arXiv subjects

Bin Han

Publications and source records attributed to Bin Han.

At least 55 records · Page 3Linked to original sources

arXiv2026

Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents

Large Language Models (LLMs) can be conditioned with explicit personality prompts, yet their behavioral realization often varies depending on context. This study examines how identical personality prompts lead to distinct linguistic, behavioral, and emotional outcomes across four conversational settings: ice-breaking, negotiation, group decision, and empathy tasks. Results show that contextual cues systematically influence both personality expression and emotional tone, suggesting that the same traits are expressed differently depending on social and affective demands. This raises an important question for LLM-based dialogue agents: whether such variations reflect inconsistency or context-sensitive adaptation akin to human behavior. Viewed through the lens of Whole Trait Theory, these findings highlight that LLMs exhibit context-sensitive rather than fixed personality expression, adapting flexibly to social interaction goals and affective conditions.

cs.CL↗

arXiv2026

VENENA: A Deceptive Visual Encryption Framework for Wireless Semantic Secrecy

Eavesdropping has been a long-standing threat to the security and privacy of wireless communications, since it is difficult to detect and costly to prevent. As networks evolve towards Sixth Generation (6G) and semantic communication becomes increasingly central to next-generation wireless systems, securing semantic information transmission emerges as a critical challenge. While classical physical layer security (PLS) focuses on passive security, the recently proposed concept of physical layer deception (PLD) offers a semantic encryption measure to actively deceive eavesdroppers. Yet the existing studies of PLD have been dominantly information-theoretical and link-level oriented, lacking considerations of system-level design and practical implementation. In this work we propose Visual ENcryption for Eavesdropping NegAtion (VENENA), an artificial intelligence-enabled framework for secure image-based communication. VENENA protects confidential messages by encoding them visually while actively deceiving eavesdroppers: legitimate receivers use artificial intelligence (AI)-based classifiers to extract true message semantics, while interceptors perceive only falsified content. The framework transmits two superimposed image components with different power levels - a high-power decoy image and a low-power correction mask - ensuring only authorized receivers with favorable channel conditions can reconstruct the true message. Experimental validation demonstrates over 93% accuracy for legitimate users while limiting eavesdropper success to 52% even when system design is fully known, validating VENENA's active defense capability for 6G semantic communication.

cs.CR↗

arXiv2026

Confusions and Erasures of Error-Bounded Block Decoders with Finite Blocklength

This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

cs.IT↗

arXiv2026

DMH-HARQ: Reliable and Open Latency-Constrained Wireless Transport Network

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design and openness paradigm as fostered by the Open-Radio Access Network (O-RAN) Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of automatic repeat request (ARQ) in multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a dynamic scheme of multi-hop hybrid ARQ (HARQ), which maximizes the end-to-end (E2E) communication reliability in the wireless transport network. We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal Dynamic Multi-Hop HARQ (DMH-HARQ) strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops.

cs.IT↗

arXiv2025

A Novel Dynamic Epidemic Model for Successive Opinion Diffusion in Social Networks

This paper proposes a dynamic epidemic model for successive opinion diffusion in social networks, extending the SHIMR model. It incorporates dynamic decision-making influenced by social distances and captures accumulative opinion diffusion caused by interrelated rumors. The model reflects the impact of rumor spread on social network structures. Simulations validate its effectiveness in explaining phenomena like the echo chamber effect and provide insights into opinion diffusion dynamics, with implications for understanding social polarization and network evolution.

cs.SI↗

arXiv2025

Joint Optimization for Security and Reliability in Round-Trip Transmissions for URLLC services

Physical layer security (PLS) is a potential solution for secure and reliable transmissions in future Ultra-Reliable and Low-Latency Communications (URLLC). This work jointly optimizes redundant bits and blocklength allocation in practical round-trip transmission scenarios. To minimize the leakage-failure probability, a metric that jointly characterizes security and reliability in PLS, we formulate an optimization problem for allocating both redundant bits and blocklength. By deriving the boundaries of the feasible set, we obtain the globally optimal solution for this integer optimization problem. To achieve more computationally efficient solutions, we propose a block coordinate descent (BCD) method that exploits the partial convexity of the objective function. Subsequently, we develop a majorization-minimization (MM) algorithm through convex approximation of the objective function, which further improves computational efficiency. Finally, we validate the performance of the three proposed approaches through simulations, demonstrating their practical applicability for future URLLC services.

