arXiv subjects

Weiwei Chen

Publications and source records attributed to Weiwei Chen.

At least 19 recordsLinked to original sources

arXiv2026

The pulsar population of Omega Centauri: New TRAPUM searches, timing, and cluster dynamics with MeerKAT and Parkes

Millisecond pulsars (MSPs) are probes of globular clusters, tracing their stellar evolution, cluster dynamics, and gravitational potential. We searched for and timed MSPs in the Omega Centauri, then investigated their high-energy emission, the cluster potential, and MSP demographics. We performed Fourier-domain acceleration and jerk searches on MeerKAT observations, followed by pulsar timing using MeerKAT and Parkes Murriyang data spanning 2021-2025. We discovered the new isolated MSP PSR J1326-4728S (PSR S, Ps=4.538 ms and DM=96.24 pc/cm3. We updated the timing solutions for PSRs A-E, obtained the first timing solutions for G, H, and K, and updated the orbital parameters for I, L, N, and Q. The latter three, I, N, and Q, have significantly longer orbital periods (Pb=1-7 days) than previously reported. Pulsars B, G, H, K, and L are black widows (Pb=2-4 hours); I is a wide-orbit (Pb=3.50 days) MSP with a light ~0.05 Mo companion; N and Q have ~0.25 Mo companions; and N shows a significant orbital eccentricity (e=0.093). We folded Fermi-LAT and NICER photons to search for high-energy pulsations, but failed to detect them from individual MSPs. Significant spin period derivatives were measured for eight MSPs and interpreted as arising from the cluster gravitational potential. The inferred line-of-sight accelerations are consistent with a King-model gravitational potential, and allow us to set an upper central black hole mass limit of Mbh<8e4 Mo at 90% confidence. The high fraction of isolated MSPs (58%) and black widow systems (63% of binaries), and possibly the eccentricity of N, are difficult to reconcile with MSP population predictions based solely on encounter rates. Instead, these properties require alternative formation channels or reflect the complex evolutionary history of Omega Centauri, with part of its MSP population having formed in denser environments than those observed today.

astro-ph.HE↗

arXiv2026

Unconventional SOC-activated Altermagnetic Spin Splitting and Half-Angle Locking of Spin Transport

Conventional altermagnetic spin splitting arises from staggered exchange and sublattice-dependent anisotropic hopping without requiring spin-orbit coupling (SOC). Here we derive a low-energy effective model showing that SOC and staggered exchange can jointly activate additional spin splitting on momentum planes where the nonrelativistic contribution vanishes. In hexagonal $α$-MnTe, this SOC-activated contribution produces d-wave spin splitting near $Γ$ with out-of-plane spin polarization. Its leading angular pattern counter-rotates by half the in-plane Néel-vector rotation angle. First-principles spin-transport calculations reveal a corresponding approximate half-angle locking between the Néel-vector orientation and the electric-field directions maximizing the signed longitudinal and transverse spin conductivities. Analytical transport calculations based on the effective model trace this locking to the same angular coupling between momentum and the Néel vector that governs the spin-splitting pattern. Our findings connect the microscopic mechanism of SOC-activated altermagnetic spin splitting to directional spin transport, providing a theoretical basis for probing Néel-vector orientation and controlling spin-current generation.

cond-mat.mtrl-sci↗

arXiv2026

Late Breaking Results: Audio-Language-Action Models with Direct Speech Conditioning for Robotic Manipulation

Vision-language-action (VLA) models enable robots to follow instructions in typed text, limiting broader deployment in natural speech. The traditional solution by prepending a speech recognition before VLA to convert speech into texts would incur additional latency and propagate errors. To address this, we present Speech-$π_{0.5}$, which maps raw speech directly to motor commands without explicit textual transcriptions. Specifically, built on $π_{0.5}$, we use a cross-modal projection to convert voice frames directly into audio tokens with significant response latency reduction. To further improve the accuracy, we adopt split LoRA adaptation with dedicated audio and task adapters, under a two-stage training. Speech-$π_{0.5}$ achieves $3\times$ lower response latency than ASR pipeline while maintaining competitive task success on LIBERO, and generalizes robustly to unseen accents where a single adapter degrades significantly.

