arXiv subjects

Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Detector-level simulation closure of radio air-shower reconstruction with native RF-chain inversion and out-of-fold endpoint interpolation

Autonomous radio arrays reconstruct air showers from trigger-selected digitized voltage traces rather than ideal electric-field footprints. We present a detector-level simulation-closure test linking RF-chain-triggered ZHAireS event packages to reconstructed direction, energy and quality diagnostics. Voltage amplitudes and station timing determine geometry through a robust joint timing--amplitude axis fit. Native-grid RF-chain inversion recovers 50--200 MHz electric-field peaks along the shower-plane v cross B axis. Symmetric four-arm iron and proton templates generated with 2 ns time bins provide endpoint estimates that are combined by a nested five-fold out-of-fold interpolation estimator. One estimator is used over the full angular range without a fitted multiplicative energy scale. Of 972 triggered event packages, 725 pass common quality selection in raw-noisy weighted, hard-gated denoised equal-weight and clean equal-weight branches. In the primary reconstructed-zenith range 60--85 degrees, 693 common events have mean energy residuals of -0.05, -0.30 and -0.66 percent with standard deviations of 11.04, 10.90 and 10.75 percent. The hard-gated denoised branch has a median angular separation of 0.052 degrees. Boundary zenith ranges are reported separately; the upper boundary has a mean energy residual of +14.2 percent and a 19.9 percent standard deviation. These results describe cross-fitted closure within a matched simulation framework, not deployed-array resolution or independent energy calibration.

astro-ph.IM↗

arXiv2026

Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding

Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains. Recently, generative recommendation (GR) has emerged. It first learns semantic identifiers (SIDs) from item semantics and formulates recommendation as autoregressive generation. However, existing methods face two critical issues: (1) they ignore collaborative correlations across domains during tokenization, and (2) they adopt inefficient decoding strategies, such as beam search, during generation, which hinders real-time deployment. To address these limitations, we propose GenCDSR, an effective and efficient generative framework for CDSR. Specifically, we design a cross-domain hybrid tokenization mechanism with a multi-tower architecture to jointly capture cross-domain commonalities and domain-specific distinctions through hierarchical shared-specific and fine-grained codebooks. Furthermore, we develop a cross-domain serial-parallel decoding strategy that leverages the hierarchical SID structure to partially parallelize generation, significantly reducing inference latency while preserving generation consistency. Experiments on three public datasets show that GenCDSR achieves an average accuracy improvement of 1.5 percent and an average inference latency reduction of 85.1 percent compared with state-of-the-art baselines. The implementation code and datasets are available online: https://github.com/Applied-Machine-Learning-Lab/RecSys2026_GenCDSR.

cs.AI↗

arXiv2026

SALMONN-2: Advancing General-Purpose Hearing Abilities with Self-Supervised Representations

Recent audio large language models (ALLMs) are typically built upon audio encoders trained with large amounts of supervised data. Since self-supervised learning (SSL) audio encoder models are known to learn general-purpose and transferable representations, we investigate whether general-purpose SSL audio representations can serve as an effective foundation for ALLMs. We present SALMONN-2, an ALLM built upon a unified SSL encoder. To better exploit the hierarchical representations learned by SSL encoders, we propose a multi-layer feature fusion (MLF) adapter that aggregates information from all encoder layers before projecting them into the language model. Beyond conventional audio understanding tasks, we further explore multimodal in-context learning (MICL) in ALLMs and study how this capability can be acquired through contextual biasing training. Experimental results show that a general-purpose SSL encoder achieves performance comparable to, or better than, specialised supervised audio encoders while providing a more balanced capability across speech, audio, music and paralinguistic tasks. SALMONN-2 further achieves state-of-the-art performance among comparable-scale open-weight models on ALLM understanding benchmarks, obtaining the best results on MMAU-Pro, MMAR and MMSU. We also show that MICL does not emerge naturally in ALLMs, but can be effectively acquired through targeted contextual biasing training.

eess.AS↗

arXiv2026

Learning Emotion from Motion: Kinetic Multi-Stream Skeleton Modeling with Metadata-Conditioned Weak Label Distributions

