arXiv subjects

Yaqi Li

Publications and source records attributed to Yaqi Li.

At least 19 recordsLinked to original sources

arXiv2026

Data-Efficient Language Modeling: From Frontier Advancement to Principle-Guided Model Improvement

Learning from limited text requires models to use context, generalize to new inputs, and retain useful capabilities. Qiushi Engine conducted a long-horizon, end-to-end autonomous research program on BabyLM 2026 Strict-Small, within 10 million corpus words and 100 million cumulative word presentations. Three stages connected frontier advancement, principle discovery, and principle-guided model improvement. Stage I combined compact restatements, budget reinvestment, and residual incremental learning to build a frontier model. Stage II found that exact repetition and aligned restatement produce different patterns of context use, depending on target relations and prediction windows. In controlled tasks, recovering familiar performance did not ensure that unseen inputs could still use learned computations. These findings support a testable data-efficient learning principle: organize experience around the contextual dependencies needed for prediction; separately design visible information, supervision, and preservation; test learning, generalization, and retention. Stage III retained source text, masked more local clues, supervised selected targets, and preserved predictions on ordinarily masked inputs. Two continuation seeds from the same parent outperformed ordinary continuation on the complete nine-metric aggregate. Overall rose from 42.02 to 42.25 across two generations; the second achieved the highest Overall in the public Strict-Small snapshot of 8 September 2026. Further studies addressed compression, relational anchors, shared representations, and measurement. Models are available on Hugging Face; code and research records accompany the GitHub repository. Together, these stages illustrate Research RSI: recursive self-improvement of the research process. Scientific understanding and method innovations change subsequent questions and designs; new experiments test and refine them.

cs.CL↗

arXiv2026

Qiushi Engine on AstaBench E2E-Bench-Hard

This report analyzes Qiushi Engine v0.8 across all 40 test tasks in AstaBench E2E-Bench-Hard, a benchmark that requires autonomous agents to carry a research question through experimental design, code implementation, actual execution, result analysis, and report delivery. Qiushi Engine is model-configurable; this evaluation selected DeepSeek deepseek-v4pro-preview as the model backend. The official AstaBench leaderboard records a score of 0.816 and an average benchmark cost of USD 15.209 per task, while the full-precision local recomputation is $81.59 \pm 1.87$. Four tasks satisfied every rubric item, yielding a full-task completion rate of 4/40 = 10% -- 7 percentage points above, and about 3.3 times, the approximately 3% best rate reported for AstaBench's official agents. Across 507 required rubric items, 416 were satisfied (82.1%). Official scoring archives and 40 Meta-Trace records show sustained production and verification of reports, code, and experimental artifacts; the principal gaps lie in repeated runs, external dependencies, specified metrics, and ablation studies. The report explains the benchmark, system workflow, aggregate results, representative cases, and limits of interpretation.

cs.AI↗

arXiv2026

FFSlim: An Efficient and Lightweight Format for Multi-modal Data Storage and Retrieval

With the rapid expansion of large-scale media-text corpora, multi-modal datasets increasingly require efficient storage and retrieval. Existing formats such as Files, TDP, and FFRecord work adequately for uni-modal data but expose fundamental limitations in multi-modal settings, including storage redundancy, massive small-file overheads, cache-unfriendly layouts, and heavy index structures. These issues jointly inflate storage and memory usage and make I/O the dominant bottleneck in real training workloads. We present FFSlim, a lightweight format for storing and retrieving multi-modal data. FFSlim improves storage efficiency and loading throughput through three components: a unified file format that removes media duplication and avoids small-file proliferation; an adaptive retrieval mechanism that enables low-overhead pair-level access and accelerates repeated media loading; and a redundancy detection and aggregation module that converts existing datasets into the FFSlim layout. The experimental results demonstrate that FFSlim achieves 2.07x and 8.26x higher data loading and write throughput on average than the strongest baseline, with minimal storage and index overhead. Consequently, these underlying I/O accelerations enable FFSlim to reduce end-to-end training time by 5.36%-14.18% across seven diverse multi-modal models.

