arXiv subjects

Le Liu

Publications and source records attributed to Le Liu.

At least 19 recordsLinked to original sources

arXiv2026

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formulation. While deterministic graph tools are invariant to such shifts, extracting topological structures from noisy text is highly fragile for LLMs, which often overfit to surface patterns. Moreover, mitigating these parsing failures via multi-agent systems incurs prohibitive latency. To address this, we propose GRAIN, a single-agent framework optimized via reinforcement learning. GRAIN models reasoning as a semantic parsing and tool-execution pipeline, guided by a Structure Invariance Reward. By validating extracted intermediate graphs against ground-truth topologies, this reward forces the LLM to learn robust text-to-structure mappings rather than memorizing linguistic artifacts. We also introduce GRIT, a benchmark evaluating sensitivity to such linguistic shifts. GRAIN outperforms multi-agent baselines by 16.45\% in accuracy with approximately 24\% lower latency. Furthermore, it demonstrates superior structural generalization, halving the out-of-distribution (OOD) gap of SFT models (from 15.77\% to 7.80\%) and maintaining robustness on large-scale graphs beyond the training distribution.

cs.AI↗

arXiv2026

SceneReGen: Generative Reconstruction of 3D Scenes from a Single Image

Single-image 3D scene reconstruction must complete partially observed objects and place them coherently in a shared observation-aligned scene frame. Object-level generative priors offer strong completion ability, but their centered, scale-normalized outputs are typically expressed in an object frame, creating a fundamental representation gap between object generation and scene reconstruction. We introduce SceneReGen, a generative reconstruction framework that reinterprets scene reconstruction as the generation and assembly of complete object assets in a shared observation-aligned scene frame. SceneReGen addresses the generation-reconstruction gap through selective pose factorization: each object's observed orientation is encoded directly in the generated mesh, while translation and scale are estimated from instance-level and global scene evidence. Given a scene image and instance masks, a geometry encoder extracts dense cues; learnable shape queries condition a pretrained DiT-based 3D generator to produce complete meshes in their observed orientations, while position queries fuse object and scene features to assemble them in the shared frame. On the 3D-FUTURE evaluation subset, SceneReGen achieves the best scene-level CD, scene-level F-Score, and 3D bounding-box IoU among the evaluated methods, ties the best object-level CD, and ranks second in object-level F-Score. Qualitative outputs in autonomous-driving and embodied-AI scenes further illustrate the potential of asset-centric reconstruction beyond indoor furniture.

cs.CV↗

arXiv2026

EGL-SCA: Structural Credit Assignment for Co-Evolving Instructions and Tools in Graph Reasoning Agents

Graph reasoning agents operating from natural-language inputs must solve a coupled problem: they must reconstruct a structured graph instance from text, decide whether existing computational assets are sufficient, interact with tools under a strict execution protocol, and satisfy an external verifier that checks structured correctness rather than textual plausibility. Existing approaches usually improve either the instruction side or the tool side in isolation, which leaves unclear what should be updated after failure. We propose EGL-SCA, a verifier-centric dual-space framework that models a graph reasoning agent using two collaborative components: an instruction-side policy space for reasoning strategies, and a tool-side program space for executable algorithmic tools. Our central mechanism is structural credit assignment, which maps trajectory evidence to conditional updates, precisely routing failures to either prompt optimization or tool synthesis and repair. To provide sufficient learning signals for dual-space adaptation, we introduce a training distribution stratified by task family, coupled with a Pareto-style retention strategy to balance success, generality, and parsimony. Experiments on four graph reasoning benchmarks show that EGL-SCA achieves a state-of-the-art 92.0\% average success rate. By effectively co-evolving instructions and tools, our framework significantly outperforms both pure-prompting and fixed-toolbox baselines.

cs.AI↗

arXiv2026

Large magnetoresistance and weak-antilocalization in the nodal-line semimetal VP2

