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Yao Yao

Publications and source records attributed to Yao Yao.

At least 19 recordsLinked to original sources
arXiv2026

Avatar Intelligence: Amplifying Human Agency Through Persistent Personal Agents

Personal AI agents are increasingly capable of handling long-horizon tasks across applications, yet users often need to invest substantial effort in specifying goals, resolving ambiguity, and verifying outcomes. We argue that progress should be measured by how effectively agents amplify human agency: enabling people to pursue their goals with less routine supervision while retaining control over consequential decisions. We advocate Avatar Intelligence, a paradigm of persistent personal agents that represent users' goals, values, and constraints through sustained decision delegation across contexts and time. We describe a maturity roadmap from human-directed execution to autonomous operation within delegated domains, and introduce the Agentic Oversight Index (AOI) as a framework for evaluating delivered value against agent-side operational cost and human oversight burden. We identify three core research challenges: identity-continuous personalization that carries user preferences across tasks, user-situated evolution through Rehearsal Engineering, and safety mechanisms that combine technical control with accountability and recourse. These directions define a research agenda for reliable decision delegation to personal agents across tasks and over time.

cs.AI↗
arXiv2026

TacClaw-Infinity: A Large-Scale Vision-Tactile-Language-Action Dataset for Dexterous Manipulation

Large-scale robot learning increasingly relies on diverse human demonstrations, yet tactile manipulation data remain difficult to scale, and existing multimodal datasets often lack consistent quality control and structured supervision. We present TacClaw-Infinity, a large-scale structured Vision-Tactile-Language-Action (VTLA) dataset for contact-rich manipulation, collected with TacClaw, a tactile-enabled UMI-style handheld interface that records synchronized visual observations, fingertip tactile signals, and end-effector trajectories without requiring a target robot during data collection. TacClaw-Infinity is built through a distributed worldwide collection across homes, laboratories, industrial workplaces, retail stores, and outdoor environments including tasks contributed by academic and industrial collaborators. Together, these efforts yield over 10,000 hours, 1 million episodes, and 1 billion frames spanning more than 1,000 task types. We develop a scalable pipeline for trajectory reconstruction, multimodal quality control, embodiment-aware filtering, and structured annotation, covering contact-centric segmentation, subtask description, and spatial grounding. We validate TacClaw-Infinity at both the data and model levels: At the data level, we assess the collection interface, the action-space compatibility of the curated demonstrations, and the structured annotations, while for the model level, we demonstrate cross-embodiment transfer across three heterogeneous robots without robot-specific demonstrations.

cs.RO↗
arXiv2026

A class of Niho type four-valued cross-correlation functions between sequences of different periods

Determining the cross-correlation distributions between $m$-sequences and their decimated sequences is an important problem in sequence design. For two positive integers $n$ and $k$ with $n=2k$, this paper investigates the cross-correlation function between a binary $m$-sequence $\{u(t)\}$ of period $2^{n}-1$ and its $2^{\gcd(s,k)}+1$ short-period decimated sequences $\{u(dt+l)\}$, where the decimation $d=\frac{2^s}{2^s+1}(2^k-1)+1$ satisfies $1 v_2(k)$. The cross-correlation function is proved to be four-valued and its value distribution is completely determined. Several previously known binary cross-correlation functions are included in our result as special cases.

cs.IT↗
arXiv2026

Can Multi-modal (reasoning) LLMs work as deepfake detectors?

Deepfake detection remains a critical challenge in the era of advanced generative models, particularly as synthetic media becomes more sophisticated. In this study, we explore the potential of state of the art multi-modal (reasoning) large language models (LLMs) for deepfake image detection such as (OpenAI O1/4o, Gemini thinking Flash 2, Deepseek Janus, Grok 3, llama 3.2, Qwen 2/2.5 VL, Mistral Pixtral, Claude 3.5/3.7 sonnet) . We benchmark 12 latest multi-modal LLMs against traditional deepfake detection methods across multiple datasets, including recently published real-world deepfake imagery. To enhance performance, we employ prompt tuning and conduct an in-depth analysis of the models' reasoning pathways to identify key contributing factors in their decision-making process. Our findings indicate that best multi-modal LLMs achieve competitive performance with promising generalization ability with zero shot, even surpass traditional deepfake detection pipelines in out-of-distribution datasets while the rest of the LLM families performs extremely disappointing with some worse than random guess. Furthermore, we found newer model version and reasoning capabilities does not contribute to performance in such niche tasks of deepfake detection while model size do help in some cases. This study highlights the potential of integrating multi-modal reasoning in future deepfake detection frameworks and provides insights into model interpretability for robustness in real-world scenarios.

cs.CV↗
arXiv2026

Can Multi-modal (reasoning) LLMs detect document manipulation?

