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Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 109 records · Page 6Linked to original sources
arXiv2026

SPEAR: A Unified SSL Framework for Learning Speech and Audio Representations

Self-supervised learning (SSL) has significantly advanced acoustic representation learning. However, most existing models are optimised for either speech or audio event understanding, resulting in a persistent gap between these two domains. We address this gap with SPEAR (SPEech and Audio Representations), a self-supervised framework that distils complementary knowledge from a speech-focused SSL teacher and a general-audio SSL teacher into a single unified model. SPEAR applies multi-codebook vector quantisation to continuous teacher representations to produce fine-grained discrete tokens that capture both semantic and acoustic information. To effectively integrate these heterogeneous representations, SPEAR jointly predicts them given a masked input with an asymmetric pre-training loss. We further improve robustness in complex sound scenes through a novel token mixing mechanism. Extensive experiments demonstrate that SPEAR consistently outperforms existing unified speech and audio models. SPEAR establishes a new state-of-the-art on the SUPERB benchmark, surpassing WavLM Large on 12 of 15 tasks, while achieving competitive performance on the HEAR benchmark. These results position SPEAR as a versatile foundation for general-purpose speech and audio representation learning. The code and pre-trained models will be released.

eess.AS↗
arXiv2026

Coincidence detection techniques for direct measurement of many-body correlations in strongly correlated electron systems

Research on strongly correlated electron systems faces a fundamental challenge due to the complex nature of intrinsic many-body correlations. A key strategy to address this challenge lies in advancing experimental methods that can directly probe and elucidate the underlying many-body correlations. In this perspective article, we discuss the theoretically proposed coincidence detection techniques, which are designed to directly measure two-body correlations in various particle-particle and particle-hole channels, with momentum, energy, and/or spatial resolution. We also explore the prospects of these coincidence detection techniques for future theoretical and experimental developments. The successful implementation and refinement of these coincidence detection techniques promise to deliver powerful new approaches for unraveling long-standing puzzles in strongly correlated electron systems, such as the enigmatic mechanism of unconventional superconductivity and the long-sought quantum spin liquids. Furthermore, these coincidence detection techniques will offer powerful new methods to investigate novel phenomena like itinerant magnetism and electronic nematicity in quantum materials.

cond-mat.str-el↗
arXiv2026

Route-Constrained Robust Fusion Estimation for MEMS/GNSS Integrated Navigation of Unmanned Ground Vehicles in GNSS Degraded Environments

To address cumulative localization drift of unmanned ground vehicles in structured road environments under severe Global Navigation Satellite System signal occlusion, this paper proposes a robust route-constrained state estimation method. During periods without satellite signals, the proposed method establishes the correspondence between the historical dead reckoning trajectory and local segments of the mission route extracted from a high-definition map, and estimates a route-referenced position via a two-dimensional rigid transformation. The estimated position is then formulated as a pseudo-position observation and incorporated into an Extended Kalman Filter update. In this way, route constraints at the road level can be continuously injected into a unified state estimation framework, thereby suppressing position deviation relative to the mission route while indirectly improving azimuth estimation. To enhance practical applicability, engineering strategies, such as trigger control, matching quality validation, route offset compensation, and single update correction limiting, are further introduced. Experiments in three representative scenarios, including a long tunnel, a multi-segment tunnel, and a curved tunnel, show that the proposed method effectively suppresses error accumulation during satellite outages, reduces the risk of large maximum deviation, and improves localization continuity and road-level usability.

cs.RO↗
arXiv2026

PMO Polaris CO survey. II. Where is the dust?

