arXiv subjects

Chuang Li

Publications and source records attributed to Chuang Li.

At least 19 recordsLinked to original sources

arXiv2026

Truly Solving the Gibbs Paradox by Local Free Space and Collision Potential

This paper argues that the Gibbs paradox can be resolved without using the concept of identical particles in quantum mechanics. The molecules in different regions of the gas can be distinguished, so there is no need to introduce the N! factor. For each molecule, the volume of its free movement space is local at every instant. Moreover, collisions are the primary way of interaction between gas molecules and the fundamental driving force for reaching equilibrium. The potential energy during collisions cannot be ignored. Based on the local free space assumption and collision potential energy, this paper uses the canonical ensemble method to rederive the entropy increment formula for gas mixing. It includes parameters such as molecular mass, effective radius, and collision characteristic time, which vary with the type of gas molecules. This solves the problem that the entropy increment of gas mixing is independent of gas properties, that is, it truly resolves the Gibbs paradox instead of providing a new conceptual explanation.

cond-mat.stat-mech↗

arXiv2026

Quantum Geometry-Driven RKKY: From Flat to Dispersive Bands

In flat-band systems, quantum metric bounds physical observables like superfluid weight and coherence length, suggesting a single geometric scale for spatial correlations. Here, we show that the RKKY exchange in an isolated filled flat band can violate this expectation. With the intraband channel absent, the exchange proceeds via virtual interband transitions across the gap; the kernel becomes the inverse-gap-weighted trace product of the real-space flat-band projector and empty-band projectors. Because the corresponding momentum-space projectors are analytic, the kernel decays exponentially, with a decay length $\xi_\text{RKKY}$ set by the closest singularities of the analytically continued projectors in the complex momentum plane. Thus, this length depends on both the flat-band geometry and the band gap. Applying this formalism to Chern flat-band systems, we find that for small gaps, $\xi_\text{RKKY}$ depends non-monotonically on the quantum metric length: it first decreases, then increases, revealing that stronger quantum geometry can shorten the magnetic exchange range. Upon restoring dispersion, a nontrivial inversion representation can force the overlap between Bloch states at antipodal Fermi points to vanish under gate tuning, producing a $1/R^3$ RKKY tail instead of the conventional $1/R^2$---a geometric selection effect.

cond-mat.str-el↗

arXiv2026

Photon-Atom Granularity Noise Thermometry

We propose granularity noise thermometry (GNT), a fluctuation-based optical thermometry scheme that exploits the intrinsic fluctuations of susceptibility arising from atomic discreteness. The power spectral density of transmitted light exhibits an excess noise above the shot-noise limit that scales linearly with the photon-to-atom ratio $\mathcal{R}$. Consequently, varying the incident power (hence $\mathcal{R}$) yields the slope $\mathcal{K}$ of this linear scaling, which directly encodes the temperature. Closed-form expressions for the polarizability moments are derived via the plasma dispersion function, which yield distinct temperature scalings: $\mathcal{K}\propto P_{\mathrm{v}}(T)/T^2$ for thermal vapors and $\mathcal{K}\propto T^{2}$ for cold atoms. While practical implementation requires careful control of technical noise and system parameters, the present framework provides a noise-based pathway for optical thermometry using atomic ensembles.

