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Xiang Li

Publications and source records attributed to Xiang Li.

At least 145 records · Page 8Linked to original sources
arXiv2026

Multimodal Adaptive Control for Safe Robotic Craniotomy Under Partial Observability

Autonomous robotic craniotomy requires continuous regulation of tool-tissue interactions to mitigate mechanical overload and thermal damage while maintaining surgical efficiency. However, this process is inherently partially observable due to unknown, time-varying tissue properties and the inability to directly measure cutting temperatures under physical occlusion. To address these challenges, we propose RL-MACRO, a cybernetic closed-loop intelligence framework that couples multimodal perception, adaptive decision-making, and robotic execution. This framework empowers the surgical robot to autonomously perceive inaccessible states from partial sensory feedback and dynamically optimize its behaviors under uncertain environment. A CNN-LSTM observer first fuses force and sound feedback to reconstruct the hidden temperature state (R^2=0.939, MAE = 1.717 deg C). This reconstructed temperature, alongside multi-sensor features, forms the belief state for an offline Implicit Q-Learning (IQL) policy. A novel dual-head Actor dynamically coordinates the feed rate, spindle speed, and cutting depth to optimize efficiency within strict safety bounds. These decisions are seamlessly translated into spatial motions via online trajectory re-planning and velocity servoing. Experiments on bovine ribs and six ex vivo goat skulls validate the system's robust perception, adaptive recovery from force/temperature excursions, and smooth execution on irregular surfaces, establishing a data-driven cybernetic paradigm for safe and efficient autonomous bone cutting.

cs.RO↗
arXiv2026

Mode-Resolved Light Scattering Recovers Polymer Thermodynamics in Solutions with Trace Large-Mass Scatterers

Light scattering provides direct access to polymer conformations and thermodynamics. However, trace large-mass scatterers such as aggregates and nanobubbles form unavoidably in polymer solutions and dominate the scattered intensity, obscuring the intrinsic polymer signal. We demonstrate that resolving the static scattering intensity by molecular mobility cleanly separates the polymer and large-scatterer contributions. Applying this mode-resolved analysis to aqueous poly(ethylene glycol) solutions, we isolate the polymer scattering even when these scatterers account for more than 90 % of the total intensity. The resolved polymer intensity recovers the universal osmotic equation of state from the dilute to the semidilute regime over 288 to 358 K. This approach establishes a reliable basis for measuring the thermodynamics of interacting macromolecules in solutions where irreproducible large-mass scatterers have precluded quantitative analysis.

cond-mat.soft↗
arXiv2026

Outstanding TC Enhancement in 5d-3d Y2NiIrO6 by Compression

Understanding and predicting the properties of 5d compounds critically depend on the identification of the superexchange interactions from which their magnetism emerges. The study of pressure effects on double perovskite Y2NiIrO6 (YNIO) provides deep insight toward this goal. At ambient pressure, YNIO is a ferrimagnetic insulator with the Ir4+-5d Jeff = 1/2 Mott-insulating state. Under physical pressure up to 17 GPa, the compound exhibits concurrent compression on Ni/Ir-O bond lengths and Ni-O-Ir bond angles, leading to an increase of the Curie temperature from 192 to 243 K. On the contrary, external pressure increases distanced Ir-Ir interaction and in turn induces magnetic frustration in Sr2IrO4/Sr3Ir2O7 due to the extended 5d orbitals. In YNIO, the rock-salt ordered Ni-Ir naturally blocks extended superexchange beyond the nearest neighbor, and in turn suppresses such magnetic frustration. Moreover, the orthogonal Ni eg-Ir t2g pathway in YNIO is robust under lattice distortion, while the superexchange is weakened by bond bending in La2NiMnO6 with a similar half-filed eg-t2g configuration. Our findings establish a framework for elucidating the mechanism of 5d-3d superexchange and guide bond-engineered magnetism in iridate-related systems.

cond-mat.str-el↗
arXiv2026

Symmetry and uniqueness of the positive solution for the critical Hartree equation on the Heisenberg group