cs.CV↗

arXiv2026

CIRRA: Dual-Level Continual Instruction Reconciliation with Ongoing Execution for Embodied Robot Agents in Interactive Household Tasks

Household robots must accommodate new user instructions while executing ongoing tasks. Existing agents often regenerate or extensively revise the remaining task sequence, introducing plan ambiguity, logical inconsistency, and redundant execution. We formulate continual instruction reconciliation and propose CIRRA (Continual Instruction Reconciliation for Robot Agents), a dual-level framework combining LLM-based semantic reasoning with rule-constrained structural integration. CIRRA first grounds incoming instructions to unique executable skills and resolves underspecified actions and execution locations. It then preserves the ongoing subtask sequence as an execution backbone and generates integration candidates by inserting incoming subtasks into location-matched segments. The semantic reasoner evaluates only modified segments to identify dependencies and conflicts and select the most logically coherent local integration. This structure-preserving process maintains alignment with ongoing execution, mitigates ambiguity and inconsistency, and reuses shared subtasks to reduce redundant execution. We also introduce CHIRP (Continual Household Instruction Reconciliation and Planning), a text-based benchmark of 120 episodes across eight household environments and six categories of everyday activities. On CHIRP, CIRRA achieves 74.2% decision agreement, exceeding the strongest replanning baseline by 30 percentage points; every correct fusion decision yields a correctly placed, conflict-free schedule. On a Unitree G1 humanoid, CIRRA interrupts ongoing skills at the correct moment in every trial and significantly outperforms all baselines on every metric.

cs.RO↗

arXiv2026

Evidence-Guided Counterfactual Learning for Long-Tailed Robotic Policies

In the practical deployment of vision--language--action policies, handling long-tailed task distributions is a common challenge due to the highly imbalanced composition of robot demonstration datasets. To investigate this issue, we study the head-task action bias in shared robot policies, particularly focusing on its relationship with task-specific visual evidence. Our experimental analysis shows that, when the visual evidence required by a tail task is insufficient, the policy increasingly falls back on action patterns reinforced by data-rich head tasks, resulting in degraded performance on under-represented tasks. Motivated by this observation, we propose Evidence-Guided Counterfactual Learning, a training framework for long-tailed robot policies. Our method identifies decision-relevant visual evidence using instruction-related and action-related signals, and constructs counterfactual observations by removing the selected evidence. The resulting counterfactual predictions are used to strengthen the association among task instructions, visual evidence, and actions, with the supervision adaptively adjusted according to the number of demonstrations available for each task. The proposed framework requires neither additional training data nor region-level annotations and incurs no additional computation during deployment. Extensive experiments across simulated benchmarks, different VLA architectures, and real-world robot platforms demonstrate that our method robustly and consistently improves long-tailed robot policy learning, particularly for tail tasks, while maintaining reliable performance on head tasks.

cs.RO↗

arXiv2026

High-Throughput Discovery of Chiral Carbon-Boron Semimetals with Weyl Topology and Phonon-Mediated Superconductivity