After growing successfully high quality VP$_2$ single crystals, we studied systematically their longitudinal $\rho_{xx}(T)$ and Hall resistivity $\rho_{yx}(T)$ at various magnetic fields, combining the electronic band and Fermi surface (FS) calculations. Band calculations reveal that VP$_2$ is a type-II nodal-line semimetal, evidenced by the Hall resistivity measurements. It is found that the magnetoresistance (MR) at higher magnetic fields exhibits a linear behavior and does not show any sign of saturation, reaching 170\% at 40 K up to 9 T, which is determined by the intrinsic electronic structure and dominated by the Lorenz force, demonstrated by the resistivity anisotropy measurements and the numerical simulations. We also found that the existence of small amount magnetic impurities (V$^{4+}$, $S=1/2$, 2.24\%) results in Kondo effect emerging in $\rho_{xx}(T)$, the conductivity at lower magnetic fields exhibits a typical weak anti-localization (WAL) behavior. These results illustrate that VP$_2$ is a platform to study the electronic transport properties of a topological material containing magnetic impurities.

cond-mat.str-el↗

arXiv2026

CoDA: Towards Effective Cross-domain Knowledge Transfer via CoT-guided Domain Adaptation

Large language models (LLMs) have achieved substantial advances in logical reasoning, yet they continue to lag behind human-level performance. In-context learning provides a viable solution that boosts the model's performance via prompting its input with expert-curated, in-domain exemplars. However, in many real-world, expertise-scarce domains, such as low-resource scientific disciplines, emerging biomedical subfields, or niche legal jurisdictions, such high-quality in-domain demonstrations are inherently limited or entirely unavailable, thereby constraining the general applicability of these approaches. To mitigate this limitation, recent efforts have explored the retrieval of cross-domain samples as surrogate in-context demonstrations. Nevertheless, the resulting gains remain modest. This is largely attributable to the pronounced domain shift between source and target distributions, which impedes the model's ability to effectively identify and exploit underlying shared structures or latent reasoning patterns. Consequently, when relying solely on raw textual prompting, LLMs struggle to abstract and transfer such cross-domain knowledge in a robust and systematic manner. To address these issues, we propose CoDA, which employs a lightweight adapter to directly intervene in the intermediate hidden states. By combining feature-based distillation of CoT-enriched reference representations with Maximum Mean Discrepancy (MMD) for kernelized distribution matching, our method aligns the latent reasoning representation of the source and target domains. Extensive experimental results on multiple logical reasoning tasks across various model families validate the efficacy of CoDA by significantly outperforming the previous state-of-the-art baselines by a large margin.

cs.AI↗

arXiv2026

Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval

Large language models (LLMs) have made notable progress in logical reasoning, yet still fall short of human-level performance. Current boosting strategies rely on expert-crafted in-domain demonstrations, limiting their applicability in expertise-scarce domains, such as specialized mathematical reasoning, formal logic, or legal analysis. In this work, we demonstrate the feasibility of leveraging cross-domain demonstrating examples to boost the LLMs' reasoning performance. Despite substantial domain differences, many reusable implicit logical structures are shared across domains. In order to effectively retrieve cross-domain examples for unseen domains under investigation, in this work, we further propose an effective retrieval method, called domain-invariant neurons-based retrieval (\textbf{DIN-Retrieval}). Concisely, DIN-Retrieval first summarizes a hidden representation that is universal across different domains. Then, during the inference stage, we use the DIN vector to retrieve structurally compatible cross-domain demonstrations for the in-context learning. Experimental results in multiple settings for the transfer of mathematical and logical reasoning demonstrate that our method achieves an average improvement of 1.8 over the state-of-the-art methods \footnote{Our implementation is available at https://github.com/Leon221220/DIN-Retrieval}.

cs.AI↗

arXiv2026

Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning

Despite its success, existing in-context learning (ICL) relies on in-domain expert demonstrations, limiting its applicability when expert annotations are scarce. We posit that different domains may share underlying reasoning structures, enabling source-domain demonstrations to improve target-domain inference despite semantic mismatch. To test this hypothesis, we conduct a comprehensive empirical study of different retrieval methods to validate the feasibility of achieving cross-domain knowledge transfer under the in-context learning setting. Our results demonstrate conditional positive transfer in cross-domain ICL. We identify a clear example absorption threshold: beyond it, positive transfer becomes more likely, and additional demonstrations yield larger gains. Further analysis suggests that these gains stem from reasoning structure repair by retrieved cross-domain examples, rather than semantic cues. Overall, our study validates the feasibility of leveraging cross-domain knowledge transfer to improve cross-domain ICL performance, motivating the community to explore designing more effective retrieval approaches for this novel direction.\footnote{Our implementation is available at https://github.com/littlelaska/ICL-TF4LR}

cs.AI↗

arXiv2026

LSA: A Long-Short-term Aspect Interest Transformer for Aspect-Based Recommendation