cs.PF↗

arXiv2026

PATE-Forensics: Perception-as-Tool for Explainable Deepfake Forensics with General-Purpose MLLMs

Existing explainable deepfake forensic methods typically rely on task-adapted MLLM to jointly address detection, localization, and explanation. Inspired by agent-style tool use, we instead introduce a Perception-as-Tool paradigm and instantiate it as PATE-Forensics, which architecturally decouples detection and localization from explanation generation while coupling detection and localization as tightly as possible within a forensic perception tool. The DINOv3-based tool couples a multi-granularity detection module that integrates global, patch-level, and segment-level evidence with a cue-guided localization module by spatializing the patch-level and segment-level evidence into forgery score maps that guide dense mask prediction. The original image and forensic perception outputs produced by the tool form structured forensic context for a general-purpose MLLM, which is guided by prompt constraints to generate explanations without task-specific fine-tuning. On DDL-X Track 3, PATE-Forensics achieves the best official score of 0.89, outperforming the second-ranked team by 0.19 points. Our code is available at https://github.com/yqli00000/PATE-Forensics.

cs.CV↗

arXiv2026

Less is More: Modality-Decoupling for General AIGC Audio-Video Detection

Generative AI has rapidly expanded audio-visual forgery beyond human-centric deepfakes into general scenes. Existing AIGC detection methods assume audio-visual content correspondence, identifying forgeries by spotting cross-modal inconsistencies. However, we empirically find that this assumption does not consistently hold in general scenarios. We argue that, for general audio-visual AIGC detection, decision-level fusion is a more robust alternative to feature-level fusion. Therefore, we propose DAV-Det, a decoupled audio-visual AIGC detection system that independently models forensic evidence from each modality. The visual detector leverages multi-granularity representations at global, patch, and segment levels to capture spatial forgery cues, while the audio detector exploits both temporal and spectral irregularities via a gated temporal-spectral dual-branch architecture to model acoustic artifacts. Our method ranks 1st in the General AIGC Audio-Video Detection Challenge of the IJCAI-ECAI 2026 DDL 2.0 Workshop, with a final score of 0.8460. Code is available at https://github.com/tuffy-studio/DAV-Det.

cs.CV↗

arXiv2026

Bridging battery design and health assessment through virtual sensing and physics-informed learning

Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence, particularly for emerging vehicle-to-grid applications with bidirectional energy flows. Yet battery management remains largely disconnected from the material and structural origins of aging, limiting both interpretable health assessment and informed battery design. Here we propose a physics-informed learning framework with virtual sensing that infers hard-to-measure design parameters, including solid-state diffusion coefficient, electrode thickness, ion concentration, and particle size, directly from standard battery management system (BMS) measurements. Across diverse fast-charging strategies and driving profiles, embedding a digital-twin-derived particle-cracking mechanism as a soft constraint reduces trajectory and lifetime prediction errors by 6-8 times relative to state-of-the-art machine learning baselines using only 2% early-life observations. We further show that accurate degradation extrapolation does not require fully resolved governing equations; validated partial mechanisms, jointly refined with limited data, provide sufficient guidance. Virtual sensing transforms standard charging signals into latent design variables without additional sensors, bridging observable battery behavior and underlying aging processes while reducing capacity loss error by up to 39%, end-of-life (EOL) error by 17%, and prediction variability by up to 54%, enabling real-time exploration of new battery configurations. More broadly, the proposed framework establishes a practical feedback loop between deployment and development, demonstrating how real-world operation can continuously inform upstream design decisions across complex multiphysics systems.

