arXiv subjects

Chong Li

Publications and source records attributed to Chong Li.

At least 19 recordsLinked to original sources

arXiv2026

EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpreting

Low-latency simultaneous speech-to-speech translation must keep pace with ongoing speech while preserving key information. To meet these demands, systems use segmentation, reformulation and condensation to reorganize and rephrase information. However, metrics developed for text translation, including BLEU and COMET, may not consistently distinguish faithful adaptations from semantic errors. We propose EviSI, a large language model evaluation agent combining Multidimensional Quality Metrics (MQM) with criteria developed with professional interpreters. Shared source evidence guides assessment across four dimensions: Anchor, Event, Logic and Fluency. Verified errors are deduplicated before deterministic scoring. On human-rated English to Chinese and Chinese to English data, EviSI recovers the aggregate English to Chinese human system ranking. Mean within-dataset Kendall correlations for system rankings reach 0.707 and 0.467, respectively, exceeding evaluated BLEU and COMET baselines. A multilingual extension to five directions without human ratings retains the dimensions and scoring rule, showing positive system ranking correlations with COMET throughout.

cs.CL↗

arXiv2026

ALOHA IRDCs Molecular Line Follow-up: I. Gas properties and kinematics

Infrared Dark Clouds are ideal sites for investigating the initial conditions of massive star and cluster formation. The A Lei Of the Habitat and Assembly of Infrared Dark Clouds (ALOHA IRDCs), a James Clerk Maxwell Telescope (JCMT) Large Program, has mapped nearby IRDCs with SCUBA-2. Complementary molecular line observations are needed to characterise the physical, kinematic, and chemical properties of the dense gas. We aim to determine the thermal, kinematic, and chemical properties of clumps identified in the ALOHA IRDCs, and to assess their evolutionary status and level of star-forming activity. We performed single-pointing K-band and W-band observations towards 56 ALOHA IRDCs clumps using the Effelsberg 100-m and Yebes 40-m telescopes, respectively. We derived NH3 kinetic temperatures using the hyperfine group ratio (HFGR) method and identified infall and shock signatures from HCO+, H13CO+, SiO, and HNCO profiles. Water masers and NH2D emission were used as complementary tracers of chemical evolution and star formation. The clumps exhibit kinetic temperatures of 15-29 K. We detect NH2D emission towards 18 sources, with NH2D centroid velocities consistent with NH3, indicating both species trace the same dense gas component. More than half of the clumps display blue-asymmetric HCO+ profiles, identifying them as infall candidates. Water masers are detected in 22 sources, with prominent velocity ranges and variability. Broad SiO emission (>~20 km/s) indicates strong shocks, while narrower extents (<~6km/s) likely trace large-scale interactions or low-velocity shocks. The widespread infall signatures, shock tracers, masers, and NH2D emission suggest that relatively quiescent, chemically young material can coexist with dynamically active gas affected by early protostellar feedback, providing insight into the coupled physical and chemical evolution of massive IRDC clumps.

astro-ph.GA↗

arXiv2026

Geometric Superconducting Diode Effect in an NbN Nanoring

Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer heterostructures, ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect realized in a structurally minimal, single-materials NbN nanoring, where inversion-symmetry breaking is introduced solely by the asymmetric geometry. The device exhibits pronounced and polarity-switchable critical-current nonreciprocity. Systematic magnetic-field and temperature-dependent measurements reveal that, at low fields, the applied magnetic field redistributes the critical current asymmetrically between opposite bias directions without significantly reducing the overall superconducting current-carrying capability. Moreover, the maximal nonreciprocity and diode efficiency exhibit distinct temperature dependence: the maximal diode efficiency follows the evolution of the energy gap, whereas the maximal nonreciprocity is more closely associated with the superfluid density. These results establish asymmetric superconducting nanorings as a minimal geometric platform for studying nonreciprocal superconducting transport and provide a simple design principle for future superconducting electronics.

cond-mat.supr-con↗

arXiv2026

Wireless millikelvin interconnects for superconducting quantum hardware

Scalable quantum computing is limited by the dense network of electrical interconnects linking cryogenic quantum processors to room-temperature control electronics. To overcome this bottleneck, considerable effort has focused on cryogenic CMOS electronics and microwave-to-optical transduction, aiming to reduce wiring complexity and thermal loading. Wireless interconnects have recently emerged as a promising complementary approach, yet their compatibility with superconducting quantum hardware remains largely unexplored. Here, we demonstrate the wireless excitation of a superconducting microwave resonator of the type routinely employed for qubit readout, operating at millikelvin temperatures inside a dilution refrigerator. By directly comparing wired and wireless operation within the same cryogenic environment, we show that wireless coupling preserves the intrinsic resonator response while revealing parasitic electromagnetic pathways arising from stray radiation within the cryostat enclosure. These results establish a framework for the co-design of wireless interconnects, cryogenic packaging and superconducting quantum hardware.