Chiral crystals provide a natural platform for unconventional topological fermions, while light-element carbon--boron frameworks are promising for phonon-mediated superconductivity owing to strong covalent bonding and high phonon frequencies. Here, we combine Wyckoff-position-based structure generation with high-throughput first-principles calculations to search for C--B crystals in selected hexagonal chiral space groups. Starting from 972 generated $\mathrm{C}_m\mathrm{B}_n$ structures ($m,n=1\text{--}4$), successive filters based on energetic stability, dynamical stability, electronic metallicity, topological band features, and thermal stability identify six stable candidates: $(\mathrm{C}_3\mathrm{B}_2)_8$, $(\mathrm{C}\mathrm{B}_2)_3$, $(\mathrm{C}\mathrm{B}_4)_6$, $(\mathrm{C}_3\mathrm{B}_2)_6$, $(\mathrm{C}_2\mathrm{B}_3)_6$, and $(\mathrm{C}\mathrm{B}_2)_6$. These materials host diverse topological semimetal states near the Fermi level, including charge-one, charge-two, and charge-three Weyl points, nodal rings, and nodal-surface states. Notably, $(\mathrm{C}_3\mathrm{B}_2)_8$ exhibits screw-symmetry-protected charge-three Weyl points and strong electron--phonon coupling, yielding a McMillan transition temperature of $31.1\,\mathrm{K}$ for the Coulomb pseudopotential parameter $μ^* = 0.10$. In addition, $(\mathrm{C}\mathrm{B}_2)_3$ hosts multiple charge-one Weyl points and pronounced anisotropic multiband superconductivity revealed by fully anisotropic Eliashberg calculations. These results extend the materials scope of topological semimetals with phonon-mediated superconductivity to chemically designed chiral light-element structures.

cond-mat.mtrl-sci↗

arXiv2026

Flash-WAM: Modality-Aware Distillation for World Action Models

World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control. Step distillation has emerged as the natural remedy, but off-the-shelf methods break down in the joint video-action setting because video and action streams use different SNR-shifted noise schedules and reach training with substantially different marginal noise distributions, an asymmetry that single-modality distillation methods cannot accommodate. We introduce \textbf{Flash-WAM}, a modality-aware step-distillation framework inspired by consistency distillation that selects the consistency function for each modality to match its noise regime: a linear-gradient-scaling parametrization for the action stream's low-noise regime, paired with a variance-preserving parametrization for the video stream's high-noise regime, grounded in a structural analysis of the consistency-function family that characterizes the achievable gradient scaling under the consistency boundary condition. Instantiated on LingBot-VA, Flash-WAM compresses inference to a single step in each modality. On RoboTwin 2.0, this reduces per-chunk latency from $8.1$ seconds to $348$ ms on NVIDIA L40S, a $23{\times}$ speedup that enables real-time inference. Flash-WAM preserves task success on simulation benchmarks ($85.5\%$ RoboTwin 2.0, $95.7\%$ LIBERO) and substantially recovers real-world performance ($60\%$ average on a Unitree G1 humanoid robot), while naive consistency distillation drops to $24\%$ at the same step budget.

cs.LG↗

arXiv2026

Q-WAM: 4-Bit Quantization of World Action Models with Action-Subspace Protection

World Action Models (WAMs) jointly generate video and robot actions through iterative diffusion and perform strongly in robotic manipulation. However, their prohibitive compute and memory costs pose substantial deployment challenges. Post-training quantization (PTQ) can reduce these costs, but existing PTQ methods such as smoothing and rotation are insufficient to maintain the precision of action generation. To overcome this limitation, we propose Q-WAM, a new 4-bit weight-activation quantization for WAMs that preserves the actions the model generates. Specifically, we introduce the \textit{Action Observability Gramian (AOG)}, which measures how much rounding errors in each weighted combination of a layer's input channels change the final action through all denoising steps. We also develop Action-Subspace Protection (ASP), which keeps the few most action-sensitive channel combinations in a tiny 16-bit low-rank branch and quantizes the complementary weights and activations to 4 bits, both as dense matrix multiplications that run efficiently on GPUs. Finally, to preserve action quality with minimal overhead, we identify the experts that matter most for the generated action by aggregating the AOG-derived action mass across the layers of each expert and apply ASP only to those experts. We evaluate Q-WAM on three WAMs, both in simulation and in real-world deployment. On the RoboTwin 2.0 benchmark, it reaches 89.6--93.0\% average success rate, within 1.1 percentage points of the 16-bit models, while reducing the memory of the quantized blocks by 3.1--3.4$\times$. Our method outperforms the strongest baseline, SVDQuant, by 2.5--8.7 percentage points. On a Unitree G1 humanoid and a bimanual UR3 robot, it improves success over SVDQuant by 12.8-17.6 percentage points.

