OpenRaiser/Intern-Atlas huggingface dataset:
LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements
Venue: arXiv cs.CL 2025
Year: 2025

The task of reading comprehension (RC), often implemented as context-based question answering (QA), provides a primary means to assess language models' natural language understanding (NLU) capabilities. Yet, when applied to large language models (LLMs) with extensive built-in world knowledge, this method can be deceptive. If the context aligns with the LLMs' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from LLMs' internal information. Conversely, using data that conflicts with the models' knowledge creates erroneous trends which distort the results. To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities. This task is entirely independent of the models' world knowledge, enabling us to evaluate LLMs' linguistic abilities without the interference of parametric knowledge. Testing ChatGPT, GPT-4, LLaMA 2 and Mixtral on such imaginary data, we uncover a class of linguistic phenomena posing a challenge to current LLMs, involving thinking in terms of alternative, hypothetical scenarios. While all the models handle simple affirmative and negative contexts with high accuracy, they are much more prone to error when dealing with modal and conditional contexts. Crucially, these phenomena also trigger the LLMs' vulnerability to knowledge-conflicts again. In particular, while some models prove virtually unaffected by knowledge conflicts in affirmative and negative contexts, when faced with more semantically involved modal and conditional environments, they often fail to separate the text from their internal knowledge.
Bounded Edit Distance: Optimal Static and Dynamic Algorithms for Small Integer Weights
Venue: arXiv cs.DS 2025
Year: 2025

The edit distance of two strings is the minimum number of insertions, deletions, and substitutions needed to transform one string into the other. The textbook algorithm determines the edit distance of length-$n$ strings in $O(n^2)$ time, which is optimal up to subpolynomial factors under Orthogonal Vectors Hypothesis. In the bounded version of the problem, parameterized by the edit distance $k$, the algorithm of Landau and Vishkin [JCSS'88] achieves $O(n+k^2)$ time, which is optimal as a function of $n$ and $k$. The dynamic version of the problem asks to maintain the edit distance of two strings that change dynamically, with each update modeled as an edit. A folklore approach supports updates in $\tilde O(k^2)$ time, where $\tilde O(\cdot)$ hides polylogarithmic factors. Recently, Charalampopoulos, Kociumaka, and Mozes [CPM'20] showed an algorithm with update time $\tilde O(n)$, which is optimal under OVH in terms of $n$. The update time of $\tilde O(\min\{n,k^2\})$ raised an exciting open question of whether $\tilde O(k)$ is possible; we answer it affirmatively. Our solution relies on tools originating from weighted edit distance, where the weight of each edit depends on the edit type and the characters involved. The textbook algorithm supports weights, but the Landau-Vishkin approach does not, and a simple $O(nk)$-time procedure long remained the fastest for bounded weighted edit distance. Only recently, Das et al. [STOC'23] provided an $O(n+k^5)$-time algorithm, whereas Cassis, Kociumaka, and Wellnitz [FOCS'23] presented an $\tilde O(n+\sqrt{nk^3})$-time solution and a matching conditional lower bound. In this paper, we show that, for integer edit weights between $0$ and $W$, weighted edit distance can be computed in $\tilde O(n+Wk^2)$ time and maintained dynamically in $\tilde O(W^2k)$ time per update. Our static algorithm can also be implemented in $\tilde O(n+k^{2.5})$ time.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches. However, CNNs struggle with long-range dependencies, while transformers are burdened by quadratic computational complexity. Mamba-based models, with their superior long-range modeling and linear efficiency, have garnered substantial attention. This study pioneers the application of Mamba to multi-class unsupervised anomaly detection, presenting MambaAD, which consists of a pre-trained encoder and a Mamba decoder featuring (Locality-Enhanced State Space) LSS modules at multi-scales. The proposed LSS module, integrating parallel cascaded (Hybrid State Space) HSS blocks and multi-kernel convolutions operations, effectively captures both long-range and local information. The HSS block, utilizing (Hybrid Scanning) HS encoders, encodes feature maps into five scanning methods and eight directions, thereby strengthening global connections through the (State Space Model) SSM. The use of Hilbert scanning and eight directions significantly improves feature sequence modeling. Comprehensive experiments on six diverse anomaly detection datasets and seven metrics demonstrate state-of-the-art performance, substantiating the method's effectiveness. The code and models are available at https://lewandofskee.github.io/projects/MambaAD.
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems
Venue: arXiv cs.CL 2025
Year: 2025

Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience. The emergence of large language models (LLMs) further enables better human-machine interaction, and facilitates the development of conversational ITSs in various disciplines such as math and language learning. In dialogic teaching, recognizing and adapting to individual characteristics can significantly enhance student engagement and learning efficiency. However, characterizing and simulating student's persona remain challenging in training and evaluating conversational ITSs. In this work, we propose a framework to construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario. We further enhance the framework with multi-aspect validation, and conduct extensive analysis from both teacher and student perspectives. Our experimental results show that state-of-the-art LLMs can produce diverse student responses according to the given language ability and personality traits, and trigger teacher's adaptive scaffolding strategies.
Generalized Linear Bandits with Limited Adaptivity
Venue: arXiv cs.LG 2025
Year: 2025

We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, $\texttt{B-GLinCB}$ and $\texttt{RS-GLinCB}$, that address, respectively, two prevalent limited adaptivity settings. Given a budget $M$ on the number of policy updates, in the first setting, the algorithm needs to decide upfront $M$ rounds at which it will update its policy, while in the second setting it can adaptively perform $M$ policy updates during its course. For the first setting, we design an algorithm $\texttt{B-GLinCB}$, that incurs $\tilde{O}(\sqrt{T})$ regret when $M = Ω( \log{\log T} )$ and the arm feature vectors are generated stochastically. For the second setting, we design an algorithm $\texttt{RS-GLinCB}$ that updates its policy $\tilde{O}(\log^2 T)$ times and achieves a regret of $\tilde{O}(\sqrt{T})$ even when the arm feature vectors are adversarially generated. Notably, in these bounds, we manage to eliminate the dependence on a key instance dependent parameter $κ$, that captures non-linearity of the underlying reward model. Our novel approach for removing this dependence for generalized linear contextual bandits might be of independent interest.
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
Venue: arXiv cs.CV 2025
Year: 2025

Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required. Recently, neural implicit representation has provided a promising direction for online interactive mapping. However, implementing open-vocabulary scene understanding capability into online neural implicit mapping still faces three challenges: lack of local scene updating ability, blurry spatial hierarchical semantic segmentation and difficulty in maintaining multi-view consistency. To this end, we proposed O2V-mapping, which utilizes voxel-based language and geometric features to create an open-vocabulary field, thus allowing for local updates during online training process. Additionally, we leverage a foundational model for image segmentation to extract language features on object-level entities, achieving clear segmentation boundaries and hierarchical semantic features. For the purpose of preserving consistency in 3D object properties across different viewpoints, we propose a spatial adaptive voxel adjustment mechanism and a multi-view weight selection method. Extensive experiments on open-vocabulary object localization and semantic segmentation demonstrate that O2V-mapping achieves online construction of language scenes while enhancing accuracy, outperforming the previous SOTA method.
Elements Allocation for Joint Active and Passive IRS Aided Wireless Communications: A Rate-Maximization Perspective
Venue: arXiv cs.IT 2025
Year: 2025

Unlike previous works that focused solely on passive intelligent reflecting surface (PIRS) or active IRS (AIRS), a novel joint AIRS and PIRS architecture has been developed to flexibly utilize their combined advantages in mitigating multiplicative path loss cost-effectively. In this paper, we consider the AIRS-PIRS jointly aided wireless point-to-point communication system with two different deployment schemes in three-dimensional (3D) space. To balance the trade-off between the square-order beamforming gain of PIRS and the unique power amplification gain of AIRS, we optimize the elements allocation and beamforming design of the two IRSs under various practical constraints from a rate-maximization perspective. Moreover, we derive a series of element-related closed-form analytical expressions and compare the performance of the two schemes. Our analysis shows that in both schemes, PIRS should be allocated more elements than AIRS, and the received signal-to-noise ratio (SNR) increases asymptotically with the cube of the number of reflecting elements, when the distance between AIRS and PIRS is sufficiently large. Last, simulation results validate our analysis and indicate that both schemes can achieve superior rate performance over various benchmarks.
Average entropy of Gaussian mixtures
Venue: arXiv cs.IT 2025
Year: 2025

We calculate the average differential entropy of a $q$-component Gaussian mixture in $\mathbb R^n$. For simplicity, all components have covariance matrix $σ^2 {\mathbf 1}$, while the means $\{\mathbf{W}_i\}_{i=1}^{q}$ are i.i.d. Gaussian vectors with zero mean and covariance $s^2 {\mathbf 1}$. We obtain a series expansion in $μ=s^2/σ^2$ for the average differential entropy up to order $\mathcal{O}(μ^2)$, and we provide a recipe to calculate higher order terms. Our result provides an analytic approximation with a quantifiable order of magnitude for the error, which is not achieved in previous literature.
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements
Venue: arXiv cs.RO 2025
Year: 2025

