arXiv subjects

Bingbing Wang

Publications and source records attributed to Bingbing Wang.

At least 19 recordsLinked to original sources

arXiv2026

Realization of Arbitrary Gauge Fields via Symmetry-Protected Zero Modes

Gauge fields are fundamental to modern physics, but prescribed gauge configurations are often difficult to implement in artificial systems. Here, we present a general scheme for realizing arbitrary static $\mathrm{O}(N)$ lattice gauge configurations using symmetry-protected zero modes of sublattice-imbalanced bipartite units. The target $\mathrm{O}(N)$ link on each bond is encoded in the connectivity and strengths of positive microscopic couplings. By decoupling the zero-mode manifold from the remaining modes, the target gauge Hamiltonian forms an exact spectral block of the microscopic tight-binding model rather than a perturbative approximation. We experimentally demonstrate this framework in acoustic crystals through a $\mathbb{Z}_2$ quadrupole topological insulator, an $\mathrm{SO}(2)$ Hofstadter model, and an $\mathrm{SO}(3)$ non-Abelian topological insulator. Our results provide a general and accessible route to gauge-field physics in artificial systems.

cond-mat.mes-hall↗

arXiv2026

RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation

Translating user intent into physical radio signals is the last critical step in wireless prototyping. It chains protocol planning, baseband synthesis, and hardware configuration. Large language models and multi-agent systems have reshaped software engineering, raising the question of whether they can solve this problem. Yet current models fail at this task, even when augmented with domain tools. Because the stages run sequentially, an error at any stage propagates downstream, so the end-to-end success rate collapses toward zero even when each stage looks locally competent. We introduce RadioMaster, a fully autonomous multi-agent framework that drives user input to verified emissions transmitted over the air. It rests on three synergistic pillars. RadioWiki grounds generation in domain knowledge to suppress hallucination. RadioAgent decomposes the fragile pipeline into independently executable and locally recoverable stages. RadioEmulator gates deployment behind closed-loop physical-layer verification. We further build RadioBench, the first benchmark for autonomous radio signal generation. Extensive real-world evaluations show that RadioMaster substantially outperforms state-of-the-art baselines in configuration viability and signal fidelity, while reducing configuration time by up to 28x.

cs.MA↗

arXiv2026

Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue

Despite substantial progress in long-context modeling, existing benchmarks remain confined to factual memory for explicit recall, failing to measure the reflective memory required to synthesize fragmented, multimodal cues into high-level interpretations. To address this gap, we introduce RefMem-Bench, a benchmark for reflective memory in long-horizon dialogue. RefMem-Bench contains 26K annotated QA instances with eight reflective-memory dimensions and three task formats, requiring models to move beyond surface-level retrieval and infer latent meanings from evidence distributed across interaction histories. To enhance reflective memory capability, we propose REflective Memory INDuction (REMIND), a hierarchical framework that treats reflective memory as progressive meaning construction. REMIND couples question-conditioned evidence retrieval, salience-aware grounding, and abstraction-level supervision, and uses Progressive Reflective Alignment to distill high-level reflective reasoning into the factual inference pathway. Experiments show RefMem-Bench poses a substantial challenge to current models, while REMIND consistently improves both answer accuracy and memory recall through progressive evidence perception, grounding, and abstraction.

cs.CL↗

arXiv2026

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current request. Yet today's agents keep what a user volunteers but rarely ask for what stays unspoken, leaving a proactivity gap in long-lived LLM agents: an agent cannot act on a preference it never obtained. As users delegate more of their affairs to agents, the impact of this gap grows. We isolate one concrete, controllable slice of this gap as Ask-to-Remember (ATR): the agent decides whether to ask now for a reusable user preference that the current task does not need but a later session with the same user will. ATR is hard even to evaluate: the right question is underdetermined and its payoff deferred to tasks that may never arise. ATRBench, to the best of our knowledge the first ATR benchmark, makes it measurable by fixing each user's preferences as hidden ground truth, so success demands asking, not recall. Across eight frontier LLM agents, defaults fall at least 62 points below an oracle handed the relevant preference, and prompting closes little of it. Diagnostics identify acquisition as the bottleneck. ATRBench surfaces this proactivity gap in current agents and offers a diagnostic testbed for closing it.

cs.CL↗

arXiv2026

Mitigating Provenance-Role Collapse in Long-Term Agents via Typed Memory Representation

