arXiv subjects

Sheng Chen

Publications and source records attributed to Sheng Chen.

At least 19 recordsLinked to original sources

arXiv2026

From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity

The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity. Large models (LMs), with strong multimodal understanding and generation capabilities, have accelerated this shift and made semantic communication (SemCom) increasingly practical. Yet current LM-driven SemCom remains fragmented: semantic representations are typically tied to specific modalities, models, or tasks. While the bit provides a universal unit for digital transport, there is still no analogous unit for representing and processing semantics, which limits interoperability, theoretical unification, and scalable system design. We argue that tokens provide a natural candidate for this missing abstraction. Two trends support this: unified multimodal LMs now encode text, images, audio, video, and robot actions in one token space, while distributed LM inference already generates substantial token-level traffic through expert routing, cache transfer, and speculative decoding. Token communication (TokenCom) emerges by unifying these trends, using the LM's native processing unit as a communication abstraction above the bit level and enabling importance assignment, error handling, and resource allocation directly at token granularity. This survey traces the evolution from LM-driven SemCom to TokenCom. We review three major directions of LM-driven SemCom: source-centric semantic coding, channel semantics for physical-layer tasks, and collaborative edge-device intelligence. We then examine the token abstraction, the transmission techniques it requires, and two emerging paradigms, namely TokenCom for LM services and for embodied and agentic intelligence. Finally, we identify open challenges toward unified, scalable, and AI-native 6G communication systems.

eess.SP↗

arXiv2026

Fluid-Dynamic Interference Modeling for LEO Mega-Constellations: A Spatiotemporal Kinetic Field Approach

Low Earth orbit (LEO) mega-constellations create a highly non-stationary interference environment that cannot be accurately captured by static stochastic-geometry snapshots. This paper proposes a kinetic interference field framework that models the constellation as a compressible fluid shell evolving under orbital kinematics. By mapping satellite motion into a continuum flux field, we derive a hydrodynamic conservation law for the aggregate interference and obtain a closed-form expression for the time-varying outage probability via moment matching. The analysis reveals that high-latitude ``interference surges'' are a direct consequence of orbital compression and boundary flux, rather than random anomalies. Numerical validation against ephemeris-driven Monte Carlo simulations confirms the accuracy of the framework across time evolution, latitude, and design parameters. Leveraging the closed-form model, we further show that the conventional $90^{\circ}$ polar-orbit design is not universally outage-optimal. Instead, an inclination angle near $79^{\circ}$ at low altitude achieves a favorable trade-off between coverage continuity and geometric interference isolation. The proposed framework provides a tractable analytical tool for interference-aware 6G non-terrestrial network (NTN) design.

cs.NI↗

arXiv2026

UW-OCDM for Low-Altitude UAV Communication and Cooperative Sensing

Integrated sensing and communications (ISAC) is a key enabler for uncrewed aerial vehicles (UAVs) in the low-altitude economy. This paper proposes an ISAC waveform that embeds a unique word (UW) into orthogonal chirp division multiplexing (OCDM), termed UW-OCDM, together with corresponding communication reception and cooperative sensing schemes for high-mobility UAV scenarios. For communication, the embedded UW enables timing synchronization and Doppler estimation and compensation without requiring a separate synchronization sequence. A sparse spatio-temporal channel estimation method exploits the common channel support across multiple receive antennas and consecutive UW observations to support reliable data demodulation. For sensing, the deterministic UW serves as a shared prior that allows distributed base stations to construct sensing dictionaries locally without exchanging random payload symbols in real time. A hierarchical multi-target detection and tracking algorithm integrates direct-path interference suppression, kinematic prediction, multi-candidate screening, off-grid refinement, residual verification, and successive interference cancellation for robust localization with reduced search complexity. Simulation results demonstrate reliable communication and localization in highly dynamic UAV scenarios, while the proposed framework retains low-complexity frequency-domain equalization and reduces transmit-reference sharing overhead and multi-static localization complexity.

eess.SP↗

arXiv2026

Proper Sea Surface Roughness Enhances the Performance of Near-Shore Maritime Networks