Aspect-based recommendation methods extract aspect terms from reviews, such as price, to model fine-grained user preferences on items, making them a critical approach in personalized recommender systems. Existing methods utilize graphs to represent the relationships among users, items, and aspect terms, modeling user preferences based on graph neural networks. However, they overlook the dynamic nature of user interests - users may temporarily focus on aspects they previously paid little attention to - making it difficult to assign accurate weights to aspect terms for each user-item interaction. In this paper, we propose a long-short-term aspect interest Transformer (LSA) for aspect-based recommendation, which effectively captures the dynamic nature of user preferences by integrating both long-term and short-term aspect interests. Specifically, the short-term interests model the temporal changes in the importance of recently interacted aspect terms, while the long-term interests consider global behavioral patterns, including aspects that users have not interacted with recently. Finally, LSA combines long- and short-term interests to evaluate the importance of aspects within the union of user and item aspect neighbors, therefore accurately assigns aspect weights for each user-item interaction. Experiments conducted on four real-world datasets demonstrate that LSA improves MSE by 2.55% on average over the best baseline.

cs.IR↗

arXiv2026

Cooperative Emission from Quantum Emitters in Hexagonal Boron Nitride Layers

Collective light emission from many-body quantum systems is a cornerstone of quantum optics, yet its implementation in solid-state platforms operating under ambient conditions remains highly challenging. Large-bandgap van der Waals materials such as hexagonal boron nitride (hBN) host stable room-temperature single-photon emitters with narrow linewidths across a broad spectral range. However, cooperative radiative effects in this system have not been previously explored. Here we demonstrate collective emission from quantum-emitter ensembles in hBN layers when the emitters are nearly indistinguishable and positioned within a sub-wavelength proximity. Using confocal microscopy and a Hanbury Brown-Twiss (HBT) configuration, we identify both isolated emitters and ensembles activated by localized electron-beam irradiation. Time-resolved photoluminescence measurements reveal a superlinear intensity enhancement and a pronounced acceleration of the radiative decay in tightly confined ensembles, with lifetimes approaching the temporal resolution of our experimental system (about 500 ps), compared to approximately 1.85 ns for single emitters or large, spatially extended ensembles. Complementary second-order photon-correlation measurements exhibit sub-Poissonian antidip consistent with emission from a few indistinguishable emitters. The simultaneous observation of lifetime shortening and enhanced emission provides direct evidence of cooperative emission at room temperature, achieved without optical cavities or cryogenic cooling. These results establish optically active defect ensembles in hBN as a scalable solid-state platform for engineered collective quantum optics in two-dimensional materials, opening avenues toward ultrabright superradiant light sources and nonclassical photonic states for quantum technologies.

quant-ph↗

arXiv2026

Fairness risk and its privacy-enabled solution in AI-driven robotic applications

Complex decision-making by autonomous machines and algorithms could underpin the foundations of future society. Generative AI is emerging as a powerful engine for such transitions. However, we show that Generative AI-driven developments pose a critical pitfall: fairness concerns. In robotic applications, although intuitions about fairness are common, a precise and implementable definition that captures user utility and inherent data randomness is missing. Here we provide a utility-aware fairness metric for robotic decision making and analyze fairness jointly with user-data privacy, deriving conditions under which privacy budgets govern fairness metrics. This yields a unified framework that formalizes and quantifies fairness and its interplay with privacy, which is tested in a robot navigation task. In view of the fact that under legal requirements, most robotic systems will enforce user privacy, the approach shows surprisingly that such privacy budgets can be jointly used to meet fairness targets. Addressing fairness concerns in the creative combined consideration of privacy is a step towards ethical use of AI and strengthens trust in autonomous robots deployed in everyday environments.

cs.RO↗

arXiv2025

A Randomized Scheduling Framework for Privacy-Preserving Multi-robot Rendezvous given Prior Information

Privacy has become a critical concern in modern multi-robot systems, driven by both ethical considerations and operational constraints. As a result, growing attention has been directed toward privacy-preserving coordination in dynamical multi-robot systems. This work introduces a randomized scheduling mechanism for privacy-preserving robot rendezvous. The proposed approach achieves improved privacy even at lower communication rates, where privacy is quantified via pointwise maximal leakage. We show that lower transmission rates provide stronger privacy guarantees and prove that rendezvous is still achieved under the randomized scheduling mechanism. Numerical simulations are provided to demonstrate the effectiveness of the method.