Long-term memory is essential for persistent LLM agents, yet prevailing architectures store historical interactions as unstructured, flat text. This unconstrained storage induces provenance-role collapse, a critical failure mode where agents suffer from source-monitoring errors. To resolve this cognitive vulnerability at the architectural level, we propose MemIR, a typed Memory Intermediate Representation that operationalizes source monitoring as a structural constraint. MemIR writes long-term memory into grounded atoms that separate raw evidence, retrieval cues, and truth-bearing claims, with factual authorization restricted to supported claim atoms. It then applies multi-route atomic projection and provenance-scoped utilization to transform heterogeneous retrieval hits into claim-centered candidate bundles and a normalized fact interface for answer generation. Experiments on LoCoMo and BEAM-100K demonstrate that MemIR consistently outperforms existing memory baselines, especially on tasks requiring source tracking, temporal grounding, and aggregation of fragmented evidence.

cs.CL↗

arXiv2026

Enabling Agile Ambient IoT Networking via a Parameterized Hybrid Radio

The emergence of Ambient IoT signals a paradigm shift toward massive batteryless networking. However, the absence of an agile physical layer substrate remains a fundamental barrier to research and standardization. Current testbeds are hindered by decoupled radio paths, high static power, and cumbersome control methods, which stifle rapid protocol prototyping. In this paper, we present Janus, the first hybrid active-passive configurable radio architected for agile Ambient IoT networking. Janus introduces a parameterized architecture that unifies passive and active transmission into a single RF front end, abstracting complex physical layer behaviors into concise parameters. This design enables a system-level control plane for dynamic mode transitions and an energy management plane for fine-grained harvesting across multiple sources. We implement a compact PCB prototype and evaluate its performance across diverse protocol landscapes, including 3GPP A-IoT, IEEE 802.11 AMP, and Bluetooth SIG. Our experimental results demonstrate that Janus achieves communication performance on par with dedicated radios while significantly reducing configuration overhead. Ultimately, Janus serves as a versatile enabler for validating emerging protocols and accelerating the standardization of next-generation low-power networks.

cs.NI↗

arXiv2026

Proactive Memory for Ad-Hoc Recall over Streaming Dialogues

Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. However, existing read-then-think memory is fundamentally misaligned with this setting, as it cannot support ad-hoc memory recall while streams unfold. To explore this challenge, we introduce \textbf{STEM-Bench}, the first benchmark for \textbf{ST}reaming \textbf{E}valuation of \textbf{M}emory. It comprises over 14K QA pairs in dialogue streams that assess perception fidelity, temporal reasoning, and global awareness under infinite-horizon constraints. The preliminary analysis on STEM-Bench indicates a critical textit{fidelity-efficiency dilemma}: retrieval-based methods use fragment context, while full-context models incur unbounded latency. To resolve this, we propose \textbf{ProStream}, a proactive memory framework for streaming dialogues built on a hierarchical structure. It enables ad-hoc memory recall on demand by reasoning over continuous streams with multi-granular distillation. Moreover, it employs Adaptive Spatiotemporal Optimization to dynamically optimize retention based on expected utility. It enables a bounded knowledge state for lower inference latency without sacrificing reasoning fidelity. Experiments show ProStream delivers higher reasoning fidelity than prior baselines while maintaining substantially lower latency than full-context alternatives.

cs.AI↗

arXiv2026

Spontaneous Fully Compensated Ferrimagnetism

We propose a general mechanism for the spontaneous emergence of filling-enforced fully compensated ferrimagnetism (fFIM), characterized by zero net magnetization yet ferromagnetic-like spin-split band structures. Using Hartree-Fock mean-field calculations of the Hubbard model, we map out the stability regime of spontaneous fFIM over a broad parameter space of interaction strength and staggered potential. We show the unique quantum-geometry-governed optical selection rules and the abundant valley- and spin-related physics of electronics and optics arising from the emergence of fFIM order, with tunable spin-polarized and valley-contrasting charge and spin currents. Furthermore, based on our theory, we demonstrate that spontaneous fFIM can be realized in nominally nonmagnetic graphene via defect engineering. Our results establish a unified framework for the mechanism, emergent properties, and materials realization of spontaneous fFIM, opening new opportunities for spintronic, valleytronic, and optoelectronic applications.

cond-mat.str-el↗

arXiv2026

SynergyKGC: Reconciling Topological Heterogeneity in Knowledge Graph Completion via Topology-Aware Synergy

Knowledge Graph Completion (KGC) fundamentally hinges on the coherent fusion of pre-trained entity semantics with heterogeneous topological structures to facilitate robust relational reasoning. However, existing paradigms encounter a critical "structural resolution mismatch," failing to reconcile divergent representational demands across varying graph densities, which precipitates structural noise interference in dense clusters and catastrophic representation collapse in sparse regions. We present SynergyKGC, an adaptive framework that advances traditional neighbor aggregation to an active Cross-Modal Synergy Expert via relation-aware cross-attention and semantic-intent-driven gating. By coupling a density-dependent Identity Anchoring strategy with a Double-tower Coherent Consistency architecture, SynergyKGC effectively reconciles topological heterogeneity while ensuring representational stability across training and inference phases. Systematic evaluations on two public benchmarks validate the superiority of our method in significantly boosting KGC hit rates, providing empirical evidence for a generalized principle of resilient information integration in non-homogeneous structured data.