eess.SY↗

arXiv2025

Network-Centric Anomaly Filtering and Spoofer localization for 5G-NR Localization in LAWNs

This paper investigates security vulnerabilities and countermeasures for the 3rd Generation Partnership Project (3GPP) Fifth Generation New Radio (5G-NR) Time Difference of Arrival (TDoA)-based unmanned aerial vehicle (UAV) localization in low-altitude urban environments. We first optimize node selection strategies under Air to Ground (A2G) channel conditions, proving that optimal selection depends on UAV altitude and deployment density, and propose lightweight User Equipment (UE)-assisted approaches that reduce overhead while enhancing accuracy. Next, we then expose critical security vulnerabilities by introducing merged-peak spoofing attacks where rogue UAVs transmit multiple 5G-NR Positioning Reference Signalss (PRSs) that merge with legitimate signals, bypassing existing detection methods. Through theoretical modeling and sensitivity analysis, we quantify how synchronization quality and geometric factors determine spoofing success probability, thereby revealing fundamental weaknesses in current 3GPP positioning frameworks. To address these vulnerabilities, we design a network-centric anomaly detection framework at the Localization Management Function (LMF) using 3GPP-specified parameters, coupled with recursive gradient descent-based robust localization that filters anomalous data while estimating UAV position. Our unified framework simultaneously provides robust victim localization and spoofer localization, enabling active attacker attribution beyond passive defense. Extensive simulations validate the effectiveness of our optimization and security mechanisms for 3GPP-compliant UAV positioning.

eess.SP↗

arXiv2025

Use Cases, Metrics, and Challenges of Nomadic Non-Public Networks for the 6G Standardization

Wireless communication is evolving with the adoption of dynamic and self-organizing networks. They are expected to play a crucial role in shaping sixth-generation (6G) systems and the ongoing standardization process. The concept of non-public networks (NPNs) introduced in fifth-generation (5G) will be enhanced by nomadic non-public networks (NNPNs), extending mobility and adaptability beyond fixed locations. These networks help overcome the limitations of traditional static infrastructures, making them applicable to areas such as emergency response, transportation, agriculture, and others. This paper examines the transition from NPNs to NNPNs, highlighting key technical aspects such as network architecture, dynamic resource allocation, and wireless backhauling. Several use cases illustrate how NNPNs improve connectivity in environments where traditional networks are limited. Additionally, the study defines Key Performance Indicators (KPIs) to evaluate NNPN applications and establishes a framework for categorizing them based on mobility and operational requirements. Despite their advantages, NNPNs introduce architectural, regulatory, and security challenges such as new approaches for handovers, spectrum policies or cross-border functionality, and trust mechanisms to maintain reliable operations. By identifying use cases, defining evaluation criteria, and addressing technical and regulatory challenges, this paper provides insights into integrating NNPNs into future 6G networks. These findings contribute to ongoing standardization efforts and emphasize the need for adaptable policies and network architectures to maximize the benefits of NNPNs in next-generation communication systems.

cs.NI↗

arXiv2025

Knowledge vs. Experience: Asymptotic Limits of Impatience in Edge Tenants

We study how two information feeds, a closed-form Markov estimator of residual sojourn and an online trained actor-critic, affect reneging and jockeying in a dual M/M/1 system. Analytically, for unequal service rates and total-time patience, we show that total wait grows linearly so abandonment is inevitable and the probability of a successful jockey vanishes as the backlog approaches towards infinity. Furthermore, under a mild sub-linear error condition both information models yield the same asymptotic limits (robustness). We empirically validate these limits and quantify finite backlog differences. Our findings show that learned and analytic feeds produce different delays, reneging rates and transient jockeying behavior at practical sizes, but converge to the same asymptotic outcome implied by our theory. The results characterize when value-of-information matters (finite regimes) and when it does not (asymptotics), informing lightweight telemetry and decision-logic design for low-cost, jockeying-aware systems.

stat.ML↗

arXiv2025

An Efficient Finite Difference-Based PML Technique for Acoustic Scattering Problems