cs.LG↗

arXiv2026

Agentic metasurface design with self-correcting language-model systems

Automated metasurface design is increasingly important, and recent advances in language-model systems are opening a route toward agentic optical design. Yet modern metasurface applications, from metalenses and holography to optical computing, require long design chains spanning modeling, simulation, coding, optimization and evaluation. These chains are error-prone, whereas existing language-model-based metasurface tools remain largely limited to simple objectives, predefined pipelines or language-to-layout generation. Here we introduce MetaDesigner, a self-correcting language-model system for agentic metasurface design. From a natural-language optical objective, MetaDesigner plans the design route, retrieves domain knowledge, invokes simulation and optimization tools, generates missing tool code and identifies errors through a dedicated Verifier. We demonstrate three tasks of increasing complexity: an RGB metalens with three independent focal spots, a six-plane full-color hologram with an average structural similarity index measure (SSIM) of 0.97, and an optoelectronic hybrid neural network for image style transfer. These tasks require 74, 136 and 90 reasoning steps, respectively, and the system self-corrects errors in frequency mapping, numerical aperture estimation, network-parameter counting and loss-function description. These results establish MetaDesigner as a self-correcting route to agentic metasurface design, where language-model systems can not only execute optical design tasks but also extend, inspect and repair the design process itself.

physics.optics↗

arXiv2026

Polar Topologies in a Ferroelastic Metal Membrane

Polar metals, materials in which electric polarisation and metallicity coexist, are exceptionally rare because itinerant electrons screen long-range dipoles and favour centrosymmetric structures. Engineering polar textures in a conducting magnet holds promise for reconfigurable spin orbit coupling and magnetoelectric functionality. Here we show that releasing epitaxial SrRuO3 films from their substrates drives a hierarchy of ferroelastic domain refinement from micrometre to nanometre length scales, and that this structural reorganisation spontaneously generates two distinct classes of emergent polar texture that are ubiquitous across the freestanding membrane. Using correlative microscopy from mesoscale electron channelling contrast imaging (ECCI) to atomic resolution scanning transmission electron microscopy (STEM), we demonstrate that electric polarisation emerges selectively at translation-inequivalent antiphase boundaries (APBs). At these boundaries multicomponent aac tilt field undergoes Neel-like interpolation that preserves the in-phase tilt component and amplifies roto flexoelectric coupling, while translation-equivalent boundaries remain nonpolar. The Neel like interpolation at hard APBs and Ising like collapse of all tilt components at easy APBs is corroborated with ab initio calculations. While embedded 90 ferroelastic walls provide an additional mechanistically distinct source of electric polarisation resulting in polar nanoclusters (4 nm). These distinct nanotextures at 90 walls from via elastic accommodation of strain mismatch between variants and rotostriction as the tilt field interpolates across the boundaries. These findings show that, in a membrane form, metal oxides provide a robust platform for hosting nanoscale ferroelastic domains that generate polar textures.

cond-mat.mtrl-sci↗

arXiv2026

Robust Deepfake Detection, NTIRE 2026 Challenge: Report

Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under exposure to even slight image degradation. In addition to weaker degradations that can accidentally occur in the image processing pipeline, there is another risk of malicious deepfakes that specifically introduce degradations, purposefully exploiting the detector's weaknesses in that regard. Here, we present an overview of the NTIRE 2026 Robust Deepfake Detection Challenge, which specifically addresses that problem. Participants were tasked with building a detector that would later be tested on an unknown test-set, which included both common and uncommon degradations of various strengths. With a total number of 337 participants and 57 submissions to the final leaderboard, the first edition of the challenge was well received. To ensure the reliability of the results, participants were given only 24h to complete the test run with no labels provided, limiting the possibility of training on the test data. Furthermore, the top solutions were scored on a private test-set to detect any such overfitting. This report presents the competition setting, dataset preparation, as well as details and performance of methods. Top methods rely on large foundation models, ensembles, and degradation training to combine generality and robustness.

cs.CV↗

arXiv2026

Spatiotemporal 2-D Polar Codes over Non-Uniform MIMO Channels: A Reliability-Aware Construction Approach