cs.AI↗

arXiv2026

Topological antilaser

Coherent perfect absorption (CPA)-the time-reversed operation of lasing at threshold-relies on finely tuned interference and is intrinsically fragile to disorder and structural imperfections. Whether absorption can be endowed with topological protection, by analogy to topological lasing, has remained an open question. Here, we experimentally demonstrate a topological antilaser: the time-reversed counterpart of a topological laser, in which chiral edge modes of a photonic lattice enable perfect light absorption protected by topology. Using a nonreciprocal microwave network with low intrinsic loss, we show that the topological antilaser preserves near-unity absorption under strong disorder, and, unlike conventional antilasers, remains functional for arbitrary placements of dissipation and input ports, even when the lattice is strongly perturbed. This robustness arises from the disorder-immune propagation and stable spatial profile of the topological edge modes. Our results establish topologically protected absorption as the missing counterpart of topological lasing, opening new directions for studying robust energy dissipation, wave control, and coherent-absorption-based detection technologies.

physics.optics↗

arXiv2025

Robust Outlier Detection and Low-Latency Concept Drift Adaptation for Data Stream Regression: A Dual-Channel Architecture

Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the continuous nature of output spaces makes distinguishing drifts from outliers inherently challenging. To address this, we propose a novel robust regression framework for joint outlier and concept drift detection. Specifically, we introduce a dual-channel decision process that orchestrates prediction residuals into two coupled logic flows: a rapid response channel for filtering point outliers and a deep analysis channel for diagnosing drifts. We further develop the Exponentially Weighted Moving Absolute Deviation with Distinguishable Types (EWMAD-DT) detector to autonomously differentiate between abrupt and incremental drifts via dynamic thresholding. Comprehensive experiments on both synthetic and real-world datasets demonstrate that our unified framework, enhanced by EWMAD-DT, exhibits superior detection performance even when point outliers and concept drifts coexist.

stat.ME↗

arXiv2025

CoreEval: Automatically Building Contamination-Resilient Datasets with Real-World Knowledge toward Reliable LLM Evaluation

Data contamination poses a significant challenge to the fairness of LLM evaluations in natural language processing tasks by inadvertently exposing models to test data during training. Current studies attempt to mitigate this issue by modifying existing datasets or generating new ones from freshly collected information. However, these methods fall short of ensuring contamination-resilient evaluation, as they fail to fully eliminate pre-existing knowledge from models or preserve the semantic complexity of the original datasets. To address these limitations, we propose \textbf{CoreEval}, a \textbf{Co}ntamination-\textbf{re}silient \textbf{Eval}uation strategy for automatically updating data with real-world knowledge. This approach begins by extracting entity relationships from the original data and leveraging the GDELT database to retrieve relevant, up-to-date knowledge. The retrieved knowledge is then recontextualized and integrated with the original data, which is refined and restructured to ensure semantic coherence and enhanced task relevance. Ultimately, a robust data reflection mechanism is employed to iteratively verify and refine labels, ensuring consistency between the updated and original datasets. Extensive experiments on updated datasets validate the robustness of CoreEval, demonstrating its effectiveness in mitigating performance overestimation caused by data contamination.

cs.CL↗

arXiv2025

PRISM of Opinions: A Persona-Reasoned Multimodal Framework for User-centric Conversational Stance Detection

The rapid proliferation of multimodal social media content has driven research in Multimodal Conversational Stance Detection (MCSD), which aims to interpret users' attitudes toward specific targets within complex discussions. However, existing studies remain limited by: **1) pseudo-multimodality**, where visual cues appear only in source posts while comments are treated as text-only, misaligning with real-world multimodal interactions; and **2) user homogeneity**, where diverse users are treated uniformly, neglecting personal traits that shape stance expression. To address these issues, we introduce **U-MStance**, the first user-centric MCSD dataset, containing over 40k annotated comments across six real-world targets. We further propose **PRISM**, a **P**ersona-**R**easoned mult**I**modal **S**tance **M**odel for MCSD. PRISM first derives longitudinal user personas from historical posts and comments to capture individual traits, then aligns textual and visual cues within conversational context via Chain-of-Thought to bridge semantic and pragmatic gaps across modalities. Finally, a mutual task reinforcement mechanism is employed to jointly optimize stance detection and stance-aware response generation for bidirectional knowledge transfer. Experiments on U-MStance demonstrate that PRISM yields significant gains over strong baselines, underscoring the effectiveness of user-centric and context-grounded multimodal reasoning for realistic stance understanding.