Skeleton-based emotion recognition from body motion remains challenging because emotional expressions are often characterized by subtle dynamic and relational motion cues, and hard labels may not fully capture ambiguity among related emotion categories. For the DIEM-A task in the MMAC ACII 2026 Challenge, we propose a multi-branch skeleton-based emotion recognition framework that combines a 6D rotation-based branch, a part-aware kinetic multi-stream branch, and a metadata-conditioned weak label distribution learning (LDL) branch. The branches are trained independently and fused by a probability-level ensemble at inference time. In 10-fold leave-performer-out cross-validation, the proposed framework improves Accuracy from 0.271 to 0.366 and Macro-F1 from 0.252 to 0.353 over the rotation-based baseline. Explainability ablations show that velocity and bone streams, as well as arm and leg regions, provide important cues for recognizing emotional body motion.

cs.CV↗

arXiv2026

Gradient regularity for nonlocal double phase equations

This paper is devoted to investigating the interior $C^{1, α}$ regularity of viscosity solutions to the nonlocal double phase equations $$ \int_{\mathbb{R}^d} \left(\frac{|u(x)-u(y)|^{p-2}(u(x)-u(y))}{|x-y|^{d+sp}}+a(x,y)\frac{|u(x)-u(y)|^{q-2}(u(x)-u(y))}{|x-y|^{d+tq}}\right)dy=0, $$ where $2\le p\le q$, $0<s\le t<1$, and $a(x, y)\ge0$. By assuming the Lipschitz continuity of $a(\cdot)$, we show that the gradient of solution is Hölder continuous, provided the distance of $tq$ and $sp$ is suitably small. As a key ingredient to this conclusion, the Lipschitz property of solutions is also established under weaker assumptions on the modulating coefficient $a(\cdot)$, which is of independent interest. Our results develop a nonlocal counterpart of the gradient regularity theory for classical double phase problems due to Colombo \& Mingione [Arch. Ration. Mech. Anal., 2015] and solve the higher regularity issue raised by De Filippis \& Palatucci [J. Differential Equations, 2019]. The core challenges consist in precisely characterizing the subtle interaction among the pointwise behaviour of the coefficient $a(\cdot)$, the growth exponents and the differentiability orders.

math.AP↗

arXiv2026

SLVMBench: Skill Learning from Video Memory

We introduce Skill Learning from Video Memory (SLVMBench), the first benchmark that jointly evaluates whether video large language models (video-LLMs) can learn skills from long video memory and apply them to real-time tasks. SLVMBench presents models with 2-3 hour video streams that contain a tutorial video embedded in a stream of arbitrary irrelevant videos, resembling real-world human learning practices. Video-LLMs are asked to apply the acquired skill to answer real-time questions about an ongoing video. Unlike long-video understanding benchmarks that emphasize passive comprehension and skill-learning benchmarks that rely on short, immediate demonstrations, SLVMBench tests the full pipeline of memorizing and extracting procedural knowledge, as well as transferring it to real-time tasks. Moreover, rigorous human annotations feature sub-second-level temporal calibration, manually engineered questions eliminating common-sense guessing, and collated tutorials to ensure coverage of the required skills. Evaluations on state-of-the-art proprietary and open-source video LLMs show that video-LLMs struggle substantially with learning and applying skill knowledge from videos. Moreover, performance degrades markedly when the skill knowledge is placed within a long video memory. These results reveal a key limitation of existing video LLMs and position SLVMBench as the first benchmark for studying real-time skill acquisition and application from long-context video memory.