Accurate performance analysis for near-shore maritime wireless communication is essential for ensuring robust and reliable operations. However, existing analytical models often rely on oversimplified propagation assumptions, such as a perfectly smooth sea surface, which fail to capture the full dynamics of the maritime channel. In this paper, we develop a physically grounded analytical framework using stochastic geometry that bridges this gap. The spatial distribution of vessels is modeled as a non-homogeneous Poisson point process to reflect realistic near-port densities. We replace the idealized smooth-sea assumption by deriving a novel reflection coefficient from the classical Rayleigh criterion, which explicitly links the path loss to the significant wave height. Integrating this roughness-aware channel model into the stochastic geometry framework, we derive new analytical expressions for the uplink coverage probability and average ergodic rate, providing the first tractable characterization of aggregate interference under such dynamic conditions. The analysis reveals a sea-state-dependent reliability--capacity trade-off: roughness-induced attenuation of the coherent specular reflection can suppress destructive-interference nulls and improve reliability-oriented coverage, while reducing high-SINR and average-rate performance. Available measurements support the underlying roughness-sensitive reflection mechanism, but direct VHF validation under rough sea conditions remains unavailable; the corresponding rough-sea results are therefore interpreted as model-based predictions. A cross-frequency ablation further confirms the wavelength dependence of the roughness effect and shows that the reflection coefficient must be evaluated for the operating frequency.

cs.NI↗

arXiv2026

Digital Tides: A Fluid-Dynamic Framework for Flux-Aware Infrastructure Provisioning in UAV Logistics Networks

The emergence of high-frequency pulsating logistics unmanned aerial vehicle (UAV) swarms gives rise to ``Digital Tides'', i.e., complex traffic dynamics that challenge sustainable resource provisioning in mobile computing networks. Conventional infrastructure provisioning strategies, which typically rely on static snapshot-based analysis and localized density estimation, fail to capture the macroscopic advection of computational workloads. As a result, reactive resource activation suffers from inherent hysteresis, yielding nominal efficiency gains at the cost of mission-critical service loss at the advancing wavefront. To address this issue, we develop a fluid-based spatiotemporal framework by explicitly solving the continuity equation to characterize the macroscopic velocity field of the workload flow. Building on this framework, we propose a flux-aware asymmetric activation strategy that leverages the derived information flux vector as a kinematic precursor of demand propagation. Unlike symmetric thresholding, the proposed control logic decouples activation and deactivation dynamics. Theoretical analysis confirms the intrinsic spatial phase-lead of the flux signal and shows that the proposed strategy generates a proactive guard ring to compensate for service setup latency, including delays caused by mobile edge computing container cold-starts. We further derive closed-form expressions for instantaneous service availability and period-average energy efficiency. In addition, we formulate a quality-of-service-penalized metric to evaluate effective energy efficiency under strict outage constraints. Numerical results show that the proposed flux-driven strategy enables zero-latency tracking of the mobile wavefront and achieves a Pareto-optimal trade-off between service reliability and energy consumption, outperforming reactive baselines in dynamic logistics corridors.

cs.NI↗

arXiv2026

Vorticity Dissipation Based Routing: A Fluid-Kinetic Framework for Loop-Free Transport in Ultra-Dense Networks

Discrete routing protocols in ultra-dense wireless networks are constrained by signaling overhead and transient routing loops that degrade radio-resource efficiency. While continuum modeling provides a scalable alternative, existing scalar density approaches lack the vector geometric structure required to characterize these topological anomalies. This paper introduces a fluid-kinetic framework, vorticity dissipation-based routing (VDR), utilizing the Helmholtz-Hodge decomposition. We demonstrate that the macroscopic traffic flux can be orthogonally decoupled into a demand-driven irrotational component and a loop-induced solenoidal component representing routing vorticity. Building on this insight, we define network vorticity as a macroscopic metric to quantify topological inefficiency. Routing optimization is formulated as a gradient flow on an enstrophy functional, yielding a vorticity dissipation equation as the governing dynamic law. Lyapunov stability analysis proves that this mechanism ensures the monotonic decay of global enstrophy toward an asymptotically loop-free equilibrium. Numerical results validate that VDR suppresses realized forwarding loops, reduces end-to-end delay, maintains robust packet delivery, and exhibits near-linear scaling under fixed-area densification while explicitly accounting for the grid-dependent Poisson-solver cost.

cs.NI↗

arXiv2026

Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?

