arXiv subjects

Hao Chen

Publications and source records attributed to Hao Chen.

At least 19 recordsLinked to original sources

arXiv2026

TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-training

Recent advances in unified multimodal models (UMMs) have led to a proliferation of architectures capable of understanding, generating, and editing across visual and textual modalities. However, developing a unified framework for UMMs remains challenging due to the diversity of model architectures and the heterogeneity of training paradigms and implementation details. In this paper, we present TorchUMM, the first unified codebase for comprehensive evaluation, analysis, and post-training across diverse UMM backbones, tasks, and datasets. TorchUMM supports a broad spectrum of models covering a wide range of scales and design paradigms. Our benchmark encompasses three core task dimensions: multimodal understanding, generation, and editing, and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models and fosters deeper insights into their strengths and limitations, facilitating the development of more capable unified multimodal systems. Code is available at: https://github.com/AIFrontierLab/TorchUMM.

cs.AI↗

arXiv2026

Growth of structure in holographic dark energy models: a posterior predictive comparison

Structure formation has been used to test the viability of the IR cutoff in the Tsallis holographic dark energy (THDE) model, and the future event horizon was found to be a viable IR cutoff (Phys. Rev. D 113, 123531 (2026)). Motivated by this, we constrain the HDE, THDE, and BHDE models using the combined Pantheon+ SN Ia, OHD, DESI DR2, and CMB distance prior datasets, and propagate the MCMC posterior samples into the linear growth sector to obtain the predicted $fσ_8(z)$ and $S_8(z)$ histories under the smooth dark energy approximation. We find that the growth predictions of HDE and BHDE are nearly indistinguishable, while those of THDE lie lowest. A posterior predictive $χ^2$ comparison shows that current $fσ_8$ measurements are statistically consistent with all three models, whereas the $S_8$ comparison yields markedly larger $χ^2$ values for HDE and BHDE and a value for THDE close to that of $Λ$CDM. As an illustrative diagnostic, we construct the $(fσ_8, S_8)$ plane and find that the trajectories of THDE and $Λ$CDM reach the vicinity of the current data region, while those of HDE and BHDE do not. Our results indicate that structure growth observables provide a promising probe for discriminating among holographic dark energy models, while a definitive discrimination will require a joint background and growth likelihood and consistent growth measurements.

gr-qc↗

arXiv2026

GlucoAgent: Personalized Glucose Forecasting from Multimodal Personal Context

Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal context that can significantly enhance glucose forecasting. However, existing methods struggle to effectively utilize these signals to improve glucose prediction because the relevance of different modalities varies drastically across individuals and time. We present GlucoAgent, an LLM-based agentic system that addresses this challenge through dynamic, case-by-case analysis at inference time. For each forecast, GlucoAgent writes and runs code over an individual's multimodal context and returns predictions with a reason and supporting evidence. The generated programs are highly diverse, ranging from retrieving similar past meals to fitting and back-testing physiological and statistical models. On CGMacros, a multimodal glucose dataset, GlucoAgent outperforms time series foundation models on every forecasting horizon, achieving an average RMSE of 22.5 mg/dL compared to 23.5 mg/dL of Chronos-2, the leading multimodal time series foundation model. Furthermore, GlucoAgent is highly effective at leveraging multimodal data. The addition of multimodal data reduces its average RMSE by 1.4 mg/dL (24.0 mg/dL to 22.5 mg/dL), compared to a marginal 0.3 mg/dL (23.8 mg/dL to 23.5 mg/dL) improvement for Chronos-2. This advantage is most pronounced in challenging post-meal windows, where GlucoAgent uses multimodal context to reduce 60-minute forecast RMSE by 4.6 mg/dL (31.0 mg/dL to 26.5 mg/dL), far exceeding the 0.8 mg/dL (31.9 mg/dL to 31.1 mg/dL) reduction achieved by Chronos-2.

cs.AI↗

arXiv2026

PyRUA-DataFarm: LLM Agent as a Cost-Efficient Teleoperator for Real-Ready Robot Data

