arXiv subjects

Weibo Gao

Publications and source records attributed to Weibo Gao.

At least 19 recordsLinked to original sources

arXiv2026

Coexistence of Superconductivity and Chern insulators with |C| $\geq 1$ in twisted rhombohedral bilayer-trilayer graphene

Rhombohedral multilayer graphene (RMG) has been demonstrated as a promising platform for investigating strongly correlated phases of matter. Here, we observe a coexistence of superconductivity and Chern insulators in a twisted rhombohedral bilayer-trilayer graphene moiré superlattice. Superconductivity, evidenced by a sharp transition with a critical temperature of ~180 mK, nonlinear I-V with a BKT temperature of 135 mK, and a signature of phase-sensitive interference patterns, emerges from a symmetry-breaking Fermi surface near v = -2 and D = 0, and it survives at a large in-plane magnetic field up to 2 T, strongly deviating from the Pauli limit. Moreover, we observe a strong modulation of the Ginzburg-Landau coherence length from 467 nm to 145 nm as the in-plane magnetic field increases from 0 to 2 T, whereas the critical current Ic barely changes. Unlike previous studies in RMG, we observe a competition between superconductivity and a correlated insulator at v = -2, monitored by displacement fields and twist angles. Note that we also observe integer Chern insulators with C = 1, 3, and -3 at v = 1/2, 1, and 3, respectively, for large D > 0 in the same superconducting device. The observations establish twisted rhombohedral graphene multilayers as a fascinating system to investigate unconventional superconductivity, high Chern number topology, and their interplay.

cond-mat.mes-hall↗

arXiv2026

Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in LLMs

Large language models (LLMs) are increasingly evaluated for fairness, yet most existing benchmarks audit one sensitive attribute at a time. This single-attribute paradigm can miss intersectional harms, where multiple attributes interact non-additively and produce failures that are invisible under marginal evaluation. We introduce ComFair, a configurable benchmark that converts existing fairness data into 479,066 controlled Dual- and Multi-attribute instances and pairs every augmented instance with its task-equivalent source. Across Recognition, Selection, Continuation, and Conversation, this pairing supports a unified audit of performance or bias-score shifts, how often added attributes introduce new errors or mask existing ones, and model-ranking stability. Experiments on ten LLMs show task- and subgroup-dependent sensitivity: discriminative tasks expose both transition directions even when aggregate changes are modest, generative tasks exhibit larger and directionally asymmetric shifts, and rankings vary with attribute composition. ComFair therefore provides a scalable controlled sensitivity audit---rather than a model of the natural joint distribution of identities---for testing the robustness of fairness-benchmark conclusions beyond single-attribute settings.

cs.AI↗

arXiv2026

Frequency-matching quantum key distribution

Quantum key distribution (QKD) enables information-theoretically secure communication against eavesdropping. However, phase instability remains a challenge across many QKD applications, particularly in schemes such as twin-field QKD and measurement-device-independent QKD. The most dominant source of phase fluctuation arises from the frequency offset between independent lasers. Here we propose a method to address this issue by employing a classical photodiode to compensate for the laser frequency difference. As an application of this method, we implement this technique in a mode-pairing QKD system, achieving an error rate approaching the theoretical limit and surpassing the linear key-rate bound over a fiber distance of 296.8 km. This approach provides a practical solution for frequency matching between independent lasers and can be extended to other fields requiring precise phase stabilization.

quant-ph↗

arXiv2026

WorldFailBench: Do World Models Understand Policy Failures?