physics.atom-ph↗

arXiv2026

Divination by Prompt: LLM-Mediated Xuanxue on Chinese Social Media

The rapid proliferation of large language models (LLMs) has produced a striking cultural practice: using conversational AI for divination. This paper offers one of the first systematic studies of LLM-mediated divination in the context of Xuanxue, an internet-native umbrella term for mystical and spiritual practices on Chinese social media. Using a mixed-methods design, we analyze 23000+ posts and comments from Xiaohongshu and conduct 32 semi-structured interviews with users and professional diviners. Users primarily consult LLMs about pragmatic concerns - romantic relationships, careers, exams, and in-game gacha draws - via two intersecting pathways: trend-driven curiosity enabled by viral visibility and zero-cost access, and event-driven anxiety under conditions of uncertainty. A defining feature is collaborative prompt refinement, which turns users into active prompt engineers. Among commenters expressing a clear stance, perceived efficacy skews positive, with "accuracy" often justified through biographical fit and retrospective confirmation, consistent with Barnum and confirmation bias. Users also develop verification practices such as repeated trials and cross-model comparison. Professional diviners, by contrast, portray LLMs as lacking the "spiritual power" required for genuine divination, reflecting both ontological commitments and economic boundary-work. We also show how participants navigate tensions between scientific and metaphysical frames when interpreting AI-generated readings. Situating these findings in anthropological and cognitive-evolutionary theories of divination, we argue that LLM divination preserves core functions of traditional practice while introducing scalability, repeatability, and prompt-driven co-production that reshape how divinatory authority is constructed and evaluated.

cs.CY↗

arXiv2026

Granularity Noise Limit in Atomic-Ensemble-Based Metrology

Conventional noise analysis in atomic-ensemble sensing assumes a continuous-medium approximation, thereby treating the atomic system as a deterministic dielectric. Here, we demonstrate that this assumption breaks down due to the discrete, particulate nature of the ensemble, giving rise to an intrinsic "atomic granularity noise" (AGN) that fundamentally competes with the optical measurement noise (OMN, typically photon shot noise). By introducing a discrete-atom statistical framework, we derive a unified noise-scaling law governed by a single dimensionless resource ratio, $\mathcal{R} = \bar{N}_{\mathrm{ph}}/\bar{N}_{\mathrm{at}}$ at (the photon-to-atom flux ratio). This law predicts a continuous crossover from an OMN-limited regime to an AGN-limited regime. Crucially, our results reveal a counter-intuitive constraint for sensor optimization: increasing optical probe power -- standard practice to mitigate OMN -- can paradoxically degrade sensitivity by driving the system into the AGN-dominated regime. Furthermore, we identify a critical resource threshold, $\mathcal{R}_{\mathrm{crit}}$, beyond which quantum-enhanced metrology using non-classical light fails to improve sensitivity, as it becomes limited by the AGN.

quant-ph↗

arXiv2026

Visualizing spin-polarization of an altermagnet KV$_2$Se$_2$O via spin-selective tunneling

Altermagnetism, a recently identified magnetic phase that combines vanishing net magnetization with momentum-dependent spin splitting, challenges the conventional dichotomy between ferromagnets and antiferromagnets. While several candidate materials have been proposed, direct experimental evidence linking crystal symmetry, electronic structure and d-wave spin polarization remains scarce. Here we report the visualization of a metallic d-wave altermagnet in KV2Se2O. Through spin-selective scanning tunneling microscopy powered by a topological insulator tip, we uncover symmetry-protected momentum-dependent spin splitting that follows a characteristic d-wave form factor. Our results establish KV2Se2O as a tunable platform to study the interplay between spin-valley locking, Fermi-surface instability and unconventional magnetism, and open a pathway toward symmetry-engineered spintronics without net magnetization.

cond-mat.mtrl-sci↗

arXiv2026

Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance

Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models-even when the guidance is correct and problem-relevant. We show that this instability arises from a previously underexplored gap between strategy usage-whether a reasoning strategy appears in successful solutions-and strategy executability-whether the strategy remains effective when instantiated as guidance for a target model. Through a controlled analysis of paired human-written and model-generated solutions, we identify a systematic dissociation between usage and executability: human- and model-derived strategies differ in structured, domain-dependent ways, leading to complementary strengths and consistent source-dependent reversals under guidance. Building on this diagnosis, we propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability by selectively retrieving and combining strategies using empirical, multi-route, source-aware signals. Across multiple mathematical reasoning benchmarks, SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance, improving accuracy by up to $+13$ points on AIME25 and $+5$ points on Apex for compact reasoning models. Code and benchmark are publicly available at: https://github.com/lwd17/strategy-execute-pipeline.