quant-ph↗

arXiv2026

Proceedings of HLPP 2026: 19th International Symposium on High-Level Parallel Programming and Applications

This volume contains the ten peer-reviewed papers presented at HLPP 2026, the 19th International Symposium on High-Level Parallel Programming and Applications, held on 9-10 July 2026 at the Institut Henri Poincare in Paris, France. The symposium covers high-level approaches to parallel programming: programming models, languages, libraries, algorithmic skeletons, compilers, and runtime systems for multi-core, GPU, and distributed platforms. The 2026 edition extended this scope towards artificial intelligence, with new topics on the parallel programming and performance of AI systems and on AI-assisted generation of parallel code, and opened with a dedicated session on parallelism and AI. Papers were selected through a single-blind review process, with three Program Committee reviews per submission, and appear in the order of the symposium program. Edited by Chong Li, Corinne Ancourt, and Gaetan Hains.

cs.DC↗

arXiv2026

Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing

Decoding language from the human brain remains a grand challenge for Brain-Computer Interfaces (BCIs). Current approaches typically rely on unimodal brain representations, neglecting the brain's inherently multimodal processing. Inspired by the brain's associative mechanisms, where viewing an image can evoke related sounds and linguistic representations, we propose a unified framework that leverages Multimodal Large Language Models (MLLMs) to align brain signals with a shared semantic space encompassing text, images, and audio. A router module dynamically selects and fuses modality-specific brain features according to the characteristics of each stimulus. Experiments on various fMRI datasets with textual, visual, and auditory stimuli demonstrate state-of-the-art performance, achieving an 8.48% improvement on the most commonly used benchmark. We further extend our framework to EEG and MEG data, demonstrating flexibility and robustness across varying temporal and spatial resolutions. To our knowledge, this is the first unified BCI architecture capable of robustly decoding multimodal brain activity across diverse brain signals and stimulus types, offering a flexible solution for real-world applications.

cs.CL↗

arXiv2026

HyperParallel-Mpipe: A Composable Algebra System for Optimizing MLLM Training over Supernode Clusters

Modern AI applications have expanded beyond text-only interaction into a wide range of multimodal scenarios, making multimodal large language models (MLLMs) crucial for both research and industry. However, compared with traditional decoder-only LLM training, large-scale MLLM training often shows much lower MFU. We analyze the key pain points in MLLM training and introduce Mpipe, which uses a schedule algebra to derive concrete runtime behavior from a compact schedule specification. From this algebra, Mpipe derives transpose, a multimodal-aware heterogeneous parallel schedule that remaps modality-encoder computation into otherwise idle pipeline regions. On Ascend 910C NPU clusters, Mpipe achieves 2.70x speedup in a small-scale setting and 1.21x speedup in a 512-card large-scale setting.

cs.DC↗

arXiv2026

Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models

Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scale 3D training data with rich surface appearance. In contrast, visual generative models are trained on datasets several orders of magnitude larger and excel at modeling complex visual patterns. Motivated by this gap, we introduce Ink3D, a framework that bridges 3D generation with large-scale video generative models to synthesize extremely complex textures. Ink3D first reconstructs a white-mesh geometry using an off-the-shelf 3D generation model. It then employs OrbitPainter, a conditional video generative model, to produce dense orbit-scan videos capturing object appearance across viewpoints. To convert these views into coherent textures, we introduce TextureOptimizer, a neural baking module that integrates dense multi-view observations while mitigating geometry inconsistencies arising from video generation. By decoupling geometry and texture synthesis and leveraging large-scale pretrained video priors, Ink3D enables significantly richer and more faithful texture generation than prior approaches.

cs.CV↗

arXiv2026

Moebius: Serving Mixture-of-Expert Models with Seamless Runtime Parallelism Switch