cs.RO↗

arXiv2026

The EDD Radio Astronomy Backend Framework

Modern digital radio astronomy receivers produce increasingly wide-bandwidth, high bit-rate data streams that necessitate the development of flexible, scalable, and maintainable backend processing and recording systems. Historically, such backend instrumentation has been tightly coupled to telescope observing modes, limiting reuse between observatories and science cases. We present the Effelsberg Direct Digitisation (EDD) backend framework, a software-defined architecture for constructing real-time radio astronomy backends on commodity off-the-shelf computing infrastructure. We describe its design, implementation, supported observing modes, and operational deployments. EDD separates a common core framework from plugin-provided observing capabilities. The core provides orchestration, telescope interfaces, pipeline lifecycle management, monitoring, and deployment tooling, while plugins implement processing pipelines for specific observing modes. The framework is designed to support both single-dish and interferometric instruments through site-specific configuration and plugin selection. EDD currently supports spectroscopy and spectropolarimetry, pulsar timing and searching, baseband recording, very long baseline interferometry, correlation, and beamforming. Operational deployments include the Effelsberg 100-m telescope, the SKA-MPI prototype dish, the Thai National Radio Telescope, and the ARGOS interferometric prototype array. By separating common services, observing-mode plugins, and site-specific configuration, it allows backend capabilities to be deployed across heterogeneous telescope environments and provides a community resource for broadband radio astronomy instrumentation.

astro-ph.IM↗

arXiv2026

The first MeerKAT S-band globular cluster pulsar survey

Globular clusters are efficient factories of recycled pulsars, but searches toward high-dispersion-measure (DM) clusters can be strongly limited near 1 GHz by dispersive smearing and interstellar scattering. We present the first MeerKAT S-band (nu ~ 2.4 GHz) pulsar survey of 14 globular clusters. High time- and frequency-resolution observations were searched using segmented acceleration and jerk techniques, followed by candidate folding and targeted folding with available timing ephemerides. We re-detected 39 known pulsars and discovered four new millisecond pulsars in Glimpse-C01: J1848-0129C, D, E, and F. Multi-epoch follow-up enabled preliminary Keplerian orbital fits for J1848-0129C and J1848-0129D. J1848-0129C is an eclipsing MSP in a ~5 d orbit, placing it among long-period eclipsing systems known as huntsman binaries, while J1848-0129D is in a ~3.4 d nearly circular orbit with a massive white-dwarf companion of about 1 solar mass. Two Glimpse-C01 pulsars show large DM offsets from the cluster average. Comparison with other Galactic globular clusters indicates that intracluster DM spreads tend to increase with foreground DM, implying that narrow DM search windows may be sub-optimal for high-DM clusters. From detections and non-detections, we infer a practical single-epoch MeerKAT S-band tied-array detectability scale of about 10-20 microJy. These results demonstrate the value of high-frequency searches for pulsars in strongly dispersed and scattered cluster environments.

astro-ph.HE↗

arXiv2026

Cross-Layer Anomalous Hall Transport driven by Néel-Vector rotating in the Altermagnet candidate V2Te2O

In van der Waals (vdW) materials, weak interlayer coupling generally suppresses vertical dispersion, reinforcing the conventional paradigm that in-plane transport dominates over cross-layer channels. Here, using first-principles calculations and magnetic symmetry analyses, we uncover a giant, symmetry-unlocked cross-layer anomalous Hall conductivity (AHC) in the vdW altermagnet V2Te2O. In the magnetic ground state with Neel vector N//z, horizontal mirror symmetry protects a spin-polarized nodal chain near the Fermi level and strictly enforces zero anomalous Hall response. Tilting the Neel vector explicitly breaks this mirror protection, allowing spin-orbit coupling to gap the nodal chain and activate a sharp cross-layer Hall response. When the Neel vector is rotated into the in-plane configuration (N//x), cross-layer orbital hybridization generates intensive Berry curvature hotspots, boosting the cross-layer component of AHC σ_{yz} to approximately 255 S/cm, which exceeds in-plane component σ_{xy} by nearly two orders of magnitude. Furthermore, varying the azimuthal angle systematically redistributes the anomalous Hall response, enabling full directional control of transverse transport. Our findings demonstrate a highly sensitive cross-layer anomalous Hall switch activated by low-barrier spin canting, offering promising avenues for directional tensor selection and low-power multi-axial vdW spintronics.