Document fraud poses a significant threat to industries reliant on secure and verifiable documentation, necessitating robust detection mechanisms. This study investigates the efficacy of state-of-the-art multi-modal large language models (LLMs)-including OpenAI O1, OpenAI 4o, Gemini Flash (thinking), Deepseek Janus, Grok, Llama 3.2 and 4, Qwen 2 and 2.5 VL, Mistral Pixtral, and Claude 3.5 and 3.7 Sonnet-in detecting fraudulent documents. We benchmark these models against each other and prior work on document fraud detection techniques using a standard dataset with real transactional documents. Through prompt optimization and detailed analysis of the models' reasoning processes, we evaluate their ability to identify subtle indicators of fraud, such as tampered text, misaligned formatting, and inconsistent transactional sums. Our results reveal that top-performing multi-modal LLMs demonstrate superior zero-shot generalization, outperforming conventional methods on out-of-distribution datasets, while several vision LLMs exhibit inconsistent or subpar performance. Notably, model size and advanced reasoning capabilities show limited correlation with detection accuracy, suggesting task-specific fine-tuning is critical. This study underscores the potential of multi-modal LLMs in enhancing document fraud detection systems and provides a foundation for future research into interpretable and scalable fraud mitigation strategies.

cs.CV↗
arXiv2026

P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning

Multimodal large language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3D generation powered by their priors, world knowledge and reasoning. Yet existing benchmarks rarely evaluate 3D modeling through code. Such modeling demands more than runnable code: from a text or visual specification, a model must generate a parametric 3D program that is geometrically precise, semantically aligned and assembly-consistent. We introduce P3D-Bench, a benchmark for parametric 3D generation. Unlike a 3D mesh, a parametric 3D program exposes explicit dimensions, construction operations and part relations, revealing whether a model recovers a design's structure, not just its appearance. Under a unified protocol, P3D-Bench covers three task families (Text-to-3D, Image-to-3D and Assembly-3D) and scores each output for executability, geometric fidelity, topology, text-grounded constraints, multiview semantic alignment and part-level structure. We construct P3D-Dataset, comprising 400 text cases, 400 image cases, and 203 annotated assemblies. Our evaluation on 100 cases from each task family yields three key findings. First, multi-part generation is substantially more challenging than single-part modeling, with models struggling to compose individual parts into a coherent structure. Second, models can often recover the global shape and semantic identity of the target object, yet fail to reproduce the precise parametric geometry specified by the input. Third, part-level modeling remains weak on assemblies, where models recover neither the geometry of each part nor the right number of parts. These results position P3D-Bench as a benchmark for evaluating precise parametric geometry and part-level structure in parametric 3D generation.

cs.CV↗
arXiv2026

Right Values, Wrong Places: Diagnosing and Fixing Record Misplacement in LLM-Generated JSON

Large language models can generate valid JSON that preserves field values yet binds them to the wrong records or hierarchy levels. We introduce JSONPlacement, a controlled task supplying all values and assignment rules for transforming flat records into hierarchical JSON. Across six open-weight models, we identify two error types: record-assignment errors and hierarchy-realization errors. Whole-record moves account for 98.6% of strictly traceable synthetic misplacements. We predict that aligning input with target traversal should reduce assignment errors, while replacing nested output with record-level paths should reduce hierarchy errors. With predictions fixed before generating new instances, we test both interventions separately and together. On synthetic instances, each nearly eliminates its targeted error while leaving the other. Combined, they achieve exact matches for three 8-12B models on all new synthetic instances and a real-data validation set. These findings motivate evaluating record placement explicitly and jointly designing input organization and output representation to preserve value-path bindings.

