguards are typically local to one lifecycle boundary or one call. Guided by this threat model, we present ClawSentry, an open-source, framework-agnostic security supervision gateway for agent runtimes. Before a skill package is ever executed, First-use Skill Package Review (FSPR) audits it under a deterministic evidence floor, escalating unresolved cases to bounded read-only agentic review (locus A). At runtime, a three-tier progressive decision engine--a deterministic L1 layer, a rule-anchored L2 semantic reviewer, and a read-only L3 evidence-seeking agent--spends contextual review only on the residual ambiguity, while a session-level anti-bypass mechanism recognizes tool-switching and rephrased retries (loci B--C); a post-action path feeds high-severity evidence non-retroactively into later review (locus D). An Agent Harness Protocol (AHP) abstraction applies one policy across Codex, Claude Code, Kimi CLI, and Gemini CLI without modifying agent internals. On SkillInject with Codex/GPT-5.4, contextual ASR falls from 39.55% to 2.61% while contextual TSR moves only from 83.78% to 83.05%. Across five Work Agents on the full SkillsSafety benchmark, ClawSentry confines ASR to 9.09--15.03% from 33.5--49.7% unprotected, and aggregate TSR on clean skills remains 98.7%.

cs.CR↗
arXiv2026

Revealed Default Under Choice Overload

Empirical and experimental studies suggest that a decision maker can remain with their default option even when preferred alternatives are available, yet standard choice data may not record which option is the default. Existing default-based models often assume that the default option is observed. In contrast, I study a decision maker subject to status quo bias and choice overload and illustrate how one can identify both the decision maker's default and preference from the observed behavior. This paper shows that anomalous choice behavior may arise from a latent default, a possibility largely overlooked in studies of standard choice data.

econ.TH↗
arXiv2026

A Symmetric Counterexample to the Snashall--Solberg Conjecture

It is shown that the trivial extension of the Xu--Snashall algebra is a symmetric counterexample to the Snashall--Solberg conjecture, which states that the Hochschild cohomology ring of a finite-dimensional algebra modulo nilpotence is a finitely generated algebra. To the best of our knowledge, this is the first known selfinjective counterexample.

math.KT↗
arXiv2026

Deformations and homotopy theory of Rota-Baxter Lie algebras

For Rota-Baxter Lie algebras, a homotopy cooperad is exhibited, whose cobar construction is shown to be the minimal model of the operad of Rota-Baxter Lie algebras by using algebraic Morse theory. The deformation complex of Rota-Baxter Lie algebras as well as the $L_\infty$-algebra structure on this complex are deduced from the minimal model and the notion of homotopy Rota-Baxter Lie algebras is given as a consequence.

math.KT↗
arXiv2026

Topologically Configurable Nonlinear Vortex Generation at van der Waals Heterostructures

van der Waals (vdW) materials offer a highly tunable and efficient platform at nanoscale for nonlinear and quantum optics. Twist-stacked vdW heterostructures enable elegant control of symmetry and interlayer coupling. Prior studies mainly focus on planar twisted interfaces, while neglecting the naturally formed and mandatory defects in such vdW heterostructures. Here, we demonstrate nonlinear singular optics with topologically configurable nonlinear vortex generation at the corner singularity of vdW heterostructures. By tailoring azimuthally discrete second-harmonic phase gradients at each interface, we obtain programmable nonlinear vortex emitters with dominant target OAM components. Nonlinear OAM beams with topological charge $\ell = 1$ and $\ell = -2$ are experimentally realized, respectively. Our work unlocks the untapped potentials of nonlinear singular optics in twisted vdW materials as a reconfigurable and lithography-free platform for nonlinear structured light generation, important in quantum nonlinear optics and related fields.

physics.optics↗
arXiv2026

StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. We bet on harness scaling to improve the execution system around an agent without changing its model weights. We introduce StateM, an agent-native runtime that organizes execution around durable states, phase-local context, checked transitions, recoverable runbooks, and versioned procedural practices that agents and users can inspect together. On Terminal-Bench 2.1, StateM raises GPT-5.5 xhigh to 92.1\%, versus 83.1\% reference and GPT-5.6 Sol Ultra at 91.9\%. The runbook transfers unchanged to GPT-5.6. With GPT-5.6 Sol xhigh, StateM reaches 95.3\% raw accuracy across 445 trials and succeeds on all 89 tasks at least once. The frozen profile raises GPT-5.6 Luna from 76.7 to 85.4\%, above the 84.9\% Sol xhigh reference. Using the same runtime, runbook structure, and golden rules, less than \$38 of adaptation raises DeepSeek-V4 Flash from 82.7 to 88.1\% under standard timeouts and to 89.1\% on an 88-task common core. Extending only the remaining latency-sensitive task matches the reported 88.8\% GPT-5.6 Sol max result. Final-score API usage is about \$15 versus \$574.68 for the GPT reference; total DeepSeek expenditure is \$52.22. On BusinessBench, family-specific runbooks built on development sets yield held-out gains of 0.55 macro and 1.34 micro points; two mechanism-matched families improve by 10.04 points. Concrete rules generalize when tasks share execution structure, while the control methodology applies broadly. StateM turns selected postmortem findings into persistent, executable preconditions and practices, making learned controls explicit and enforceable through stateful controls. Code at github.com/henryqin1997/statem.