We apply the moving plane method in integral forms to classify the positive solutions of the critical Hartree equation on Heisenberg group \begin{equation}\label{0.1} -Δ_{\mathbb{H}}u=\left(\int_{\mathbb{H}^{n}}\frac{|u(ξ)|^{Q^{\ast}_μ}}{|ζ^{-1}ξ|^μ}\mathrm{d}ξ\right)|u|^{Q^{\ast}_μ-2}u,~~~ζ,ξ\in\mathbb{H}^{n}, \end{equation} where $Δ_{\mathbb{H}}$ denotes the Kohn Laplacian, $u(ξ)$ is a real-valued function, $Q=2n+2$ is the homogeneous dimension of $\mathbb{H}^{n}$, $μ\in (0,Q)$ is a real parameter and $Q^{\ast}_μ=\frac{2Q-μ}{Q-2}$ is the upper critical exponent associated with the Hardy-Littlewood-Sobolev inequality on the Heisenberg group. By introducing the $\mathbb{H}$-reflection, we prove that the solutions of (\ref{0.1}) are cylindrical, upto Heisenberg translation and suitable scaling of function \begin{equation*}\label{0.2} u_{0}(ζ)=u_{0}(z,t)=\left((1+|z|^{2})^{2}+t^{2}\right)^{-\frac{Q-2}{4}},~~~ζ=(z,t)\in \mathbb{H}^{n}. \end{equation*} Furthermore, we show that these positive solutions are also CR inversion-symmetric with respect to the unit CC sphere. Consequently, we establish the uniqueness of positive solutions to equation (\ref{0.1}).

math.AP↗
arXiv2026

Arm-Aware Guided Dexterous Grasp Generation with Arm-Agnostic Grasp Models

Dexterous grasp generation that considers arm-related constraints is crucial in real-world scenarios involving arm environment collision avoidance, workspace boundary grasps, and consecutive grasping. Existing hand-centric grasp models, which primarily focus on the floating hand's pose, are insufficient for such cases. Conventional arm-aware methods either rely on rejection sampling to discard infeasible samples or require retraining on arm-specific data, leading to low sample efficiency under adverse conditions or limited generalization across different robots and environments. To overcome these limitations, this letter presents an arm-aware dexterous grasp generation framework that leverages pretrained arm-agnostic grasp models while integrating arm and environmental information only at inference time. Specifically, we formulate arm-aware constrained grasp generation as a joint optimization of hand pose and arm configuration, and derive closed-form gradients for arm-related constraints. Assuming the hand pose distribution is represented by a diffusion model, we prove that gradient-based optimization is equivalent to guided diffusion sampling, steering near-feasible samples toward the feasible region. Through comprehensive evaluation involving 10k objects across 6 scenarios, we demonstrate that the proposed framework generates feasible grasps in highly constrained settings with significantly higher probability, highlighting its advantages in real-world applications. Supplementary materials and appendix are available at https://arm-aware-dexgrasp.github.io/.

cs.RO↗
arXiv2026

Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport

Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current vision models either lack contextual reasoning (cell foundation models) or cannot operate at the cell level (pathology MLLMs). We present Loki-OT, which propagates region-level tissue reasoning to individual cell predictions via Unbalanced Optimal Transport, using MLLM-derived density priors as soft guidance for ambiguous cell reassignment. Loki-OT is motivated by the observation that pretrained cell foundation model features already encode discriminative information, including tissue context, but standard cell-level supervision fails to use tissue context effectively. The resulting transport plan is distilled into a lightweight student MLP classifier that learns context-aware decision boundaries within the pretrained feature space. On the independent TCGA-BRCA cohort, Loki-OT achieved lower patient-level MAE than the fully supervised in-domain PanopTILs classifier and improved F1 in epithelium-rich mimicry tissues, using 278 weak region-level MLLM estimates built on a general-domain cell foundation model. Code: https://github.com/xiangli980/Lymphocyte_Mimicry_Correction_via_Loki_OT

cs.CV↗
arXiv2026

Vision-Based Tactile Intelligence for Robotics: Sensing, Learning, and Embodied Manipulation

Tactile sensing is essential for robots in contact-rich tasks, yet many tactile sensors still provide sparse, low-dimensional signals that do not capture sufficient information for complex robotic perception and interaction. Vision-based tactile sensors (VBTSs) offer a powerful alternative by con-verting contact-induced deformation of a soft interface into im-ages. The image-based formulation gives VBTSs high-resolution, information-rich tactile observations that enable complex robotic tasks. This review surveys the full VBTS pipeline and treats sensing hardware, learning methods, simulation, and datasets as an integrated sensing-and-learning system. We 1) organize representative VBTSs into a hardware taxonomy structured by deformable elastomer design, sensor size and shape, and optical system design to guide future sensor development; 2) present a hierarchical view of learning-based tactile intelligence from low-level signal understanding to task-level policies and foundation models; and 3) examine simulation platforms and tactile datasets as a scaling layer, together with sim-to-real transfer and cross-sensor adaptation for training, benchmarking, and deployment. Finally, we identify open challenges and future directions for VBTSs in robotics. By providing a holistic view of how hardware, AI architectures, simulation, and datasets interact, this review aims to advance tactile intelligence for contact-rich robotic tasks.