cs.AI↗

arXiv2026

Altermagnetic Even-Odd Effects in CsV$_2$Te$_2$O Josephson Junctions

The interplay between conventional superconductivity and unconventional magnetism offers an exciting platform for realizing exotic superconducting phenomena. Here, we investigate Josephson effects in planar and vertical junctions based on CsV$_2$Te$_2$O-family materials, which host hidden $d$-wave altermagnetism with G-type antiferromagnetic order. In monolayer-based planar junctions, the quasi-1D, nearly flat, spin-polarized bands of the altermagnet, when coupled to $s$-wave superconductors, produce a \textit{fully} spin-polarized supercurrent with strong directional anisotropy -- a spin-selective Josephson effect. In multilayers, we uncover an \textit{altermagnetic even-odd effect}: spin-polarized supercurrents persist only in odd-layer planar junctions but cancel exactly in even layers. Thus, layer parity acts as a switch for spin-polarized supercurrent. In vertical junctions, odd-layer barriers enhance equal-spin triplet transport while even layers favor opposite-spin transport, yielding a robust period-two oscillation in the total supercurrent with layer number. These layer-parity-dependent responses represent a general even-odd effect in hidden altermagnets, applicable to diverse magnetic and transport phenomena.

cond-mat.supr-con↗

arXiv2025

Field-free Josephson diode and tunable $\phi_0$-junction in chiral kagome antiferromagnets

The recent realization of superconducting proximity effect in chiral antiferromagnets (cAFMs) opens a new route to nonreciprocal superconducting transport of fundamental interest and practical importance. Using microscopic modeling and symmetry analysis, we show that Josephson junctions formed by conventional $s$-wave superconductors (SCs) and cAFMs on the kagome lattice exhibit Josephson diode effects and anomalous phase shifts ($\phi_0$-junction state) when space inversion $\mathcal{I}$, time-reversal $\mathcal{T}$, and combined mirror-time-reversal $\mathcal{TM}_z$ symmetries are simultaneously broken. We propose two setups to realize these phenomena and achieve high diode efficiency. (i) An SC/cAFM/SC junction with spin-orbit coupling, which enables a field-free diode effect with a robust tunable $\phi_0$-junction state. (ii) An SC/cAFM/cAFM$^\prime$/SC junction, where two cAFM layers with different in-plane order orientations, under an out-of-plane Zeeman exchange field, produces significant diode effect and anomalous phase shifts. These results establish a direct link between $\mathcal{TM}_z$ symmetry breaking and nonreciprocal superconductivity, suggesting cAFMs as versatile platforms for symmetry-engineered Josephson diodes and tunable $\phi_0$-junctions.

cond-mat.supr-con↗

arXiv2025

Inter-orbital spin-triplet superconductivity from altermagnetic fluctuations

Altermagnetic (AM) fluctuations are a new class of collinear spin fluctuations whose role in mediating superconductivity faces a fundamental tension: their $\Gamma$-point peak favors intra-orbital spin-triplet pairing, while their spin compensation favors inter-orbital singlets. Here, we demonstrate that inversion-symmetry-broken AM fluctuations generically resolve this competition in favor of spin-triplet pairing. As a proof of concept, we study a minimal two-orbital model with two van Hove singularities. The broken inversion symmetry induces momentum-orbital locking: the same orbital dominates at opposite momenta, enhancing the triplet channel. Crucially, a subdominant fluctuation channel arising from inter-van-Hove nesting provides an internal Josephson coupling that locks the phase difference between triplet pairs on different orbitals. We find this coupling changes sign ($+$ to $-$) upon a crossover from AM-dominant to ferromagnetic-dominant fluctuations. The resulting $\pi$-phase difference manifests as a $\tau_z$-type order parameter, $c_{k,1\uparrow}c_{-k,1\uparrow} - c_{k,2\uparrow}c_{-k,2\uparrow}$. Although intra-orbital in the original basis, its orbital-nontrivial character, as manifested by its equivalence to inter-orbital pairing under rotation, defines a general \textit{inter-orbital spin-triplet superconductivity}. This state is distinct from the $\tau_0$-triplet pairing mediated by ferromagnetic fluctuations, as evidenced by the canceled intra-orbital supercurrent in a Josephson junction between them.