Mixture-of-Experts (MoE) architectures scale large language models (LLMs) to hundreds of billions of parameters. Serving a single MoE model requires multiple GPUs operating in parallel, typically through tensor parallelism (TP) or expert parallelism (EP). The optimal choice depends on the number of in-flight requests: TP is faster at low concurrency, whereas EP wins at high concurrency. Production workloads cross this boundary continually: online serving sees bursty arrivals that subside into quiet periods, and reinforcement-learning rollouts begin as a high-concurrency burst that decays into a long tail of stragglers. Pinning either layout therefore forfeits performance when the workload crosses to the other side. We present Moebius, a serving system that switches between EP and TP at runtime without restarting the engine or dropping in-flight requests. Our key insight is that EP and TP are two layouts of one model, not two models: they compute the same function over byte-identical expert weights and KV cache, so a switch changes only which rank owns each slice. Moving those owner-changed slices is the sole irreducible cost, and modern high-bandwidth GPU interconnects make it fast enough to do between decode steps without draining in-flight requests. Moebius preserves each parallelism's runtime resident, and reshards the single copy of expert weights and KV cache at fixed addresses with fused GPU-to-GPU transfer kernels. On 8x H200 GPUs serving Qwen3-235B-A22B, Moebius matches the better static parallelism at every operating point, and beats it on RL rollouts by 1.16-1.25x across steps. Each switch completes in 215-434 ms, and Moebius holds both layouts resident with only 2.4% memory overhead.

cs.DC↗

arXiv2026

TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment

Tokenization is a foundational step in the text process of Large Language Models (LLMs). Texts must be first tokenized into token IDs, which are then input to LLMs. Inefficient tokenization results in long token-ID sequences and will slow down the training and inference of LLMs. The fine-grained knowledge transfer between LLMs, like token-level distillation, is also impeded by the mismatch in vocabulary. To bridge this gap, we introduce a method named TokAlign++ to improve vocabulary adaptation performance by learning better token alignment lexicon. The source and target vocabularies are taken as two different languages, and the bilingual token alignment lexicon is learned from monolingual token representations. Model parameters are rearranged following this bilingual lexicon for new vocabulary, and progressively fine-tuned for adaptation. Experimental results on 15 languages show that our method boosts the multilingual text compression rates and preserves most of the multilingual ability of vanilla models. It costs as few as 1k steps to restore the performance of the vanilla model. After unifying vocabularies between vanilla models, token-level distillation remarkably improves the base model with only 235M tokens.

cs.CL↗

arXiv2026

Floquet dynamical quantum phase transitions in periodically flux-quenched systems

Floquet dynamical quantum phase transitions (FDQPTs) reveal many nonequilibrium critical phenomena in periodically driven quantum systems, and their underlying mechanisms have attracted deep attention in recent years. In this paper, we consider an extended XY spin chain under a periodic flux-quench protocol, and demonstrate the effect of the flux difference within each micromotion period on the emergence of FDQPTs, by analyzing physical quantities such as the Loschmidt echo, rate function, and dynamical topological order parameter (DTOP), etc. We also generalize the concept of quench fidelity to periodically driven systems, i.e., Floquet quench fidelity, and discuss the necessary and sufficient conditions for FDQPTs. In contrast to conventional single-quench scenarios, the occurrence of FDQPTs is determined by the requirement of Floquet fidelity condition and segment duration. Our framework may be applied generally to arbitrary periodically driven parameters, providing fundamental insights into how periodic protocols control nonequilibrium phase transitions in quantum many-body systems.

quant-ph↗

arXiv2026

Nanoscale electrothermal-switch superconducting diode for electrically programmable superconducting circuits

Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire. This mechanism gives rise to two coexisting nonreciprocal transport regimes-one associated with a nonreciprocal superconducting-to-normal transition and the other with ratchet-like vortex dynamics-both originating from the same electrothermal-switch process. The diode exhibits efficiencies up to 42% and 60% for the two regimes, respectively, and can be electrically switched on, off, or reversed in polarity in situ by applying a small gate current. These capabilities enable programmable superconducting circuits that realize electrically reconfigurable full-wave and half-wave rectification. The lithography-compatible design, high performance, and gate-controlled functionality establish a scalable platform for programmable superconducting electronics and hybrid quantum systems.

cond-mat.supr-con↗

arXiv2026

MERIT: Multilingual Expert-Reward Informed Tuning for Chinese-Centric Low-Resource Machine Translation

Neural machine translation (NMT) from Chinese to low-resource Southeast Asian languages remains severely constrained by the extreme scarcity of clean parallel corpora and the pervasive noise in existing mined data. This chronic shortage not only impedes effective model training but also sustains a large performance gap with high-resource directions, leaving millions of speakers of languages such as Lao, Burmese, and Tagalog with persistently low-quality translation systems despite recent advances in large multilingual models. We introduce \textbf{M}ultilingual \textbf{E}xpert-\textbf{R}eward \textbf{I}nformed \textbf{T}uning (\textbf{MERIT}), a unified translation framework that transforms the traditional English-centric ALT benchmark into a Chinese-centric evaluation suite for five Southeast Asian low-resource languages (LRLs). Our framework combines language-specific token prefixing (LTP) with supervised fine-tuning (SFT) and a novel group relative policy optimization (GRPO) guided by the semantic alignment reward (SAR). These results confirm that, in LRL{\textrightarrow}Chinese translation, targeted data curation and reward-guided optimization dramatically outperform mere model scaling.

