arXiv subjects

Ke Zhou

Publications and source records attributed to Ke Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning

Synthesis planning for inorganic materials requires predicting both synthesizability and viable routes by linking microscopic thermodynamic stability with macroscopic synthesis methods, precursors, and processing conditions. Here, we develop an interpretable Physics-Informed Retrieval-Augmented Generation Language Model (PIRAG-LM) for end-to-end inorganic crystal synthesis planning. We construct a material-centered Structured Synthesis Knowledge Base (SSKB) containing route-level records for 13,820 experimentally synthesized inorganic crystals. PIRAG-LM retrieves historical precedents using chemical, structural, and thermodynamic similarity, then employs a structured LLM reasoning module to propose routes, precursors, and processing conditions and assess thermodynamic feasibility, kinetics, and accessibility. It achieves 91.4% accuracy in synthesis-method prediction, compared with 72.1% for the LLM alone, and generalizes to materials reported after the knowledge cutoff. Because the framework relies on retrieval rather than parametric memorization, its performance can be improved by expanding the SSKB without retraining the language model. Guided by PIRAG-LM, we experimentally synthesize five new compounds: BaMo0.3In0.7O2.95, BaNb0.4In0.6O2.9, Hg[B(CN)4]2, CoCo(CN)6, and SrNb2Fe2(PO4)6, via solid-state and solution routes. These results demonstrate an interpretable machine-learning approach that helps bridge computational materials discovery and experimental realization.

cond-mat.mtrl-sci↗

arXiv2026

TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics

Cloud Virtual Disk (CVD) placement in Cloud Block Storage (CBS) is critical for resource efficiency and performance isolation. Existing schemes prioritize spatial load balancing by dispersing disks across pods based on configuration-derived load estimates. However, overload risk in CBS is fundamentally temporal. Even when average load is balanced, pods can still suffer transient congestion when the peaks of co-located disks align in time. Achieving complementary placement, which co-locates CVDs with offset peaks, is hard at provisioning time because new disks have no history from which to infer temporal phase. We present TIDAL, a CVD placement framework that recovers phase-aware signals for cold-start placement from an underused source: tenant-provided names and identifiers in provisioning metadata. TIDAL first uses LLMs to recover application semantics from noisy metadata such as project, VM, and disk names. It then translates these semantics into phase-aware temporal signals to guide complementary placement. To satisfy control-plane constraints, TIDAL adopts an offline-to-online design with teacher-student distillation, regex-based filtering, and prefix-aware caching, enabling CPU-only inference with millisecond-level latency. Evaluations driven by production traces show that TIDAL reduces overload frequency by 79.1% and P95 overload duration by 73.7% compared with the strongest baselines.

cs.OS↗

arXiv2026

HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling

Agentic LLM applications increasingly execute user requests as multi-step workflows involving planning, tool use, branching, refinement, and synthesis. In such settings, users experience the end-to-end latency of an entire workflow, not the latency of any single LLM call. In this paper, we study how to schedule online agentic workflows across heterogeneous prefill-decode disaggregated LLM serving clusters to efficiently meet workflow-level latency objectives. The problem is challenging because workflow dependencies are revealed incrementally at runtime, calls have heterogeneous prompts, outputs, and KV-cache requirements, and the prefill and decode stages impose different compute, memory, and transfer constraints across heterogeneous GPUs. To solve this problem, we present HexAGenT, a workflow-aware scheduler for a heterogeneous prefill-decode inference service. HexAGenT models each request as an online-revealed DAG, maintains a running estimate of the workflow's standalone completion horizon, prioritizes ready calls by projected risk of missing that horizon, and jointly selects prefill placement, decode placement, and local queue priority while accounting for KV-cache capacity and cross-stage transfer latency. Across representative agentic workloads and heterogeneous A100/H100/H200 clusters, HexAGenT reduces the SLO scale required for timely workflow completion by an average of 20.1% at 95% attainment and 33.0% at 99% attainment, with maximum reductions of 45.0% and 80.5%, respectively.