Robot policy evaluation informs deployment decisions and policy improvement, but physical testing and simulator preparation require substantial effort. World models offer a complementary route through closed-loop interaction in generated environments. Recent benchmarks have demonstrated their potential as policy evaluators through outcome-based evaluation (i.e., assessing task success or failure). However, such evaluation does not establish whether world models faithfully simulate how policies fail. Misrepresenting failures can lead to incorrect diagnoses of policy weaknesses and make policies appear ready for deployment even when small scene changes cause them to fail. We therefore ask: do world models faithfully simulate how and at which stages policies fail, and under what conditions they transition from success to failure? To answer this question, we introduce WorldFailBench with three evaluation tasks. Failure reason tests whether models reproduce the observable errors policies make. Failure stage assesses whether failures concentrate at the same stages of full-task execution. Failure boundary examines whether models reproduce the conditions under which policies transition from success to failure under controlled scene changes in simulation. The benchmark covers manipulation tasks in the LIBERO and RoboTwin simulation environments and real-world manipulation tasks performed by the G1 humanoid robot. Experiments across seven world models reveal substantial gaps in their understanding of policy failures, providing guidance for developing more reliable world models for policy evaluation.

cs.RO↗

arXiv2026

SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning

Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from multiple sampled responses. However, existing TTRL methods typically apply the same expensive sampling and optimization procedure to all test problems, despite substantial differences in their training utility. In this paper, we study whether such problems can be identified before costly test-time training, without access to ground-truth answers. We uncover a stable relationship between internal response representations and problem-level empirical accuracy. Specifically, we define COS as the cosine similarity between a model's response representation and a correctness-related direction constructed from an independent question-answering dataset. COS consistently correlates with problem solvability across different models, datasets, and experimental settings, and we further provide a theoretical analysis explaining this relationship. Building on this finding, we propose SelectTTRL, a representation-guided data selection method that ranks test problems using COS and applies TTRL only to a selected percentile interval. SelectTTRL leaves the standard TTRL pipeline unchanged for the selected problems, while avoiding unnecessary multi-response sampling and reinforcement learning updates on the remaining ones. Experiments across multiple reasoning benchmarks demonstrate that SelectTTRL substantially reduces test-time computation while maintaining or improving the performance of standard TTRL. Code is available at https://github.com/heyiliyi/SelectTTRL-public.

cs.LG↗

arXiv2026

From Learner Behavior to Reusable Skills for Effective and Efficient Learner Simulation

Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine-grained learning behaviors, existing approaches often need to repeatedly process a growing interaction history to reconstruct the learner. This introduces additional context and inference costs and makes the acquired learner-specific simulation capability difficult to reuse across different LLMs. We therefore propose Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill. The Skill captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration without reconstructing the learner from scratch. Experiments show that Learner2Skill more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.

cs.AI↗

arXiv2026

Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models

Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SLMs) are easier to deploy locally yet remain weaker in reasoning. This capability-deployment gap has motivated LLM-SLM collaboration, which aims to improve SLM reasoning using LLM capabilities while preserving the deployment advantages of SLMs. Existing approaches mainly follow two paradigms. Knowledge distillation uses LLM-generated answers and reasoning trajectories to train SLMs offline, but requires parameter updates and additional training. Alternatively, online collaboration routes difficult problems to an LLM or leverages LLM-generated guidance and corrections when an SLM encounters difficulties. Although effective, online collaboration requires repeated LLM access. Moreover, the guidance produced for a particular problem is discarded after inference and cannot benefit subsequent problems involving similar reasoning states. In the paper, we focus on a more constrained setting in which the LLM is accessed only offline, the SLM parameters remain fixed, and online inference is performed solely by the SLM. To this end, we propose Reusable Latent Correction (RLC), which converts one-off natural-language guidance from a black-box LLM into persistent corrective experiences in the hidden space of an SLM. RLC stores these experiences in an external bank and retrieves them according to the SLM's current reasoning state, enabling the SLM to reuse LLM-derived corrections during inference without any online LLM calls. Experiments across multiple reasoning benchmarks and SLM scales show that RLC consistently improves SLM reasoning without parameter updates or online LLM calls. Code is available at https://github.com/ZBH031/reusable-latent-correction.

cs.CL↗

arXiv2026

Generalized Reimpell-Werner Iteration

Quantum measurements and channels determine how information is extracted, encoded, and transmitted in quantum protocols. Optimizing their performance often requires numerical methods that remain practical as Hilbert space dimensions increase. The Reimpell-Werner iteration offers a practical approach to these tasks through repeated matrix updates that respect the constraints. Here, we generalize this iteration to linear objectives with arbitrary Hermitian cost matrices. We prove that the iterates converge to a global optimum whenever the initialization satisfies suitable support overlap conditions. For each fixed problem, choice of iteration parameters, and admissible initialization, $\mathcal{O}(1/\varepsilon)$ iterations suffice asymptotically to bring the objective value within $\varepsilon$ of the optimum. These results provide a rigorous foundation for the iteration and broaden the class of optimization problems to which its convergence guarantees apply.

