Retrieval-Augmented Generation (RAG) has shown remarkable results to address the hallucinations of Large Language Models (LLMs) , which usually uses a large external corpus to supplement knowledge. However, with the development of LLMs, the internal knowledge of LLMs has expanded significantly, thus causing significant knowledge redundancy between the external corpus and LLMs. On the one hand, the indexing cost of dense retrieval is highly related to the corpus size and thus significant redundant knowledge intensifies the dense retrieval’s workload. On the other hand, the redundant knowledge in the external corpus is not helpful to LLMs and our exploratory analysis shows that it instead hurts the RAG performance on those questions which the LLM can answer by itself. To address these issues, Zero-RAG is proposed to tackle these challenges. Specifically, a Mastery-Score metric is introduced to identify and prune redundant knowledge in the RAG corpus. After pruning, answers to “mastered” questions rely primarily on internal knowledge of the LLM. To better exploit this capacity, a Query Router and a Noise-Tolerant Tuning strategy are employed, thereby mitigating the distraction of irrelevant documents and enhancing the utilisation of internal knowledge under the pruned corpus. Experimental results show that Zero-RAG prunes the Wikipedia corpus by 30% and accelerates the retrieval stage by 27%, without compromising RAG’s performance (average degradation <2 points).
Negative focuses are the most prominent negated texts for a negative cue or verbal negation in a negative statement. Although previous work adopted sequence labelling framework using LSTM and CRF networks, Negative Focus Detection (NFD) is still faced with several disadvantages involving with data limitation, coarse-grained encoding, and insufficient dependencies of the sequence of words. To solve these problems, we firstly apply data augmentation driven by Large Language Models (LLMs) to produce more samples. Then, we propose a novel HyperGraph attention LSTM network (LSTM-HyG) to capture high-level semantics for sentences, and negated verbs, negative cues. Finally, we predict negative focuses by a fine-grained label scheme that can learn adequate sequential dependency relationship of words. Experimental results on PB-FOC and CNeSp datasets can prove that our proposed model is superior to state-of-the-arts.
Reachability query, which determines whether a path exists between any two nodes in a graph, has been the cornerstone of graph operation research for decades. Traditional algorithms, such as depth-first search and transitive closure, are often limited by either slow query or substantial storage. Extensive efforts have increasingly focused on designing reachability indexes to balance the tradeoff between index space and query time. However, existing solutions are limited to specific tradeoff points and lack theory for adjustable, quantifiable tradeoffs. In this work, we introduce Reachability-state Encoding (RE), which adjusts the number of encoded node pairs per chunk to affect compression ratio and decoding time, thus allowing for a smooth transition between the minimal storage and the fastest query. We establish theoretical upper bounds for the minimum encoding length of RE in relation to the size and entropy of reachability-states, which quantifies the limits of compression within the encoding process and ensures superior performance in dense graphs compared to traditional methods. We have implemented a prototype algorithm, RE-toy, to validate the effectiveness of RE. Through experiments conducted on both synthetic and real-world graphs, RE-toy consistently achieves numerous optimal tradeoff points in space-time, demonstrating robust performance across graphs of varying densities. RE thus provides a practical and adaptable approach for managing the balance between storage demands and computational efficiency in reachability queries.
The increasing popularity of container technology raises significant challenges in efficiently storing millions of container images in registries to enable fast on-demand image pulling. This is further complicated by (1) registries are geographically distributed, with independent and heterogeneous storage resources; (2) container images are pulled in layers, but can be stored at different levels of granularity, i.e., layer-level or file-level, each with varying storage requirement and pulling latency. To address the above challenges, we propose MIS, a multi-granularity image storage strategy, for distributed registries to determine the storage granularity and schedule image storage collaboratively, aiming to reduce the image pulling latency while improving the storage utilization. We formulate the image storage problem into a nonlinear mixed-integer programming form with NP-hardness by incorporating both layer-level and file-level storage constraints. We propose a low computational complexity algorithm via randomized rounding with a guaranteed approximation ratio. Extensive experimental results demonstrate the effectiveness of our strategy, with image pulling latency reductions of 28.67%, 21.69%, and 28.94% respectively compared to the state-of-the-art solutions.