cs.RO↗
arXiv2026

Thinking with Gaze: Sequential Eye-Tracking as Visual Reasoning Supervision for Medical VLMs

Vision--language models (VLMs) process images as visual tokens, yet their intermediate reasoning is often carried out in text, which can be suboptimal for visually grounded radiology tasks. Radiologists instead diagnose via sequential visual search; eye-tracking captures this process as time-ordered gaze trajectories that reveal how evidence is acquired over time. We use eye-gaze as supervision to guide VLM reasoning by introducing a small set of dedicated gaze tokens. These tokens are trained to predict gaze-selected image patch indices in temporal order, encouraging the model to follow human-like evidence acquisition and integration. Experiments on MIMIC-EYE and multiple external zero-shot benchmarks show consistent gains over baselines, achieving state-of-the-art in-domain performance and improved out-of-domain robustness. These results highlight temporally ordered gaze as an effective supervision signal for learning visually grounded medical reasoning.

cs.CV↗
arXiv2026

Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset

Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet datasets with stylized ground truth forces existing methods to rely on self-supervised or proxy objectives, which limits model capability. To mitigate this gap, we design a data construction pipeline to build TST100K, a large-scale dataset of 100,000 content-reference-stylized triplets. At the core of this pipeline, we train a tone style scorer to ensure strict stylistic consistency for each triplet. In addition, existing methods typically extract content and reference features independently and then fuse them in a decoder, which may cause semantic loss and lead to inappropriate color transfer and degraded visual aesthetics. Instead, we propose ICTone, a diffusion-based framework that performs tone transfer in an in-context manner by jointly conditioning on both images, leveraging the semantic priors of generative models for semantic-aware transfer. Reward feedback learning using the tone style scorer is further incorporated to improve stylistic fidelity and visual quality. Experiments demonstrate the effectiveness of TST100K, and ICTone achieves state-of-the-art performance on both quantitative metrics and human evaluations. The project page is available online: https://dengyuhai.github.io/ICTone_Project/.

cs.CV↗
arXiv2026

Valhalla: A Layered Knowledge-State and Service-Governance Framework for Long-Term Scientific Knowledge Work

As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity. However, most structured knowledge systems remain node-centric, representing files, concepts, results, and judgments as nodes and relations in a graph. While suitable for personal knowledge management, such structures often depend on individual organizational practices, limiting knowledge sharing, integration, and reorganization across users. This paper presents Valhalla, a layered knowledge-state and service-governance framework for long-term scientific knowledge work. Valhalla replaces flat graphs with layered encapsulation and stable semantic boundaries through a five-layer File-Resource-Entity-Relationship-Graph (FREG) model. File and Resource preserve source identity and provenance, Entity represents knowledge objects, Relationship captures semantic judgments, and Graph provides task-oriented knowledge views, enabling knowledge states from different researchers to be exchanged and reorganized under a unified structure. We further introduce a Router-Contract-Workflow service-governance architecture, inspired by the microkernel paradigm, to constrain how language models access, modify, and extend knowledge states while maintaining structural consistency and auditable operational boundaries. We implement a Valhalla prototype and validate knowledge ingestion, cross-member integration, and scientific writing support through an antibody-design review task comprising 26 paper resources, 80 knowledge entities, and 92 semantic relations. Rather than proposing a new knowledge-extraction algorithm, Valhalla offers a paradigm for organizing collaborative scientific knowledge, transforming individualized knowledge structures into transferable and reorganizable shared knowledge states.

q-bio.NC↗
arXiv2026

Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data

Many stochastic approximation (SA) algorithms evolve along a single trajectory, making uncertainty quantification challenging under nonlinear dynamics and Markov dependence. We develop an online inference framework for nonlinear SA with decreasing step sizes. Under local stability and verifiable conditions, we establish a functional central limit theorem for the partial-sum path. The proof uses a Poisson-equation decomposition to handle Markov dependence and a uniform bound to control endpoint-dependent remainders induced by decreasing step sizes. The resulting path limit yields self-normalized confidence intervals without estimating the asymptotic variance. Our primary construction uses a five-dimensional polynomial-series normalizer, has an asymptotic Student $t_5$ pivot, and requires only constant memory. We apply the framework to Q-learning, including asynchronous tabular, projected linear, and entropy-regularized updates, as well as SGD for generalized linear models with Markov data and inference for the identified product in low-rank adaptation (LoRA). Across four settings, the polynomial-series method achieves near-nominal coverage, with shorter confidence intervals and lower computational cost than online bootstrap.