This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, containing over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations.
A Survey and Future Outlook on Indoor Location Fingerprinting Privacy Preservation
Venue: arXiv cs.CR 2025
Year: 2025

The pervasive integration of Indoor Positioning Systems (IPS) arises from the limitations of Global Navigation Satellite Systems (GNSS) in indoor environments, leading to the widespread adoption of Location-Based Services (LBS) in places such as shopping malls, airports, hospitals, museums, corporate campuses, and smart buildings. Specifically, indoor location fingerprinting (ILF) systems employ diverse signal fingerprints from user devices, enabling precise location identification by Location Service Providers (LSP). Despite its broad applications across various domains, ILF introduces a notable privacy risk, as both LSP and potential adversaries inherently have access to this sensitive information, compromising users' privacy. Consequently, concerns regarding privacy vulnerabilities in this context necessitate a focused exploration of privacy-preserving mechanisms. In response to these concerns, this survey presents a comprehensive review of Indoor Location Fingerprinting Privacy-Preserving Mechanisms (ILFPPM) based on cryptographic, anonymization, differential privacy (DP), and federated learning (FL) techniques. We also propose a distinctive and novel grouping of privacy vulnerabilities, adversary models, privacy attacks, and evaluation metrics specific to ILF systems. Given the identified limitations and research gaps in this survey, we highlight numerous prospective opportunities for future investigation, aiming to motivate researchers interested in advancing ILF systems. This survey constitutes a valuable reference for researchers and provides a clear overview for those beyond this specific research domain. To further help the researchers, we have created an online resource repository, which can be found at \href{https://github.com/amir-ftlz/ilfppm}{https://github.com/amir-ftlz/ilfppm}.
PRAM: Place Recognition Anywhere Model for Efficient Visual Localization
Venue: arXiv cs.CV 2025
Year: 2025

Visual localization is a key technique to a variety of applications, e.g., autonomous driving, AR/VR, and robotics. For these real applications, both efficiency and accuracy are important especially on edge devices with limited computing resources. However, previous frameworks, e.g., absolute pose regression (APR), scene coordinate regression (SCR), and the hierarchical method (HM), have limited either accuracy or efficiency in both indoor and outdoor environments. In this paper, we propose the place recognition anywhere model (PRAM), a new framework, to perform visual localization efficiently and accurately by recognizing 3D landmarks. Specifically, PRAM first generates landmarks directly in 3D space in a self-supervised manner. Without relying on commonly used classic semantic labels, these 3D landmarks can be defined in any place in indoor and outdoor scenes with higher generalization ability. Representing the map with 3D landmarks, PRAM discards global descriptors, repetitive local descriptors, and redundant 3D points, increasing the memory efficiency significantly. Then, sparse keypoints, rather than dense pixels, are utilized as the input tokens to a transformer-based recognition module for landmark recognition, which enables PRAM to recognize hundreds of landmarks with high time and memory efficiency. At test time, sparse keypoints and predicted landmark labels are utilized for outlier removal and landmark-wise 2D-3D matching as opposed to exhaustive 2D-2D matching, which further increases the time efficiency. A comprehensive evaluation of APRs, SCRs, HMs, and PRAM on both indoor and outdoor datasets demonstrates that PRAM outperforms ARPs and SCRs in large-scale scenes with a large margin and gives competitive accuracy to HMs but reduces over 90\% memory cost and runs 2.4 times faster, leading to a better balance between efficiency and accuracy.
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
Venue: arXiv cs.CV 2025
Year: 2025

Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments. Leveraging near-infrared (NIR) images to assist visible RGB image denoising shows the potential to address this issue, becoming a promising technology. Nonetheless, existing works still struggle with taking advantage of NIR information effectively for real-world image denoising, due to the content inconsistency between NIR-RGB images and the scarcity of real-world paired datasets. To alleviate the problem, we propose an efficient Selective Fusion Module (SFM), which can be plug-and-played into the advanced denoising networks to merge the deep NIR-RGB features. Specifically, we sequentially perform the global and local modulation for NIR and RGB features, and then integrate the two modulated features. Furthermore, we present a Real-world NIR-Assisted Image Denoising (Real-NAID) dataset, which covers diverse scenarios as well as various noise levels. Extensive experiments on both synthetic and our real-world datasets demonstrate that the proposed method achieves better results than state-of-the-art ones. The dataset, codes, and pre-trained models will be publicly available at https://github.com/ronjonxu/NAID.
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
Venue: arXiv cs.CL 2025
Year: 2025