Teleoperation remains the default way to collect robot demonstrations, but every episode costs robot and operator time, and every new task starts from scratch. We ask whether a coding agent can take over the teleoperator's job. In our DataFarm, the agent receives a data request and one calibration episode of the robot in the target scene and rebuilds the task in simulation: it writes the scene, an expert controller, and a success judge. The simulator supplies exact actions, and a video model conditioned on real images renders each rollout as photographic video, generating all camera views together consistently. The output is real-ready data: synchronized camera views labelled with the robot's actions, on which a policy can be trained directly and deployed on the real robot. Independent judges certify the expert and every rendered episode before admission. On five real-robot tasks, policies trained only on farm data match policies trained on as many teleoperated episodes (93 vs. 84 successes in 100 trials), and under an unseen tablecloth and coloured light they keep 87 successes where the teleoperation-trained policies fall to 46. Adapting a task is fully automatic and costs \$21 of API usage on average, paid once per task, plus \$5 for a render contract; every further episode costs \$0.80 on average for simulation, rendering, and review, with no robot or operator. One hour of three-view data for a task costs \$107 on average, these costs included, slightly below a quote of \$117 for teleoperation in China and below \$290 in labour alone in North America.

cs.RO↗

arXiv2026

Learning to Adjudicate: Verifier-Guided Multi-Agent Reasoning for E-Commerce Relevance

Relevance modeling is fundamental to e-commerce search, determining how well retrieved products satisfy the needs expressed in a user's query. Accurate judgments require interpreting user intent, comparing product attributes, and applying business-specific matching criteria. Current methods based on large language models (LLMs) leverage contextual understanding and reasoning, focusing on incorporating domain-specific context and enhancing reasoning performance. Despite these advances, difficult query-product pairs remain challenging when evidence from different perspectives supports conflicting conclusions. A single reasoning pass may fail to reconcile these conflicts and ensure that decisive evidence guides the final judgment. To address this challenge, we propose Collaborative Adjudication with Debate and Replay Experience (CADRE), a framework for evidence-based adjudication in e-commerce relevance assessment. CADRE organizes three expert agents sharing a frozen backbone to analyze product attributes, user intent, and business rules in separate contexts, while a trainable Verifier assesses their supporting evidence against the applicable matching criteria to determine relevance. Under expert disagreement, a Verifier-guided debate elicits targeted clarification and revision of disputed interpretations before final adjudication. We optimize the Verifier through multi-turn group relative policy optimization and adapt successful-experience replay so that trajectories yielding correct terminal labels can be reused alongside fresh rollouts in subsequent updates. For efficient online serving, we use CADRE as an offline teacher to distill its relevance judgments into a lightweight student model. Offline evaluations and online A/B tests on an e-commerce search advertising platform demonstrate that CADRE improves relevance classification and key business and relevance satisfaction metrics.

cs.IR↗

arXiv2026

Molecular Curvature Enables Tubular π-Stacked Pathways in Three-Dimensional Conductive Metal-Organic Frameworks

Conductive metal-organic frameworks (MOFs) are commonly constructed from planar aromatic ligands, whose geometries favor conventional π-stacking arrangements. However, intrinsically twisted ligands offer an alternative geometric approach to directing aromatic packing and constructing unconventional framework architectures. Here, we employ intrinsically twisted, butterfly-shaped dibenzo[g,p]chrysene-2,3,6,7,10,11,14,15-octaol (DBC) ligands to construct a series of lanthanide MOFs. Single-crystal X-ray diffraction reveals an interpenetrated three-dimensional framework in which π-π interactions between the aromatic fragments generate axially extended tubular π-stacked pathways, alongside one-dimensional channels rich in hydrogen-bonding sites. Lanthanide substitution preserves the overall tubular framework topology while modulating aromatic stacking distances and local pore environments. PrDBC single crystals exhibit mixed electron-proton transport, with electron and proton conductivities reaching 1.62 x 10-3 and 4.37 x 10-4 S cm-1, respectively, at 338 K and 30% relative humidity. This work highlights molecular curvature as a structural design strategy for constructing three-dimensional conductive MOFs that integrate tubular aromatic stacks with tunable pore environments for mixed electron-proton transport.

cond-mat.mtrl-sci↗

arXiv2026

4K Image Super-Resolution on Mobile NPUs: Mobile AI 2026 Challenge Report

Image super-resolution is widely used on mobile devices for upscaling various types of image data. This creates a need for efficient deep learning-based models that are not only performant but can also be deployed on neural processing units (NPUs) present in the majority of mobile and IoT chipsets. In this Mobile AI challenge, we address this problem and propose the participants to design fast solutions that can upsample images from Full HD to 4K resolution while running on popular mobile NPUs from Qualcomm and MediaTek. For this, the participants were provided with the DIV2K dataset and trained models to do an efficient 3X image upscaling. The competition had two tracks: in the first track, the models had to be fully quantized (INT8), and the runtime of all models was evaluated on the Qualcomm Snapdragon 8 Elite Gen 5 NPU. In the second track, the participants were developing floating-point solutions that were evaluated on the MediaTek Dimensity 9500 NPU. Top solutions proposed in this challenge are able to reconstruct 4K images under 5 milliseconds while reaching a PSNR score of 30 dB. A comprehensive description of the models developed in the challenge is provided in this paper.

