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Bin Shen

Publications and source records attributed to Bin Shen.

At least 19 recordsLinked to original sources
arXiv2026

Complex Magnetic Behavior of the Ce sawtooth chains in CeRhSn$_2$

Conflicting reports exist on the ground state of the intermetallic compound CeRhSn$_2$. This can be rooted in the sawtooth-like arrangement of two inequivalent Ce sites in the unit cell, which suggests potential geometric magnetic frustration. To resolve, we conducted a comprehensive study on high-quality single crystals of CeRhSn$_2$ by means of magnetization ($M$), specific heat ($C_p/T$), and resistivity ($\rho$). The system exhibits strong magnetic anisotropy, confirming the $b$-axis as the easy magnetic axis. We establish three successive transitions, an AFM order at $T_{N} = 3.65$K, a first-order FM order at $T_{C} = 1.7$K and final transition, at $T = 1.5$K. The transition temperatures are highly field-directional dependent: in a magnetic field, the lowest transition is immediately suppressed while $\mathbf{H} \parallel b$ rapidly merges $T_{C}$ and $T_{N}$ into a single second-order transition. Conversely, $\mathbf{H}\parallel c$ suppresses the FM order and reduces $T_{N}$. Additional ab initio calculations affirm the FM ground state of CeRhSn$_2$. The observation of an enhancement of the Sommerfeld coefficient ($\gamma = 76.5$mJ/mol$\cdot$K$^2$) may arise from geometric frustration, but it is most consistently attributed to weak Kondo hybridization as frustration cannot be conclusively established through our data.

cond-mat.str-el↗
arXiv2026

Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling

Multi-class event streams arise in numerous real-world applications, where uncovering structured, interpretable inter-event relationships, together with accurate prediction, remains a central challenge. Existing neural point process models are highly expressive but encode event interactions in a black-box manner, preventing explicit discovery of structured dependencies. In this paper, we propose a structured neural marked point process (SNMPP) that achieves high modeling flexibility while enabling explicit event-wise and class-wise relationship discovery from data. Our model constructs a product-form neural influence kernel composed of a signed interaction network over event types and a delay-aware monotonic temporal network. This design enables explicit characterization of inter-class influence topology -- including excitation, inhibition, and neutrality -- while flexibly capturing diverse temporal decay patterns and potential influence delays. For efficient learning, we develop a stratified Monte Carlo estimator for stochastic training. Extensive experiments on synthetic and real-world benchmark datasets validate the ability of our approach to uncover structured relationships and deliver strong predictive performance.

cs.LG↗
arXiv2026

EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models

While large vision-language models (VLMs) are increasingly adopted as the perceptual backbone for embodied agents, existing benchmarks often rely on question-answering or multiple-choice formats. These protocols allow models to exploit linguistic priors rather than demonstrating genuine visual grounding. To address this, we present EPIC-Bench, Embodied PerceptIon BenChmark, a fine-grained grounding benchmark designed to systematically evaluate the visual perceptual capabilities of VLMs in real-world embodied environments. Comprising 6.6k meticulously annotated tuples (Image, Text, Mask), EPIC-Bench spans 23 fine-grained tasks across three core stages of the embodied interaction pipeline: Target Localization, Navigation, and Manipulation. Extensive evaluations of over 89 leading VLMs reveal that while advanced reasoning models show promise, current VLMs universally struggle with complex visual-text alignment for physical interactions. Specifically, models exhibit critical bottlenecks in multi-target counting, part-whole relationship understanding, and affordance region detection. EPIC-Bench provides a robust foundation and actionable insights for advancing the next generation of vision-driven embodied models.

cs.CV↗
arXiv2026

Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events

Events in spatiotemporal systems are ubiquitous, yet modeling their complex distributions remains challenging. Existing point process models often rely on strong structural assumptions and are typically limited to autoregressive, event-by-event prediction. As a result, they struggle to support broader inference tasks such as inverse inference, trajectory reconstruction, and recovery of missing event locations. We introduce Arbitrarily Conditioned Hierarchical Flows (ARCH), a hierarchical flow matching framework for spatiotemporal event modeling. ARCH is expressive enough to capture complex event distributions while enabling tractable and accurate computation of conditional intensities, which quantify instantaneous event risk. Built on a history-encoder-generative-decoder architecture, ARCH introduces a hybrid masking strategy for flexible conditioning on arbitrary observed events. This enables a unified treatment of forecasting, inverse inference, and partial trajectory recovery within a single framework. Experiments on synthetic and real-world datasets show that ARCH consistently outperforms existing baselines across both prediction and conditional inference tasks.