The acoustic scattering problem is modeled by the exterior Helmholtz equation, which is challenging to solve due to both the unboundedness of the domain and the high dispersion error, known as the pollution effect. We develop high-order compact finite difference methods (FDMs) in polar coordinates to numerically solve the problem with multiple arbitrarily shaped scatterers. The unbounded domain is effectively truncated and compressed via perfectly matched layers (PMLs), while the pollution effect is handled by the high order of our method and a novel pollution minimization technique. This technique is easy to implement, rigorously proven to be effective and shows superior performance in our numerous numerical results. The FDMs we propose in regular polar coordinates achieve fourth consistency order. Yet, combined with exponential stretching and mesh refinement, we can reach sixth consistency order by slightly enlarging the stencil at certain locations. Our numerical examples demonstrate that the proposed FDMs are effective and robust under various wavenumbers, PML layer thickness and shapes of scatterers.

math.NA↗

arXiv2025

Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning

In this technical report, we present the Ring-linear model series, specifically including Ring-mini-linear-2.0 and Ring-flash-linear-2.0. Ring-mini-linear-2.0 comprises 16B parameters and 957M activations, while Ring-flash-linear-2.0 contains 104B parameters and 6.1B activations. Both models adopt a hybrid architecture that effectively integrates linear attention and softmax attention, significantly reducing I/O and computational overhead in long-context inference scenarios. Compared to a 32 billion parameter dense model, this series reduces inference cost to 1/10, and compared to the original Ring series, the cost is also reduced by over 50%. Furthermore, through systematic exploration of the ratio between different attention mechanisms in the hybrid architecture, we have identified the currently optimal model structure. Additionally, by leveraging our self-developed high-performance FP8 operator library-linghe, overall training efficiency has been improved by 50%. Benefiting from the high alignment between the training and inference engine operators, the models can undergo long-term, stable, and highly efficient optimization during the reinforcement learning phase, consistently maintaining SOTA performance across multiple challenging complex reasoning benchmarks.

cs.LG↗

arXiv2025

Automatic Refinement of Force Fields Based on Phase Diagrams

Exact characterization of phase transitions requires sufficient configurational sampling, necessitating efficient and accurate potential energy surfaces. Molecular force fields with computational efficiency and physical interpretability are desirable but challenging to refine for complex interactions. To address this, we propose a force field refinement strategy with phase diagrams as top-down optimization targets based on automatic differentiation. Using gas-liquid co-existence as a paradigm, we employ an enhanced sampling technique and design a differentiable loss function to evaluate force fields' depiction of phase diagrams. The refined force fields produce gas-liquid phase diagrams matching well with targets for two modeling systems, which confirms our approach as an effective automated force field development framework for phase transition studies.

cond-mat.stat-mech↗

arXiv2025

Chronicles of Jockeying in Queuing Systems

Emerging trends in communication systems, such as network softwarization, functional disaggregation, and multi-access edge computing (MEC), are reshaping both the infrastructural landscape and the application ecosystem. These transformations introduce new challenges for packet transmission, task offloading, and resource allocation under stringent service-level requirements. A key factor in this context is queue impatience, where waiting entities alter their behavior in response to delay. While balking and reneging have been widely studied, this survey focuses on the less explored but operationally significant phenomenon of jockeying, i.e. the switching of jobs or users between queues. Although a substantial body of literature models jockeying behavior, the diversity of approaches raises questions about their practical applicability in dynamic, distributed environments such as 5G and Beyond. This chronicle reviews and classifies these studies with respect to their methodologies, modeling assumptions, and use cases, with particular emphasis on communication systems and MEC scenarios. We argue that forthcoming architectural transformations in next-generation networks will render many existing jockeying models inapplicable. By highlighting emerging paradigms such as MEC, network slicing, and network function virtualization, we identify open challenges, including state dissemination, migration cost, and stability, that undermine classical assumptions. We further outline design principles and research directions, emphasizing hybrid architectures and decentralized decision making as foundations for re-conceptualizing impatience in next-generation communication systems.

cs.NI↗

arXiv2025

Adaptive Decentralized Queue Disclosure for Impatient Tenants in Edge and Non-terrestrial Systems