Controllability has become a crucial aspect of trustworthy machine learning, enabling learners to meet predefined targets and adapt dynamically at test time without requiring retraining as the targets shift. We provide a formal definition of controllable learning (CL), and discuss its applications in information retrieval (IR) where information needs are often complex and dynamic. The survey categorizes CL according to what is controllable (e.g., multiple objectives, user portrait, scenario adaptation), who controls (users or platforms), how control is implemented (e.g., rule-based method, Pareto optimization, hypernetwork, and others), and where to implement control (e.g., pre-processing, in-processing, post-processing methods). Then, we identify challenges faced by CL across training, evaluation, task setting, and deployment in online environments. Additionally, we outline promising directions for CL in theoretical analysis, efficient computation, empowering large language models, application scenarios, and evaluation frameworks.
Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies. Not surprisingly, this area has attracted considerable attention from researchers, who have proposed a wide range of methods based on various backbones. However, the evaluation of the area often exhibits three systemic limitations: 1) Failure to account for the full spectrum of stock movement patterns observed in dynamic financial markets. (Diversity Gap); 2) The absence of unified assessment protocols undermines the validity of cross-study performance comparisons. (Standardization Deficit); 3) Neglect of critical market structure factors, resulting in inflated performance metrics that lack practical applicability. (Real-World Mismatch). Addressing these limitations, we propose Financial Time Series Benchmark (FinTSB), a comprehensive and practical benchmark for financial time series forecasting (FinTSF). To increase the variety, we categorize movement patterns into four specific parts, tokenize and pre-process the data, and assess the data quality based on some sequence characteristics. To eliminate biases due to different evaluation settings, we standardize the metrics across three dimensions and build a user-friendly, lightweight pipeline incorporating methods from various backbones. To accurately simulate real-world trading scenarios and facilitate practical implementation, we extensively model various regulatory constraints, including transaction fees, among others. Finally, we conduct extensive experiments on FinTSB, highlighting key insights to guide model selection under varying market conditions. Overall, FinTSB provides researchers with a novel and comprehensive platform for improving and evaluating FinTSF methods. The code is available at the website of github.com/TongjiFinLab/FinTSB.
Generative model (GM)-assisted product design has become increasingly popular. The key is to find generated samples (GSs) satisfying the design goal from the GM’s latent space (GLS). Most existing works rely on defining objective functions or writing prompts to search for GSs in the GLS. Unlike them, we propose a progressive approach that relies on multiple rounds of neighborhood exploration to choose desirable GSs from the GLS. Notably, the approach allows users to concretize and refine their goals during the exploration, thus applying to abstract or unspecific goals, which is unavailable for all existing techniques. The approach integrates two techniques to solve the challenges of achieving and applying it. First, many GSs are highly similar or irrelevant to the neighborhood center. Those GSs do not allow users to make comprehensive comparisons for rational choices and cannot be excluded by classic methods. Thus, we propose a method to avoid collecting them, which makes collected GSs have representative feature variations from the neighborhood center. Second, we need a system for applying the approach. The system should fulfill many visualization requirements to efficiently drive exploration and keep it always in the right direction. Thus, we followed the mountain-climbing metaphor to design the system and developed a series of visual and quantitative techniques to achieve these requirements. Cases on multiple real-world datasets and GMs, results of quantitative experiments, and performance and feedback of participants in user studies prove the approach’s effectiveness and usability.
Recent advancements in native 3D generation have demonstrated remarkable capabilities in producing high-quality 3D assets from image or text prompts. However, these methods face a critical challenge: insufficient alignment between generated meshes and input conditions. In this paper, we propose Multimodal Triplane Diffusion Transformer to address the issue, featuring two core components: A triplane-based 3D variational autoencoder that compresses point clouds, sampled uniformly from mesh surfaces and concentrated near sharp edges, into a triplane latent space, and a diffusion model trained on this latent space, empowered by multimodal diffusion transformer blocks to establish cross-modality interactions between latent representations and conditions. Extensive experiments demonstrate that our method achieves not only superior generalization capability but also significantly enhanced geometric alignment with input images compared to state-of-the-art approaches in the image-to-3D task.