cs.AI↗
arXiv2026

Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning

Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to rigid, handcrafted models. Conversely, deep neural network (DNN)-based compression methods, while effective, demand substantial computational resources, hindering deployment in resource-constrained settings. To address these challenges, we propose a novel tri-plane context tree (TCT)-based method for lossless volumetric medical image compression that delivers high performance without relying on DNNs or external training data. To exploit intra-slice and inter-slice redundancies, we introduce a compact tri-plane context representation that decomposes complex 3D context modeling into efficient 2D modeling on three orthogonal planes. By integrating this representation with a context tree framework, we develop an input-specific TCT model employing an adaptive binary tree structure. At each tree node, the model dynamically selects from a suite of tri-plane based predictors and contextual feature extractors, enabling data-adaptive context modeling tailored to local structural characteristics. Instead of offline training, we sample a subset of the input volume to learn the TCT model by optimizing the minimum description length (MDL) through iterative construction and pruning. With the learned TCT model, each pixel retrieves its corresponding context, computes the prediction residual using the predictor dictated by the context, and performs entropy encoding based on the associated histograms. Experimental results demonstrate that the proposed method achieves compression performance on par with recent DNN-based methods on multiple datasets, while maintaining low computational cost and fast coding speeds, making it highly applicable in practice.

eess.IV↗
arXiv2026

The origin of the galaxy size-stellar metallicity relation: A semi-analytical perspective

Stellar metallicity encodes the integrated effects of gas inflow, star formation, and feedback-driven outflow, yet its connection to galaxy structure remains poorly understood. Using SDSS-IV MaNGA, we present the direct observational evidence that, at fixed stellar mass, smaller central galaxies are systematically more metal-rich, with a Spearman's rank correlation coefficient reaching $R_{\rm s}\approx -0.4$. The semi-analytical model L-GALAXIES reproduces this anti-correlation, albeit with a stronger amplitude ($R_{\rm s}\approx -0.8$). Within this framework, the trend cannot be explained by differences in gravitational potential depth or star formation history. Instead, smaller galaxies attain higher stellar metallicities because their elevated star formation efficiencies accelerate chemical enrichment, and, at fixed stellar mass, they inhabit less massive haloes, which makes their recycled inflows more metal-rich. The gas-regulator model demonstrates that star formation efficiency affects stellar metallicity when the system has not long remained in equilibrium, which is shown to be the case for central galaxies with $M_{\rm star}\lesssim 10^{10.5}\rm M_\odot$ in both L-GALAXIES and observation. The model also suggests a testable signature that, at fixed stellar mass, larger or lower-metallicity galaxies should inhabit more massive haloes than their smaller and higher-metallicity counterparts, providing a direct and testable imprint of the galaxy size-stellar metallicity relation on the galaxy-halo connection.

astro-ph.GA↗
arXiv2026

Gravitational potential drives the concentration dependence of the stellar mass-halo mass relation

We investigate the origin of the scatter in the stellar mass-halo mass (SMHM) relation using the \colibre cosmological hydrodynamical simulations. At fixed halo mass, we find a clear positive correlation between stellar mass and halo concentration, particularly in low-mass haloes between $10^{11}$ and $10^{12}\,\rm M_\odot$, where all halo properties are computed from the corresponding dark-matter-only simulation. Two scenarios have been proposed to explain this trend: the earlier formation of higher-concentration haloes allows more time for star formation, or the deeper gravitational potential wells of higher-concentration haloes enhance baryon retention. To distinguish between them, we examine correlations between halo concentration, stellar mass, stellar age, and stellar metallicity. While, at fixed halo mass, halo concentration correlates with stellar age, stellar age itself shows only a weak correlation with stellar mass, indicating that early formation alone cannot account for the concentration-dependence in the scatter of the SMHM relation. In contrast, both stellar metallicity and halo concentration exhibit correlations with stellar mass. The connection between halo concentration and stellar metallicity persists even when simultaneously controlling for both halo mass and stellar mass. These results support the scenario in which the deeper gravitational potentials in higher-concentration haloes suppress feedback-driven outflows, thereby enhancing both baryon and metal retention.

