arXiv subjects

Tao Li

Publications and source records attributed to Tao Li.

At least 73 records · Page 4Linked to original sources

arXiv2026

Large Language Models for Mobile GUI Text Input Generation: An Empirical Study

Mobile apps have become essential, making quality assurance increasingly important. GUI testing is widely used for automated exploration, yet text-input components remain a major obstacle, as many UI pages require semantically appropriate text inputs before proceeding. Large Language Models have shown promise in generating context-aware text, but the effectiveness of different UI representations, feedback mechanisms, and human intervention remains unclear. This paper presents a large-scale empirical study addressing these gaps. We evaluate nine state-of-the-art LLMs across 115 real-world apps, comparing three UI-context prompting settings: extracted textual context, UI-hierarchy XML, and screenshot-based vision input. Results show extracted context and XML achieve comparable page-pass-through rates of 71.4% and 71.0%, while vision-based input reaches 65.1% but incurs substantially higher token costs. In bug-detection experiments with 37 real-world text-input bugs, LLMs generating invalid inputs detect about 51% of issues across all evaluated models. A feedback-enhanced protocol, incorporating execution outcomes into subsequent attempts, improves average PPTRs to 69.2-73.8% and raises bug-detection rates to 51.0-64.5%. Human testers further refine inputs, yielding additional gains. We integrate the process into DroidBot, augmenting its UI-exploration capabilities. We derive actionable insights on context selection, cost-effectiveness, feedback strategies, and human-LLM collaboration, advancing both knowledge and practice in Android testing.

cs.SE↗

arXiv2026

LLMCFG-TGen: Using LLM-Generated Control Flow Graphs to Automatically Create Test Cases from Use Cases

Appropriate test-case generation is critical in software testing and significantly impacts testing quality. Requirements-Based Test Generation (RBTG) derives test cases from software requirements to verify whether system behavior aligns with user needs and expectations. Requirements are often documented in Natural Language (NL), with use-case descriptions being a popular method for capturing functional behaviors and interaction flows in a structured, readable form. Recently, Large Language Models (LLMs) have shown strong potential for automating test generation from NL requirements. However, existing LLM-based approaches often fail to ensure comprehensive and non-redundant coverage, and may not adequately capture complex conditional logic, leading to incomplete test cases. To address these limitations, we propose an end-to-end approach called Test Generation based on LLM-generated Control Flow Graphs (LLMCFG-TGen), which generates test cases from NL use-case descriptions. It consists of three steps: (1) CFG Generation, where an LLM transforms a use case into a structured JSON-based Control Flow Graph capturing all potential branches; (2) Test-Path Extraction, where the CFG is traversed to derive execution paths; and (3) Test-Case Creation, where test cases are generated from these paths. We evaluate the approach on six use-case datasets across diverse domains. Results show that LLMs can effectively construct structured CFGs from NL use cases. Compared with two baselines, LLMCFG-TGen produces more complete and structurally consistent test cases by better capturing behavioral logic and execution flows. Both LLM-based and practitioner-based evaluations further confirm improved comprehensiveness and logical coherence while reducing manual effort.

cs.SE↗

arXiv2026

ATGBuilder: Feature-Assisted Graph Learning for Activity Transition Graph Construction with Seed Supervision

Android applications are organized around activities that provide visual Graphical User Interface (GUI) containers that host the UI and handle user interaction events. Activity Transition Graphs (ATGs) have been widely used to model apps' GUI navigation. However, the construction of high-quality ATGs is challenging: ATGs based on static analysis may miss acceptable transitions and may extract infeasible ones; while dynamically explored ATGs can yield incomplete transitions. Recent learning-based approaches can treat ATG construction as a seed-supervised link-prediction task. However, the use of activity-layout and widget-trigger information for ATG construction remains limited. We propose ATGBuilder, a feature-assisted graph-learning approach for seed-supervised ATG construction. ATGBuilder uses a Large Language Model (LLM) to summarize UI activity metadata from layouts into compact textual functionality summaries. ATGBuilder explicitly models widget-trigger information into the edge attribute: It then uses an auxiliary widget-attribute reconstruction objective on this information during model training. ATGBuilder's performance was evaluated across a series of ablations on the frontmatter corpus, and an experiment on benchmark using manually-checked ground-truth ATGs. Experiments on multiple benchmarks show that ATGBuilder significantly outperforms state-of-the-art methods. We further demonstrate its effectiveness by improving automated GUI exploration tools through better navigation guidance.