cs.CV↗

arXiv2026

Local Fan-type obstructions and Ore-type conditions for rainbow Hamiltonicity in graph systems

Let $G=(G_1,...,G_n)$ be a graph system on a common $n$-vertex set $V$. For $v\in V$, set \[ δ(v)=\min_{i\in[n]}d_{G_i}(v), \qquad E_{\cap}=\bigcap_{i=1}^n E(G_i), \] and let $d^c(u,v)$ denote the length of a shortest rainbow $u$--$v$ path. We study Hamiltonicity conditions imposed only on pairs at small rainbow distance. Suppose that every $G_i$ is $2$-connected and that \[ δ(u),δ(v)<\frac n2, \qquad d^c(u,v)\le2 \quad\Longrightarrow\quad uv\in E_{\cap}. \] Our main result shows that any system satisfying this local Fan-type condition but containing no rainbow Hamilton cycle must satisfy \[ E_{\cap}=\varnothing, \qquad \sum_{v\in V}δ(v)\le\frac{n^2-1}{2}. \] Consequently, either $E_{\cap}\ne\varnothing$ or $\sum_{v\in V}δ(v)\ge\lceil n^2/2\rceil$ guarantees a rainbow Hamilton cycle. We further prove that the local Ore-type condition \[ δ(u)+δ(v)<n, \qquad d^c(u,v)\le2 \quad\Longrightarrow\quad uv\in E_{\cap} \] already forces a rainbow Hamilton cycle. Along the way, the Fan-type hypothesis yields a rainbow Hamilton path and a rainbow $(n-1)$-cycle. For diagonal systems, our conditions reduce to the classical Fan and local Ore conditions. The proofs combine rainbow rotation--extension with a double-counting argument in an auxiliary digraph. Keywords: graph system; rainbow Hamilton cycle; rainbow distance; Fan-type condition; Ore-type condition; rotation--extension.

math.CO↗

arXiv2026

Dimension Changes the Scale-Signal Channel in Nearest-Neighbor Two-Sample Testing

Nearest-neighbor two-sample tests are often analyzed with dimension fixed. We study the power of the generalized edge-count test on a directed 1-nearest-neighbor graph. This statistic combines a weighted sum and a difference of the two within-sample edge counts. For balanced isotropic Gaussian samples with $d/\log N\toγ>0$, we derive the connection probabilities under local location and scale alternatives. Under location alternatives, the two counts have equal mean shifts, which cancel in their difference. Under scale alternatives, the two counts instead have mean shifts of opposite signs. We also derive the growth rate of the null expectation $\mathbb{E}_0C_3$, where $C_3$ counts pairs of directed edges sharing exactly one vertex. On an explicit range of $γ$, we establish local-power limits strictly above the test level for scale alternatives in the difference component at the scale $|\varepsilon_N|\asymp (\mathbb{E}_0C_3)^{1/2}/(Nd)$. Here $\mathbb{E}_0C_3$ gives the variance order of the difference count under these local alternatives. At this scale, the standardized drift of the weighted sum vanishes. When $d=1$, the weighted component instead detects local Gaussian scale alternatives while the difference component has no limiting drift. Thus dimension changes both the magnitude of a detectable scale difference and the component of the test that detects it.

math.ST↗

arXiv2026

META-Robot: Self-Cognition and Capability Scaling in Agentic Robots

LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. Expanding their skill repertoires alone offers limited guidance on how to assess new capabilities or identify what remains beyond their reach. We introduce META-Robot, a framework that couples self cognition with capability scaling by expanding the criteria through which an agent evaluates and develops its skills. At its core, action-level predicates make execution outcomes explicit and available for LLM reasoning. The agent uses these assessments to identify its own limitations, extend relevant evaluation criteria, and guide the structured expansion of its skills. Proprioceptive feedback further grounds this process in the robot's physical state, enriching the evidence available for self-cognition. Experiments in simulation and on real robots demonstrate stronger overall task performance, generalization to new tasks, continued expansion of skills and evaluation criteria, and effective use of non-visual proprioceptive signals.