cs.CL↗

arXiv2026

Bringing Your Portrait to 3D Presence

We present a unified framework for reconstructing animatable 3D human avatars from a single portrait across head, half-body, and full-body inputs. Our method tackles three bottlenecks: pose- and framing-sensitive feature representations, limited scalable data, and unreliable proxy-mesh estimation. We introduce a Dual-UV representation that maps image features to a canonical UV space via Core-UV and Shell-UV branches, eliminating pose- and framing-induced token shifts. We also build a factorized synthetic data manifold combining 2D generative diversity with geometry-consistent 3D renderings, supported by a training scheme that improves realism and identity consistency. A robust proxy-mesh tracker maintains stability under partial visibility. Together, these components enable strong in-the-wild generalization. Trained only on half-body synthetic data, our model achieves state-of-the-art head and upper-body reconstruction and competitive full-body results. Extensive experiments and analyses further validate the effectiveness of our approach.

cs.CV↗

arXiv2026

Dynamic Manifold Hopfield Networks for Context-Dependent Associative Memory

Neural population activity in cortical and hippocampal circuits can be flexibly reorganized by context, suggesting that cognition relies on dynamic manifolds rather than static representations. However, how such dynamic organization can be realized mechanistically within a unified dynamical system remains unclear. Continuous Hopfield networks provide a classical attractor framework in which neural dynamics follow gradient descent on a fixed energy landscape, constraining retrieval within a static attractor manifold geometry. Extending this approach, we introduce Dynamic Manifold Hopfield Networks (DMHN), continuous dynamical models in which contextual modulation dynamically reshapes attractor geometry, transforming a static attractor manifold into a context-dependent family of neural manifolds. In DMHN, network interactions are learned in a data-driven manner, to intrinsically deform the geometry of its attractor manifold across cues without explicit context-specific parameterization. As a result, in associative retrieval, DMHN achieve substantially higher capacity and robustness than classical and modern Hopfield networks: when storing $2N$ patterns in a network of $N$ neurons, DMHN attain reliable retrieval with an average accuracy of 64%, compared with 1% and 13% for classical and modern variants, respectively. Together, these results establish dynamic reorganization of attractor manifold geometry as a principled mechanism for context-dependent remapping in neural associative memory.

cs.LG↗

arXiv2026

Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems

This paper investigates numerical solution methods for the Schatten-$p$ quasi-norm regularized problem with $p \in [0,1]$, which has been widely studied for finding low-rank solutions of linear inverse problems and gained successful applications in various mathematics and applied science fields. We propose a dynamic proximal gradient algorithm that, through the use of the Cayley transformation, avoids computationally expensive singular value decompositions at each iteration, thereby significantly reducing the computational complexity. The algorithm incorporates two step size selection strategies: an adaptive backtracking search and an explicit step size rule. We establish the sublinear convergence of the proposed algorithm for all $p \in [0,1]$ within the framework of the Kurdyka-Lojasiewicz property. Notably, under mild assumptions, we show that the generated sequence converges to a stationary point of the objective function of the problem. For the special case when $p=1$, the linear convergence is further proved under the strict complementarity-type regularity condition commonly used in the linear convergence analysis of the forward-backward splitting algorithms. Preliminary numerical results validate the superior computational efficiency of the proposed algorithm.

math.OC↗

arXiv2026

DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening

Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.

cs.LG↗

arXiv2026

The existence of solutions of Schrödinger equations with essence resonance

The current paper investigates a class of asymptotically linear Schrodinger equations. The Palais-Smale condition fails to hold in this case. Especially under the hypothesis (V2), the lack of compactness occurs at the interaction between nonlinear term and continuum spectrum. For this reason, we introduce a bootstrap iteration approach for elliptic equation on RN. The iteration is self-contained and can be regarded as a generalization of Agmon-Douglis-Nirenberg theorem. The proof characterizes iteration steps independent of the choice of the parameter, which are indeed manipulated by intrinsic natures of potentials and nonlinear terms, and furthermore presents precise estimates for asymptotically linear functions or continuous nonlinear terms restricted on a bounded domain in RN. Additionally, a comparison theorem for the spectrum of Schrodinger operator is also established in this paper. With above preparations, we can get a nontrivial solution without mountain pass geometry, and more importantly make an explicit description of nondegeneracy of solutions with monotonicity hypothesis.

math.AP↗