cs.CL↗

arXiv2025

MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection

Multimodal Stance Detection (MSD) is a crucial task for understanding public opinion on social media. Existing methods predominantly operate by learning to fuse modalities. They lack an explicit reasoning process to discern how inter-modal dynamics, such as irony or conflict, collectively shape the user's final stance, leading to frequent misjudgments. To address this, we advocate for a paradigm shift from *learning to fuse* to *learning to reason*. We introduce **MIND**, a **M**eta-cognitive **I**ntuitive-reflective **N**etwork for **D**ual-reasoning. Inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop. It first generates a rapid, intuitive hypothesis by querying evolving Modality and Semantic Experience Pools. Subsequently, a meta-cognitive reflective stage uses Modality-CoT and Semantic-CoT to scrutinize this initial judgment, distill superior adaptive strategies, and evolve the experience pools themselves. These dual experience structures are continuously refined during training and recalled at inference to guide robust and context-aware stance decisions. Extensive experiments on the MMSD benchmark demonstrate that our MIND significantly outperforms most baseline models and exhibits strong generalization.

cs.CL↗

arXiv2025

Comprehensive and Efficient Distillation for Lightweight Sentiment Analysis Models

Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions and large-scale user texts. Despite the promising results, two key challenges remain: (1) manually written instructions are limited in diversity and quantity, making them insufficient to ensure comprehensive coverage of distilled knowledge; (2) large-scale user texts incur high computational cost, hindering the practicality of these methods. To this end, we introduce CompEffDist, a comprehensive and efficient distillation framework for sentiment analysis. Our framework consists of two key modules: attribute-based automatic instruction construction and difficulty-based data filtering, which correspondingly tackle the aforementioned challenges. Applying our method across multiple model series (Llama-3, Qwen-3, and Gemma-3), we enable 3B student models to match the performance of 20x larger teacher models on most tasks. In addition, our approach greatly outperforms baseline methods in data efficiency, attaining the same performance level with only 10% of the data.

cs.CL↗

arXiv2025

Universal valley filtering via uniform dissipation and velocity contrast

Valley, as a ubiquitous degree of freedom in lattices, has found wide applications in both electronic and classical-wave devices in recent years. However, achieving valley-polarized states, a prerequisite for valley-based operations, still remains challenging. Here, we propose and experimentally demonstrate a universal non-Hermitian mechanism for valley filtering using only uniform background dissipation, which creates a propagation length contrast between valleys through their intrinsic group velocity differences. We implement this concept in an acoustic crystal, observing switchable and robust valley polarization of sound through large-scale field mapping. Remarkably, our approach is solely based on uniform loss, without the need for any special lattice structures, tailored excitations, or external fields. We further provide designs of our non-Hermitian valley filter on photonic and electronic platforms. Our results offer a simple and effective solution to valley-polarized state generation and may advance the development of novel valley-based devices in both classical and quantum regimes.

cond-mat.mes-hall↗

arXiv2025

Transfer Learning in Regression with Influential Points

Regression prediction plays a crucial role in practical applications and strongly relies on data annotation. However, due to prohibitive annotation costs or domain-specific constraints, labeled data in the target domain is often scarce, making transfer learning a critical solution by leveraging knowledge from resource-rich source domains. In the practical target scenario, although transfer learning has been widely applied, influential points can significantly distort parameter estimation for the target domain model. This issue is further compounded when influential points are also present in source domains, leading to aggravated performance degradation and posing critical robustness challenges for existing transfer learning frameworks. In this study, we innovatively introduce a transfer learning collaborative optimization (Trans-CO) framework for influential point detection and regression model fitting. Extensive simulation experiments demonstrate that the proposed Trans-CO algorithm outperforms competing methods in terms of model fitting performance and influential point identification accuracy. Furthermore, it achieves superior predictive accuracy on real-world datasets, providing a novel solution for transfer learning in regression with influential points

stat.ME↗

arXiv2025

Relaxation and Its Effects on Electronic Structure in Twisted Systems: An Analytical Perspective

Lattice relaxation profoundly reshapes electronic structures in twisted materials. Prevailing treatments, however, typically rely on large-scale density functional theory (DFT), which is computationally costly and mechanistically opaque. Here, we develop a unified analytical framework to overcome these limitations. From continuum elastic theory, we derive closed-form solutions for both in-plane and out-of-plane relaxation fields. We further introduce an analytical phase factor expansion theory that maps relaxation into the electronic Hamiltonian. By applying this framework, the relaxation-mediated single-particle and many-body topological phase transitions in twisted MoTe$_{2}$ is accurately captured, and the evolution of flat bands in magic-angle graphene is quantitatively reproduced. Our work transforms the research of moir\'e relaxation from black-box numerical fitting to an analytical paradigm, offering fundamental insights, exceptional efficiency, and general applicability to a wide range of twisted materials.

cond-mat.mtrl-sci↗