math.ST↗
arXiv2026

Has ACL Lost Its Crown? A Decade-Long Quantitative Analysis of Scale and Impact Across Leading AI Conferences

The recent surge of language models (LMs) has rapidly expanded NLP/AI research, driving an exponential rise in submissions and acceptances at major conferences. Yet this growth has been shadowed by escalating concerns over conference quality, such as plagiarism, reviewer inexperience, and collusive bidding. However, existing studies rely largely on qualitative accounts, for example expert interviews and social media discussions, lacking longitudinal empirical evidence. To fill this gap, we conduct a ten-year empirical study (2014-2024) spanning seven leading conferences. We build a four-dimensional bibliometric framework covering conference scale, core citation statistics, impact dispersion, and cross-venue and journal influence. Notably, we further propose a metric called Quality-Quantity Elasticity (QQE), which measures the elasticity of citation growth relative to acceptance growth. We highlight two key findings. First, conference expansion does not lead to proportional growth in scholarly impact, as QQE consistently declines over time across all venues. Second, ACL has not lost its crown, continuing to outperform other NLP conferences in median citations, milestone contributions, and citation coverage. This study provides the first decade-long, cross-venue empirical evidence on the evolution of major NLP/AI conferences. Our code is available at https://anonymous.4open.science/r/acl-crown-analysis-38D5.

cs.DL↗
arXiv2026

OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation

Contact-rich manipulation tasks, such as wiping and assembly, require accurate perception of contact forces, friction changes, and state transitions that cannot be reliably inferred from vision alone. Despite growing interest in visuo-tactile manipulation, progress is constrained by two persistent limitations: existing datasets are small in scale and narrow in task coverage, and current methods treat tactile signals as passive observations rather than using them to model contact dynamics or enable closed-loop control explicitly. In this paper, we present \textbf{OmniViTac}, a large-scale visuo-tactile-action dataset comprising $21{,}000+$ trajectories across $86$ tasks and $100+$ objects, organized into six physics-grounded interaction patterns. Building on this dataset, we propose \textbf{OmniVTA}, a world-model-based visuo-tactile manipulation framework that integrates four tightly coupled modules: a self-supervised tactile encoder, a two-stream visuo-tactile world model for predicting short-horizon contact evolution, a contact-aware fusion policy for action generation, and a 60Hz reflexive controller that corrects deviations between predicted and observed tactile signals in a closed loop. Real-robot experiments across all six interaction categories show that OmniVTA outperforms existing methods and generalizes well to unseen objects and geometric configurations, confirming the value of combining predictive contact modeling with high-frequency tactile feedback for contact-rich manipulation. All data, models, and code will be made publicly available on the project website at https://mrsecant.github.io/OmniVTA.

cs.RO↗
arXiv2026

Fermionic Lattice Supersolidity in the Attractive Three-Color Fermi-Hubbard Model

The recent experimental realization of the half-filled three-color Fermi-Hubbard model on a square optical lattice provides a novel platform for exploring exotic states of matter beyond conventional SU(2) systems. In this Letter, we investigate the three-color Fermi-Hubbard model with color-dependent attractive interactions using determinant quantum Monte Carlo simulations. We find that, at quantum degenerate temperatures, a lattice Fermi supersolid state emerges from the interplay between a moderately-to-strongly interacting two-color subsystem and a weakly coupled third-color environment. This supersolid state, characterized by the coexistence of charge-density-wave and color-superfluid orders, is highly promising for experimental detection with current techniques. Our results demonstrate that the attractive three-color Fermi-Hubbard model on a square optical lattice offers an experimentally accessible system for exploring the supersolidity of ultracold lattice fermions, requiring only a simple lattice geometry and easily tunable onsite interactions.

cond-mat.str-el↗
arXiv2026

ATP: Anatomical Torque with Passivity-based Control Framework for Safe Upper-Limb Exoskeleton Assistance

Providing assistance across diverse movements is a central objective of exoskeletons, and anatomical knowledge can enable responsive support that generalizes across tasks. However, anatomical assistance has mainly been studied for lower-limb exoskeletons, where periodic, weight-bearing motions impose lower demands on torque precision. Extending such assistance to complex, nonperiodic upper-limb movements remains challenging. This paper proposes Anatomical Torque with Passivity-Based Control (ATP) for safe upper-limb exoskeleton assistance. First, a scalable musculoskeletal simulation framework trains a unified reinforcement-learning muscle controller that generalizes across upper-limb movements and generates anatomical reference torques without complex biomechanical computations. Second, an online torque-refinement scheme adapts the reference to diverse movements, suppresses tendon-induced spikes, and incorporates a learned anomaly score for safe and comfortable assistance. Third, an interaction torque controller delivers assistance through a cable-driven compliant exoskeleton without constraining motion to predefined trajectories, while an energy tank preserves passivity with theoretical guarantees on torque tracking and system passivity. Simulations and real-world experiments show accurate tracking of long-duration motion sequences and generalization to real-time human movements. The controller achieves accurate torque tracking while preserving passivity and resumes tracking after energy-tank replenishment. An EMG study with five participants further shows reduced target-muscle activity during static and dynamic tasks compared with gravity compensation and open-loop assistance, with reductions of up to 48% relative to movement without the exoskeleton in a dynamic multi-joint task.