astro-ph.GA↗
arXiv2026

Experimental quantum telecloning across silicon photonic chips

Telecloning -- the combination of quantum teleportation and cloning -- offers a powerful mechanism to disseminate unknown quantum states to multiple spatially separated recipients with optimal fidelity. Despite its conceptual importance for quantum networks, an experimental demonstration of symmetric qubit quantum telecloning remains elusive, particularly due to the challenges of generating multipartite entangled resource states and implementing stable multi-photon interference across distributed nodes. Here, we realize the optimal 1 to 2 symmetric quantum telecloning using a scalable silicon photonic platform. We implement a six-photon protocol using two independent, fiber-linked photonic chips: one generating a heralded input state and the other preparing a four-photon entangled resource state. By performing an interchip Bell-state measurement, we successfully distribute the input state into two optimal clones at remote nodes. We observe an interchip cloning fidelity of 78.45 $\pm$ 1.39%, exceeding the classical limit of 2/3 by 8 standard deviations. Our results demonstrate the robust generation and manipulation of complex multi-photon states between integrated chips, providing a foundational building block for large-scale multi-party quantum networks.

quant-ph↗
arXiv2026

Photonic realization of a subgraph extraction in a quantum random network

Understanding how complex connectivity emerges in networks is a fundamental challenge in classical and quantum science. In classical random networks, complex subgraphs typically require relatively high connection probabilities, whereas quantum random network theory predicts that such structures can arise at a single, lower threshold through entanglement and local operations. Here, using an integrated silicon photonic chip, we experimentally realize a quantum subgraph predicted by quantum random network theory in a four-node quantum random network. Our integrated platform exploits probabilistic photon-pair sources and coherent control of path modes to prepare a structured quantum subgraph through local transformations and postselection, operating in a threshold regime that differs from classical random networks. We verify that the subgraph state exhibits genuine high-dimensional multipartite entanglement across the nodes, providing experimental evidence that quantum entanglement enables connectivity structures beyond classical accessibility.

quant-ph↗
arXiv2026

Adversarial Concept Distillation for One-Step Diffusion Personalization

Recent progress in accelerating text-to-image diffusion models enables high-fidelity synthesis within a single denoising step. However, customizing the fast one-step models remains challenging, as existing methods consistently fail to produce acceptable results, underscoring the need for new methodologies to personalize one-step models. Therefore, we propose One-step Personalized Adversarial Distillation (OPAD), a framework that combines teacher-student distillation with adversarial supervision. A multi-step diffusion model serves as the teacher, while a one-step student model is jointly trained with it. The student learns from alignment losses that preserve consistency with the teacher and from adversarial losses that align its output with real image distributions. Beyond one-step personalization, we further observe that the student's efficient generation and adversarially enriched representations provide valuable feedback to improve the teacher model, forming a collaborative learning stage. Extensive experiments demonstrate that OPAD is the first approach to deliver reliable, high-quality personalization for one-step diffusion models; in contrast, prior methods largely fail and produce severe failure cases, while OPAD preserves single-step efficiency.

cs.CV↗
arXiv2026

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement

With the growth of gaming, animation, and virtual reality industries, the demand for efficient generation of stylized 3D assets is rapidly increasing. However, existing approaches still struggle to jointly preserve style fidelity, geometric consistency, and generation efficiency, as most of them still rely on indirect 2D-to-3D stylization pipelines. This motivates a native 3D stylization framework that can explicitly disentangle style from geometry while remaining efficient. To this end, we propose DreamStyle3D, an efficient framework for stylized 3D asset generation built on a Decoupled Dual Cross-Attention mechanism. Our method explicitly separates geometric and stylistic features to enable efficient style injection while preserving structural consistency, and further adopts a lightweight training strategy to enhance style consistency and model generalization. In addition, we build an automated data pipeline and construct a dataset of about 15K content-style-stylized triplets for training and evaluation. Extensive experiments demonstrate that our DreamStyle3D can generate high-fidelity, geometrically consistent stylized 3D assets within 10 seconds, substantially improving efficiency while maintaining superior style quality and offering a new solution for 3D content creation. The project is available at https://github.com/NK-JittorCV/nk-3D/tree/main/models/DreamStyle3D.

cs.CV↗
arXiv2026

XEWorld: Can Action-Conditioned World Models Generalize to Unseen Robot Embodiments?