cs.DC↗

arXiv2026

FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances

Modern retrieval systems increasingly require integrating approximate nearest neighbor search (ANNS) with complex attribute filtering to handle hybrid queries in applications such as recommendation systems and retrieval-augmented generation (RAG). While HNSW-based inline-filtering methods show promise, existing approaches struggle to deliver high throughput under low-selectivity scenarios while balancing search efficiency, filtering generality, and index connectivity. To address these challenges, we propose FAVOR, an efficient filter-agnostic vector ANNS that supports arbitrary filtering conditions while maintaining stable performance across varying selectivity levels. FAVOR introduces three novel features: (1) an integrated architecture that unifies selectivity estimation and filtered ANNS execution, providing a cohesive solution for hybrid vector-attribute queries; (2) a HNSW-based inline-filtering algorithm that introduces an exclusion distance mechanism to dynamically reshape the vector distance distribution, pushing non-target vectors away from the query while promoting valid candidates toward the query, thus improving search efficiency without compromising generality or graph connectivity; and (3) a selectivity-driven search selector that estimates query selectivity and dynamically routes queries between a pre-filtering brute-force algorithm for low-selectivity cases and an optimized HNSW-based search algorithm for other scenarios, ensuring consistent performance. Extensive experiments on real-world datasets demonstrate that FAVOR achieves a 1.3-5$\times$ higher QPS at $Recall@10 = 95\%$ compared to state-of-the-art methods for arbitrary filtering conditions, while maintaining competitive performance even against tailored solutions in some filtering conditions.

cs.IR↗

arXiv2026

Frictionless Love: Associations Between AI Companion Roles and Behavioral Addiction

AI companion chatbots increasingly shape how people seek social and emotional connection, sometimes substituting for relationships with romantic partners, friends, teachers, or even therapists. When these systems adopt those metaphorical roles, they are not neutral: such roles structure people's ways of interacting, distribute perceived AI harms and benefits, and may reflect behavioral addiction signs. Yet these role-dependent risks remain poorly understood. We analyze 248,830 posts from seven prominent Reddit communities describing interactions with AI companions. We identify ten recurring metaphorical roles (for example, soulmate, philosopher, and coach) and show that each role supports distinct ways of interacting. We then extract the perceived AI harms and AI benefits associated with these role-specific interactions and link them to behavioral addiction signs, all of which has been inferred from the text in the posts. AI soulmate companions are associated with romance-centered ways of interacting, offering emotional support but also introducing emotional manipulation and distress, culminating in strong attachment. In contrast, AI coach and guardian companions are associated with practical benefits such as personal growth and task support, yet are nonetheless more frequently associated with behavioral addiction signs such as daily life disruptions and damage to offline relationships. These findings show that metaphorical roles are a central ethical design concern for responsible AI companions.

cs.CY↗

arXiv2026

Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning

Prior work has mapped which workplace tasks are exposed to AI, but less is known about whether workers perceive these tasks as meaningful or as busywork. We examined: (1) which dimensions of meaningful work do workers associate with tasks exposed to AI; and (2) how do the traits of existing AI systems compare to the traits workers want. We surveyed workers and developers on a representative sample of 171 tasks and use language models (LMs) to scale ratings to 10,131 computer-assisted tasks across all U.S. occupations. Worryingly, we find that tasks that workers associate with a sense of agency or happiness may be disproportionately exposed to AI. We also document design gaps: developers report emphasizing politeness, strictness, and imagination in system design; by contrast, workers prefer systems that are straightforward, tolerant, and practical. To address these gaps, we call for AI whose design explicitly focuses on meaningful work and worker needs, proposing a five-part research agenda.

cs.CY↗

arXiv2026

SoulX-FlashHead: Oracle-guided Generation of Infinite Real-time Streaming Talking Heads

Achieving a balance between high-fidelity visual quality and low-latency streaming remains a formidable challenge in audio-driven portrait generation. Existing large-scale models often suffer from prohibitive computational costs, while lightweight alternatives typically compromise on holistic facial representations and temporal stability. In this paper, we propose SoulX-FlashHead, a unified 1.3B-parameter framework designed for real-time, infinite-length, and high-fidelity streaming video generation. To address the instability of audio features in streaming scenarios, we introduce Streaming-Aware Spatiotemporal Pre-training equipped with a Temporal Audio Context Cache mechanism, which ensures robust feature extraction from short audio fragments. Furthermore, to mitigate the error accumulation and identity drift inherent in long-sequence autoregressive generation, we propose Oracle-Guided Bidirectional Distillation, leveraging ground-truth motion priors to provide precise physical guidance. We also present VividHead, a large-scale, high-quality dataset containing 782 hours of strictly aligned footage to support robust training. Extensive experiments demonstrate that SoulX-FlashHead achieves state-of-the-art performance on HDTF and VFHQ benchmarks. Notably, our Lite variant achieves an inference speed of 96 FPS on a single NVIDIA RTX 4090, facilitating ultra-fast interaction without sacrificing visual coherence.