Touchpoint selection in conversion attribution, namely identifying meaningful touchpoints contributing to conversions, is essential for e-commerce recommendation and online advertising. Current selection methods rely heavily on collaborative-filtering-based heuristics, which fail to align with user-perceived semantic intent. Through human annotation, we reveal a significant semantic gap: many implicitly-related, semantically relevant touchpoints remain undetected by existing rules. Therefore, we systematically evaluate the capability of Large Language Models (LLMs) in identifying these hidden associations. Our evaluation shows that while LLMs effectively uncover a substantial portion of implicitly-related touchpoints, significant room for improvement remains in their selection performance. Furthermore, we analyze the impact of different prompting strategies and foundation model choices on identification performance, providing valuable insights into their reasoning patterns and effectiveness. These insights offer a new roadmap for transitioning conversion attribution from mechanical rule-matching to human-aligned semantic reasoning. Moreover, we leverage the LLM-attributed conversion labels for enhancing industrial CVR model training and achieve significant offline performance gains, showing the potential of LLMs in conversion attribution.

cs.CL↗

arXiv2026

AsymSpec: Efficient Cloud-Edge Speculative Decoding over Asymmetric Networks

Cloud-edge speculative decoding places a lightweight draft model at an edge gateway and a higher-quality target model in the cloud, but inserts communication into every speculative block. Under a constrained uplink, candidate messages may queue while the verifier is idle. Stop-and-wait scheduling leaves edge compute underutilized; optimistic same-request runahead can waste work when a rejection or an unexpected bonus token invalidates dependent drafts. We present AsymSpec, which addresses uplink-gated verification and invalid dependent work with two corresponding mechanisms. Its asymmetric verification protocol keeps the common-path acceptance upload compact and moves richer, rejection-only correction information to the downlink. A total-variation (TV) certificate for the residual distribution determines whether a small target top-K response suffices; if not, the protocol progressively escalates through proposal-based exact recovery before falling back to the full distribution. Its confirmed-prefix pipeline exposes only independent, valid requests to the edge scheduler and lets the cloud re-batch arrived blocks, hiding verification waits when another confirmed-prefix request is ready without using same-request runahead. Across three draft-target pairs, two workloads, and three asymmetric network profiles, our end-to-end evaluation shows that AsymSpec delivers 2.82-28.03$\times$ the output-token throughput of the strongest baseline.

cs.DC↗

arXiv2026

RAC: Reference-Aware Activation Compression for Communication-Efficient Split LLM Inference

Large language model (LLM) agents repeatedly process long, privacy-sensitive contexts, while cloud-only deployment exposes user data beyond the trusted endpoint and fully local deployment often requires costly hardware. Split inference offers a middle ground by executing the model head, tail, and tools locally and the middle layers in the cloud, but its local-cloud-local path transfers boundary hidden states at every invocation and creates a critical communication bottleneck. We present \system, a reference-aware codec that retrieves exact-token historical spans for prefill uplinks, reuses the reconstructed uplink state for same-round prefill downlinks, and generates boundary-specific decode references with lightweight causal predictors. RAC applies grouped affine alignment and calibrated residual quantization with optional prefill outliers, while sender-side wire-format reconstruction synchronizes subsequent references and offline calibration accounts for quality and packed representation costs. Across three models and nine evaluated model-link pairs, Raw-to-RAC mean time to first token (TTFT) and time per output token (TPOT) ratios are 1.24-2.72$\times$ and 1.01-2.79$\times$, while the 12 non-perplexity task-score changes range from $-0.40$ to $+2.50$ points.