Dust plays critical chemical and dynamical roles in the interstellar medium (ISM), but its specific association with molecular and atomic gas remains difficult to isolate. Combining the PMO Polaris CO Survey (PPCOS), EBHIS \ion{H}{I} data, and \textit{Planck} dust maps, this study investigates dust distributions across multiple gas components in the Polaris Flare. We employ multi-technique linear decomposition -- including full-spectrum fitting and a regularization approach -- to reconstruct the dust distribution from multi-component gas emissions. This framework quantifies dust contributions from CO-associated, \ion{H}{I}-associated, and CO-dark molecular gas phases. CO-associated dust accounts for 20--40\% of the total dust mass, whereas dust in the broad \ion{H}{I} (warm neutral medium, WNM) component is negligible. Instead, \ion{H}{I}-associated dust concentrates primarily within the narrow cold neutral medium (CNM) and a distinct, ultra-narrow component with a velocity width comparable to the \ion{H}{I} spectral resolution. Residual dust at atomic-to-molecular (\ion{H}{I}--CO) interfaces contributes 4--10\% to the global dust mass, but exceeds 25\% at molecular cloud boundaries, confirming a substantial presence of CO-dark molecular gas. Furthermore, the velocity fields of dust-associated \ion{H}{I} closely match those of CO, indicating active dynamical coupling between CO-emitting gas and the surrounding CNM. Guided by these results, we present a stepwise schematic cartoon illustrating the coupling between multi-phase gas structures, molecular formation, and dust growth.

astro-ph.GA↗
arXiv2026

Co-VLA: Coordination-Aware Structured Action Modeling for Dual-Arm Vision-Language-Action Systems

Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end learning, leveraging large vision-language backbones with continuous action prediction. However, as bimanual tasks become tightly coupled and execution constraints become critical, implicit coordination alone is insufficient to ensure reliable, interpretable, and stable behavior. In this work, we propose Co-VLA, a coordination-aware bimanual manipulation framework introducing explicit structural priors into VLA models. We instantiate our method on a state-of-the-art vision-language backbone by replacing its monolithic action head with a Structured Action Expert (SAE) designed for bimanual coordination. Specifically, we introduce explicit structure at the action generation level with a modular coordination-aware loss that shapes shared and residual latents according to task-specific structures. The shared latent encodes task-level coordination intent, while residual latents capture execution adjustments for each arm. At deployment, a Latent-Aware Controller (LAC) interprets the learned representations to modulate synchronization strength, execution asymmetry, smoothness, and safety constraints in real time. LAC operates at the joint-command level and remains compatible with standard control pipelines without requiring force or impedance control. Experiments across simulation and real-world benchmarks show Co-VLA significantly outperforms monolithic baselines, achieving a 27% success rate gain in tight-coordination tasks, more than doubling performance in OOD real-world scenarios (from 13% to 27%), and reducing task completion time by up to 25%.

cs.RO↗
arXiv2026

PMO Polaris CO survey. I. A 100 deg$^2$ view of the Polaris Flare

Large-area CO surveys are essential for studying molecular cloud dynamics and evolution; however, most have focused on the Galactic plane, leaving high-latitude clouds less explored. We present the PMO Polaris CO Survey (PPCOS), which maps a 100~deg$^2$ region of the Polaris Flare in the $J=1-0$ transitions of $^{12}$CO, $^{13}$CO, and C$^{18}$O using the Delingha 13.7~m telescope. As the first large-area CO survey at high Galactic latitude ($|b| > 20^{\circ}$) with sub-arcminute resolution, PPCOS achieves sensitivities of $\sim$0.46~K for $^{12}$CO and $\sim$0.23~K for $^{13}$CO and C$^{18}$O at a spectral resolution of 0.16~km~s$^{-1}$ and an angular resolution of 50\arcsec. The $^{12}$CO emission reveals seven distinct complexes, where only $\sim$10\% of pixels display multiple velocity components, alongside a global velocity gradient of 0.18~km~s$^{-1}$~pc$^{-1}$. Typical line widths are $1.2 \pm 0.6$~\mbox{km~s$^{-1}$} for $^{12}$CO, while $^{13}$CO components are systematically narrower ($\lesssim 0.7\,ΔV_{\rm ^{12}CO}$). The $^{12}$CO/$^{13}$CO intensity ratios (5--25) indicate widespread $^{12}$CO optical thickness, resembling conditions found in giant molecular clouds (GMCs). Globally, the CO emission divides into two groups: a major group aligned with the velocity gradient and a secondary group elongated perpendicular to it, possibly regulated by large-scale coherent dynamics. We propose a three-layer hierarchy: a dynamically assembling and dispersing periphery traced by $^{12}$CO, a more stable intermediate kernel traced by $^{13}$CO, and gravitationally bound compact cores traced by C$^{18}$O. No young stellar objects are firmly associated with the molecular gas. PPCOS provides an ideal laboratory for studying turbulence, hierarchical structure, and early cloud evolution in a nearby, relatively simple molecular cloud.