With the increasing demand for ultra-reliable and low-latency communication (URLLC), spatiotemporal two-dimensional (2-D) channel coding has received growing interest. By leveraging the spatial degrees of freedom in massive multiple-input multiple-output (MIMO) systems, it shortens the time-domain blocklength, thereby reducing latency and enhancing reliability. However, existing spatiotemporal coding schemes typically assume uniform reliability across spatial streams. This assumption does not hold in practical MIMO channels, where the underlying propagation environment generally leads to unequal spatial-eigenmode gains and reliabilities, making the conventional Gaussian-approximation-based construction for 2-D polar codes less effective. This paper investigates spatiotemporal 2-D polar coding over non-uniform MIMO channels, where the spatial domain exhibits inherently heterogeneous signal-to-noise ratios (SNRs). We propose a reciprocal channel approximation (RCA)-based reliability-aware 2-D polar coding framework that accurately characterizes such heterogeneous SNRs without relying on log-likelihood-ratio distribution assumptions. Simulation results demonstrate that the proposed RCA-based spatiotemporal 2-D polar coding scheme achieves clear performance gains and strong robustness, confirming its effectiveness in jointly exploiting temporal and spatial polarization for URLLC in practical MIMO systems.

cs.IT↗

arXiv2026

Leave No Stone Unturned: Uncovering Holistic Audio-Visual Intrinsic Coherence for Deepfake Detection

The rapid progress of generative AI has enabled hyper-realistic audio-visual deepfakes, intensifying threats to personal security and social trust. Most existing deepfake detectors rely either on uni-modal artifacts or audio-visual discrepancies, failing to jointly leverage both sources of information. Moreover, detectors that rely on generator-specific artifacts tend to exhibit degraded generalization when confronted with unseen forgeries. We argue that robust and generalizable detection should be grounded in intrinsic audio-visual coherence within and across modalities. Accordingly, we propose HAVIC, a Holistic Audio-Visual Intrinsic Coherence-based deepfake detector. HAVIC first learns priors of modality-specific structural coherence, inter-modal micro- and macro-coherence by pre-training on authentic videos. Based on the learned priors, HAVIC further performs holistic adaptive aggregation to dynamically fuse audio-visual features for deepfake detection. Additionally, we introduce HiFi-AVDF, a high-fidelity audio-visual deepfake dataset featuring both text-to-video and image-to-video forgeries from state-of-the-art commercial generators. Extensive experiments across several benchmarks demonstrate that HAVIC significantly outperforms existing state-of-the-art methods, achieving improvements of 9.39% AP and 9.37% AUC on the most challenging cross-dataset scenario. Our code and dataset are available at https://github.com/tuffy-studio/HAVIC.

cs.CV↗

arXiv2026

Helium-Cooled Cryogenic STEM Imaging and Ptychography for Atomic-Scale Study of Low-Temperature Phases

Much of the exotic functionality of prime interest in quantum materials emerges from structural and electronic ground states that can only be accessed at cryogenic temperatures. Understanding device operation therefore requires structural characterization under the same low-temperature conditions at which these functional phases exist, as room-temperature measurements often probe a different structural state. Achieving atomic-resolution in scanning transmission electron microscopy imaging and particularly 4D-STEM electron ptychography at liquid helium temperature has remained extremely challenging because even small amounts of drift, vibration, and thermal instability associated with the cryogen can disrupt the stringent stability requirements of atomic-resolution STEM. In this work we demonstrate atomic-resolution STEM and multislice electron ptychography at temperatures as low as 20 K using a commercial helium cooled holder. We find that rapid scans and a multi-stage registration workflow are critical to reducing artifacts associated with cryogenic instability for atomic-resolution imaging, while for ptychography scan position correction including compensation for coupling between probe aberrations and position refinement is necessary for successful reconstructions. Together these results establish a pathway for reliable atomic-resolution STEM and ptychography at low temperature, enabling direct visualization of structural ground states relevant to quantum technology.

cond-mat.mtrl-sci↗

arXiv2026

Dual-Agent Multiple-Model Reinforcement Learning for Event-Triggered Human-Robot Co-Adaptation in Decoupled Task Spaces