cs.DC↗

arXiv2026

ClawRec: A Claw-Native Recommender System

Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localized interaction traces and ranking items from isolated candidate spaces. This design is poorly suited to real-world tasks that unfold through searches, content consumption, and comparisons across multiple information sources. Claw-style personal agents, with persistent access to authorized cross-platform context, create an opportunity for recommendation to operate around the user rather than any single platform. In this paper, we introduce Claw-native recommender systems, a new paradigm that moves beyond platform-local ranking to produce unified, complementary recommendation slates spanning diverse sources and content forms. To instantiate this paradigm, we present ClawRec, the first recommender system designed to operate natively in this environment. ClawRec maintains an evidence-linked, temporally structured user state that connects cross-platform behaviors with cross-source recommendations. It organizes retrieval around functional source roles and selects candidates according to their marginal utility, producing non-redundant slates aligned with the user's active task. To enable rigorous evaluation, we introduce ClawRec-SimBench, a benchmark constructed from sequences of concrete life events and cross-platform behavior trajectories. Experiments show that ClawRec outperforms the strongest baselines, achieving an NDCG@20 of 0.6134 (+0.1126) and a Hit@20 of 0.6944 (+0.0854), while also improving user state quality and temporal alignment. Our code and dataset are available at https://github.com/RUCAIBox/ClawRec.

cs.IR↗

arXiv2026

Scaling Unmodified Multithreaded Applications with Elastic CXL-based Distributed Shared Memory

While CXL presents a promising hardware substrate for Distributed Shared Memory (DSM), seamlessly scaling multithreaded applications across multiple nodes remains a formidable challenge. Existing CXL-based DSMs fall short: they require manual code modifications to share non-heap data, employ rigid data placement policies that fail under diverse and dynamic workloads, and suffer from severe page-fault processing overheads in sub-microsecond ($\mu\mathrm{s}$) environments. We present xDSM, a full-space, elastic DSM system built over CXL that transparently scales unmodified multithreaded applications. To eliminate the burden of manual code rewrites, xDSM employs an OS-runtime co-design that establishes a globally coordinated address space, seamlessly sharing all memory segments. To mask CXL access penalties, xDSM abandons static placement rules in favor of a dynamic, latency-driven policy that actively balances data between local DRAM and CXL memory. Finally, to resolve the fundamental tension between high base-page fault overheads and severe huge-page false sharing, xDSM introduces spatial locality-aware elasticity, dynamically coalescing and splitting pages on the fly to amortize processing costs. Evaluated across diverse workloads using 15 system configurations, xDSM outperforms CXL-only baselines by 1.5$\times$ to 2.2$\times$ and state-of-the-art hybrid DSMs by 1.1$\times$ to 2.2$\times$, while achieving near-linear scalability.

cs.OS↗

arXiv2026

Fluid-Spatiotemporal Stochastic Geometry: Information Flow in Non-Stationary Fields

The fundamental limits of information flow in spatial networks are usually characterized under stationary spatial point processes, but this assumption cannot capture non-stationary regimes where the node intensity field evolves continuously in space and time. This paper develops Fluid-Spatiotemporal Stochastic Geometry (F-STSG), treating dynamic network topology as a hydrodynamic limit of the discrete node constellation. We formulate the identification of latent network dynamics as an inverse boundary value problem and, using the minimum kinetic energy principle from optimal transport, establish the existence and uniqueness of a scalar potential field governing the compressive evolution of network load. The resulting field-theoretic formulation couples continuous Lagrangian transport with discrete Eulerian interference geometry. Based on this model, we derive the information flux vector as a sufficient statistic for macroscopic advection and the material derivative as a kinematic predictor of topological divergence. We further characterize non-stationary network limits through energy-density scaling and source-channel interpretation, showing how coordination overhead, topology deformation, and control signaling requirements are linked to the kinematic entropy of the evolving network topology.

cs.NI↗

arXiv2026

Symmetry-Selective Strain Control of Spin-Momentum Locking and Spin Transport in Two-Dimensional Pentagonal Altermagnets