cond-mat.str-el↗

arXiv2025

From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs

Recent reasoning LLMs (RLMs), especially those trained with verifier-based reinforcement learning, often perform worse with few-shot CoT than with direct answering. We revisit this paradox using high-quality reasoning traces from DeepSeek-R1 as demonstrations and find that adding more exemplars consistently degrades accuracy, even when demonstrations are optimal. A detailed analysis reveals two mechanisms behind this decline: (i) semantic misguidance, where high textual similarity leads the model to treat the target as the same as the exemplar and to copy intermediate steps verbatim; and (ii) strategy transfer failure, where the model struggles to extract useful reasoning strategies and apply them to target questions. Guided by these, we introduce Insight-to-Solve (I2S), a sequential test-time procedure that turns demonstrations into explicit, reusable insights and derives a target-specific reasoning trace; optionally, the reasoning is self-refined for coherence and correctness (I2S+). Extensive experiments on diverse benchmarks show that I2S and I2S+ consistently outperform both direct answering and test-time scaling baselines across open- and closed-source models. Even for GPT models, our method helps: on AIME'25, GPT-4.1 rises by +14.0%, and o1-mini improves by +2.7% on AIME and +1.7% on GPQA, indicating that in-context demonstrations can be harnessed effectively via insight-refine-solve framework.

cs.CL↗

arXiv2025

Spin-Polarized Josephson Supercurrent in Nodeless Altermagnets

Long-range propagation of equal-spin triplet Cooper pairs typically occurs in ferromagnet/$s$-wave superconductor junctions, where net magnetization plays a crucial role. Here, we propose a fundamentally different scenario in which Josephson supercurrents mediated exclusively by spin-triplet pairings emerge in systems with \textit{zero} net magnetization. We identify collinear altermagnets, particularly a subclass termed nodeless altermagnets, as ideal platforms to realize this phenomenon. These materials host spin-split Fermi surfaces that do not intersect altermagnetic nodal lines and support maximal spin-valley polarization, yielding fully spin-polarized electronic states at each valley. Consequently, Josephson junctions based on nodeless altermagnets sustain supercurrents solely through spin-polarized triplet pairing correlations, simultaneously contributed by spin-up Cooper pairs from one valley and spin-down Cooper pairs from the other. Furthermore, controlling the relative local inversion-symmetry breaking at the two interfaces enables a robust 0--$\pi$ transition without fine tuning, while adjusting the junction orientation allows a crossover between pure triplet and mixed singlet-triplet states. Our work thus establishes nodeless altermagnets as a unique platform for altermagnetic superconductors with magnetization-free spin-polarized supercurrents.

cond-mat.supr-con↗

arXiv2025

SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) systems require Large Language Models (LLMs) to generate responses that are faithful to the retrieved context. However, faithfulness hallucination remains a critical challenge, as existing methods often require costly supervision and post-training or significant inference burdens. To overcome these limitations, we introduce Self-Supervised Faithfulness Optimization (SSFO), the first self-supervised alignment approach for enhancing RAG faithfulness. SSFO constructs preference data pairs by contrasting the model's outputs generated with and without the context. Leveraging Direct Preference Optimization (DPO), SSFO aligns model faithfulness without incurring labeling costs or additional inference burden. We theoretically and empirically demonstrate that SSFO leverages a benign form of \emph{likelihood displacement}, transferring probability mass from parametric-based tokens to context-aligned tokens. Based on this insight, we propose a modified DPO loss function to encourage likelihood displacement. Comprehensive evaluations show that SSFO significantly outperforms existing methods, achieving state-of-the-art faithfulness on multiple context-based question-answering datasets. Notably, SSFO exhibits strong generalization, improving cross-lingual faithfulness and preserving general instruction-following capabilities. We release our code and model at the anonymous link: https://github.com/chkwy/SSFO