cs.CV↗

arXiv2026

video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding

Video large language models (LLMs) are often constrained by computation and memory budgets, leading them to use reduced frame rates and spatial resolutions, which may cause them to miss critical information for question answering (QA). A practical and efficient solution is a two-stage paradigm: first perform coarse video understanding to localize relevant segments, and then re-watch these segments at higher temporal or spatial fidelity. In this paper, we present video-SALMONN-R$^3$, the first end-to-end video-LLM that enables re-watch through reinforcement learning without relying on chain-of-thought (CoT) cold-start. This design removes the need for costly CoT data annotations and avoids CoT-based supervised fine-tuning (SFT), which can otherwise degrade the pretrained video understanding abilities. To address the mismatch between the reasoning-first behavior induced by re-watch and the answer-first tendency of pretrained video-LLMs, we propose a re-answer strategy, in which the model first produces a direct answer in the first watch and then refines it after re-watching. Finally, to improve question adherence during re-watching, we propose a re-ask mechanism that re-injects the query when revisiting localized segments. Experimental results show that video-SALMONN-R$^3$ consistently outperforms both the base model and the QA-SFT baseline, while surpassing prior re-watch-based approaches with significantly lower computational cost. Code, models, and data will be publicly released upon acceptance.

cs.CV↗

arXiv2026

Quantifying Entrainment Evidence: A Comparison of Frequentist and Bayesian Approaches for Information Processing Pathway Maps

Information Processing Pathway Maps (IPPMs) offer a scalable framework for formalizing the complex sequence of mathematical transformations applied to sensory stimuli. These maps chart the latency and cortical expression of computational steps, relying on statistical inference to link model outputs with observed neural activity. Traditionally, this mapping has relied on frequentist hypothesis testing. However, determining which of several competing computational models best explains neural data is a problem of model adjudication, arguably better suited to probabilistic inference. Here, we present a direct comparison between the established frequentist approach and a novel Bayesian framework for mapping cortical entrainment. While the Bayesian formulation retains the core strength of IPPMs -- generating explicit predictions of time-varying neural signals -- it fundamentally alters the selection criterion, shifting from rejecting a null hypothesis to quantifying the relative evidence for competing computational hypotheses. We evaluate the performance and interpretability of both approaches using an auditory neuroimaging dataset to reconstruct a known loudness-processing pathway. We discuss the implications of this shift for systems neuroscience, specifically regarding the handling of collinear models and the robust accumulation of evidence.

q-bio.NC↗

arXiv2026

Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention

We introduce a controlled subspace intervention framework to investigate how self-supervised Vision Transformers (ViTs) encode dense geometric information. While linear probing is widely used to assess geometric representations, it treats features as a black box, failing to disentangle the underlying topology. To address this issue, we decompose the weights of converged linear probes to isolate the low-rank subspaces containing explicit geometric signals using Singular Value Decomposition (SVD). Our perspective yields three key insights: (1) Pre-training objectives determine how features are encoded. DINOv2 aligns spatial features for efficient linear extraction, while Masked Autoencoders (MAE) tend to disperse these signals, requiring a broader spatial context. (2) Explicit geometric representations are highly compressible, suggesting dense predictive heads could potentially be constrained to low-rank subspaces with minimal performance loss. (3) The layer-wise task affinity suggests that geometric precision peaks at intermediate layers before yielding to semantic abstraction in the final layers. By connecting internal encoding mechanics with downstream performance, these findings provide a basis for effective feature selection and lightweight decoder design. The source code is available at https://github.com/Zhou-Weichen/Geosubprobe.

cs.CV↗

arXiv2026

Random gas motions inside sub-parsec scale supercritical filaments

Supercritical gas filaments in molecular clouds host the dense cores in which new stars form. The mechanisms governing their formation and subsequent gas accretion remain poorly understood. In this study, we conduct a statistical analysis of a large sample of sub-parsec supercritical filaments using H13COp J=1-0 data from the ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS) Survey. We identified velocity-coherent filaments in position-position-velocity (PPV) space and systematically examined velocity gradients both along and perpendicular to their skeletons. Our analysis uncovers a remarkable result: at scales of ~ 0.1-1 pc, the local velocity gradients within these supercritical filaments show no preferred alignment with the filament skeletons and exhibit no correlation with the local gravitational field. This random orientation suggests the presence of chaotic gas motions deep inside these dense structures. These findings may indicate that turbulence-rather than gravity-dominates gas dynamics and structural evolution at small scales, even in regions on the verge of star formation, challenging the paradigm of gravity-dominated structure formation within molecular clouds. This scenario should be further tested by more state-of-the-art simulations. This study offers key observational insights into the roles of turbulence and gravity in establishing the initial conditions for star formation.