cs.SE↗

arXiv2026

Bridging the Post-discharge Gap: A Traceable Multi-agent Framework for Safe and Continuous Care

Post-discharge clinical follow-up is critical for maintaining continuity of care and mitigating long-term health risks. However, traditional follow-up paradigms suffer from shortage of health workforce, fragmented patient histories, and information silos across clinical departments. While large language models have demonstrated potential in medical question-answering, their deployment in continuous care is hindered by hallucination risks and a fundamental inability to reason over longitudinal, patient-specific constraints. Here we present Healink, a memory-enhanced multi-agent framework to support AI-assisted post-discharge follow-up by generating prescription-grounded, traceable responses that improved completeness and perceived clinical utility in retrospective and physician-blinded evaluations. The architecture seamlessly integrates a triage routing mechanism, a unified memory enhancement module utilizing a robust relational database for optimal latency, and a strict constraint-based retrieval-augmented generation engine. By vectorizing historical clinical records and employing weighted similarity functions across diverse phenotypic and intervention dimensions, Healink ensures precise inter-patient and intra-patient case matching while actively preventing cross-departmental drug conflicts. We evaluated Healink on a dataset comprising 400 continuous and 85 highly complex real-world follow-up cases, alongside the webMedQA benchmark. In a rigorous single-blind evaluation conducted by clinical experts, the framework outperformed human physician baselines in both authoritativeness and clinical safety. By generating a traceable, white-box evidence chain, Healink provides a scalable, safe, and highly effective paradigm for intelligent patient management, ultimately enhancing societal healthcare outcomes.

cs.MA↗

arXiv2026

Efficient Minimal Solvers for Visual-Inertial Relative Pose Estimation in Multi-Camera Systems

Estimating the relative poses of multi-camera systems is a fundamental problem in computer vision, with critical applications in autonomous vehicles, mobile devices, and unmanned aerial vehicles (UAVs). However, existing solutions often suffer from high computational complexity or rely on an excessive number of point correspondences, limiting their real-world applicability. To address these limitations, we propose two efficient minimal solvers for estimating the relative poses of multi-camera systems using a novel parameterization. The first solver leverages the vertical direction prior provided by Inertial Measurement Units (IMUs), while the second utilizes the rotation axis direction prior from IMUs. Our methods require only four point correspondences and reduce the problem of multi-camera relative pose estimation to solving a univariate 6th-degree polynomial, a significant improvement over existing approaches, which typically involve 8th-degree polynomials. This reduction in computational complexity and correspondence requirements makes our solvers particularly effective when integrated into RANSAC frameworks, demonstrating strong potential for visual odometry applications. Through rigorous evaluations on synthetic data and the KITTI benchmark, our methods achieved superior computational efficiency and competitive accuracy compared to state-of-the-art algorithms.

cs.CV↗

arXiv2026

Efficient Minimal Solvers for Relative Pose Estimation in Autonomous Driving Applications

With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localization and environment perception, demanding high real-time performance and robustness. Existing methods, however, often involve high computational costs and rely heavily on abundant feature matches, limiting their applicability in time-sensitive driving scenarios. To address these limitations, this paper introduces a unified framework for efficient relative pose estimation, built upon a novel translation parameterization and first-order rotation approximation. Within this framework, we propose three efficient minimal solvers specifically designed for autonomous vehicles. The first solver integrates the vertical direction prior from Inertial Measurement Units (IMUs), the second utilizes the rotation axis direction prior during steering maneuvers, and the third is designed for planar motion - a realistic assumption for ground vehicles operating on structured roads. By reducing both the minimal number of point correspondences and the algebraic complexity, our methods enable faster hypothesis generation within RANSAC-based pipelines, improving suitability for real-time systems. Extensive experiments on synthetic datasets and the KITTI autonomous driving benchmark demonstrate that the proposed solvers achieve a favorable balance between speed and accuracy compared to existing state-of-the-art algorithms.

cs.RO↗

arXiv2026

In-Context Reinforcement Learning via Communicative World Models

Reinforcement learning (RL) agents often struggle to generalize to new tasks and contexts without updating their parameters, mainly because their learned representations and policies are overfit to the specifics of their training environments. To boost agents' in-context RL (ICRL) ability, this work formulates ICRL as a two-agent emergent communication problem and introduces CORAL (Communicative Representation for Adaptive RL), a framework that learns a transferable communicative context by functionally separating latent representation learning from control. In CORAL, an Information Agent (IA) is pre-trained as a world model on a diverse distribution of tasks. Its objective is not direct return maximization, but world modeling and distilling its understanding into concise messages. The emergent communication protocol is shaped by a novel Causal Influence Loss, which measures the effect that the message has on the next action. During deployment, the previously trained IA serves as a fixed contextualizer for a new Control Agent (CA), which learns to solve tasks by interpreting the provided communicative context. Our experiments demonstrate that this approach enables the CA to achieve significant gains in sample efficiency and successfully perform zero-shot adaptation with the help of pre-trained IA in diverse online and offline environments, validating the efficacy of learning a transferable communicative representation.