astro-ph.GA↗
arXiv2026

Augmenting Dysarthric Speech Severity Assessment with MOS Supervision

Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness. Automatic utterance-level assessment of dysarthric speech can support scalable speech monitoring and therapy-related analysis. Yet training such systems is bottlenecked by the scarcity of clinically annotated dysarthric speech. This work proposes to augment dysarthric speech assessment using data from speech synthesis evaluations, specifically human-annotated utterances with Mean Opinion Score (MOS) labels from the QualiSpeech corpus. Experiments show that fine-tuning on speech synthesis assessment data consistently improves performance on both intelligibility and naturalness prediction, while joint training yields gains primarily on naturalness. These results suggest that synthesis artifacts and dysarthric speech share perceptual commonalities, and speech synthesis evaluation corpora offer a practical augmentation source that reduces reliance on scarce clinical annotations.

eess.AS↗
arXiv2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is optimized for instant response generation and high capability per output token, whereas Ring-2.6 is tailored for deeper reasoning and more advanced agentic workflows. Instead of training from scratch, we upgrade the Ling-2.0 base model through architectural migration pre-training and large-scale post-training. This upgrade is guided by a unified co-design of model architecture, optimization objectives, serving systems, and agent training environments, enabling improvements in both model capability and deployment efficiency. At the architectural level, we introduce a hybrid linear attention design that integrates Lightning Attention with MLA, improving the efficiency of long-context training and decoding. To further enhance token efficiency, we optimize capability per output token through Evolutionary Chain-of-Thought, Linguistic Unit Policy Optimization, bidirectional preference alignment, and shortest-correct-response distillation. For agentic capabilities, we propose KPop, a reinforcement learning framework designed to support stable training of Ring-2.6-1T on large-scale environment-grounded data. KPop improves training efficiency through asynchronous scheduling across coding, search, tool use, and workflow execution, enabling scalable learning from complex agent-environment interactions. Together, Ling-2.6 and Ring-2.6 provide a practical pathway toward efficient, scalable, and open agentic systems. We open-source all checkpoints in the 2.6 family to support further research and development in practical agentic intelligence.

cs.CL↗
arXiv2026

Investigating Metamorphic Fuzz Oracle Enhancement via Large Language Models

Fuzz drivers are essential components of greybox fuzzing, as they encapsulate target interfaces, define test spaces, and largely determine fuzzing effectiveness. Existing fuzz drivers typically rely on crash-based oracles for security testing, overlooking library functionality and limiting bug detection capability. In this paper, we present the first study on metamorphic-based fuzz oracle enhancement (MFOE), which augments existing fuzz drivers with metamorphic-based oracles derived from metamorphic relations (MRs). Since constructing and integrating such oracles requires substantial domain knowledge, automating MFOE is challenging. To address this challenge, we propose MetaFOE, an LLM-based framework that automatically generates and integrates metamorphic-based oracles. We evaluate MetaFOE on OSS-Fuzz drivers using three modern LLMs and five prompt strategies. MetaFOE generates 3,475 MRs, of which 77.3% are applicable, and implements 12,351 meta drivers, with 6,228 being valid. After three hours of fuzzing, the valid meta drivers improve edge coverage by an average of 18.7% and trigger 1,528 unique crashes. Our results demonstrate both the effectiveness of metamorphic-based oracle enhancement and the feasibility of using LLMs to automate MFOE, providing valuable insights for advancing greybox fuzzing.

