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Chiral superconductors are unconventional superconducting states that break time reversal symmetry spontaneously and typically feature Cooper pairing at non-zero angular momentum. Such states may host Majorana fermions and provide an important platform for topological physics research and fault-tolerant quantum computing. Despite of intensive search and prolonged studies of several candidate systems, chiral superconductivity has remained elusive so far. Here we report the discovery of unconventional superconductivity in rhombohedral tetra-layer graphene. We observed two superconducting states in the gate-induced flat conduction bands with Tc up to 300 mK and charge density ne as low as 2.4*1011 cm-2, appearing robustly in three different devices, where electrons reside close to a proximate WSe2 layer, far away from WSe2, and in the absence of WSe2 respectively. Spontaneous time-reversal-symmetry-breaking (TRSB) due to electron's orbital motion is found, and several observations indicate the chiral nature of these superconducting states, including 1. In the superconducting state, Rxx shows fluctuations at zero magnetic field and magnetic hysteresis versus an out-of-plane magnetic field B, which are absent from all other superconductors; 2. one superconducting state develops within a spin- and valley-polarized quarter-metal phase, and is robust against the neighboring spin-valley-polarized quarter-metal state under B; 3. the normal states show anomalous Hall signals at zero magnetic field and magnetic hysteresis. We also observed a critical B > 0.9 Tesla, higher than any graphene superconductivity reported so far and indicates a strong-coupling superconductivity close the BCS-BEC crossover. Our observations establish a pure carbon material for the study of topological superconductivity, and pave the way to explore Majorana modes and topological quantum computing.

Rhombohedral multilayer graphene, with its flat electronic bands and concentrated Berry curvature, is a promising material for the realization of correlated topological phases of matter. When aligned to an adjacent hexagonal boron nitride (hBN) layer, the graphene develops narrow minibands with non-trivial topology. By tuning an externally-applied electric displacement field, the conduction electrons can either be pushed towards or away from the moir\'e superlattice. Motivated by the recent observation of the fractional quantum anomalous Hall effect (FQAHE) in the moir\'e-distant case, we study the opposite moir\'e-proximal case, where the superlattice potential is considerably stronger. We explore the physics within the moir\'e conduction bands through capacitance measurements that allow us to determine the inverse electronic compressibility and extract energy gaps of incompressible states. We observe integer and fractional Chern insulator states at superlattice filling factors v = 1, 2/3, and 1/3 with Streda slopes of -1, -2/3, and -1/3, respectively. Remarkably, the v = 1/3 state persists down to a magnetic field of 0.2 T. In addition, we also observe numerous trivial and topological charge density waves. We map out a phase diagram that is highly sensitive to both displacement and magnetic fields, which tune the system between various ground states by modifying the band dispersion and the structure of the electronic wavefunctions. This work demonstrates displacement field control of topological phase transitions in the moir\'e-proximal limit of rhombohedral pentalayer graphene, creating a highly-tunable platform for studying the interplay between intrinsic band topology and strong lattice effects.

Electrons in topological flat bands can form novel topological states driven by the correlation effects. The penta-layer rhombohedral graphene/hBN moire superlattice has been shown to host fractional quantum anomalous Hall effect (FQAHE) at ~400 mK, triggering discussions around the underlying mechanism and the role of moire effects. In particular, novel electron crystal states with non-trivial topology have been proposed. Here we report DC electrical transport measurement in rhombohedral penta- and tetra-layer graphene/hBN moire superlattices at electronic temperatures down to ~40 mK. We observed two more FQAH states in the penta-layer devices than previously reported. In a new tetra-layer device, we observed FQAHE at filling factors v = 3/5 and 2/3 at 300 mK. With a small bias current and the lowest temperature, we observed a new extended quantum anomalous Hall (EQAH) state and magnetic hysteresis, where Rxy = h/e2 and vanishing Rxx span a wide range of moire filling factor v from 0.5 to up to 1.3. By increasing the temperature or current, FQAHE can be recovered -- suggesting the break-down of the EQAH states and a phase transition into the fractional quantum Hall liquid. Furthermore, we observed displacement field-induced quantum phase transitions from the EQAH states to Fermi liquid, FQAH liquid and the likely composite Fermi liquid. Our observation establishes a new topological phase of electrons with quantized Hall resistance at zero magnetic field, and enriches the emergent quantum phenomena in materials with topological flat bands.

Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. However, it has been increasingly challenging to evaluate the reasoning capability of LLMs. Concretely, existing outcome-based benchmarks begin to saturate and become less sufficient to monitor the progress. To this end, we present a process-based benchmark MR-BEN that demands a meta reasoning skill, where LMs are asked to locate and analyse potential errors in automatically generated reasoning steps. MR-BEN is a comprehensive benchmark comprising 5,975 questions collected from human experts, covering various subjects such as physics, chemistry, logic, coding, and more. Through our designed metrics for assessing meta-reasoning on this benchmark, we identify interesting limitations and weaknesses of current LLMs (open-source and closed-source models). For example, open-source models are seemingly comparable to GPT-4 on outcome-based benchmarks, but they lag far behind on our benchmark, revealing the underlying reasoning capability gap between them. Our dataset and codes are available on https://randolph-zeng.github.io/Mr-Ben.github.io/.

Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works have focused on improving the model performance, the privacy issue has only received comparatively less attention. In this paper, we review recent advancements in privacy within LLM-based recommendation, categorizing them into privacy attacks and protection mechanisms. Additionally, we highlight several challenges and propose future directions for the community to address these critical problems.

Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content. One potential approach to mitigate these issues is learning from human or external feedback (e.g. tools). In this paper, we introduce an intrinsic self-correct reasoning framework for LLMs that eliminates the need for human feedback, external tools, and handcraft prompts. The proposed framework, based on a multi-step reasoning paradigm \textbf{Le}arning from \textbf{Co}rrectness (\textsc{LeCo}), improves reasoning performance without needing to learn from errors. This paradigm prioritizes learning from correct reasoning steps, and a unique method to measure confidence for each reasoning step based on generation logits. Experimental results across various multi-step reasoning tasks demonstrate the effectiveness of the framework in improving reasoning performance with reduced token consumption., Comment: Accepted to COLM 2024

We present CNER-UAV, a fine-grained \textbf{C}hinese \textbf{N}ame \textbf{E}ntity \textbf{R}ecognition dataset specifically designed for the task of address resolution in \textbf{U}nmanned \textbf{A}erial \textbf{V}ehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and \textbf{L}arge \textbf{L}anguage \textbf{M}odel annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at \url{https://github.com/zhhvvv/CNER-UAV}., Comment: Accepted by TheWebConf'24 (WWW'24) as a Resource Paper

Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior. Recently, large language models (LLMs) have gained prominence for their capabilities in understanding and reasoning over textual semantics, and have found utility in various domains, including recommendation. Conventional recommendation methods and LLMs each have their strengths and weaknesses. While conventional methods excel at mining collaborative information and modeling sequential behavior, they struggle with data sparsity and the long-tail problem. LLMs, on the other hand, are proficient at utilizing rich textual contexts but face challenges in mining collaborative or sequential information. Despite their individual successes, there is a significant gap in leveraging their combined potential to enhance recommendation performance. In this paper, we introduce a general and model-agnostic framework known as \textbf{L}arge \textbf{la}nguage model with \textbf{m}utual augmentation and \textbf{a}daptive aggregation for \textbf{Rec}ommendation (\textbf{Llama4Rec}). Llama4Rec synergistically combines conventional and LLM-based recommendation models. Llama4Rec proposes data augmentation and prompt augmentation strategies tailored to enhance the conventional model and LLM respectively. An adaptive aggregation module is adopted to combine the predictions of both kinds of models to refine the final recommendation results. Empirical studies on three real-world datasets validate the superiority of Llama4Rec, demonstrating its consistent outperformance of baseline methods and significant improvements in recommendation performance.

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized \textit{prompts} to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning techniques to align LLMs with human preferences, promising more effective recommendations. However, existing methods suffer from several limitations. The full potential of LLMs is not fully elicited due to low-quality tuning data and the overlooked integration of conventional recommender signals. Furthermore, LLMs may generate inconsistent responses for different ranking tasks in the recommendation, potentially leading to unreliable results. In this paper, we introduce \textbf{RecRanker}, tailored for instruction tuning LLMs to serve as the \textbf{Ranker} for top-\textit{k} \textbf{Rec}ommendations. Specifically, we introduce an adaptive sampling module for sampling high-quality, representative, and diverse training data. To enhance the prompt, we introduce a position shifting strategy to mitigate position bias and augment the prompt with auxiliary information from conventional recommendation models, thereby enriching the contextual understanding of the LLM. Subsequently, we utilize the sampled data to assemble an instruction-tuning dataset with the augmented prompts comprising three distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further propose a hybrid ranking method to enhance the model performance by ensembling these ranking tasks. Our empirical evaluations demonstrate the effectiveness of our proposed RecRanker in both direct and sequential recommendation scenarios.