cs.LG↗
arXiv2026

SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication

With the evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness. However, existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss. To address this issue, this paper proposes a Semantic Port-Aware Adaptive-Rate Transmission Protocol (SPAT) for semantic communication. The proposed framework jointly embeds source and destination port information into semantic representations, thereby reducing dependence on explicit port headers while enabling robust port-aware transmission. Furthermore, a differentiated semantic processing mechanism is developed for uplink and downlink scenarios, where port identification is introduced for uplink service recognition and destination-aware conditional gating is designed for downlink selective decoding. In addition, an adaptive-rate controller is incorporated to dynamically adjust the number of transmitted semantic channels according to channel conditions and feature importance, thereby improving both robustness and transmission efficiency. Experimental results on the AFHQ and ImageNet-10 datasets, together with real-world experimental measurements, demonstrate that SPAT consistently outperforms TCP, UDP, and SITP in reconstruction quality across different SNRs while maintaining low-latency transmission.

eess.SP↗
arXiv2026

Kronecker-Structured Nonparametric Spatiotemporal Point Processes

Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical Poisson and Hawkes processes rely on restrictive parametric assumptions that limit their ability to capture complex interaction patterns, while recent neural point process models increase representational capacity but integrate event information in a black-box manner, hindering interpretable relationship discovery. To address these limitations, we propose a Kronecker-Structured Nonparametric Spatiotemporal Point Process (KSTPP) that enables transparent event-wise relationship discovery while retaining high modeling flexibility. We model the background intensity with a spatial Gaussian process (GP) and the influence kernel as a spatiotemporal GP, allowing rich interaction patterns including excitation, inhibition, neutrality, and time-varying effects. To enable scalable training and prediction, we adopt separable product kernels and represent the GPs on structured grids, inducing Kronecker-structured covariance matrices. Exploiting Kronecker algebra substantially reduces computational cost and allows the model to scale to large event collections. In addition, we develop a tensor-product Gauss-Legendre quadrature scheme to efficiently evaluate intractable likelihood integrals. Extensive experiments demonstrate the effectiveness of our framework.

cs.LG↗
arXiv2026

Magnetic and electric properties of the metallic kagome antiferromagnet CrRhAs

CrRhAs is an antiferromagnetic kagome metal predicted to host a nontrivial spin texture with vector spin chirality [Huang \textit{et al.}, \textit{npj Quantum Mater.} \textbf{8}, 32 (2023)]. We report the synthesis and basic characterization of CrRhAs single crystals, which exhibit an antiferromagnetic transition with $T_{\rm N}$ = 150~K, evidenced by electrical transport, heat capacity, and magnetization measurements. Hall resistivity varies linearly with magnetic field, i.e., there is no nonlinear Hall contribution. Intriguingly, the Hall coefficient changes sign between the configurations of $j \parallel ab, H \perp ab$ and $j \parallel c, H \perp c$, which is likely connected to a peculiar topology of the Fermi surface. Furthermore, for $j \parallel ab$, the Hall coefficient shows a pronounced and continuous enhancement below $T_{\rm N}$, signaling a significant reconstruction of the Fermi surface or an extra scattering from the magnons. Our results offer guidance for exploring anomalous electric transport phenomena in exotic magnetic systems.

cond-mat.str-el↗
arXiv2026

Query Suggestion for Retrieval-Augmented Generation via Dynamic In-Context Learning

Retrieval-augmented generation with tool-calling agents (agentic RAG) has become increasingly powerful in understanding, processing, and responding to user queries. However, the scope of the grounding knowledge is limited and asking questions that exceed this scope may lead to issues like hallucination. While guardrail frameworks aim to block out-of-scope questions (Rodriguez et al., 2024), no research has investigated the question of suggesting answerable queries in order to complete the user interaction. In this paper, we initiate the study of query suggestion for agentic RAG. We consider the setting where user questions are not answerable, and the suggested queries should be similar to aid the user interaction. Such scenarios are frequent for tool-calling LLMs as communicating the restrictions of the tools or the underlying datasets to the user is difficult, and adding query suggestions enhances the interaction with the RAG agent. As opposed to traditional settings for query recommendations such as in search engines, ensuring that the suggested queries are answerable is a major challenge due to the RAG's multi-step workflow that demands a nuanced understanding of the RAG as a whole, which the executing LLM lacks. As such, we introduce robust dynamic few-shot learning which retrieves examples from relevant workflows. We show that our system can be self-learned, for instance on prior user queries, and is therefore easily applicable in practice. We evaluate our approach on three benchmark datasets based on two unlabeled question datasets collected from real-world user queries. Experiments on real-world datasets confirm that our method produces more relevant and answerable suggestions, outperforming few-shot and retrieval-only baselines, and thus enable safer, more effective user interaction with agentic RAG.