cs.SE↗
arXiv2026

Towards Paradigm-General Suicide Risk Detection via Speech LLM

Suicide risk among adolescents remains a critical public health concern, and speech provides a non-invasive and scalable approach for its detection. Speech-based suicide risk assessment commonly relies on carefully designed speech elicitation paradigms (\textit{e.g.,} verbal fluency, reading, or question answering) to probe cognitive and affective states. Existing approaches, however, typically focus on one single paradigm at a time. This paper, for the first time, investigates cross-paradigm approaches that unify diverse speech elicitation paradigms within a single model. Specifically, we use a speech LLM as backbone with a mixture of DoRA experts (MoDE) to capture complementary cues across assessments dynamically, tested on 1,223 participants across ten speech elicitation paradigms. Results show that MoDE outperforms both paradigm-specific and conventional joint-learning models. Moreover, it can generalise to unseen paradigms and provide better confidence calibration.

eess.AS↗
arXiv2026

Towards Diverse Scientific Hypothesis Search with Large Language Models

Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many discovery settings, the goal is not to identify a single best hypothesis since validation can be noisy and expensive, and scientists benefit from a set of high-quality alternative hypotheses that hedge against downstream uncertainty for the best solutions. Nevertheless, commonly used evolutionary search recipes tend to prioritize optimization over exploration in hypothesis generation, and the resulting selection pressure during the search process leads to diversity collapse. Motivated by these limitations, we formulate hypothesis search as a sampling problem, where the objective is to efficiently produce diverse, high-quality hypotheses under a fixed validation budget. Building on this perspective, we propose \ours, an evolutionary framework inspired by the classical parallel tempering algorithm that searches hypotheses at multiple temperature levels and enables principled information exchange across temperatures to improve exploration without disrupting convergence. Across domains including molecular discovery, equation discovery, and algorithm discovery, our approach consistently improves both hypothesis quality and diversity under the same validation budget, and produces candidates that remain robust under more expensive downstream computational validations.

cs.LG↗
arXiv2026

Detectability to extreme mass ratio inspirals with alternative space-based detector networks

Extreme mass ratio inspirals (EMRIs) are among the primary targets for space-based gravitational-wave (GW) detectors, providing valuable opportunities to study stellar-mass compact objects orbiting supermassive black holes (SMBHs) and to probe gravity in the strong-field regime. As the LISA, TAIJI, and TianQin missions are expected to operate around 2035, joint observations by multiple detectors may provide enhanced measurement capabilities compared to individual missions. In this work, we investigate the detectability and parameter-constraint prospects for EMRIs using global detector networks composed of LISA, TianQin, and two alternative TAIJI orbital configurations. Employing Fisher information matrix analysis, we find that joint observations improve source localization relative to a standalone LISA mission, with sky-localization uncertainties reduced by up to two orders of magnitude for a one-month observation period. We further find that a three-detector network (LISA-TAIJI-TianQin) operating for one month yields parameter constraints comparable to, and in some cases tighter than, those obtained from a one-year observation by LISA alone. This result indicates that network observations can partially compensate for shorter observation durations in parameter inference. Furthermore, we evaluate measurement uncertainties in concurrent dual-signal scenarios: (i) two EMRIs orbiting the same SMBH and (ii) two EMRIs with identical intrinsic parameters but different sky locations and orientations. The results indicate that differences in the detector responses associated with the source geometries reduce correlations between overlapping signals, allowing the sources to remain distinguishable even for a standalone mission. The inclusion of multiple detector constellations further improves source localization and tightens parameter constraints in these concurrent-source scenarios.