Altermagnets are compensated magnets featuring momentum-dependent nonrelativistic spin splitting generated by nontrivial operations connecting opposite-spin sublattices. A direct symmetry-based route to control this spin splitting is to modify the real-space operations that define the altermagnetic spin-momentum locking (SML). Here, we develop a strain-resolved symmetry framework for two-dimensional pentagonal altermagnets, classifying whether uniaxial and shear strain tensors preserve, reconstruct, or eliminate the SML. Using the above criterion combined with first-principles screening, we identify 94 stable altermagnetic candidates from 3330 materials. These candidates cover all type-III spin Laue groups of orthorhombic lattices and are classified into three strain-response types: Type-I preserves the SML; Type-II reconstructs the SML through partial symmetry breaking while retaining essential altermagnetic features; and Type-III destroys the altermagnetic SML. Representative materials further demonstrate this classification: ferroelastic $\alpha$-CoS$_2$ exhibits ferroelastically switchable SML and reverses the sign of the off-diagonal spin conductivity; shear-strained $\alpha$-CoP$_2$ undergoes a $g$- to $d$-wave reconstruction of the SML, activating off-diagonal spin conductivity; and uniaxially strained FeSSe realizes strain-selected spin-valley transport. This work provides theoretical and material guidance for strain-controlled transport in two-dimensional orthorhombic altermagnets.

cond-mat.mtrl-sci↗

arXiv2026

Sensing-Assisted Predictive Beamforming for UAV-Enabled Ocean Monitoring Networks

This paper investigates a sensing-assisted predictive beamforming framework for UAV--buoy maritime monitoring by explicitly accounting for wave-induced buoy dynamics and residual sea clutter. A frame-based UAV mission workflow is first established, where the UAV transmits integrated sensing and communication signals to acquire buoy echoes and to support subsequent uplink beam alignment. To characterize short-horizon buoy motion, a correlated-acceleration state-space model is developed by combining a Singer process for wave-driven excitation with a slowly varying current-drift term. Given the resulting nonlinear reflection, Doppler, and delay measurements, the posterior Fisher information matrix and the corresponding posterior Cram\'er--Rao bound (PCRB) are derived, and the predicted horizontal-position PCRB is adopted as the sensing metric. A per-frame worst-buoy design is then formulated to jointly optimize sensing power allocation and UAV position under uplink-rate, UAV-power, and mobility constraints. By exploiting a Schur-complement reformulation and a lagged successive convex approximation, the resulting subproblem is converted into a convex conic program with tractable complexity. Simulation results show that the proposed scheme maintains robust prediction and communication performance under denser buoy deployments and harsher sea conditions, and outperforms several baseline designs. In particular, the pronounced root mean square error (RMSE) degradation of the communication-only benchmark confirms that sensing-assisted state refinement is essential for accurate predictive beamforming in dynamic maritime environments. Compared with a full first-order Taylor expansion method, it achieves a more attractive performance--complexity tradeoff for online deployment.

eess.SP↗

arXiv2026

FastContext: Training Efficient Repository Explorer for Coding Agents

Large Language Model (LLM) coding agents have achieved strong results on software engineering tasks, yet repository exploration remains a major bottleneck: locating relevant code consumes substantial token budget and pollutes the agent's context with irrelevant snippets. In most agents, the same model explores the repository and solves the task, leaving exploratory reads and searches in the solver's history. We present FastContext, a dedicated exploration subagent that separates repository exploration from solving. Invoked on demand, FastContext issues parallel tool calls and returns concise file paths and line ranges as focused context. FastContext is powered by specialized exploration models spanning 4B--30B parameters. We bootstrap them from strong reference-model trajectories and refine them with task-grounded rewards for broad first-turn search, multi-turn evidence gathering, and precise citation generation. Across SWE-bench Multilingual, SWE-bench Pro, and SWE-QA, integrating FastContext into Mini-SWE-Agent improves end-to-end resolution rates up to 5.5% while reducing coding-agent token consumption up to 60%, with marginal overhead. These results show that repository exploration can be separated from solving and handled effectively by specialized models. Code and data: https://github.com/microsoft/fastcontext