The quantum anomalous Hall effect (QAHE) is a robust topological phenomenon featuring quantized Hall resistance at zero magnetic field. We report the QAHE in a rhombohedral pentalayer graphene/monolayer WS2 heterostructure. Distinct from other experimentally confirmed QAHE systems, this system has neither magnetic element nor moir\'e superlattice effect. The QAH states emerge at charge neutrality and feature Chern numbers C = +-5 at temperatures up to about 1.5 K. This large QAHE arises from the synergy of the electron correlation in intrinsic flat bands of pentalayer graphene, the gate-tuning effect, and the proximity-induced Ising spin-orbit-coupling. Our experiment demonstrates the potential of crystalline two-dimensional materials for intertwined electron correlation and band topology physics, and may enable a route for engineering chiral Majorana edge states., Comment: to be published in Science

General-purpose text decoding approaches are usually adopted for dialogue response generation. Although the quality of the generated responses can be improved with dialogue-specific encoding methods, conversational decoding methods are still under-explored. Inspired by \citet{wu2023learning} that a good dialogue feature space should follow the rules of locality and isotropy, we present a fine-grained conversational decoding method, termed \textit{isotropic and proximal search (IPS)}. Our method is designed to generate the semantic-concentrated response, while still maintaining informativeness and discrimination against the context. Experiments show that our approach outperforms existing decoding strategies in the dialogue field across both automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our approach., Comment: Accepted to EMNLP 2023 Main Conference

The fractional quantum anomalous Hall effect (FQAHE), the analog of the fractional quantum Hall effect1 at zero magnetic field, is predicted to exist in topological flat bands under spontaneous time-reversal-symmetry breaking. The demonstration of FQAHE could lead to non-Abelian anyons which form the basis of topological quantum computation. So far, FQAHE has been observed only in twisted MoTe2 (t-MoTe2) at moire filling factor v > 1/2. Graphene-based moire superlattices are believed to host FQAHE with the potential advantage of superior material quality and higher electron mobility. Here we report the observation of integer and fractional QAH effects in a rhombohedral pentalayer graphene/hBN moire superlattice. At zero magnetic field, we observed plateaus of quantized Hall resistance Rxy = h/(ve^2) at filling factors v = 1, 2/3, 3/5, 4/7, 4/9, 3/7 and 2/5 of the moire superlattice respectively. These features are accompanied by clear dips in the longitudinal resistance Rxx. In addition, at zero magnetic field, Rxy equals 2h/e^2 at v = 1/2 and varies linearly with the filling factor-similar to the composite Fermi liquid (CFL) in the half-filled lowest Landau level at high magnetic fields. By tuning the gate displacement field D and v, we observed phase transitions from CFL and FQAH states to other correlated electron states. Our graphene system provides an ideal platform for exploring charge fractionalization and (non-Abelian) anyonic braiding at zero magnetic field, especially considering a lateral junction between FQAHE and superconducting regions in the same device., Comment: Nature, in press

We investigate the orbit-to-charge conversion in YIG/Pt/nonmagnetic material (NM) trilayer heterostructures. With the additional Ru layer on the top of YIG/Pt stacks, the charge current signal increases nearly an order of magnitude in both longitudinal spin Seebeck effect (SSE) and spin pumping (SP) measurements. Through thickness dependence studies of the Ru metal layer and theoretical model, we quantitatively clarify different contributions of the increased SSE signal that mainly comes from the inverse orbital Hall effect (IOHE) of Ru, and partially comes from the orbital sink effect in the Ru layer. A similar enhancement of SSE(SP) signals is also observed when Ru is replaced by other materials (Ta, W, and Cu), implying the universality of the IOHE in transition metals. Our findings not only suggest a more efficient generation of the charge current via the orbital angular moment channel but also provides crucial insights into the interplay among charge, spin, and orbit.

Perpendicular synthetic antiferromagnets (SAFs) are of interest for the next generation ultrafast, high density spintronic memory and logic devices. However, to energy efficiently operate their magnetic order by current-induced spin orbit torques (SOTs), an unfavored high external field is conventionally required to break the symmetry. Here, we theoretically and experimentally demonstrate the field-free SOT switching of a perpendicular SAF through the introduction of interlayer Dzyaloshinskii-Moriya interaction (DMI). By macro-spin simulation, we show that the speed of field-free switching increases with the in-plane mirror asymmetry of injected spins. We experimentally observe the existence of interlayer DMI in our SAF sample by an azimuthal angular dependent anomalous Hall measurement. Field-free switching is accomplished in such a sample and the strength of the effective switching field demonstrates its origin from interlayer DMI. Our results provide a new strategy for SAF based high performance SOT devices.