cs.LG↗

arXiv2026

Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter

Low-rank adaptation (LoRA) enables parameter-efficient specialization of foundation models, but the proliferation of task-specific adapters fragments capabilities across many adapters, complicating reuse and deployment. We study the problem of merging $T$ LoRAs into a single rank-$r$ LoRA, thereby preserving the benefits of low-rank structure. Existing Merge-then-Compress pipelines treat the rank constraint as an afterthought: they merge adapters in the full parameter space, then compress the merged result to rank $r$ via truncated SVD. However, full-parameter merging may destroy the low-rank structure, making it difficult for subsequent compression to recover an effective rank-$r$ LoRA. We propose Compress-then-Merge (CtM), a reversed pipeline that enforces the rank-$r$ bottleneck before merging: CtM computes shared $r$-dimensional subspaces using only the LoRA weights to capture cross-adapter common structure, projects each adapter into the shared subspaces to obtain $r\times r$ coordinates, and then applies standard merging rules in this reduced space. CtM guarantees a rank-$r$ LoRA by construction, avoiding post-hoc truncation, and enables efficient computation in the core space spanned by concatenated LoRA factors. Experiments across multiple models and tasks show that CtM consistently outperforms existing single-LoRA-output baselines while narrowing the performance gap to full-parameter merging methods.

cs.LG↗

arXiv2026

A Unified Variational Design of Predictive Mirror Descent in Convex Games under Stochastic Feedback

Mirror descent provides a geometric framework for learning in games, but its last-iterate behavior can fail in weakly stable regimes, where the dynamics may exhibit rotational or recurrent transients. Predictive mirror methods mitigate this issue by modifying the feedback entering the mirror update, yet standard predictive variants are typically introduced algorithmically and analyzed one at a time. This letter gives a variational route to predictive feedback by constructing a stochastic mirror differential game with an auxiliary memory state. Its stage cost couples two Fenchel terms: a strategic term evaluated at a predicted profile and a corrective term driven by realized feedback. The resulting equilibrium feedback induces two-channel predictive mirror dynamics in general mirror geometry. Under local mirror regularity, a quantitative local Bregman growth condition, and bounded Brownian diffusion, we establish finite-horizon local terminal-time bounds in expectation and with high probability, together with an exit-probability estimate for the localization neighborhood. The result provides a unified variational construction of the induced predictive-memory mirror flow together with a local stochastic certificate for last-iterate performance near stable equilibria.

math.OC↗

arXiv2026

Med-URWKV†: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation

Medical image segmentation is a fundamental task in computer-aided diagnosis and treatment. Existing approaches based on CNNs, ViTs, Mamba, and hybrid models still suffer from limitations such as restricted receptive fields, high computational cost, or insufficient accuracy. Recently, Vision Receptive-field Weighted Key-Value (VRWKV) models have emerged as a promising alternative,delivering strong long-range dependency modeling for visual tasks. However, current studies on VRWKV-based medical image segmentation mainly focus on hybrid architectures trained from scratch, while the potential of large-scale pretrained pure VRWKV models remains unexplored. In this work, we systematically investigate the effectiveness of pure VRWKV architectures for medical image segmentation. We construct Med-URWKV-T and Med-URWKV-S by reusing pretrained VRWKV encoders at different scales and pairing them with pure VRWKV decoders, enabling a comprehensive evaluation of pretrained pure VRWKV models in this domain. To further enhance performance, we propose two VRWKV-compatible modules: a Frequency-Aware Wavelet Attention (FAWA) module, which exploits wavelet transforms to capture edge details and structural characteristics, and a Multi-Scale Channel Fusion (MSCF) module, which integrates multi-scale features to strengthen informative channel representations. By incorporating them into Med-URWKV-T, we obtain the enhanced model Med-URWKV†. Extensive experiments on five medical image segmentation datasets demonstrate that Med-URWKV achieves performance comparable to or superior to state-of-the-art methods and carefully designed hybrid VRWKV architectures. Moreover, Med-URWKV† further improves segmentation accuracy, surpassing Med-URWKV-S while using only half of its parameter count, and achieves the highest average Dice similarity coefficient of 88.00%. The codes will be released.