cs.CL↗
arXiv2025

Magnetic field and pressure tuning of the heavy fermion antiferromagnet CePdIn

Frustrated Kondo lattices are ideal platforms for studying how both the Kondo effect and quantum fluctuations compete with the magnetic exchange interactions that drive magnetic ordering. Here, we investigate the effect of tuning the heavy-fermion compound CePdIn, which crystallizes in the geometrically frustrated ZrNiAl-type structure, using applied magnetic fields and hydrostatic pressure. At ambient pressure, CePdIn exhibits two magnetic transitions, one at $T_{\rm{N}} \approx 1.65$ K and another at $T_{\rm{M}} \approx 1.15$ K, which are both suppressed by applied $c$-axis fields. Upon applying pressure in zero magnetic field, there is a non-monotonic evolution of $T_{\rm{N}}$, which decreases to 0.8 K at 2.3 GPa, before abruptly increasing to 1.5 K at 2.6 GPa. At higher pressures, $T_{\rm{N}}$ has a weak pressure dependence, and vanishes near 5 GPa. Together with the high-pressure phase being more robust to applied fields, these results suggest two distinct antiferromagnetic phases in CePdIn, which are separated near 2.6 GPa, and this change may be driven by the evolution of the underlying electronic structure due to enhanced Kondo hybridization under pressure.

cond-mat.str-el↗
arXiv2025

Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization

Developing a universal and versatile embodied intelligence system presents two primary challenges: the critical embodied data bottleneck, where real-world data is scarce and expensive, and the algorithmic inefficiency of existing methods, which are resource-prohibitive. To address these limitations, we introduce Deliberate Practice Policy Optimization (DPPO), a metacognitive ``Metaloop'' training framework that dynamically alternates between supervised fine-tuning (competence expansion) and reinforcement learning (skill refinement). This enables automatic weakness identification and targeted resource allocation, specifically designed to maximize learning efficiency from sparse, finite data. Theoretically, DPPO can be formalised as a unified preference-learning framework. Empirically, training a vision-language embodied model with DPPO, referred to as Pelican-VL 1.0, yields a 20.3% performance improvement over the base model and surpasses open-source models at the 100B-parameter scale by 10.6%. We are open-sourcing both the models and code, providing the first systematic framework that alleviates the data and resource bottleneck and enables the community to build versatile embodied agents efficiently.

cs.AI↗
arXiv2025

Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence

This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source embodied multimodal brain model. Its core advantage lies in the in-depth integration of data power and intelligent adaptive learning mechanisms. Specifically, metaloop distilled a high-quality dataset from a raw dataset containing 4+ billion tokens. Pelican-VL 1.0 is trained on a large-scale cluster of 1000+ A800 GPUs, consuming over 50k+ A800 GPU-hours per checkpoint. This translates to a 20.3% performance uplift from its base model and outperforms 100B-level open-source counterparts by 10.6%, placing it on par with leading proprietary systems on well-known embodied benchmarks. We establish a novel framework, DPPO (Deliberate Practice Policy Optimization), inspired by human metacognition to train Pelican-VL 1.0. We operationalize this as a metaloop that teaches the AI to practice deliberately, which is a RL-Refine-Diagnose-SFT loop.

cs.LG↗
arXiv2025

Pressure tuning of competing interactions on a honeycomb lattice

Magnetic exchange interactions are mediated via orbital overlaps across chemical bonds. Thus, modifying the bond angles by physical pressure or strain can tune the relative strength of competing interactions. Here we present a remarkable case of such tuning between the Heisenberg (J) and Kitaev (K) exchange, which respectively establish magnetically ordered and spin liquid phases on a honeycomb lattice. We observe a rapid suppression of the Neel temperature (TN) with pressure in Ag3LiRh2O6, a spin-1/2 honeycomb lattice with both J and K couplings. Using a combined analysis of x-ray data and first-principles calculations, we find that pressure modifies the bond angles in a way that increases the |K/J| ratio and thereby suppresses TN. Consistent with this picture, we observe a spontaneous onset of muon spin relaxation (muSR) oscillations below TN at low pressure, whereas in the high-pressure phase, oscillations appear only when T < TN/2. Unlike other candidate Kitaev materials, Ag3LiRh2O6 is tuned toward a quantum critical point by pressure while avoiding a structural dimerization in the relevant pressure range.

cond-mat.str-el↗
arXiv2025

Shi-type and Hamilton-type gradient estimates for a general parabolic equation under compact Finsler $CD(-K,N)$ geometric flows