cs.RO↗
arXiv2026

Engram-E2VID: Reference-Based Event-to-Video Reconstruction via Generative Activation of Appearance Engrams

Reference-based event-to-video reconstruction aims to recover target RGB frames from a reference frame and the event stream captured over the reference-to-target interval. Although events provide fine-grained temporal cues, they encode sparse and asynchronous log-intensity changes rather than absolute appearance, making faithful reconstruction intrinsically challenging. The central challenge lies in associating event-derived target-time structures with relevant appearance information from the reference frame, especially under complex motion and long temporal intervals. In this work, we propose Engram-E2VID, a structure-guided framework that reconstructs target frames through the generative activation of appearance engrams. Specifically, the reference frame is encoded into token-space appearance engrams, while the event stream and reference context are transformed into a target-time motion-structure scaffold that captures motion boundaries and event-induced structural changes. Within a one-step diffusion backbone, scaffold-derived structural tokens progressively interact with and activate relevant appearance engrams across layers. This token-space association allows target structures to access reference appearance without relying on direct pixel-wise correspondence, while the diffusion prior complements uncertain or newly revealed regions. Across three benchmarks, Engram-E2VID improves PSNR by up to 3.29 dB and reduces LPIPS by up to 0.08 over the strongest same-input baseline, while degrading more slowly as the reconstruction interval increases.

cs.CV↗
arXiv2026

From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models

Economic World Models (EWMs) are generative economic models that simulate how economies evolve from within by modeling heterogeneous agents, their beliefs and actions, and the market and institutional mechanisms through which their interactions produce aggregate outcomes. This paper develops an implementation roadmap for building economic world models as generative engines in which heterogeneous agents act, interact, adapt, and co-evolve with markets and institutions, thereby producing economic dynamics from the inside. We organize EWM systems into a six-level capability ladder, from fixed rule-based agent worlds to adaptive and LLM-based agent worlds, self-evolving agents, evolving institutional worlds, and sim-to-real economic twins aligned with real observations. A systematic literature survey across these levels reveals that existing work remains concentrated in lower-level agent and simulation environments, while systems with self-evolving agents, endogenous institutions, persistent empirical alignment, and validated economic mechanisms remain rare. By translating the EWM agenda into an implementation blueprint, this paper aims to accelerate the development of the next generation of economic simulation environments that can serve as high-fidelity sandboxes for human decision-makers and as training, planning, evaluation, and safety substrates for AI agents. We release a curated paper list and related resources to support future research.

cs.AI↗
arXiv2026

Fifty Years of SAR Automatic Target Recognition: The Road Forward

Synthetic Aperture Radar (SAR) imaging is capable of observing objects in nearly all weather and illumination conditions, and has become an indispensable means of information acquisition for analysis and recognition of objects and scenes. SAR Automatic Target Recognition (SAR ATR) has been one of the most fundamental and challenging problems in remote sensing image analysis. Nowadays, the artificial intelligence (AI) technology, represented by large models and AI agents, has transformed the research paradigm, profoundly influenced various research fields, and continues to evolve at an unprecedented pace. However, the huge potential of AI for SAR image analysis remains locked. To unlock the potential of AI in SAR image understanding, the research community should rethink how to enable bidirectional empowerment between AI and SAR image understanding and strive to achieve substantial breakthroughs at critical bottlenecks. Given this period of remarkable evolution, this paper offers the first comprehensive review of SAR ATR, tracing its development and milestones over the past five decades and providing the research community with a clear roadmap. This survey includes approximately 260 research contributions, covering critical aspects of SAR ATR: pivotal challenges, important datasets, the merits and limitations of representative methods, evaluation metrics, and state-of-the-art performance. Finally, we finish the survey by identifying promising directions for future research. Looking ahead, we call for significant attention on three fundamental pillars: the curation of high-quality large-scale datasets, the design of fair and comprehensive evaluation benchmarks, and the fostering of safe open-source ecosystems.

eess.IV↗