cs.RO↗

arXiv2026

FuzzAgent: Multi-Agent System for End-to-End Evolutionary Library Fuzzing

Library fuzzing synthesizes harnesses that expose new API interactions, but deploying it end to end still requires extensive project-specific integration. Existing LLM-based systems automate individual steps, yet they treat execution outcomes as coarse signals: a stalled campaign does not reveal which state blocks progress, and a crash trace does not reveal whether the library or the harness is at fault. We present FuzzAgent, a multi-agent system that builds and evolves a complete fuzzing campaign from a source tree alone. Specialized agents acquire project knowledge and repair campaign artifacts automatically. Hierarchical coverage diagnosis localizes blockers from modules down to branches, and dependency-aware generation converts them into harnesses with the required API lifecycle. Crash validation recovers runtime object states to distinguish harness misuse from library bugs. We evaluate FuzzAgent on 20 real-world C/C++ libraries against five state-of-the-art baselines (OSS-Fuzz, OSS-Fuzz-Gen, PromptFuzz, PromeFuzz, and ProteusFuzz). FuzzAgent completes the full fuzzing lifecycle for all 20 libraries without human intervention at $3.17 per library. On the 20 libraries, it reaches 179619 branches, exceeding OSS-Fuzz, PromptFuzz, ProteusFuzz, PromeFuzz, and OSS-Fuzz-Gen by 45.1%, 73.2%, 87.7%, 92.1%, and 191.2%, respectively. FuzzAgent also identifies 102 new library bugs, 90 of which have already been acknowledged or fixed by upstream maintainers.

cs.SE↗

arXiv2026

Approximation of the relaxed Euler system at the level of the compressible Navier-Stokes equations

We study the global well-posedness of the Cauchy problem in $\mathbb R^d$, $d\geq2$, for the relaxed compressible Euler system with a Maxwell-type constitutive law for the stress tensor. We show that the corresponding compressible Navier-Stokes solutions serve as both relaxation limits and large-time asymptotic profiles of the global relaxed Euler solutions constructed here. Given a Navier-Stokes background satisfying suitable boundedness and decay assumptions, we construct global smooth relaxed Euler solutions for each fixed $α\in(0,1]$ and sufficiently small $\varepsilon>0$. Here, the background need not be small. After subtracting the stress initial layer, the approximation error is $O(\varepsilon^α)$ uniformly in time. Under suitable additional conditions on the initial perturbations, this error decays in $L^2$ faster than the decay of standard Navier-Stokes background, uniformly in $\varepsilon$, identifying the background solution as a large-time asymptotic profile. As an application, we obtain the corresponding small-data results using classical Navier-Stokes theory to verify the background assumptions.

math.AP↗

arXiv2026

PHKT:Personalized Dynamic Hypergraph-enhanced KAN-Transformer for Multi-behavior Sequential Recommendation

In multi-behavior recommendation, auxiliary behaviors such as clicks, add-to-cart, and purchases can provide richer supervisory information for predicting target behaviors. Although existing graph and hypergraph methods are capable of modeling high-order relationships among users, items, and behaviors, they still have limitations in heterogeneous semantics, user-specific weighting, and sequence dependency modeling. While standard Transformers excel at sequence modeling, their shared feedforward mapping struggles to accommodate the differentiated requirements of heterogeneous latent patterns in multi-behavior scenarios. To address this, this paper proposes the Personalized Hypergraph-enhanced Kolmogorov-Arnold Network Transformer (PHKT). Specifically, we design a personalized dynamic hypergraph module that performs behavior-aware weighting of item similarities based on users' historical behavior sequences to capture user-specific heterogeneous high-order relationships. Meanwhile, a Transformer is used as the temporal backbone to model the evolution of short- and long-term preferences, and KAN is introduced to replace the traditional MLP in the feedforward network to enhance fine-grained modeling capability for nonlinear responses to different latent patterns. Experiments on three real datasets, Tmall, RetailRocket, and IJCAI, show that PHKT consistently outperforms nine strong baseline models across multiple evaluation metrics, demonstrating its effectiveness in multi-behavior preference modeling and target behavior prediction.

cs.IR↗

arXiv2026

scTREAT: Latent Diffusion for Single-Cell Drug-Response Prediction

Generating drug-induced transcriptional responses requires a model to account for cellular background together with molecular identity and dose. We present scTREAT, the first diffusion framework to factorize state and drug guidance for single-cell drug-response prediction. A shared latent expression space connects baseline and response, while Guided Decomposable Attention (GD-Attn) injects a baseline representation and a dose-conditioned molecular embedding. Independent channel dropout exposes four conditioning states during training; inference combines three conditional predictions with separately specified state and drug coefficients, and dose modulates drug-guidance strength. On condition-level pseudobulk profiles derived from the Tahoe-100M single-cell atlas, scTREAT achieves the best value on 10 of 12 metrics for unseen cell line-drug combinations (UC) and 11 of 12 for unseen drugs (UD). Ablations examine condition injection, molecular representation, and guidance design. On UD, the relative gains over the second-best model reach 36.11% and 34.21% for DEG logFC-Spearman and logFC-Pearson. These results demonstrate the effectiveness of combining shared latent expression modeling with explicit state and drug conditioning for response prediction.