quant-ph↗

arXiv2026

DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation

On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questions the student already answers correctly, and how often each model succeeds varies across tasks. OPD ignores these outcomes and, on average, pushes down even the student's correct responses; gating feedback by student correctness fixes the direction but uses the teacher in the same way whether or not it succeeded. We introduce DuoOPD, in which the student's outcome sets the direction of feedback and the joint teacher-student outcome decides how the teacher supports it: when only the teacher succeeds, its verified answer becomes context for scoring the student's failed response, and when only the student succeeds, a weight shared within the task reinforces the whole response. A single rule covers all four outcome combinations without task-specific settings. Across Qwen3 and Llama, DuoOPD outperforms all five baselines in mean macro accuracy, improving over OPD by 2.58 and 5.98 percentage points, and it also leads on two further task mixtures spanning scientific calculation, instruction following, and code generation. Ablations show that outcome-based direction alone stays near the gated baseline, while the joint-outcome designs supply most of the gain.

cs.LG↗

arXiv2026

Block-Sparse Attention with Semantic-Geometric Decoupled Routing

Long-context inference has become a defining capability of large language models, but exact dense attention remains costly due to its quadratic scaling with sequence length. Block-sparse attention offers a hardware-friendly alternative by routing each query block to a small set of relevant key blocks, yet accurate training-free block routing remains difficult. Existing routers often pool post-RoPE token representations, which entangles semantic aggregation with RoPE-induced geometry and attenuates local positional cues through high-frequency phase cancellation. To resolve this mismatch, we propose \textbf{Semantic-Geometric Decoupled Routing}, a training-free block routing framework that shifts semantic aggregation to the pre-RoPE space and reconstructs geometric bias with an offline structural prior and relative block distances. This decomposition yields an explicit closed-form block routing score without token-level search or post-hoc calibration. Experiments on long-context text and video tasks show that our method approaches full-attention accuracy across 4K--128K contexts, keeps routing overhead below 3.4 ms, and achieves a 5.03$\times$ speedup over FlashAttn at a 128K context length.

cs.CL↗

arXiv2026

Experimental demonstration of asynchronous measurement-device-independent quantum cryptographic conferencing

Quantum network enable a variety of quantum information processing tasks, where multi-user quantum communication is one of the important objectives. Quantum cryptographic conferencing (QCC) serves as an essential solution to establish secure keys to realize secure multi-user communications. However, existing QCC implementations have been fundamentally limited by the low probability of multi-user coincidence detection to measure or construct the Greenberger-Horne-Zeilinger (GHZ) entangled state. In this work, we report the experimental realization of QCC eliminating the need for coincidence detection, where the GHZ state is constructed by correlating detection events occurring within the coherence time, thereby greatly enhancing the success probability of GHZ-state measurement. Meanwhile, to establish and maintain high-visibility GHZ measurement among three independent users, we developed a three-party phase compensation scheme combined with precise temporal and polarization alignment within a time-bin-phase encoding framework. Furthermore, we designed an efficient pairing strategy to simplify subsequent data processing and enhance processing efficiency. Based on these techniques, we successfully performed QCC experiments over total loss of 66.3 dB, with the channel loss of 51.24 dB, achieving secure key rates of 5.4 bit/s, whereas previous QCC experiments have been limited to 100 km. The results establish a new regime of scalable, multi-user quantum communication and paving the way for metropolitan quantum networks.