eess.IV↗

arXiv2026

Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language

Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for gentle manipulation due to scarce aligned vision-tactile-language data and the lack of effective closed-loop force feedback mechanisms. To address these challenges, we introduce Tabero, a benchmark and model suite for gentle, language-conditioned robotic manipulation that demands fine-grained contact force perception. First, the Tabero benchmark addresses the scarcity of tactile data by presenting a data-efficient pipeline that repurposes open-source robot manipulation trajectories to generate diverse vision-tactile-language tasks, and establishes a multidimensional evaluation protocol that measures task success alongside physical interaction quality. Second, we propose Tabero-VTLA, an architecture with a decoupled force-position command interface; the resulting force-position commands are executed by a fixed hybrid controller to enable real-time, force-aware manipulation. Evaluated on Tabero, our model maintains high task success while reducing average grip force by over 70\% under gentle instructions, demonstrating its ability to modulate interaction forces based on multimodal experience. Our code is publicly available at https://github.com/NathanWu7/Tabero.

cs.RO↗

arXiv2026

Beyond Similarity: Task-Aligned Retrieval for Language Models

Retrieval-augmented generation (RAG) ranks passages by semantic similarity to the input, implicitly assuming that semantic similarity is a reliable indication of applicability in downstream tasks. This assumption breaks down when task success depends not on topical relevance but on applying the correct rules, constraints, or procedural guidance. In such settings, the most useful context may be the rule triggered by the input rather than the most semantically similar passage. We propose Task-Aligned Retrieval (TAG), a retrieval framework that replaces similarity-based retrieval with applicability-based rule selection. TAG transforms source documents into traceable condition-action rules, identifies which rules apply to a given input through pairwise LLM judgments, and generates the output conditioned only on the selected actions. We empirically observe that across Wikipedia NPOV rewriting, HumanEval with PEP~8 compliance, and NBA transaction reasoning on RuleArena, TAG consistently outperforms standard RAG, with the largest gains in high-mismatch settings (up to 12.2\%) while reducing retrieved context by up to 93\%. These results suggest that, in rule- and instruction-governed tasks, retrieval should optimize for applicability rather than for semantic similarity alone.

cs.IR↗

arXiv2026

Metasurface Holography on a Relative-Phase Manifold for Stable and High Fidelity Tweezer-Array Generation

We present a new holographic approach for generating large scale, polarization resolved optical tweezer arrays. By analyzing the ideal Jones fields that realize a target pattern, we identify that the fundamental degrees of freedom are the relative phases of the individual tweezers, rather than the full spatial phase profile. Leveraging this insight, we formulate a reverse projection optimization that adjusts only a small set of phase parameters to approximate the ideal operator within the physical constraints of a metasurface. This produces significantly higher fidelity and robustness than Gerchberg_Saxton type algorithms. Experimentally, we demonstrate H, V, L, and R polarized tweezer arrays using a single layer metasurface. A key advantage of our method is its phase stability, yielding strong resistance to optical aberrations and enabling coherent global phase modulation such as forming vortex tweezer lattice, without degrading trap quality. This framework provides a conceptually clear and experimentally powerful route for scalable optical field synthesis.

physics.optics↗

arXiv2026

Real-World Scene Recovery for Scattering-Degraded Images Using Spatial and Frequency Priors

Scene recovery from real-world images degraded by scattering effects, such as haze, sandstorm, underwater, and remote sensing conditions, remains a fundamental yet challenging problem in computer vision. Existing methods either rely on a single prior, which is inherently insufficient to characterize diverse scattering degradations, or employ deep networks trained on synthetic data, which often suffer from limited generalization to real-world scenarios. In this paper, we propose Spatial and Frequency Priors (SFP) for real-world scene recovery under scattering-induced degradations. In the spatial domain, we observe that the inverse of a scattering-degraded image reveals a projection along its spectral direction that correlates with the underlying scene transmission. Based on this observation, a spatial prior is formulated to estimate the transmission map, enabling effective recovery of scene radiance under scattering effects. In the frequency domain, we design an adaptive frequency enhancement strategy guided by two novel priors. The first prior assumes that the mean intensity of the direct current (DC) components across channels in degraded images approximates that of the corresponding clear images. The second prior is based on the observation that, in clear images, low radial frequencies within a narrow band contribute only a small proportion of the overall spectrum. These priors enable targeted compensation for scattering-induced attenuation across different frequency bands. Finally, a weighted fusion of the spatial and frequency domain results is performed to obtain the final recovered image. Extensive experiments on diverse real-world scattering-degraded scenarios verify that our SFP achieves superior performance and strong generalization capability compared to state-of-the-art methods.