cs.AI↗
arXiv2026

Retrospective Progress-Aware Self-Refinement for LLM Agent Training

Long-horizon LLM-based agents receive rich environmental observations during interaction, yet outcome rewards provide limited explicit supervision about how individual actions advance task completion. We investigate whether agents can turn this interaction evidence into useful training signals through retrospective progress assessment. A WebShop pilot shows that direct progress prompting reduces task success, whereas hindsight-annotated demonstrations improve it. We introduce RePro, Retrospective Progress-Aware Training, with a forward-then-reflect rollout: the agent estimates progress while acting, then reassesses each step using the completed trajectory and outcome. After warmup with externally generated demonstrations, policy optimization combines self-generated progress differences, online-retrospective alignment, and format rewards with environment feedback, requiring neither a separate process reward model nor ongoing teacher annotation. Experiments on WebShop, ALFWorld, and Sokoban show that RePro enhances the Qwen family's performance, with up to 11.57% success rate gains.

cs.CL↗
arXiv2026

An End-to-End Framework for Modelling Pneumatic Soft Robots Based on Differentiable Finite Element Methods

Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulties in accurate system identification and dynamics modelling limit their applications in precise robotics tasks. Prior modelling approaches typically suffer from trade-offs in accuracy, computational efficiency, or speed. The differentiable finite element method (FEM) offers a promising balance between these desiderata for soft robot modelling, and enables the use of gradients for efficient calibration and trajectory optimisation. In this paper, we propose an end-to-end differentiable FEM-based framework designed to streamline modelling and system identification for pneumatic soft robots, enabling the generation of reliable models for downstream tasks. The framework automates the conversion of computer-aided designs into voxelised tetrahedral viscoelastic FEM meshes that accurately represent the robot's geometry and actuation mechanism. By integrating easily acquired point cloud data with differentiable FEM, the framework achieves precise identification of material and dynamic actuation parameters using a minimal experimental setup. Additionally, the differentiable nature of the model facilitates trajectory optimisation by leveraging gradients from robot dynamics and contact interactions. We validate the framework by modelling a complex bellow-shaped pneumatic soft robot and demonstrate its efficacy in real-world motion planning tasks. Experimental results indicate high modelling accuracy, with a maximum positional error of less than 3 mm, and successful application in tasks such as path following and grasping.

cs.RO↗
arXiv2026

Obtaining Game-Stationary Points for Smooth Nonconvex-Nonconcave Minimax Problems via First-Order Methods

Minimax optimization is a fundamental framework in machine learning, robust optimization, and game theory, yet finding first-order stationary points of general nonconvex-nonconcave minimax problems remains challenging without additional structural assumptions. Existing guarantees often rely on global PL- or KL-type conditions that connect max-player stationarity to global inner optimality, or on Minty-type conditions that impose a global relation on the game gradient field relative to a reference solution; local KL variants relax the former requirement but typically require initialization and tracking within a near-optimal region. Such conditions may be difficult to satisfy in many applications. In contrast, we develop a first-order method that finds an $ε$-stationary point within $\widetilde{O}(ε^{-2})$ first-order iterations under a local inverse-Lipschitz regularity condition around approximate max-player stationary points, together with a compactness condition on a penalty sublevel set. Our condition places no optimality requirement on stationary points of the inner maximization problem: they need not be globally, or even locally, maximizing. We further provide sufficient conditions for the required regularity. In the unconstrained setting, it follows from uniform nonsingularity of the maximization-variable Hessian near stationary points; for constrained upper Moreau envelopes, it follows from standard KKT regularity conditions. These results establish first-order complexity guarantees for classes of nonconvex-nonconcave minimax problems not covered by the above PL-, KL-, or Minty-type frameworks.

math.OC↗
arXiv2026

Gel-Confined Rolling-Circle Amplification Enables Sensitive Single-Cell Proteoform Analysis