cs.CV↗

arXiv2026

Region-aware Spatiotemporal Modeling with Collaborative Domain Generalization for Cross-Subject EEG Emotion Recognition

Cross-subject EEG-based emotion recognition (EER) remains challenging due to strong inter-subject variability, which induces substantial distribution shifts in EEG signals, as well as the high complexity of emotion-related neural representations in both spatial organization and temporal evolution. Existing approaches typically improve spatial modeling, temporal modeling, or generalization strategies in isolation, which limits their ability to align representations across subjects while capturing multi-scale dynamics and suppressing subject-specific bias within a unified framework. To address these gaps, we propose a Region-aware Spatiotemporal Modeling framework with Collaborative Domain Generalization (RSM-CoDG) for cross-subject EEG emotion recognition. RSM-CoDG incorporates neuroscience priors derived from functional brain region partitioning to construct region-level spatial representations, thereby improving cross-subject comparability. It also employs multi-scale temporal modeling to characterize the dynamic evolution of emotion-evoked neural activity. In addition, the framework employs a collaborative domain generalization strategy, incorporating multidimensional constraints to reduce subject-specific bias in a fully unseen target subject setting, which enhances the generalization to unknown individuals. Extensive experimental results on SEED series datasets demonstrate that RSM-CoDG consistently outperforms existing competing methods, providing an effective approach for improving robustness. The source code is available at https://github.com/RyanLi-X/RSM-CoDG.

cs.CV↗

arXiv2026

Ion Clustering Regulated by Extreme Nanoconfinement Enables Mechanosensitive Nanochannels

Mechanosensitive ion nanochannels regulate transport by undergoing conformational changes within nanopores. However, achieving precise control over these conformational states remains a major challenge for both artificial soft or solid pores. Here, we propose an alternative mechanism that modulates the charge carrier density inside nanopores, inspired by transistors in solid-state electronics. This strategy leverages a novel phenomenon of confinement-regulated ion clustering in two-dimensional extremely confined nanochannels, revealed by extensive $\mu$s-scale enhanced-sampling molecular simulations based on an \emph{ab initio}-refined force field and nucleation theory. The resulting \emph{force-ion transistor} enables mechanically gated control of ion transport and provides a conceptual foundation for designing ionic mechanical logic gates. Our findings offer new insights into piezochannel mechanosensing and electromechanical coupling in biosystems beyond conformational signaling, opening pathways to integrate artificial ion channels with neuromorphic devices for processing mechanical stimuli.

physics.chem-ph↗

arXiv2025

SoulX-FlashTalk: Real-Time Infinite Streaming of Audio-Driven Avatars via Self-Correcting Bidirectional Distillation

Deploying massive diffusion models for real-time, infinite-duration, audio-driven avatar generation presents a significant engineering challenge, primarily due to the conflict between computational load and strict latency constraints. Existing approaches often compromise visual fidelity by enforcing strictly unidirectional attention mechanisms or reducing model capacity. To address this problem, we introduce \textbf{SoulX-FlashTalk}, a 14B-parameter framework optimized for high-fidelity real-time streaming. Diverging from conventional unidirectional paradigms, we use a \textbf{Self-correcting Bidirectional Distillation} strategy that retains bidirectional attention within video chunks. This design preserves critical spatiotemporal correlations, significantly enhancing motion coherence and visual detail. To ensure stability during infinite generation, we incorporate a \textbf{Multi-step Retrospective Self-Correction Mechanism}, enabling the model to autonomously recover from accumulated errors and preventing collapse. Furthermore, we engineered a full-stack inference acceleration suite incorporating hybrid sequence parallelism, Parallel VAE, and kernel-level optimizations. Extensive evaluations confirm that SoulX-FlashTalk is the first 14B-scale system to achieve a \textbf{sub-second start-up latency (0.87s)} while reaching a real-time throughput of \textbf{32 FPS}, setting a new standard for high-fidelity interactive digital human synthesis.