cs.CL↗

arXiv2025

Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-based Reranking

We tackle the challenge of integrating large language models (LLMs) with external recommender systems to enhance domain expertise in conversational recommendation (CRS). Current LLM-based CRS approaches primarily rely on zero/few-shot methods for generating item recommendations based on user queries, but this method faces two significant challenges: (1) without domain-specific adaptation, LLMs frequently recommend items not in the target item space, resulting in low recommendation accuracy; and (2) LLMs largely rely on dialogue context for content-based recommendations, neglecting the collaborative relationships among item sequences. To address these limitations, we introduce the CARE (Contextual Adaptation of Recommenders) framework. CARE (a) integrates external recommender systems as domain experts, producing candidate items through entity-level insights, and (b) customizes LLMs as rerankers to enhance the accuracy by leveraging contextual information. Our results demonstrate that incorporating CARE framework significantly enhances recommendation accuracy of LLMs by an average of 54% and 25% for ReDial and INSPIRED datasets. The most effective CARE strategy involves LLMs selecting and reranking candidate items that external recommenders provide based on contextual insights.

cs.IR↗

arXiv2025

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer parameters than several competitors, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance research in reasoning and agentic AI systems. Code, models, and more information are available at https://github.com/zai-org/GLM-4.5.

cs.CL↗

arXiv2025

Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

q-fin.CP↗

arXiv2025

FLTG: Byzantine-Robust Federated Learning via Angle-Based Defense and Non-IID-Aware Weighting

Byzantine attacks during model aggregation in Federated Learning (FL) threaten training integrity by manipulating malicious clients' updates. Existing methods struggle with limited robustness under high malicious client ratios and sensitivity to non-i.i.d. data, leading to degraded accuracy. To address this, we propose FLTG, a novel aggregation algorithm integrating angle-based defense and dynamic reference selection. FLTG first filters clients via ReLU-clipped cosine similarity, leveraging a server-side clean dataset to exclude misaligned updates. It then dynamically selects a reference client based on the prior global model to mitigate non-i.i.d. bias, assigns aggregation weights inversely proportional to angular deviations, and normalizes update magnitudes to suppress malicious scaling. Evaluations across datasets of varying complexity under five classic attacks demonstrate FLTG's superiority over state-of-the-art methods under extreme bias scenarios and sustains robustness with a higher proportion(over 50%) of malicious clients.

cs.CR↗

arXiv2025

Vortex States and Coherence Lengths in Flat-Band Superconductors

Superconductivity in flat-band systems, governed by quantum metric of Bloch states rather than the BCS framework, exhibits unique phenomena due to the vanishing electron group velocity. Here, we propose the vortex states and vortex size as direct probes to explore the quantum geometry effects in flat-band superconductors. We show that flat-band vortex bound states are sharply localized near the vortex core, and the energy gap between the lowest two bound states is on the order of the bulk superconducting gap. Both the spatial spread and energy scales of bound states are controlled by the flat-band's quantum metric length. Moreover, the vortex size at zero temperature, set by the quantum metric length, is atomic in scale and independent of interaction strength. Near $T_c$, the vortex size corresponds to the Ginzburg-Landau coherence length, diverges as $\xi\sim \sqrt{T_c/(T_c-T)}\xi_0$, where $\xi_0$ depends linearly on the quantum metric length. Thus, the quantum metric serves as the lower bound for vortex state spread and vortex size. We also introduce perturbations to make the flat band dispersive, and distinguish flat-band vortices from BCS-like vortices. Our results establish vortices as universal probes of quantum geometry in flat-band superconductors.

cond-mat.supr-con↗