We study how queue-state information disclosures affect impatient tenants in multi-tenant edge systems. We propose an information-bulletin strategy in which each queue periodically broadcasts two Markov models. One is a model of steady-state service-rate behavior and the other a model of the queue length inter-change times. Tenants autonomously decide to renege or jockey based on this information. The queues observe tenant responses and adapt service rates via a learned, rule-based predictive policy designed for decentralized, partially-observed, and time-varying environments. We compare this decentralized, information-driven policy to the classical, centralized Markov Decision Process (MDP) hedging-point policy for M/M/2 systems. Numerical experiments quantify the tradeoffs in average delay, impatience and robustness to stale information. Results show that when full, instantaneous state information and stationarity hold, the hedging-point policy yields less impatience but this diminishes as information becomes partial or stale. The rule-based predictive policy on the other hand is more robust to staleness in dispatched information, making it conducive for conditions typical of edge cloud and non-terrestrial deployments.

eess.SY↗

arXiv2025

Flexible and High-Performance Radio Access Networks for upcoming Sixth-Generation (6G) Systems

The collaborative research project 6G-ANNA develops concepts for the 6G radio access network (RAN) architecture and technology components. Previous RAN generations have become inherently more complex and reach their limits in handling foreseen future traffic demands with their diverse characteristics in an efficient manner, e.g., for the use-case of mobile eXtended Reality (XR) on a massive scale. One main objective of 6G is to regain both operational and energy efficiency, i.e., by simplification and automation. To achieve this, in this paper a flexible 6G RAN functional architecture and protocol stack as well as implementation and deployment options are described. We outline how performance is optimized by distributed Multiple Input Multiple Output (MIMO) and distributed Carrier Aggregation (CA), and furthermore, how adaptiveness and scalability is enabled by Cloud RAN and service orchestration. Finally, the proposed zero-trust framework mitigates security risks in the described 6G RAN architecture.

cs.NI↗

arXiv2025

Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans

Large Language Models (LLMs) are increasingly deployed in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored. This study assesses the behavioral alignment of personality-prompted LLMs in adversarial dispute resolution by simulating multi-turn conflict dialogues that incorporate negotiation. Each LLM is guided by a matched Five-Factor personality profile to control for individual variation and enhance realism. We evaluate alignment across three dimensions: linguistic style, emotional expression (e.g., anger dynamics), and strategic behavior. GPT-4.1 achieves the closest alignment with humans in linguistic style and emotional dynamics, while Claude-3.7-Sonnet best reflects strategic behavior. Nonetheless, substantial alignment gaps persist. Our findings establish a benchmark for alignment between LLMs and humans in socially complex interactions, underscoring both the promise and the limitations of personality conditioning in dialogue modeling.

cs.CL↗

arXiv2025

Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?

This study proposes a framework that employs personality prompting with Large Language Models to generate verbal and nonverbal behaviors for virtual agents based on personality traits. Focusing on extraversion, we evaluated the system in two scenarios: negotiation and ice breaking, using both introverted and extroverted agents. In Experiment 1, we conducted agent to agent simulations and performed linguistic analysis and personality classification to assess whether the LLM generated language reflected the intended traits and whether the corresponding nonverbal behaviors varied by personality. In Experiment 2, we carried out a user study to evaluate whether these personality aligned behaviors were consistent with their intended traits and perceptible to human observers. Our results show that LLMs can generate verbal and nonverbal behaviors that align with personality traits, and that users are able to recognize these traits through the agents' behaviors. This work underscores the potential of LLMs in shaping personality aligned virtual agents.

cs.HC↗

arXiv2025

Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses

We explore the application of large language models (LLMs) to empower domain experts in integrating large, heterogeneous, and noisy urban spatial datasets. Traditional rule-based integration methods are unable to cover all edge cases, requiring manual verification and repair. Machine learning approaches require collecting and labeling of large numbers of task-specific samples. In this study, we investigate the potential of LLMs for spatial data integration. Our analysis first considers how LLMs reason about environmental spatial relationships mediated by human experience, such as between roads and sidewalks. We show that while LLMs exhibit spatial reasoning capabilities, they struggle to connect the macro-scale environment with the relevant computational geometry tasks, often producing logically incoherent responses. But when provided relevant features, thereby reducing dependence on spatial reasoning, LLMs are able to generate high-performing results. We then adapt a review-and-refine method, which proves remarkably effective in correcting erroneous initial responses while preserving accurate responses. We discuss practical implications of employing LLMs for spatial data integration in real-world contexts and outline future research directions, including post-training, multi-modal integration methods, and support for diverse data formats. Our findings position LLMs as a promising and flexible alternative to traditional rule-based heuristics, advancing the capabilities of adaptive spatial data integration.

cs.AI↗