gr-qc↗
arXiv2026

TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders

Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals. We introduce TRL-Bench, a multi-granular tabular representation learning (TRL) benchmark that standardizes cross-paradigm representation-level evaluation: each encoder exports row-, column-, or table embeddings through its supported wrapper, and shared lightweight heads probe them across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment spanning all three granularities). To support this standardized setting, we release curated benchmark assets and task reformulations, including 50 OpenML tables with 123 verified targets, 16 row-pair linkage rewrites, and a 47,772-table DLTE lake derived from 1,379 parent tables. Across 20 models and 16 tasks, TRL-Bench shows that once downstream conditions are standardized, encoder quality is capability-specific rather than captured by a single leaderboard. In TRL-CTbench, generic text encoders often lead on tasks with strong surface-text signal, while tabular specialists win where their pretraining objective aligns with the task. In TRL-Rbench, within-table prediction and cross-table linkage favor different training regimes, with atomic linkage performance correlating strongly with the row-matching stage of DLTE pipelines. In TRL-DLTE, the strongest pipelines combine capability-matched specialists rather than reuse a single encoder, and top end-to-end quality depends on non-additive compositional fit rather than per-stage marginal rank alone. TRL-Bench provides a common protocol for measuring reusable signal in exported tabular representations under shared downstream conditions. Code and data: https://github.com/LOGO-CUHKSZ/TRL-Bench

cs.AI↗
arXiv2026

A Mixed Model Approach for Estimating Regional Functional Connectivity from Voxel-level BOLD Signals

Resting-state brain functional connectivity quantifies the synchrony between activity patterns of different brain regions. In functional magnetic resonance imaging, each region comprises a set of spatially contiguous voxels at which blood-oxygen-level-dependent signals are acquired. The ubiquitous Correlation of Averages (CA) estimator, and other similar metrics, are computed from spatially aggregated signals within each region, and remain the quantifications of inter-regional connectivity most used by neuroscientists. Their popularity is primarily due to computational simplicity despite their demonstrable bias and lack of statistically principled justification. By leveraging linear mixed-effects models, both inter-regional and intra-regional correlation and measurement error can be explicitly modeled as signal variability sources. A novel computational pipeline, focused on subject-level inter-regional correlation parameters of interest, is developed to address the challenges of applying maximum likelihood estimation to such structured, high-dimensional spatiotemporal data. Simulation results confirm the superiority of the proposed estimator relative to CA in terms of both decreased bias and accurate confidence interval coverage across simulation settings. The proposed method is also applied to construct individual human brain networks for subjects from a Human Connectome Project test-retest database. Concordances between inter-regional correlation estimates demonstrate the potentially substantial scientific benefits of the proposed approach that reliably produces more consistent results than CA for test-retest scans of the same subject.

stat.ME↗
arXiv2026

Coexistence Regime and Thermal Crystallization in the cavity-mediated extended Bose-Hubbard Model

By means of path integral- Monte Carlo, we study the finite-temperature behavior of the extended Bose-Hubbard model with cavity-mediated long-range interactions at unit filling. At zero temperature, the system supports superfluid, Mott-insulating, supersolid, and charge-density-wave phases, with a strongly first-order transition between superfluid and charge density wave states characterized by a broad coexistence region. Focusing on this coexistence regime, we explore how the dominant order evolves with temperature. When the system is initialized in a superfluid state, the superfluid density is progressively suppressed upon heating, and a normal fluid is stabilized. Upon further increasing the temperature, a thermally assisted emergence of crystalline order occurs which eventually melts into the normal fluid. In contrast, simulations initialized in a charge-density-wave configuration display a smooth thermal melting of density order, with no reemergence of superfluid coherence. Overall, our results show that metastability persists at low temperatures, but ultimately disappears at higher temperatures, where thermally induced crystallization takes place.

cond-mat.quant-gas↗
arXiv2026

Attention Sink in Transformers: A Survey on Utilization, Interpretation, and Mitigation