cond-mat.mtrl-sci↗

arXiv2026

Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions

Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 million inspection records with supplemental demographic, economic, and environmental indicators extracted from the Statistical Yearbook. A three-stage pretraining design leverages partial supervision from the Wilson interval (capturing both safety and risk rankings), together with semi-supervised label refinement, to effectively utilize historical records even when local sample sizes are insufficient. Experimental evaluations on data from 2022 show that the proposed approach outperforms baselines significantly. A subsequent field experiment in collaboration with the Zhejiang Provincial Administration for Market Regulation further demonstrates improved detection rates and more efficient allocation of inspection resources compared to a manually developed plan. Observations of regulatory decision-making reveal a threshold-based heuristic employed by inspectors, hinting that additional training or decision-support interfaces could further enhance the impact of AI-generated risk scores. Overall, these findings underscore that a rigorous integration of large-scale public inspection data, Wilson interval-based confidence modeling, and advanced deep learning can facilitate earlier and more granular identification of food safety threats. By reducing reliance on reactive measures alone, the proposed framework has the potential to advance proactive, data-driven oversight of the global food supply.

cs.AI↗

arXiv2026

VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting

Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, current VLA models suffer from two drawbacks: (i) generation of massive tokens leading to high inference latency and increased training cost, and (ii) insufficient utilization of generated actions resulting in potential performance loss. To address these issues, we develop a training framework to finetune VLA models for generating significantly fewer action tokens with high parallelism, effectively reducing inference latency and training cost. Furthermore, we introduce an inference optimization technique with a novel voting-based ensemble strategy to combine current and previous action predictions, improving the utilization of generated actions and overall performance. Our results demonstrate that we achieve superior performance compared with state-of-the-art VLA models, achieving significantly higher success rates and 39$\times$ faster inference than OpenVLA with 46 Hz throughput on edge platforms, demonstrating practical deployability. The code is available at https://github.com/LukeLIN-web/VOTE.

cs.CV↗

arXiv2026

Task-Oriented Multimodal Edge Intelligence via Integrated Sensing-Communication-Computation

Integrated sensing, communication, and computation (ISCC) has recently emerged as a unified framework for enabling edge intelligence. However, existing ISCC designs predominantly rely on single-modal sensing, which is inherently vulnerable to occlusions, environmental uncertainties, and modality-specific failures, leading to degraded robustness in real-world deployments. This motivates the need for multi-modal ISCC, yet its design remains insufficiently explored. Compared with the single-modal case, multi-modal ISCC is more challenging because heterogeneous modalities enlarge data dimensionality and tighten communication/computation/energy budgets, while inter-modal correlations further complicate performance characterization. To address these challenges, we propose a task-oriented multi-modal ISCC framework that integrates device-side feature extraction with edge-side joint multi-modal inference. A central component of our approach is the maximal coding rate reduction (MCR^2) criterion, which enables each device to learn compact and discriminative task-relevant features, offering clear advantages over conventional cross-entropy-based extractors. We further leverage MCR^2 as a principled metric for edge-side sensing evaluation. On this basis, we formulate a sensing accuracy maximization problem under delay and resource constraints and develop an efficient block coordinate descent (BCD) algorithm after transforming the problem into a more tractable equivalent form. Focusing on a human activity recognition task, we conduct extensive experiments on publicly available datasets to evaluate the performance of the proposed ISCC framework. The results demonstrate that our approach consistently outperforms three baseline schemes under limited resource conditions.