cs.CV↗

arXiv2025

Extreme Nanoconfinement Reshapes the Self-Dissociation of Water

Water's ability to self-dissociate into H$_3$O$^+$ and OH$^-$ ions is central to acid-base chemistry and bioenergetics. Recent experimental advances have enabled the confinement of water down to the nanometre scale, even to the single-molecule limit, yet how this process is altered at the extreme nanoconfinement remains unclear. Using \emph{ab-initio} calculations and enhanced-sampling machine-learning potential molecular dynamics, we show that monolayer-confined water exhibits a markedly lower barrier to auto-dissociation than bulk water. Confinement restructures both intramolecular bonding and the intermolecular hydrogen-bond network, while enforcing quasi-2D dipolar correlations that amplify dielectric fluctuations. Our results imply that two-dimensional confined water could act as a \emph{superdielectric} medium and may exhibit \emph{superionic} behavior, as observed in recent experiments. These findings reveal confinement as a powerful route to enhanced proton activity, shedding light on geochemical niches, biomolecular environments, and nanofluidic systems where water's chemistry is fundamentally reshaped.

cond-mat.mes-hall↗

arXiv2025

Intrinsic Fracture Nonreciprocity at the Nanoscale

We reveal intrinsic fracture nonreciprocity, manifesting as directional asymmetry in crack resistance, in two-dimensional heterostructures engineered through lattice-mismatched interfaces. Density-functional theory combined with machine-learning molecular dynamics show that intrinsic lattice mismatch between bonded component crystals imprints asymmetric prestrain states at crack tips, governing bond-breaking thresholds through charge redistribution. The failure criterion obeys a universal exponential scaling law between normalized charge density and bond strain, insensitive to bonding chemistry and local atomic environment. The magnitude of nonreciprocity scales systematically with lattice mismatch, reaching 49% at 10% mismatch. Validation across hexagonal, square, rectangular, and oblique two-dimensional lattices confirms universality, establishing interface strain engineering as a general design principle that bridges electronic structure to nanoscale failure, enabling rational design of damage-tolerant nanostructures.

cond-mat.mtrl-sci↗

arXiv2025

Learning an Efficient Optimizer via Hybrid-Policy Sub-Trajectory Balance

Recent advances in generative modeling enable neural networks to generate weights without relying on gradient-based optimization. However, current methods are limited by issues of over-coupling and long-horizon. The former tightly binds weight generation with task-specific objectives, thereby limiting the flexibility of the learned optimizer. The latter leads to inefficiency and low accuracy during inference, caused by the lack of local constraints. In this paper, we propose Lo-Hp, a decoupled two-stage weight generation framework that enhances flexibility through learning various optimization policies. It adopts a hybrid-policy sub-trajectory balance objective, which integrates on-policy and off-policy learning to capture local optimization policies. Theoretically, we demonstrate that learning solely local optimization policies can address the long-horizon issue while enhancing the generation of global optimal weights. In addition, we validate Lo-Hp's superior accuracy and inference efficiency in tasks that require frequent weight updates, such as transfer learning, few-shot learning, domain generalization, and large language model adaptation.

cs.LG↗

arXiv2025

Portfolio selection with exogenous and endogenous transaction costs under a two-factor stochastic volatility model

In this paper, we investigate a portfolio selection problem with transaction costs under a two-factor stochastic volatility structure, where volatility follows a mean-reverting process with a stochastic mean-reversion level. The model incorporates both proportional exogenous transaction costs and endogenous costs modeled by a stochastic liquidity risk process. Using an option-implied approach, we extract an S-shaped utility function that reflects investor behavior and apply its concave envelope transformation to handle the non-concavity. The resulting problem reduces to solving a five-dimensional nonlinear Hamilton-Jacobi-Bellman equation. We employ a deep learning-based policy iteration scheme to numerically compute the value function and the optimal policy. Numerical experiments are conducted to analyze how both types of transaction costs and stochastic volatility affect optimal investment decisions.

q-fin.MF↗

arXiv2025

On the joint estimation of flow fields and particle properties from Lagrangian data