astro-ph.GA↗

arXiv2026

UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation

Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this paradigm to the tactile domain, where both object-level semantics and sensor-level configurations jointly determine the meaning of touch. To address this gap, we propose UniTac, the first UMM designed for tactile understanding and generation. UniTac models the tactile process as a transition from non-contact to contact, capturing the physical interaction between sensors and objects through a dual-level representation that encodes both sensor and object attributes. For tactile understanding, UniTac introduces two tasks, object property description and sensor identification, to enhance reasoning over physical and cross-sensor information. For tactile generation, we design a two-stage training paradigm consisting of reconstruction and alignment, together with a sensor-prior-based sampling strategy that simulates realistic tactile contact. Trained on large-scale multi-sensor datasets, UniTac achieves state-of-the-art performance in tactile understanding and generates realistic tactile signals across sensors.

cs.RO↗

arXiv2026

Anomalous Air Showers and What They Reveal About Hadronic Interactions and Cosmic-ray Masses

The identification of the sources and acceleration mechanisms of cosmic rays require precise measurements of their mass composition. Currently, the most reliable method is to measure the atmospheric depth at which cosmic ray air showers in our atmosphere reach their maximum (\Xmax). However, the hadronic interaction properties that govern the longitudinal development of air showers are not precisely known, which is a major source of systematic uncertainty on the mass composition. SKA-Low will observe cosmic rays in the 10$^{16}$ - 10$^{18}$ eV energy range with unprecedented resolution and bandwidth. This allows for a much more detailed reconstruction of the longitudinal shower evolution, which can be used to gain better understanding of the hadronic interactions, as well as the primary mass composition. After the first interaction of the cosmic ray with an atom in an air molecule, the secondary particles still carry a significant fraction of the total energy. When one of these particle travels very far before interacting again, it produces a sub-shower that can be recognized as a secondary bump in the longitudinal profile. Simulations have demonstrated that SKA-Low can resolve such double bump profiles by virtue of its high antenna density and broad bandwidth. In this chapter, we demonstrate how double-bump showers and other anomalous longitudinal developments can be used to constrain hadronic interaction properties, and to determine the mass composition of cosmic rays in the Galactic-to-extragalactic transition region.

astro-ph.HE↗

arXiv2026

MemLeak: Diagnosing Information Leaks in Multimodal Agent Memory

When a multimodal AI agent is asked to forget a fact, current memory systems usually delete the text entry and report success. We find that the fact can remain recoverable from retained user images, including images tagged to entirely different facts, because VLMs use implicit visual cues at inference time. We introduce the Information Provenance Graph (IPG), a taxonomy that classifies memory representations by deletion affordance. The IPG reveals that deletion fails through multiple channels. Our benchmark, MemLeak, measures this across a deletion cascade: direct probing of deletion-capable systems yields <1%, but retained correlated text enables 18.3% recovery, and retained images enable 12.0% recovery (0.0% blind baseline, 0.3% FPR) -- with 47% of image leaks not text-recoverable. Content-aware semantic deletion reduces the image residual to 2.0%. The residual appears across multiple VLMs, a production memory system, and real Unsplash-licensed photographs. Dual-annotator human validation (kappa = 0.88) confirms judge reliability.

cs.LG↗

arXiv2026

Orlicz Potential Theory: Balayage, Riesz Measures, and Very Weak Solutions

We develop a nonlinear potential theory for elliptic equations with Orlicz growth under general monotonicity and growth conditions, without any homogeneity or scaling assumptions. The lack of scaling invariance prevents the use of many classical tools from nonlinear potential theory. To overcome this difficulty, we establish a new framework that includes global Hölder regularity for obstacle problems, a balayage theory, the construction and analysis of Riesz measures associated with superharmonic functions, the identification of capacitary potentials, capacitary estimates for polar sets, and the quasicontinuity of superharmonic functions. As an application of this theory, we prove that the classes of superharmonic functions and renormalized solutions to elliptic measure data problems coincide. This extends the classical equivalence theory from the homogeneous $p$-growth setting to general Orlicz growth and is new even for power-growth operators without homogeneity assumptions.