The rapid growth of Internet of Things (IoT) devices has increased security risks due to cryptographic misuse in firmware, including vulnerable algorithms and misconfigurations. Accurate identification of cryptographic algorithms in firmware is fundamental for firmware analysis, while existing tools, usually designed for x86 platform, struggle with IoT-specific architectures, file formats, and instruction sets. Currently, it lacks a unified and scalable evaluation framework, and standard datasets. Besides, there are challenges to be addressed: unverified tool applicability, unexplored impacts of instruction sets and compilation optimizations, and insufficient empirical data on cryptographic implementations. To this end, this study proposes a modular framework for IoT firmware cryptographic algorithm identification, which supports plug-and-play integration of tools via standardized interfaces, a three-dimensional evaluation metric (algorithm types, function counts, constant quantities) and a standardized test dataset covering seven cryptographic libraries, six instruction set architectures, and four compilation optimization levels. Through four experimental studies including tool performance comparison, compilation optimization impact analysis, architecture difference evaluation, and real-world firmware cryptographic usage investigation, it demonstrates that constant-based identification techniques achieve optimal performance in IoT scenarios while revealing the impact mechanisms of ISA architectures and compilation optimizations on identification effectiveness. It also provides methodological guidance and empirical data foundations for IoT firmware cryptographic algorithm identification studies in the future.
Generative artificial intelligence is capable of generating high-quality, fine-grained 3D assets with realistic visual effects. These 3D assets are often utilized in various scenarios that demand different levels of security permissions. As such, they need to display tiered visual effects while ensuring that the content remains protected. Recently, several researchers have proposed that traditional security encryption methods might not be sufficient to fulfill these requirements. We conduct an in-depth analysis of the structure of generative 3D assets and their visually salient features. Based on this analysis, we propose an encryption scheme for generative 3D assets that achieves tiered visual effects upon decryption. Specifically, a novel coordinate fine-grained regularization method is employed to partition different sub blocks, and ring encryption is implemented, followed by generating keys at different levels, ultimately constructing an encryption scheme with tiered visual effects. Experimental results demonstrate that the proposed scheme not only meets diverse security requirements but also effectively addresses the complexity issues in processing large-scale 3D assets.
With the rapid growth of Internet of Things (IoT), ensuring the security of devices in resource-constrained environments has become increasingly critical. In this context, the design of lightweight cryptographic algorithms is essential, with a particular emphasis on constructing optimal linear layers to enhance their efficiency and security. An optimal linear layer should simultaneously achieve strong cryptographic properties, while minimizing the number of exclusive OR (XOR) gates required for implementation. Previous research has primarily focused on optimizing existing linear layers. Only a few studies have explored the construction of new linear layers, often employing classical structures such as Feistel and Lai-Massey. However, these studies typically achieve optimality only for specific dimensions or branch numbers. Furthermore, while they aim to construct high-dimensional linear layers, they often result in increased costs for lower-dimensional cases.
In this paper, we present a novel method for constructing optimal linear layers by formulating the problem as a satisfiability (SAT) problem. A major obstacle in previous research was the inability to efficiently search for optimal linear layers as the dimension increased. We overcome this challenge by using our key insight into the relationship between linear layers and the arrangement of XOR gates in their implementation. This understanding allows us to extend the constructible dimension up to 14, significantly beyond what was previously achievable. Moreover, we are able to construct optimal linear layers for arbitrary branch numbers in dimensions ranging from 4 to 12. Finally, we construct linear layers for dimensions ranging from 4 to 14 that match or surpass the best known results in both cryptographic properties and implementation cost. Furthermore, we systematically construct involutory linear layers for dimensions 4 to 14, addressing a gap that has remained open in previous research.