This paper presents a shared-control rehabilitation policy for a custom 6-degree-of-freedom (6-DoF) upper-limb robot that decomposes complex reaching tasks into decoupled spatial axes. The patient governs the primary reaching direction using binary commands, while the robot autonomously manages orthogonal corrective motions. Because traditional fixed-frequency control often induces trajectory oscillations due to variable inverse-kinematics execution times, an event-driven progression strategy is proposed. This architecture triggers subsequent control actions only when the end-effector enters an admission sphere centred on the immediate target waypoint, and was validated in a semi-virtual setup linking a physical pressure sensor to a MuJoCo simulation. To optimise human--robot co-adaptation safely and efficiently, this study introduces Dual Agent Multiple Model Reinforcement Learning (DAMMRL). This framework discretises decision characteristics: the human agent selects the admission sphere radius to reflect their inherent speed--accuracy trade-off, while the robot agent dynamically adjusts its 3D Cartesian step magnitudes to complement the user's cognitive state. Trained in simulation and deployed across mixed environments, this event-triggered DAMMRL approach effectively suppresses waypoint chatter, balances spatial precision with temporal efficiency, and significantly improves success rates in object acquisition tasks.

cs.RO↗

arXiv2026

Observation of spin-valley locked nodal lines in a quasi-2D altermagnet

The interplay among quantum degrees of freedom-spin, orbital and momentum-has emerged as a fertile ground for realizing magnetic quantum states with transformative potential for electronic and spintronic technologies. Prominent examples include ferromagnetic Weyl semimetals and antiferromagnetic axion insulators. Recently, altermagnets(AMs) have been identified as a distinct spin-splitting class of collinear antiferromagnets(AFMs), characterized by crystal symmetry that connects magnetic sublattices in real space and enforces C-paired spin-momentum locking in reciprocal space. These materials combine the advantages of nonrelativistic spin-polarization akin to FMs and vanished net-magnetization as AFMs, making them highly promising for spintronic applications. Furthermore, they introduce nontrivial spin-momentum locking spin texture as an additional degree of freedom for realizing novel quantum phases. In this work, we report the discovery of a new type of spin-valley-locked nodal line phase in the layered AM Rb-intercalated V{_2}Te{_2}O. By combining high-resolution spin and angle-resolved photoemission spectroscopy with first-principles calculations, we observe the coexistence of both spinless and spinful nodal lines near the Fermi level. Remarkably, the spinful nodal lines exhibit uniform spin polarization within each valley, while displaying opposite spin polarizations across symmetry-paired valleys-a unique feature we term spin-valley-locked nodal lines, which is exclusive to AMs. Direct measurements of out-of-plane band dispersion using a side-cleaving technique reveal the two-dimensional nature of these nodal lines. Our findings not only unveil a previously unexplored topological phase in AMs where valley-locked spin as an additional quantum character but also establish RbV{_2}Te{_2}O as a promising platform for spintronics, valleytronics, and moire-engineered quantum devices.

cond-mat.mtrl-sci↗

arXiv2025

OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction

The human hand is our primary interface to the physical world, yet egocentric perception rarely knows when, where, or how forcefully it makes contact. Robust wearable tactile sensors are scarce, and no existing in-the-wild datasets align first-person video with full-hand touch. To bridge the gap between visual perception and physical interaction, we present OpenTouch, the first in-the-wild egocentric full-hand tactile dataset, containing 5.1 hours of synchronized video-touch-pose data and 2,900 curated clips with detailed text annotations. Using OpenTouch, we introduce retrieval and classification benchmarks that probe how touch grounds perception and action. We show that tactile signals provide a compact yet powerful cue for grasp understanding, strengthen cross-modal alignment, and can be reliably retrieved from in-the-wild video queries. By releasing this annotated vision-touch-pose dataset and benchmark, we aim to advance multimodal egocentric perception, embodied learning, and contact-rich robotic manipulation.