math.AP↗

arXiv2026

Investigating forward-backward asymmetry in D-meson production and anisotropic flow in p-Pb collisions at the LHC

We investigate the forward--backward asymmetry in the production and elliptic flow of prompt D0 mesons in proton--lead (p--Pb) collisions at$\sqrt{s_{\mathrm{NN}}}=8.16$ TeV using the heavy-flavor improved string-melting version of the AMPT model. The model calculations provide a simultaneous description of nuclear modification factor $R_{\mathrm{pPb}}$ and $v_2$ in forward and backward rapidities. We find that the observed asymmetry arises from the interplay of initial-state cold nuclear matter effects and final-state partonic interactions, with the competition between coalescence and fragmentation playing a critical role in shaping the transverse momentum and rapidity dependence of both observables. This work suggests that a partonic medium is formed in high-multiplicity p-Pb collisions at LHC energies.

nucl-th↗

arXiv2026

Measuring High-Energy Cosmic Particles with the SKA

The origin of high-energy cosmic rays remain one of astrophysics' greatest unsolved mysteries. SKA-Low will be able to measure air showers initiated by cosmic rays with unprecedented precision in the PeV - EeV energy range, covering the critical transition region between Galactic and extragalactic sources. SKA-Low's densely instrumented core and broad bandwidth will allow for measurements of individual air showers with a level of detail unmatched by any existing or planned detector. The depth of shower maximum, the primary mass-sensitive observable, will be reconstructed with a resolution of better than 8~g/cm$^2$, a significant improvement over existing methods. Additionally, new reconstruction methods are expected to enable full air shower reconstruction across a wide energy range, down to PeV levels. At these energies, efficient photon/hadron separation may offer an opportunity to measure PeV gamma-ray air showers. Furthermore, SKA-Low opens a window into studying high-energy hadronic interactions, including via the unique channel of anomalous air showers. This combination of measurements provides a unique opportunity to investigate the origins and physics of high-energy cosmic rays. A dedicated particle detector array will provide triggered readout of raw antenna-level voltage buffers, enabling fully commensal cosmic-ray observations alongside regular operations. We outline our science case and discuss the observational strategy, signal properties and detector design underpinning these measurements. We also summarize the accompanying book chapters, which address composition measurements in the Galactic-to-extragalactic transition region, next-generation interferometric reconstruction techniques, hadronic interaction physics through anomalous air showers, the prospects for detecting PeV gamma-rays from Galactic sources, and the related project of imaging lightning using SKA-Low.

astro-ph.HE↗

arXiv2026

Using SKA-Low to Detect PeV Gamma-rays from Galactic Sources

Detecting so called PeVatrons is considered one of the prime goals of $γ$-ray astronomy. PeVatrons are astrophysical objects in the Galaxy that are sources of cosmic rays exceeding PeV ($10^{15}$ eV) energies, the highest in our Galaxy. Their nature is unknown as of now, with some candidates reaching barely above PeV energies just having been identified. Serendipitously, the energy threshold of air shower detection using radio emission, has been proven at 50 PeV. There is a case to be made that SKA-Low with its unprecedented number of antennas, can reach lower in energy, while the size of the core is sufficiently large provide a significant effective area to measure PeV fluxes. While this promises a novel angle towards understanding the cosmic ray accelerators in our Galaxy, it also would be the first detection of $γ$-ray air showers using radio emission.

astro-ph.HE↗

arXiv2026

Interferometric Analysis of Air-shower Radio Emission in the Near Field with an Information Field Theory Approach

Current reconstruction techniques for air-shower radio emission generated by cosmic rays have shown great success, having been applied to several radio detectors over the last decade. Nevertheless, they are limited by their high computational cost, simplified approximations, and signal information used for reconstruction. As such, advanced analyses are required to not only be able to perform a holistic reconstruction of all parameters, but also to conduct near-field interferometry of the air shower. This can be achieved through Information Field Theory (IFT), an imaging reconstruction framework based on Bayesian inference that can extract all available information within the signal to infer distributions of field-like quantities. In this chapter, we highlight current novel approaches that use IFT for air shower reconstruction, and the potential of their applicability towards SKA-Low.

astro-ph.IM↗