quant-ph↗

arXiv2026

Self-Trapping Enabled Highly Bright Momentum-Indirect Interlayer Excitons

Interlayer excitons in two dimensional material heterostructures exhibit large exciton binding energies and long lifetimes, making them ideal platforms for studying excitonic devices and many body quantum phenomena. However, the spatially separated electron and hole nature of IXs reduces their oscillator strength by two orders of magnitude compared to intralayer excitons. Achieving high efficiency IX emission remains challenging and requires optimal material selection with appropriate momentum matching and meticulous device fabrication. Here we demonstrate a highly bright momentum indirect IX emission within heterostructures formed between 2D perovskites and monolayer transition metal dichalcogenides. The quantum yield of IX emission reaches 35.2% on average, over 50 times higher than that of the corresponding constituent TMD monolayer, with the highest value exceeding 60%. Notably, the radiative recombination efficiency of this momentum indirect IX exceeds that of momentum direct IXs in monolayer TMD-based heterostructures by two orders of magnitude. We suggest that the remarkably bright IX emission in our heterostructure originates from IX self trapping, induced by strong exciton phonon coupling arising from the soft lattice nature of the 2D perovskite. Our findings provide new insights into achieving high IX emission efficiency and open new avenues for exploring long lifetime excitonic devices.

cond-mat.mes-hall↗

arXiv2026

UniRRM: Unified Reasoning Reward Models Across Languages and Evaluation Paradigms

Reinforcement learning (RL) excels on tasks with verifiable rewards, but in open-ended tasks, the reliability of reward models remains a key challenge. Existing solutions either depend on costly proprietary LLM-as-a-Judge systems or opaque scalar reward models that lack interpretability. Recent works on generative reward models offer a promising alternative, but they remain constrained by static evaluation criteria, fragmented evaluation paradigms, and limited multilingual support. To address these challenges, we introduce \textbf{MixReward}, a large-scale multilingual dataset spanning six domains and 103 languages, containing both pairwise and listwise data, and propose \textbf{UniRRM}, a unified reasoning reward model supporting multiple languages and evaluation paradigms. UniRRM uses a staged reasoning chain to dynamically generate task-generic and instruction-specific criteria, enabling fine-grained, input-adaptive judgments while maintaining consistency across languages. Experiments demonstrate that UniRRM-8B and UniRRM-14B achieve performance close to the state-of-the-art for models of comparable size across multiple benchmarks, and are effective for unseen evaluation paradigms. In addition, ablation studies validate the reliability and effectiveness of UniRRM.

cs.CL↗

arXiv2026

Cavity-enhanced superconductivity in the two-dimensional limit of NbSe2

Vacuum electromagnetic fluctuations have emerged as a means of controlling collective quantum phases without external driving. Cavity-induced modification of superconductivity has been widely predicted. What sets the size of the effect, and which microscopic channel carries it, remain open. Here we couple few-layer NbSe2 to a terahertz complementary split-ring resonator (CSRR) and show that the enhancement grows sharply on approaching the two-dimensional limit. In bilayer NbSe2 the superconducting transition temperature rises by 10%, from 3.02 K to 3.41 K, on a cavity resonant at 0.92 THz - roughly four times the shift measured in a ten-layer device at the same resonance. Within a single device the shift maps onto the simulated cavity field profile, falling from 0.39 K at the field maximum to zero outside the resonator, with the lower critical field following the same spatial ordering; because all regions are measured on one continuous flake in a single cooldown, sample-to-sample variation is excluded by construction. The frequency dependence is non-monotonic, with suppression below resonance and maximal enhancement near 0.96 THz. Quantum electrodynamical density functional theory calculations show that cavity coupling redistributes spectral weight in the Eliashberg function, weakening the total electron-phonon coupling while hardening the logarithmic average phonon frequency; competition between the two reproduces a sign change in Tc. These results identify dimensionality, local field amplitude and detuning as the control parameters of cavity-enhanced superconductivity, and point to electron-phonon reweighting as its microscopic origin.