cs.SE↗

arXiv2026

Dual-Stream MLP is All You Need for CTR Prediction

Click-through rate (CTR) prediction holds a pivotal role in online advertising and recommendation systems, where even small improvements can significantly boost revenue. Existing research primarily focuses on designing dual-stream architectures to capture effective complex feature interactions from both explicit and implicit perspectives. However, these approaches are faced with two major challenges: 1) the high complexity of feature interaction learning, which increases computational demands and the overfitting risk, and 2) the imbalance between explicit and implicit modules, where one module's output may dominate the final prediction. To address these issues, in this paper, we propose Dual-Stream MLP (DS-MLP), a novel feature interaction framework for the CTR prediction task. Specially, it leverages knowledge distillation to consolidate the capacity of learning explicit feature interaction into a main MLP network, while a parallel MLP simultaneously captures implicit feature interactions as a complement. To effectively optimize the dual-stream MLP architecture, we further design a specific learning approach with two alignment strategies for enhancing the compatibility of the two MLP components. Experiments demonstrate that DS-MLP, though merely a vanilla MLP structure (the final model), can achieve state-of-the-art performance across three widely used benchmarks, offering a scalable and efficient solution for large-scale recommendation systems. Our code is available at https://github.com/RUCAIBox/DS-MLP.

cs.IR↗

arXiv2026

Satisfiability Solving with LLMs: A Matched-Pair Evaluation of Reasoning Capability

Large language models (LLMs) are increasingly used for tasks that implicitly reduce to Boolean satisfiability (SAT), yet their reasoning ability on SAT remains unclear. We present a systematic study of LLMs on 2-SAT and 3-SAT, together with two canonical reductions, Vertex Cover and discrete 3D packing, to probe representation-invariant reasoning. We first evaluate models using conventional metrics, including accuracy, precision, recall, and F1, as well as the SAT phase-transition setting. We find that these metrics can be misleading: many models obtain high scores by over-predicting satisfiable formulas, fail to reproduce the classical easy-hard-easy signature around the 3-SAT threshold, and degrade sharply as the number of variables grows. To address this problem, we introduce a paired-formula protocol based on minimally different satisfiable and unsatisfiable instances, together with Accurate Differentiation Rate (ADR), which requires both members of each pair to be classified correctly. ADR separates reasoning-oriented models from heuristic ones and correlates with witness validity. Beyond CNF, we test cross-representation consistency by converting CNF to Vertex Cover and 3-SAT to discrete 3D packing. Model decisions on CNF and on the corresponding graph or packing instances agree for most models on more than 80 percent of instances, suggesting stable decision rules across representations. Overall, our results show that SAT is a conservative probe for LLM reasoning, and that paired evaluation with ADR provides a more faithful and representation-robust assessment than conventional metrics.

cs.AI↗

arXiv2026

An Adaptive Log-Laguerre Spectral Method for the Radial Dirac Equation: Resolving Asymptotic Decay and Core Singularities in Atomic Calculations

The high-precision solution of the radial Dirac equation is fundamental to relativistic quantum chemistry, essential for reliable pseudopotential generation and all-electron electronic structure methods. Capturing both the non-polynomial singularities at the origin and the state-dependent asymptotic decay on semi-infinite domains presents a significant computational challenge. In this work, we propose the Adaptive Log-Laguerre Spectral Method (ALLSM), a novel coupled spectral-element solver that seamlessly integrates three advanced mathematical methodologies into a unified framework. Specifically, Generalized Log-Orthogonal Functions (GLOFs) are deployed in the near-core region to intrinsically approximate complex $r^s$ singular behaviors without requiring prior knowledge of the exact analytical exponent $s$. Concurrently, an adaptive Laguerre spectral method is employed to dynamically capture diverse exponential tails on $[0, \infty)$, avoiding artificial domain truncation. To structurally guarantee spectral purity across this bipartite basis, the framework rigorously incorporates the Inverse Dirac Operator Method (IDOM), effectively eliminating variational collapse and spurious states. Validated across diverse physical regimes, including Coulomb, finite-nucleus, and screened potentials, the proposed solver restores exponential convergence and consistently achieves relative accuracies of $10^{-10}$. This work provides a robust, pollution-free computational kernel for atomic structure calculations, establishing a highly reliable numerical standard for complex molecular simulations.

math.NA↗