q-bio.QM↗

arXiv2026

Decoupling and Regulating Modality Heads for Fake News Detection under Missing Modality

Multimodal news often loses its image or text as it spreads across Web platforms. Existing methods treat this as information loss, assuming that the original veracity label remains valid. This assumption fails for fake news reusing authentic material, whose remainder may be genuine. Detectors should therefore decouple modality cues and adapt their contributions to modality availability. However, coarse news labels provide little guidance for modality decoupling, while training on complete inputs can induce modality bias. To address these challenges, we propose Head-wise Modality Decoupling and Regulation (HMDR) for multimodal large language models. First, Modality Head Decoupling decouples modality representations through soft head grouping by attention preference. Second, Modality Head Regulation regulates head contributions through compensation for imbalanced modality attention. Finally, Modality Invariance Learning promotes invariance by aligning representations of each modality under replacement of the other. Experiments on two benchmarks show that HMDR improves detection under missing modalities while maintaining performance on complete inputs.

cs.CV↗

arXiv2026

Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation

In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage 'grasp-then-plan' framework that generates grasp candidates and performs downstream motion planning conditioned on the selected grasp. Given a failed manipulation trajectory, we learn a failure attribution model that generalizes to unseen grasps and produces a stable distribution over failure modes for diagnosis-guided optimization. Based on these attribution results, we then optimize both modules in a diagnosis-driven manner: on the grasping side, we inject task-level priors and risk penalties into grasp candidate scoring and optimization to suppress unstable or task-incompatible grasps; on the planning side, we target high-risk initial states through data collection and fine-tuning to address genuine planning bottlenecks. We evaluate the proposed framework in both simulation and real-robot experiments, and show that GTP-FA improves the corresponding base learners across RL, IL, diffusion-policy, and VLA-based settings, achieving substantially higher overall task success rates.

cs.RO↗

arXiv2026

When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement

Autonomous agents increasingly depend on harnesses that govern interaction, memory, tools, and execution around large language models (LLMs). While recent work has begun to evolve these harnesses from execution experience, the supervision used to guide evolution often remains relatively static, making it difficult to track changing behavioral weaknesses across rounds and potentially limiting generalization to unseen tasks. To address this issue, we propose RuHarness, a rubric--harness co-evolution framework for continual agent self-improvement. RuHarness continually discovers and reassesses behavioral criteria from execution trajectories, prioritizes the most relevant unresolved weaknesses, and uses them to guide targeted harness updates while keeping the task objective fixed. Extensive experiments across diverse agent tasks and backbone models show that RuHarness outperforms strong harness-evolution baselines, particularly on unseen held-out tasks. Further analyses show that adaptive rubric prioritization tracks shifting weaknesses and supports more sustained improvement and stable generalization across evolution rounds.

cs.AI↗

arXiv2026

Euclidean Crowding-Driven Adaptive Euclidean-Hyperbolic Graph Learning

Hyperbolic graph representation learning has emerged as a powerful paradigm for modeling hierarchical data. Unlike Euclidean space, which exhibits polynomial volume growth, hyperbolic space grows exponentially with radius, naturally accommodating tree-like structures without the geometric distortion known as Euclidean crowding. Despite strong empirical performance, the theoretical mechanisms underlying hyperbolic embeddings remain poorly understood. In this work, we provide a geometric quantifcation research for Euclidean crowding, a generalization-theoretic explanation for the superiority of hyperbolic space on hierarchical data, and further develop an adaptive Euclidean-hyperbolic fusion learning framework toward bridging this gap. Specifically, we \textbf{first} establish a geometric quantification of Euclidean crowding by analyzing layer-wise minimum separation in node embeddings, proving that fixed-dimensional Euclidean embeddings of tree-like graphs inevitably exhibit layer-wise crowding that leads to weaker margin-based generalization bounds. \textbf{Second}, we provide a generalization-bound for the superiority of hyperbolic geometry on hierarchical data. \textbf{Third}, we propose EH-Fusion, a geometry-adaptive framework that lifts Euclidean GNN representations into hyperbolic space via exponential mapping and adaptively fuses both geometries based on node-level neighborhood expansion patterns. Extensive experiments validate our theoretical insights and demonstrate the effectiveness of EH-Fusion on benchmark datasets.

cs.LG↗