As the foundational architecture of modern machine learning, Transformers have driven remarkable progress across diverse AI domains. Despite their transformative impact, a persistent challenge across various Transformers is Attention Sink (AS), in which a disproportionate amount of attention is focused on a small subset of specific yet uninformative tokens. AS complicates interpretability, significantly affecting the training and inference dynamics, and exacerbates issues such as hallucinations. In recent years, substantial research has been dedicated to understanding and harnessing AS. However, a comprehensive survey that systematically consolidates AS-related research and offers guidance for future advancements remains lacking. To address this gap, we present the first survey on AS, structured around three key dimensions that define the current research landscape: Fundamental Utilization, Mechanistic Interpretation, and Strategic Mitigation. Our work makes a pivotal contribution by highlighting the key concepts and main trends in the field, guiding researchers through the evolution of AS-related studies. We envision this survey as a valuable resource, empowering researchers to effectively manage AS within the current Transformer paradigm, while simultaneously inspiring innovative advancements for the next generation of Transformers. The paper list of this work is available at https://github.com/ZunhaiSu/Awesome-Attention-Sink.

cs.LG↗
arXiv2026

GPU optical photon Monte Carlo for noble liquid detectors: validation against Geant4 in a large liquid argon TPC benchmark

Optical photon Monte Carlo simulation is a computational bottleneck for noble liquid Time Projection Chambers. Design studies require repeated, geometry dependent simulations of timing, wavelength shifting, and optical response, while reconstruction and particle identification workflows need labeled optical datasets. We present Simphony, a GPU optical simulation tool, formerly EIC-Opticks, built on Opticks with CUDA and NVIDIA OptiX. Simphony implements a GPU version of the Geant4 G4OpWLS wavelength-shifting model and returns Monte Carlo truth for detected hits with low per-photon overhead. We validate Simphony against Geant4 11.3.2 in a simplified 14.7 kt liquid argon Time Projection Chamber benchmark with a two-stage wavelength-shifting shell and idealized photon counting detector. For three paired 2.5 GeV electron simulations, each producing about 61 M optical photons, the integrated detected-photon ratio agrees with Geant at the subpercent level. The detected arrival time and wavelength spectra give $χ^2/\mathrm{ndf}$ values of 0.98 and 1.08. Contained muon and near-Cerenkov-threshold proton samples give $R_N=1.0017\pm0.0008$ and $R_N=1.0005\pm0.0014$, confirming agreement for distinct source topologies. On an NVIDIA RTX 4090, a stacked launch of four 2.5 GeV electron events transports 243 M optical photons in $3.03\pm0.06$ s, giving $80.2\pm1.6$ M photons s$^{-1}$. Relative to a single-thread Geant reference and including GPU overheads and host-device transfers, the optical transport speedup is $1053\pm55$; the end-to-end wall time acceleration is $89\pm5$. These results show that Simphony can make explicit optical photon Monte Carlo practical for detector development studies and for generating machine learning optical response datasets.

physics.ins-det↗
arXiv2026

MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models

The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding. While current VLMs excel at event- or story-level understanding, their ability to capture fine-grained motion details remains limited, primarily due to their focus on high-level static semantic structures and macro-event logic. In contrast, Video Diffusion Models (VDMs) are adept at modeling dynamic motion patterns, benefiting from large-scale video data and the intrinsic requirement of temporal generation. In this paper, we introduce MotionEnhancer, a novel approach that leverages motion priors distilled from a powerful video diffusion model as auxiliary supervision to enhance the motion understanding capability of a VLM via attention alignment. MotionEnhancer comprises two simple parameter-free modules, Motion-sensitive Head Selection (MHS) and Motion-salient Text Token Identification (MTTI), to directly extract and optimize motion-related attentions from the VDM in a computation-only manner. MotionEnhancer provides a scalable solution for motion understanding without additional training parameters, modifications to existing architectures, or tool calling. Extensive experiments demonstrate that MotionEnhancer can achieve consistent improvements over state-of-the-art VLMs on two motion-level video understanding benchmarks, especially on motion-related metrics.

cs.CV↗