We numerically investigate the feasibility and limits of jointly estimating flow fields and unknown particle properties (e.g., position, size, and density) from Lagrangian particle tracking (LPT) data. LPT offers time-resolved, volumetric measurements of particle trajectories, which are markers of the carrier fluid motion. However, experimental tracks are spatially sparse and potentially noisy, and the problem of reconstructing flow fields may be further complicated by inertial particle transport, such that particle slip velocities must be determined to access the velocity field of the carrier fluid. To address this problem, we develop a data assimilation framework that couples an Eulerian representation of the flow with Lagrangian particle models, enabling the simultaneous inference of carrier fields and particle properties under the governing equations of disperse multiphase flow. We show that flow fields and particle properties can be jointly estimated in three representative regimes: (1) In a turbulent boundary layer with noisy tracer tracks (St to 0), flow fields and true particle positions are jointly estimated, which amounts to a physics-informed particle tracking problem; (2) in homogeneous isotropic turbulence seeded with inertial particles (St ~ 1-5), we demonstrate simultaneous recovery of flow states and particle diameters, showing the feasibility of implicit particle characterization; and (3) in a compressible, shock-dominated flow, we report the first joint reconstructions of velocity, pressure, density, and inertial particle properties (diameter and density), highlighting both the potential and certain limits of joint estimation in supersonic regimes. A systematic sensitivity study reveals how the seeding density, noise level, and Stokes number govern reconstruction accuracy for our method.

physics.flu-dyn↗

arXiv2025

Intent-Driven Storage Systems: From Low-Level Tuning to High-Level Understanding

Existing storage systems lack visibility into workload intent, limiting their ability to adapt to the semantics of modern, large-scale data-intensive applications. This disconnect leads to brittle heuristics and fragmented, siloed optimizations. To address these limitations, we propose Intent-Driven Storage Systems (IDSS), a vision for a new paradigm where large language models (LLMs) infer workload and system intent from unstructured signals to guide adaptive and cross-layer parameter reconfiguration. IDSS provides holistic reasoning for competing demands, synthesizing safe and efficient decisions within policy guardrails. We present four design principles for integrating LLMs into storage control loops and propose a corresponding system architecture. Initial results on FileBench workloads show that IDSS can improve IOPS by up to 2.45X by interpreting intent and generating actionable configurations for storage components such as caching and prefetching. These findings suggest that, when constrained by guardrails and embedded within structured workflows, LLMs can function as high-level semantic optimizers, bridging the gap between application goals and low-level system control. IDSS points toward a future in which storage systems are increasingly adaptive, autonomous, and aligned with dynamic workload demands.

cs.AR↗

arXiv2025

Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

Meta-learning is a powerful paradigm for tackling few-shot tasks. However, recent studies indicate that models trained with the whole-class training strategy can achieve comparable performance to those trained with meta-learning in few-shot classification tasks. To demonstrate the value of meta-learning, we establish an entropy-limited supervised setting for fair comparisons. Through both theoretical analysis and experimental validation, we establish that meta-learning has a tighter generalization bound compared to whole-class training. We unravel that meta-learning is more efficient with limited entropy and is more robust to label noise and heterogeneous tasks, making it well-suited for unsupervised tasks. Based on these insights, We propose MINO, a meta-learning framework designed to enhance unsupervised performance. MINO utilizes the adaptive clustering algorithm DBSCAN with a dynamic head for unsupervised task construction and a stability-based meta-scaler for robustness against label noise. Extensive experiments confirm its effectiveness in multiple unsupervised few-shot and zero-shot tasks.

cs.LG↗

arXiv2025

XBOF: A Cost-Efficient CXL JBOF with Inter-SSD Compute Resource Sharing

Enterprise SSDs integrate numerous computing resources (e.g., ARM processor and onboard DRAM) to satisfy the ever-increasing performance requirements of I/O bursts. While these resources substantially elevate the monetary costs of SSDs, the sporadic nature of I/O bursts causes severe SSD resource underutilization in just a bunch of flash (JBOF) level. Tackling this challenge, we propose XBOF, a cost-efficient JBOF design, which only reserves moderate computing resources in SSDs at low monetary cost, while achieving demanded I/O performance through efficient inter-SSD resource sharing. Specifically, XBOF first disaggregates SSD architecture into multiple disjoint parts based on their functionality, enabling fine-grained SSD internal resource management. XBOF then employs a decentralized scheme to manage these disaggregated resources and harvests the computing resources of idle SSDs to assist busy SSDs in handling I/O bursts. This idea is facilitated by the cache-coherent capability of Compute eXpress Link (CXL), with which the busy SSDs can directly utilize the harvested computing resources to accelerate metadata processing. The evaluation results show that XBOF improves SSD resource utilization by 50.4% and saves 19.0% monetary costs with a negligible performance loss, compared to existing JBOF designs.

cs.OS↗