Protein abundance alone does not capture the molecular diversity generated by protein processing and post-translational modification, yet most single-cell protein assays do not resolve these proteoform states. Here, we develop RCAmp-scWB, a post-separation amplification strategy that performs rolling-circle amplification directly within the polyacrylamide gel after single-cell protein electrophoresis. Reaction-transport modeling identifies a balance between reagent access and confinement of the template-associated amplification product that supports signal amplification while retaining the electrophoretically encoded spatial readout. Using purified protein standards, RCAmp-scWB produced a 4.0-7.8-fold steeper concentration-response slope than conventional single-cell western blotting. Across six human cancer cell lines, RCAmp-scWB quantified protein abundance and resolved distinct Vimentin and PD-L1 proteoform states whose relative abundance and single-cell distributions were not captured by total protein measurements alone. Extension to individual small extracellular vesicles further revealed source-dependent shifts in Vimentin proteoform composition despite comparatively similar abundance of one major Vimentin species. By separating electrophoretic molecular discrimination from signal amplification, RCAmp-scWB enables proteoform-resolved analysis of heterogeneous cells and small extracellular vesicles.

q-bio.QM↗
arXiv2026

Antiblockade Quantum Batteries

Blockade, a phenomenon in which one particle hinders the transport of another, constitutes a core research topic in mesoscopic and microscopic physics. Benefiting from the unique single-particle characteristics, it underpins versatile applications spanning from quantum computation to quantum metrology. Nevertheless, in the context of quantum energy?storage devices, i.e., quantum batteries (QBs), blockade triggered by the intrinsic level splitting is in turn detrimental to energy accumulation. Here we propose an antiblockade charging protocol enabled by deliberately engineering a bright-dark interaction. Rooted in a manifold of equally-spaced dressed-dark states, this elaborate configuration opens up a new charging channel facilitating an antiblockade energy flow and outperforms the Rabi-split blockade design with at least a 20-fold enhancement in energy storage. Additionally, we further illustrate the achievement of superextensive (quadratic) charging characteristic by operating the system in the bad-cavity limit. In view of its inherent generality, our scheme offers a universal and feasible pathway toward high-performance quantum energy architectures.

quant-ph↗
arXiv2026

Searching for Type Ia Supernovae in the Dark Energy Spectroscopic Instrument

With the development of large-scale photometric surveys, an increasing number of supernova candidates are being discovered, leading to a rapidly growing demand for supernova spectra. In addition to equipping photometric surveys with follow-up spectroscopic facilities, archival spectra from large multi-object spectroscopic surveys can be mined to provide spectroscopic classifications for candidates and to find supernovae missed by previous surveys. In this work, we combine Principal Component Analysis (PCA), the Local Outlier Factor (LOF) algorithm, and the supernova classification tool SNID to search for Type Ia supernovae among 1,757,303 galaxy spectra from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). We finally obtain 247 Type Ia supernovae and 17 supernovae of other types. Among these, 202 supernovae lack classification records in the Transient Name Server (TNS) and represent newly identified SNe. These results demonstrate the potential of multi-object spectroscopic surveys to supplement supernova samples, particularly for transients missed by traditional photometric surveys.

astro-ph.HE↗
arXiv2026

LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction

Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce LingBot-Map, a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture. A defining aspect of LingBot-Map lies in its carefully designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and long-range drift correction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around 20 FPS on 518 x 378 resolution inputs over long sequences exceeding 10,000 frames. Extensive evaluations across a variety of benchmarks demonstrate that our approach achieves superior performance compared to both existing streaming and iterative optimization-based approaches.

cs.CV↗
arXiv2026

C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video

High-quality demonstrations for dexterous robot manipulation are costly and difficult to collect, whereas monocular human videos provide a scalable source of diverse manipulation behaviors. However, transferring such demonstrations to dexterous robots remains challenging: monocular hand-object interaction (HOI) reconstruction often produces temporally unstable contacts and physically implausible interactions, while conventional retargeting methods struggle to preserve task-relevant contacts and local interaction geometry across different hand embodiments. We present C2Dex, a video-to-dexterous-manipulation framework built around a shared interaction representation: stable object-side contacts recovered by aggregating noisy frame-wise observations in the canonical object space. These stable contacts serve a dual role: as trajectory-level constraints that guide reconstruction toward temporally coherent and physically plausible human HOI trajectories, and as explicit transfer targets for the dexterous hand, where Laplacian interaction optimization preserves the local hand-object geometry across embodiments and residual reinforcement learning refines the trajectory in simulation. Experiments on DexYCB and TACO show that C2Dex achieves end-to-end trajectory success rates of 57.78% and 26.67%, respectively, substantially outperforming the strongest baselines (17.78% and 10.00%) under identical evaluation criteria. Real-robot replay experiments further demonstrate physical feasibility across diverse contact-rich manipulation tasks. Project page: https://k-jie.github.io/C2Dex/