Recently, the Li-Yau-type gradient estimates for positive solutions to parabolic equations \begin{equation} \partial_t u=\Delta u+\mathcal{R}_1u+\mathcal{R}_2u^{\alpha}+\mathcal{R}_3u(\log u)^{\beta},\notag \end{equation} under the general compact Finsler $CD(-K,N)$ geometric flow are studied. Here $\mathcal{R}_1$,$\mathcal{R}_2$,$\mathcal{R}_3$ $\in$ $ C^{1}(M,[0,T])$, $\alpha$ and $\beta$ are both positive constants, $T$ is the maximal existence time for the flow. However, compared with the Riemannian case, the curvature conditions impose stricter derivative bounds on the development term in the geometric flow, as well as on the derivative bounds of the distortion of the manifold. In this manuscript, we present Shi-type and Hamilton-type gradient estimates to demonstrate the possibility of removing such conditions.

math.DG↗
arXiv2025

Modeling and Performance Analysis for Semantic Communications Based on Empirical Results

Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, we propose an Alpha-Beta-Gamma (ABG) formula to model the relationship between the end-to-end measurement and SNR, which can be applied for both image reconstruction tasks and inference tasks. Specifically, for image reconstruction tasks, the proposed ABG formula can well fit the commonly used DL networks, such as SCUNet, and Vision Transformer, for semantic encoding with the multi scale-structural similarity index measure (MS-SSIM) measurement. Furthermore, we find that the upper bound of the MS-SSIM depends on the number of quantized output bits of semantic encoders, and we also propose a closed-form expression to fit the relationship between the MS-SSIM and quantized output bits. To the best of our knowledge, this is the first theoretical expression between end-to-end performance metrics and SNR for semantic communications. Based on the proposed ABG formula, we investigate an adaptive power control scheme for semantic communications over random fading channels, which can effectively guarantee quality of service (QoS) for semantic communications, and then design the optimal power allocation scheme to maximize the energy efficiency of the semantic communication system. Furthermore, by exploiting the bisection algorithm, we develop the power allocation scheme to maximize the minimum QoS of multiple users for OFDMA downlink semantic communication Extensive simulations verify the effectiveness and superiority of the proposed ABG formula and power allocation schemes.

cs.LG↗
arXiv2025

Pressure-tuned spin chains in brochantite, Cu$_4$SO$_4$(OH)$_6$

Using high-pressure single-crystal x-ray diffraction combined with thermodynamic measurements and density-functional calculations, we uncover the microscopic magnetic model of the mineral brochantite, Cu$_4$SO$_4$(OH)$_6$, and its evolution upon compression. The formation of antiferromagnetic spin chains with the effective intrachain coupling of $J\simeq 100$\,K is attributed to the occurrence of longer Cu--Cu distances and larger Cu--O--Cu bond angles between the structural chains within the layers of the brochantite structure. These zigzag spin chains are additionally stabilized by ferromagnetic couplings $J_2$ between second neighbors and moderately frustrated by several antiferromagnetic couplings that manifest themselves in the reduced N\'eel temperature of the material. Pressure tuning of the brochantite structure keeps its monoclinic symmetry unchanged and leads to the growth of antiferromagnetic $J$ with the rate of 3.2\,K/GPa, although this trend is primarily caused by the enhanced ferromagnetic couplings $J_2$. Our results show that the nature of magnetic couplings in brochantite and in other layered Cu$^{2+}$ minerals is controlled by the size of the lattice translation along their structural chains and by the extent of the layer buckling.

cond-mat.str-el↗
arXiv2025

Pressure-induced strange metal phase in a metallic kagome ferromagnet

Strange metallicity with $T$-linear electrical resistance preceding high-$T_c$ superconductivity remains an enigmatic, yet crucial, signature of correlation physics. Using electrical transport and magnetization measurements up to 50 GPa, we show that such a strange-metal phase is formed in pressurized kagome ferromagnet CrNiAs. In contrast to other kagome materials, a linear suppression of the Curie temperature is found, with the ferromagnetic quantum critical point at $p_{\rm{c}} \approx 12.5$ GPa. Remarkably, from $p_{\rm{c}}$ up to the highest measured pressure, characteristic strange-metal behavior is observed, whereas magnetic field reinstates the Fermi liquid. Electronic structure calculations reveal robust weakly dispersive bands persisting unchanged beyond $p_{\rm{c}}$, possibly at the origin of the $T$-linear electrical resistance. This establishes pressurized kagome ferromagnets as an intriguing platform for strange-metal behavior.

cond-mat.str-el↗
arXiv2025

A Liouville-type theorem for Finslerian exponentially harmonic functions

In this manuscript, we investigate the exponentially harmonic equation on noncompact forward complete Finsler metric measure spaces. We demonstrate that this Finslerian equation represents a critical point of an exponential energy functional. Furthermore, we establish that any bounded solution to this equation is constant, provided that the mixed weighted Ricci curvature is nonnegative and certain additional non-Riemannian tensors are bounded.

math.DG↗