eess.SY↗

arXiv2025

Privacy protection under the exposure of systems' prior information

For systems whose states implicate sensitive information, their privacy is of great concern. While notions like differential privacy have been successfully introduced to dynamical systems, it is still unclear how a system's privacy can be properly protected when facing the challenging yet frequently-encountered scenario where an adversary possesses prior knowledge, e.g., the steady state, of the system. This paper presents a new systematic approach to protect the privacy of a discrete-time linear time-invariant system against adversaries knowledgeable of the system's prior information. We employ a tailored \emph{pointwise maximal leakage (PML) privacy} criterion. PML characterizes the worst-case privacy performance, which is sharply different from that of the better-known mutual-information privacy. We derive necessary and sufficient conditions for PML privacy and construct tractable design procedures. Furthermore, our analysis leads to insight into how PML privacy, differential privacy, and mutual-information privacy are related. We then revisit Kalman filters from the perspective of PML privacy and derive a lower bound on the steady-state estimation-error covariance in terms of the PML parameters. Finally, the derived results are illustrated in a case study of privacy protection for distributed sensing in smart buildings.

eess.SY↗

arXiv2025

Graph Neural AI with Temporal Dynamics for Comprehensive Anomaly Detection in Microservices

This study addresses the problem of anomaly detection and root cause tracing in microservice architectures and proposes a unified framework that combines graph neural networks with temporal modeling. The microservice call chain is abstracted as a directed graph, where multidimensional features of nodes and edges are used to construct a service topology representation, and graph convolution is applied to aggregate features across nodes and model dependencies, capturing complex structural relationships among services. On this basis, gated recurrent units are introduced to model the temporal evolution of call chains, and multi-layer stacking and concatenation operations are used to jointly obtain structural and temporal representations, improving the ability to identify anomaly patterns. Furthermore, anomaly scoring functions at both the node and path levels are defined to achieve unified modeling from local anomaly detection to global call chain tracing, which enables the identification of abnormal service nodes and the reconstruction of potential anomaly propagation paths. Sensitivity experiments are then designed from multiple dimensions, including hyperparameters, environmental disturbances, and data distribution, to evaluate the framework, and results show that it outperforms baseline methods in key metrics such as AUC, ACC, Recall, and F1-Score, maintaining high accuracy and stability under dynamic topologies and complex environments. This research not only provides a new technical path for anomaly detection in microservices but also lays a methodological foundation for intelligent operations in distributed systems.

cs.LG↗

arXiv2025

Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales

Chain-of-thought (CoT) distillation aims to enhance small language models' (SLMs) reasoning by transferring multi-step reasoning capability from the larger teacher models. However, existing work underestimates rationale quality, focusing primarily on data quantity, which may transfer noisy or incorrect information to the student model. To address the above issues, we proposed \textbf{M}odel-\textbf{O}riented \textbf{R}ationale \textbf{S}election \textbf{D}istillation (MoRSD), which can discern and select high quality rationales for distillation to improve performance further. We further propose a Rationale Difficulty (RD) metric to measure the ability of the student model to generate the correct answer under a given rationale. Compared to the baseline, we achieved 4.6$\%$ average improvement on seven datasets over three tasks, using fewer rationales by controlling their accuracy, diversity, and difficulty. Our results reveal that a small portion of the high quality rationales can enhance the reasoning ability of student models than the entire dataset. Our method promises to be a possible solution for efficient CoT distillation. Our code will be released in https://github.com/Leon221220/MoRSD.

cs.CL↗

arXiv2025

From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement

Chain-of-Thought (CoT) reasoning improves performance on complex tasks but introduces significant inference latency due to verbosity. We propose Multiround Adaptive Chain-of-Thought Compression (MACC), a framework that leverages the token elasticity phenomenon--where overly small token budgets can paradoxically increase output length--to progressively compress CoTs via multiround refinement. This adaptive strategy allows MACC to determine the optimal compression depth for each input. Our method achieves an average accuracy improvement of 5.6 percent over state-of-the-art baselines, while also reducing CoT length by an average of 47 tokens and significantly lowering latency. Furthermore, we show that test-time performance--accuracy and token length--can be reliably predicted using interpretable features like perplexity and compression rate on the training set. Evaluated across different models, our method enables efficient model selection and forecasting without repeated fine-tuning, demonstrating that CoT compression is both effective and predictable. Our code will be released in https://github.com/Leon221220/MACC.