cs.RO↗
arXiv2026

Procedura: Agentic 3D Modeling with Procedural Control

Native 3D generators now recover impressive mesh geometry from a single image. However, a dense mesh stays soft where a machined object should be sharp, it carries no part decomposition, and it exposes no parameter a user could edit. To address this, we explore the paradigm of 3D shape as code, leveraging and scaling the coding ability of an LLM for 3D modeling. We introduce Procedura, a novel 3D modeling agent framework that writes an object as a procedural assembly, a parametric program whose named parts are joined by typed, machine-checkable mates. From a text prompt, the agent plans the object as an assembly graph and writes the program part by part, solving each placement from the mated frames rather than guessing it, and admitting a part only once compile, mate, and connectivity checks pass. A decoupled vision critic then refines the assembly one diagnosed fix at a time. Moreover, the same graph carries per-part materials and a simulator-validated articulation. We evaluate on P3D-Bench under its assembly judge, and with the same judge on MechBench-36, our hard-surface benchmark. On both, Procedura outperforms state-of-the-art native 3D generators and every prior 3D-code agent on judged quality, produces the sharpest edges of any method we evaluate, and is the only one whose output is an editable, part-structured program.

cs.CV↗
arXiv2026

Theoretical emission lines and metallicity calibrations of H II regions in ASTRID simulation

We present a theoretical framework to derive redshift-dependent metallicity calibrations for galaxies at $z$=2-7. The ionization parameter ($U$) and gas pressure ($P$) in our approach are not assumed, but are predicted self-consistently. By combining the ASTRID cosmological simulation with stellar population synthesis (SPS) and MAPPINGS V photoionization modeling, we evolve young star clusters under an analytic wind-driven bubble model. This directly couples stellar feedback to the local ISM density, allowing \hii{} region properties to emerge from the underlying physics rather than being treated as free parameters. The emission-line predictions are validated against observed star-formation rate indicators (deviation <0.05 dex) and the \oiii{} luminosity function. We derive calibrations for common optical (e.g. R23, O3N2, N2, O32) and UV (e.g. C3O3, N3O3) diagnostics. We find significant redshift evolution in these relations, driven primarily by changing ionization conditions. A Bayesian analysis quantifies calibration performance under varying signal-to-noise, enabling diagnostic recommendations as a function of redshift and data quality. The R23 calibration performs well at all redshifts with minimal error in our model, while nitrogen- and carbon-based calibrations are highly sensitive to the abundance enrichment process and should be used with caution. These results provide a practical framework for interpreting JWST spectroscopy and tracing chemical evolution from cosmic noon to the epoch of reionization.

astro-ph.GA↗
arXiv2026

Feature Learning for the High Dimensional Stationary Schödinger Equation with Deep Ritz Method

This paper investigates feature learning within the framework of the deep Ritz method for solving the stationary Schrödinger equation with Neumann boundary conditions. We first analyze the convergence of Riemannian gradient descent in an agnostic setting, where the hypothesis function is restricted to a single-index model while the PDE solution is arbitrary. We prove that gradient descent reaches an approximate global minimum: after T = O(log(1/ε)) iterations, the loss is within εof a constant multiple of the optimal loss. We then examine the loss landscape when the source term of the PDE itself follows a single-index model, considering hypothesis functions given by either a single-index model or a two-neuron multi-index model. In the single-index case, we show that the minimum Ritz energy is attained at the feature vector aligned with that of the source term. In the two-neuron case, we study the landscape of regularized Ritz losses and characterize how a second feature emerges, given that the first feature is aligned with the source, as the regularization parameter varies. Finally, numerical experiments are presented to validate the feature emergence theory in the two-neuron setting.

math.OC↗