Action-conditioned world models are promising learned simulators for robotic manipulation, yet evaluating them exclusively on training robots fails to reveal whether they capture physical dynamics or merely memorize visual patterns. To answer whether a model can faithfully render a robot it has never seen, we introduce XEWorld, a controlled cross-embodiment testbed for world models that isolates embodiments by evaluating held-out robots within physically identical scenes. Our systematic analysis uncovers a shared architectural bottleneck: current models act primarily as 2D visual pattern matchers whose generalization is governed by visual similarity rather than physical kinematic similarity. Driven by this limitation, they struggle to translate abstract numeric joint actions into coherent visual trajectories, and fail to predict dynamic visual changes from static initial observations. Consequently, successfully rendering an unseen embodiment zero-shot strictly requires heavily grounded cues, specifically pixel-space actions and explicit spatial-temporal alignment. Even when bypassing this zero-shot barrier via few-shot adaptation, the forced appearance recovery triggers catastrophic forgetting of seen embodiments. Together, these failures expose a critical inability to apply learned physical dynamics to novel visual appearances, highlighting that achieving true cross-embodiment generalization requires architectural innovations that decouple visual appearance from underlying physical dynamics.

cs.RO↗
arXiv2026

Ruffled Feathers: Merger-driven galaxy size growth and structural transformation in EAGLE

Galaxy mergers drive both the size growth and the transformation from discs to spheroids, yet the prescriptions used to model these processes in semi-analytic frameworks have not been tested against the realistic merger population in cosmological hydrodynamical simulations. Using $\approx 4{,}500$ mergers identified in the EAGLE simulation, we test an energy-conservation estimator for post-merger galaxy sizes and quantify merger-driven morphological transformation. The predicted remnant half-stellar-mass radius matches the simulated descendant size with a scatter of $\approx 0.12$-$0.15$ dex and no significant systematic dependence on progenitor properties, while a commonly used dissipation correction applied to gas-rich mergers under-predicts the post-merger size by up to $\approx 0.4$ dex in a cosmological context and increases the overall scatter. The per-merger size growth increases monotonically with the stellar mass ratio of the merging pair, from $\lesssim 0.03$ dex for minor mergers to $\approx 0.10$ dex for equal-mass mergers. From the energy-conservation estimator, we analytically derive the size growth efficiency per unit accreted stellar mass, $η\equiv \mathrm{d}\log_{10} r_{\star}/\mathrm{d}\log_{10} M_{\star}$, and show that $η$ reaches $\approx 2$ only in the idealised limit of collisionless minor mergers with zero orbital energy; as $η$ is highly sensitive to the orbital energy at the time of merging, the minor merger channel cannot be established as the driver of the rapid size growth of massive galaxies without better constraints on this quantity. Beyond the size growth, mergers systematically reduce rotational support and increase triaxiality in proportion to mass ratio, but even the most nearly equal-mass mergers do not always fully destroy the disc, in tension with the complete disc destruction assumed in several semi-analytic models.

astro-ph.GA↗
arXiv2026

Inflow-driven galaxy evolution - I. Revealing the physics of the fundamental metallicity relation

We present a unified physical framework for the fundamental metallicity relation (FMR), based on the mass-continuity equations. The FMR is not merely the anti-correlation between star formation rate (SFR) and gas metallicity ($Z_{\rm g}$) at fixed stellar mass ($M_\star$); it is a redshift-invariant surface in the $(M_\star,{\rm SFR},Z_{\rm g})$ space. We construct a minimal cosmological gas flow model, calibrated to reproduce the mass-metallicity relation, star-forming main sequence, and stellar-to-halo mass relation at $z=0-3$, and show that the FMR emerges as a prediction of the calibrated physics. Through controlled experiments that progressively simplify the model, we reveal that in a universe where both the star formation efficiency ($ε$) and mass-loading factor ($η$) are constants, the FMR reduces to a universal scaling between $Z_{\rm g}$ and $M_\star/$SFR, whose shape traces the transition from inflow-driven regime to equilibrium. The specific parameterisation of the observed FMR is not a fundamental symmetry but a contingent consequence of how $ε$ and $η$ depend on stellar mass and redshift. We show that the gaseous FMR (gFMR), defined in the $(M_\star,M_{\rm g},Z_{\rm g})$ space, is more fundamental than the standard FMR: in the inflow-driven limit, $Z_{\rm g}$ is proportional to $M_\star/M_{\rm g}$, and the approach to equilibrium is governed by $M_\star/M_{\rm g}$ and $η$ alone. We derive an analytic solution for an idealised version of the model that provides closed-form expressions relating $Z_{\rm g}$, $M_{\rm g}/M_\star$, and $η$, and show this framework accurately reproduces the cosmological gas flow model. By establishing the physical origin of the FMR and its connection to the more fundamental gFMR, we provide the theoretical foundation to turn metallicity scaling relations into precision probes of the baryon cycle over cosmic history.

astro-ph.GA↗