cs.CL↗

arXiv2025

Topological nontrivial berry phase in altermagnet CrSb

The study of topological properties in magnetic materials has long been one of the forefront research areas in condensed matter physics. CrSb, as a prototypical candidate material for altermagnetism, has attracted significant attention due to its unique magnetic properties. This system provides a novel platform for exploring the intrinsic relationship between altermagnetic order and exotic topological states. In this study, we combine systematic electrical transport experiments with first-principles calculations to investigate the possible realization mechanisms of topological semimetal states in CrSb and their manifestations in quantum transport phenomena. Our high field magneto-transport measurements reveal that the magnetoresistance of CrSb exhibits no sign of saturation up to 35 T, following a distinct power-law dependence with an exponent of 1.48. The nonlinear Hall resistivity further indicates a multiband charge transport mechanism. Under high magnetic fields, we observe pronounced Shubnikov-de Haas (SdH) quantum oscillations and discernible Zeeman-effect-induced band splitting at 1.6 K. Systematic Fermi surface and band calculations combined with Berry phase analysis confirm the nontrivial topological character of this material (with a Berry phase approaching {\pi}). These findings not only provide crucial experimental evidence for understanding the electronic structure of CrSb, but also establish an important foundation for investigating topological quantum states in altermagnets.

cond-mat.mtrl-sci↗

arXiv2025

Observation of Integer and Fractional Chern insulators in high Chern number flatbands

Moir\'e flatbands with high Chern numbers (C>1) offer opportunities to study the fractional quantum anomalous Hall effects that go beyond the Landau level paradigm with C=1, which remain unexplored yet. Here, we target the novel topological phases in high Chern number flatbands by designing a new moir\'e system, i.e., twisted rhombohedral trilayer-bilayer graphene. We observe quantized anomalous Hall effects (QAH) with C = 3 at v = 1 and v = 3, demonstrating the high Chern number nature of the flat band from continuum calculations. By fractionally filling the flat band, we observe QAH with C = 2 at even-denominator fractional filling v = 3/2, as well as QAH with C = 3 continuously from v = 1 to the even-denominator v = 3/2 at zero magnetic fields. Most importantly, we observe, for the first time, evidence of an FCI with C = -6/5 at v = 12/5, corresponding to 2/5 filling of a high Chern number flat band with C = -3, verified by both Streda formula analysis and a fractionally QAH. It is also worth noting that Streda formula analysis reveals a signature of another FCI with C = -3/2 at v = 5/2 under finite magnetic fields. Our results demonstrate the tRTBG, which can be naturally extended to other twisted graphene moir\'e superlattices based on rhombohedral graphene multilayers, as a novel platform for hosting unconventional high Chern number correlated topology in the ultra-strong correlated regime that is beyond the paradigm of fractional phases with C < 1.

cond-mat.str-el↗

arXiv2025

Single Photon Emitters in Ultra-Thin Hexagonal Boron Nitride Layers

Single-photon emitters (SPE) in hexagonal boron nitride (h-BN) are promising for applications ranging from single-photon sources to quantum sensors. Previous studies exclusively focused on the generation and characterization of SPEs in relatively thick h-BN layers ($\geq$ 30 nm). However, for electrical and magnetic sensing applications, the thickness of the h-BN limits the attainable spatial resolution. Here, we report the observation of blue-wavelength emitters (B-centers) activated by electron beam irradiation in ultra-thin ($\simeq$ 3 nm) h-BN. These SPEs in ultra-thin flakes exhibit reduced brightness, broader zero-phonon line, and enhanced photobleaching. Remarkably, upon encapsulation in thicker h-BN, we restore their brightness, narrow linewidth 230$\mu$eV at 5K, resolution limited), suppress photobleaching, and confirm single-photon emission with $ g^{(2)}(0) < 0.4$ at room temperature. The possibility of generating SPEs in a few-layer h-BN and their subsequent incorporation into a van der Waals heterostructure paves the way for achieving quantum sensing with unprecedented nanometer-scale spatial resolution.

cond-mat.mes-hall↗