eess.SP↗

arXiv2026

Toward Efficient Sensing in Multi-Device ISCC by Removing Frequency Domain Redundancy

Integrated sensing, communication, and computation (ISCC) is envisioned as a key enabler for intelligent services in future wireless networks. However, in multi-device ISCC systems, directly offloading full orthogonal frequency division multiplexing (OFDM) sensing data to the edge may incur excessive overhead, thereby limiting sensing performance under practical resource constraints. In this paper, we propose a subcarrier selection-based sensing framework for multi-device ISCC systems, where frequency-domain redundancy in OFDM sensing data is removed during local preprocessing to reduce sensing data transmission and processing overhead. Based on the proposed framework, we establish analytical models for sensing accuracy, delay, and energy consumption, and formulate a sensing accuracy maximization problem under practical resource constraints. To solve this problem, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Experiments on commodity wireless devices validate the effectiveness of the proposed framework and show that it consistently outperforms three baseline schemes under various resource constraints.

eess.SP↗

arXiv2026

Harvesting AI Computation at the Edge via Generic Approximation

With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands. However, they are often underutilized and suffer from considerable computational waste due to temporal or spatial redundancy in processing. Conversely, general-purpose processing engines at the edge may struggle with compute-intensive tasks such as signal processing and complex numerical operations because of stringent resource constraints. To address this imbalance, we propose a framework that harvests unused AI computation resources using general-purpose approximation techniques. The core idea is to automatically convert traditional computing tasks into neural network models via a representative neural architecture search (NAS) method. These approximate versions of general-purpose tasks are then deployed on AI engines during their idle periods. Specifically, we introduce a runtime scheduler that offloads these tasks to AI chips without compromising the performance of primary AI workloads, thereby alleviating the burden on general-purpose processors. Experiments on a representative AIoT processor show that our proposed AI computation harvesting strategy delivers substantial performance improvements across a set of edge processing tasks.

cs.AR↗

arXiv2026

ASAP: A Disaggregated and Asynchronous Inference System for MoE Prefill

Mixture-of-Experts (MoE) models have become the de facto standard for scaling large language models. To maintain computational efficiency, modern MoE serving systems typically employ a hybrid parallelism strategy, combining Data Parallelism (DP) for attention stages with Expert Parallelism (EP) for MoE stages. However, this design necessitates frequent global synchronization barriers between attention DP groups and experts. In online serving, significant variance in request arrival rates and sequence lengths inherently leads to DP imbalance, causing severe synchronization stalls that degrade Time-to-First-Token (TTFT) and system throughput. We present ASAP, an asynchronous inference system specifically designed to accelerate the prefill phase of MoE models. ASAP disaggregates the attention and MoE stages and implements a fully asynchronous execution pipeline. This is achieved through a suite of specialized asynchronous communication primitives and four coordinated optimizations across request scheduling and model execution, which collectively dismantle global synchronization barriers. We implement and evaluate ASAP on CloudMatrix384 super-nodes, demonstrating that it improves SLO-compliant prefill throughput by 90% compared to state-of-the-art synchronous serving solutions.

cs.DC↗

arXiv2026

Human Cognition in Machines: A Unified Perspective of World Models

This report of world models distinguishes prior works by the cognitive functions they innovate. Many works claim an almost human-like cognitive capability in their world models. To evaluate these claims requires a proper grounding in first principles from human and machine cognition theory. In moving towards human-like world models we present a conceptual unified framework for world models that fully incorporates all the cognitive functions (i.e., memory, perception, language, reasoning, imagining, motivation, and metacognition) and identify gaps in existing research as a guide for future states of the art. In particular, we find that motivation (especially intrinsic motivation) and metacognition remain drastically under-researched, and we propose concrete directions to address these gaps informed by active inference and global workspace theory. We also introduce epistemic world models, a new category encompassing agent frameworks for scientific discovery that operate over structured knowledge. Our taxonomy, applied to video, embodied, and epistemic world models, suggests research directions where prior taxonomies have not.

cs.RO↗