cs.CV↗

arXiv2026

Probing scalar-neutrino and scalar-dark-matter interactions with PandaX-4T

Scalar-mediated interactions may exist among neutrinos, dark matter particles, or between the two. Double $β$-decay experiments provide a powerful tool to probe such exotic interactions. Using $^{136}$Xe double $β$-decay data from PandaX-4T, we perform the first direct spectral search in the energy range of 20 to 2800~keV, setting the most stringent limits to date on scalar-mediated neutrino self-interactions for mediator masses below 2~MeV$/c^2$. These results place significant constraints on models invoking such interactions to alleviate the Hubble Tension. Assuming the same scalar also mediates dark matter self-interactions, constraints on the dark matter-scalar interactions can be placed in conjunction with cosmological constraints.

hep-ex↗

arXiv2026

OneVoice: One Model, Triple Scenarios-Towards Unified Zero-shot Voice Conversion

Recent progress of voice conversion~(VC) has achieved a new milestone in speaker cloning and linguistic preservation. But the field remains fragmented, relying on specialized models for linguistic-preserving, expressive, and singing scenarios. We propose OneVoice, a unified zero-shot framework capable of handling all three scenarios within a single model. OneVoice is built upon a continuous language model trained with VAE-free next-patch diffusion, ensuring high fidelity and efficient sequence modeling. Its core design for unification lies in a Mixture-of-Experts (MoE) designed to explicitly model shared conversion knowledge and scenario-specific expressivity. Expert selection is coordinated by a dual-path routing mechanism, including shared expert isolation and scenario-aware domain expert assignment with global-local cues. For precise conditioning, scenario-specific prosodic features are fused into each layer via a gated mechanism, allowing adaptive usage of prosody information. Furthermore, to enable the core idea and alleviate the imbalanced issue (abundant speech vs. scarce singing), we adopt a two-stage progressive training that includes foundational pre-training and scenario enhancement with LoRA-based domain experts. Experiments show that OneVoice matches or surpasses specialized models across all three scenarios, while verifying flexible control over scenarios and offering a fast decoding version as few as 2 steps. Audio samples are available on demo page.

eess.AS↗

arXiv2026

Statistical sensitivity of neutrinoless double-beta decay exchange mechanism discrimination by tracking experiments

Reconstruction of the individual energies and the opening angle between the electrons emitted in neutrinoless double-beta decay can probe the nature of the beyond-the-Standard-Model exchange mechanism that underlies the process. Although it is often stated that discrimination of the mechanism would require such measurements to be performed with high statistics, we show that this is not the case. If a single mechanism dominates the process, its discrimination at the 1$σ$ level is already achieved with just a few well-reconstructed events; only $\sim$10 such events are required to reach 3$σ$-level discovery sensitivity. In the presence of realistic reconstruction uncertainties, this requirement increases to $\sim$25 events, indicating that substantial discrimination power is retained as long as backgrounds remain small. We conclude that the pursuit of tracking detectors for exchange-mechanism discrimination remains valuable even for ``discovery-class'' experiments in which only a few signal counts are expected.

hep-ph↗

arXiv2026

Structure preserving quaternion conjugate gradient-type methods for solving non-Hermitian quaternion linear systems

In this paper, we consider the non-Hermitian quaternion linear systems arising from color image restoration and three-dimensional signal filtering problems. For exploring to solve such systems, we present two innovative structure-preserving conjugate gradient-type methods, QNHERLQ and QNHERQR, which are based on the unitary equivalence transformations of the non-Hermitian quaternion matrices to tridiagonal forms, called quaternion Saunders-Simon-Yip tridiagonalization procedure. The proposed tridiagonalization procedure for non-Hermitian quaternion matrices is closely related to the quaternion Lanczos process for Hermitian matrices, and is very different from the quaternion Lanczos biorthogonalization process for non-Hermitian matrices. The convergence of QNHERLQ and QNHERQR is discussed, which depends on the singular values of the coefficient matrix. Also we show that both algorithms have the finite termination property and constant costs per iteration step. Numerical results illustrate that the proposed algorithms are with the robustness and effectiveness compared with QGMRES and QQMR.

math.NA↗