cond-mat.supr-con↗

arXiv2026

Topologically Configurable Nonlinear Vortex Generation at van der Waals Heterostructures

van der Waals (vdW) materials offer a highly tunable and efficient platform at nanoscale for nonlinear and quantum optics. Twist-stacked vdW heterostructures enable elegant control of symmetry and interlayer coupling. Prior studies mainly focus on planar twisted interfaces, while neglecting the naturally formed and mandatory defects in such vdW heterostructures. Here, we demonstrate nonlinear singular optics with topologically configurable nonlinear vortex generation at the corner singularity of vdW heterostructures. By tailoring azimuthally discrete second-harmonic phase gradients at each interface, we obtain programmable nonlinear vortex emitters with dominant target OAM components. Nonlinear OAM beams with topological charge $\ell = 1$ and $\ell = -2$ are experimentally realized, respectively. Our work unlocks the untapped potentials of nonlinear singular optics in twisted vdW materials as a reconfigurable and lithography-free platform for nonlinear structured light generation, important in quantum nonlinear optics and related fields.

physics.optics↗

arXiv2026

Experimental High-Dimensional Quantum Overlapping Tomography

Large-scale quantum systems have advanced rapidly via the exploration of more particles and higher dimensions, offering great potential for developing quantum technologies. However, their characterization becomes prohibitive with increasing local dimensionality and particle number. Here we propose high-dimensional quantum overlapping tomography based on a graph-theoretic formulation, which allows one to efficiently reconstruct few-body marginals of multipartite high-dimensional quantum systems. We experimentally realize it on a photonic four-party entangled state in a $4 \times 4 \times 2 \times 2$ system. Using measurements in mutually unbiased bases, we reconstruct all six two-body marginals with only 25 projective measurement settings, compared with 94 and 225 settings for independent tomography of all two-body reduced states and full state tomography, respectively. The reconstructed marginals reveal a layered entanglement structure vital for high-dimensional quantum networks. We further show that these marginals enable more noise-resilient certification of multipartite high-dimensional entanglement than the fidelity-based criterion. Our work thus offers a scalable route for learning multidimensional quantum systems.

quant-ph↗

arXiv2026

AI-generated Images Challenge Visual Trust in High-risk Scenarios

Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation. Yet existing detection benchmarks rarely examine synthetic images in public- and individual-safety contexts, where misleading visual content may carry substantial risks. Here we introduce SafeIMG, a safety-oriented benchmark spanning 12 public- and individual-safety scenarios generated using GPT Image 2. Unlike benchmarks centred on generic imagery and image-level labels, SafeIMG evaluates not only whether detectors recognise synthetic images, but also whether their decisions reflect human-identified anomalies. To this end, SafeIMG provides human annotations that localise suspicious regions and explain local artefacts and higher-level commonsense or physical inconsistencies. We evaluate specialized synthetic-image detectors and vision-language models (VLMs), and find that neither provides reliable detection. The strongest VLM identifies only 49.5% of generated images, whereas the best specialised detector identifies 33.1%, compared with 81.7% accuracy for human evaluators. Model explanations cover only 29.8\% of human-annotated anomalies and predominantly capture local defects in text, faces and hands. Their coverage falls to 15.0% for commonsense conflicts and 12.0% for physical inconsistencies, while detection performance deteriorates further after dissemination-induced image degradation. These findings show that current detectors lack the accuracy, explanatory alignment and robustness needed to evaluate AI-generated images reliably across public- and individual-safety settings.

cs.CV↗

arXiv2026

Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call

Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement. Learner simulators play a critical role in simulating scalable learner behavior without the need for continuous involvement of real learners. However, existing methods are predominantly \textbf{individual-centric}, pairing a simulator with each learner to iteratively infer latent knowledge states from dense interaction histories, which is both data- and computation-intensive, and fragile in cold-start scenarios. We propose a \textbf{cohort-aware roll-call simulation paradigm} that first constructs cohort-level proficiency priors and refines individual learner states through a small number of targeted diagnostic queries. Based on this paradigm, we introduce \textbf{Edu-Theater}, an LLM-powered agent system that performs cohort-aware learner simulation via a teacher agent and retrospective roll-call probing over learner logs. Edu-Theater enables scalable future behavior simulation without the need for dense per-learner histories. Experiments on two real-world datasets demonstrate that Edu-Theater achieves higher simulation accuracy with significantly fewer LLM calls, producing synthetic data that enhances downstream applications such as adaptive testing.

cs.LG↗