cs.CV↗

arXiv2025

Quantifying Phase Transformations in Alloying Anodes via In-Situ Liquid Cell Hard X-ray Spectroscopy and Cryogenic Microscopy

Understanding electrochemical phenomena at complex liquid solid interfaces requires linking real time structural dynamics with atomic scale interfacial chemistry. Here, we integrate operando synchrotron X-ray fluorescence and diffraction with high resolution cryogenic electron and ion multi model microscopy to provide a mechanistic understanding of Pt based alloying anodes across length scales. We directly observe the initial lithiation driven formation of Li2Pt and its evolution to a stable LiPt intermetallic phase during extended cycling via a solid solution type reaction mechanism. Simultaneously, the solid electrolyte interphase transitions from an unstable carbonate rich to a stable LiF dominated composition, confirmed by cryogenic scanning transmission electron microscopy and electron energy loss spectroscopy. Crucially, cryogenic atom probe tomography reveals spatially distinct compositional regimes within the alloy anode, including lithium flux limited, heterogeneous interfacial zone and a diffusion controlled, homogeneous LiPt alloy bulk. This nanoscale compositional gradient rationalises the emergent solid solution reaction mechanism and highlights how kinetic limitations and interface dynamics govern alloy formation and electrochemical stability. Our findings demonstrate a broadly applicable correlative framework bridging operando structural dynamics with near atomic resolution interfacial chemistry, advancing the rational design of durable alloy electrodes for next generation energy storage.

cond-mat.mtrl-sci↗

arXiv2025

A One Dimensional (1D) Computational Fluid Dynamics Study of Fontan-Associated Liver Disease (FALD)

Fontan-Associated Liver Disease (FALD) is a disorder arising from hemodynamic changes and venous congestion in the liver. This disease is prominent in patients with hypoplastic left heart syndrome (HLHS). Although HLHS patients typically survive into adulthood, they have reduced cardiac output due to their univentricular physiology (i.e., a Fontan circuit). As a result, they have insufficient blood delivery to the liver. In comparison, patients with double outlet right ventricle (DORV), also having a univentricular circuit, have lower incidence of FALD. In this study, we use a patient-specific, one-dimensional computational fluid dynamics (1D-CFD) model to predict hemodynamics in the liver of an HLHS patient and compare predictions with an age- and size-matched DORV control patient. Additionally, we simulate FALD conditions in the HLHS patient to predict hemodynamic changes across various stages of disease progression. Our results show that the HLHS patient has a higher portal venous pressure compared to the DORV patient. This difference is exacerbated as FALD conditions progress. The wall shear stress (WSS) is also higher than normal for the HLHS patient, suggesting vascular remodeling. WSS decreases slightly under FALD conditions, consistent with the development of portal hypertension. Perfusion analysis gives insight into regions of liver tissue at risk for fibrosis development, showing increasing pressures and reduced flow throughout the liver tissue fed by the portal vein under FALD conditions. Our results provide insight into the specific hemodynamic changes in Fontan circulation that can cause FALD.

physics.med-ph↗

arXiv2025

Polarization Domain Mapping From 4D-STEM Using Deep Learning

Polarization in ferroelectric domains arises from atomic-scale structural variations that govern macroscopic functionalities. The interfaces between these domains known as domain walls host distinct physical responses, making their identification and control critical. Four dimensional scanning transmission electron microscopy (4DSTEM) enables simultaneous acquisition of real and reciprocal-space information at the atomic scale, offering a powerful platform for domain mapping. However, conventional analyses rely on computationally intensive processing and manual interpretation, which are time consuming and prone to misalignment and diffraction artefacts. Here, we present a convolutional neural network that, with minimal training, classifies polarization directions from diffraction data and segments domains in real space. We further introduce an adaptive sampling strategy that prioritizes images from domain wall regions, reducing the number of training images required while improving accuracy and interpretability. We demonstrate this approach for domain mapping in ferroelectric boracite, Cu3B7O13Cl.

cond-mat.mtrl-sci↗