Commit-and-prove succinct non-interactive arguments of knowledge (CP-SNARKs) are an important class of SNARKs that allow proving the knowledge of a witness committed beforehand. They are crucial in proving composite statements that combine algebraic and non-algebraic statements. However, existing CP-SNARKs only support constraint systems with degree-2 gates, limiting their ability to express non-algebraic statements succinctly. We propose a new family of CP-SNARKs for Customizable Constraint Systems. The new family, named CP-SuperSpartan, supports high-degree gates, eliminates expensive fast Fourier transform operations and supports a general commit-and-prove relation including multiple commitments to different slots of the witness. CP-SuperSpartan has several instantiations based on different multilinear polynomial commitment schemes (PCS). 1) When using pairing-based PCS, CP-SuperSpartan provides the same universally updatable setup as LegoSNARK (CCS’19), but it reduces the proof size and verifier complexity from to while maintaining the same prover complexity, where is the circuit size. 2) When using discrete-logarithm-based PCS, CP-SuperSpartan provides a transparent setup and achieves proof size while the smallest proof size of existing transparent CP-SNARKs is . 3) When using PCS based on interactive oracle proof of proximity, CP-SuperSpartan provides a transparent setup and firstly achieves prover complexity, proof size and verifier complexity while the number of public-key operations for both the prover and verifier is independent of .
Decentralized data sovereignty and secure data exchange are regarded as foundational pillars of the new era. Attribute-based encryption (ABE) is a promising solution that enables fine-grained access control in data sharing. Recently, Hohenberger et al. (Eurocrypt 2023) introduced registered ABE (RABE) to eliminate trusted authority and gain decentralization. Users generate their own public and secret keys and then register their keys and attributes with a transparent key curator. However, RABE still suffers from heavy decryption overhead. A natural approach to address this issue is to outsource decryption to a decryption cloud server (DCS). In this work, we propose the first auditable RABE scheme with reliable outsourced decryption (ORABE) based on blockchain. First, we achieve verifiability of transform ciphertext via a verifiable tag mechanism. Then, the exemptibility, which ensures that the DCS escapes false accusations, is guaranteed by zero knowledge fraud proof under the optimistic assumption. Additionally, our system achieves fairness and auditability to protect the interests of all parties through blockchain. Finally, we give concrete security and theoretical analysis and evaluate our scheme on Ethereum to demonstrate feasibility and efficiency.
A functional commitment (FC) commits to a value , and can later generate a proof for a function value with respect to some function . In contrast, the dual functional commitment (dual FC) allows a committer to commit to a function , and later produces an opening proof for the function value given an input . We propose a new construction of dual FC scheme from lattices. Our dual FC scheme can support arbitrary circuits of bounded sizes. Moreover, our dual FC scheme enjoys computational binding, and we prove the computational binding property of our dual FC based on the -succinct - assumption, a falsifiable generalization of the -succinct assumption, in the random oracle model. In addition, our dual FC scheme is quasi-succinct with a succinct commitment and a quasi-succinct opening proof.
The original Grover’s algorithm suffers from the souffle problem, which means that the success probability of quantum search decreases dramatically if the iteration time is too small or too large from the right time. To overcome the souffle problem, the fixed-point quantum search with an optimal number of queries was proposed [Phys. Rev. Lett. 113, 210501 (2014)], which always finds a marked state with a high probability when a lower bound of the proportion of marked states is given. The fixed-point quantum search relies on a key lemma regarding the explicit formula of recursive quasi-Chebyshev polynomials, but its proof is not given explicitly. In this work, we give a detailed proof of this lemma, thus providing a sound foundation for the correctness of the fixed-point quantum search. This lemma may be of independent interest as well, since it expands the mathematical form of the recursive relation of Chebyshev polynomials of the first kind, and it also constitutes a key component in overcoming the souffle problem of quantum walk-based search algorithms, for example, robust quantum walk search on complete bipartite graphs [Phys. Rev. A 106, 052207 (2022)]. The lemma is also central to a recently proposed quantum algorithm named quantum phase discrimination, which has become a fundamental subroutine in quantum search on graphs [arxiv: 2504.15194]. Hopefully, more applications of the lemma will be found in the future.
Quantum simulation is a rapidly advancing field poised to revolutionize our understanding of complex quantum systems by harnessing the unique capabilities of quantum computers. In this review, we present a concise overview of key developments in quantum simulation algorithms, with a focus on time-independent digital Hamiltonian simulation of quantum dynamics. We review seminal methods—from traditional Trotter formulas to techniques such as truncated Taylor series combined with the linear combination of unitaries, and quantum signal processing. Additionally, we examine error analyses and recent innovations that enhance the efficiency and accuracy of quantum simulations.