We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection. The intuition behind such a criterion is that, the pretrained LLM has the prior knowledge about OOD data due to its large amount of training data, and once finetuned with the in-distribution data, the LLM has sufficient knowledge to distinguish their difference. Leveraging the power of LLMs, we show that, the likelihood ratio can serve as an effective OOD detection criterion. Moreover, we apply the proposed LLM-based likelihood ratio to detect OOD questions in question-answering (QA) systems, which can be used to improve the performance of specialized LLMs for general questions. Given that likelihood can be easily obtained by the loss functions within contemporary neural network frameworks, it is straightforward to implement this approach in practice. Since both the pretrained LLMs and its various finetuned models are widely available from online platforms such as Hugging Face, our proposed criterion can be effortlessly incorporated for OOD detection without the need for further training. We conduct comprehensive evaluation across on multiple settings, including far OOD, near OOD, spam detection, and QA scenarios, to demonstrate the effectiveness of the method. Code can be found at https://github.com/andiac/LLMOODratio
T-REX: Mixture-of-Rank-One-Experts with Semantic-aware Intuition for Multi-task Large Language Model Finetuning
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) encounter significant adaptation challenges in diverse multitask finetuning. Mixture-of-experts (MoE) provides a promising solution with a dynamic architecture, enabling effective task decoupling. However, scaling up the number of MoE experts incurs substantial parameter and computational overheads and suffers from limited performance gain due to naive routing mechanisms. In this paper, we design a novel framework, mix\underline{\textbf{T}}ure\underline{\textbf{-}}of-\underline{\textbf{R}}ank-on\underline{\textbf{E}}-e\underline{\textbf{X}}perts (\texttt{T-REX}), which leverages the combination of ultra-low rank experts to construct LoRA weights on pretrained LLMs. The rank-1 experts enable a mix-and-match mechanism to quadratically expand the vector subspace of experts with linear parameter overheads, achieving approximate error reduction with optimal efficiency. In addition, T-REX offers implicit guidance to the router, leveraging the inherent semantic clustering of training embeddings as prior knowledge, enabling optimized feature allocation across experts for a smoother convergence. Extensive theoretical and empirical results demonstrate that T-REX achieves superior efficiency and generalizability across diverse tasks. Compared with other LoRA-based methods, T-REX achieves up to 1.78\% mean accuracy improvement with around 30\%-40\% less trainable parameters across 14 public datasets. \href{https://github.com/RoyZry98/T-REX-Pytorch}{Code} is available.
Proof-of-Learning with Incentive Security
Venue: arXiv cs.CR 2025
Year: 2025

Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. However, the substantial energy expenditure stemming from computationally intensive yet meaningless tasks has raised considerable concerns surrounding traditional PoW approaches, The PoS mechanism, while free of energy consumption, is subject to security and economic issues. Addressing these issues, the paradigm of Proof-of-Useful-Work (PoUW) seeks to employ challenges of practical significance as PoW, thereby imbuing energy consumption with tangible value. While previous efforts in Proof of Learning (PoL) explored the utilization of deep learning model training SGD tasks as PoUW challenges, recent research has revealed its vulnerabilities to adversarial attacks and the theoretical hardness in crafting a byzantine-secure PoL mechanism. In this paper, we introduce the concept of incentive-security that incentivizes rational provers to behave honestly for their best interest, bypassing the existing hardness to design a PoL mechanism with computational efficiency, a provable incentive-security guarantee and controllable difficulty. Particularly, our work is secure against two attacks, and also improves the computational overhead from $Θ(1)$ to $O(\frac{\log E}{E})$. Furthermore, while most recent research assumes trusted problem providers and verifiers, our design also guarantees frontend incentive-security even when problem providers are untrusted, and verifier incentive-security that bypasses the Verifier's Dilemma. By incorporating ML training into blockchain consensus mechanisms with provable guarantees, our research not only proposes an eco-friendly solution to blockchain systems, but also provides a proposal for a completely decentralized computing power market in the new AI age.
SQIAsignHD: SQIsignHD Adaptor Signature
Venue: arXiv cs.CR 2025
Year: 2025

Adaptor signatures can be viewed as a generalized form of standard digital signature schemes by linking message authentication to the disclosure of a secret value. As a recent cryptographic primitive, they have become essential for blockchain applications, including cryptocurrencies, by reducing on-chain costs, improving fungibility, and enabling off-chain payments in payment-channel networks, payment-channel hubs, and atomic swaps. However, existing adaptor signature constructions are vulnerable to quantum attacks due to Shor's algorithm. In this work, we introduce $\mathsf{SQIAsignHD}$, a new quantum-resistant adaptor signature scheme based on isogenies of supersingular elliptic curves, using SQIsignHD - as the underlying signature scheme - and exploiting the idea of the artificial orientation on the supersingular isogeny Diffie-Hellman key exchange protocol, SIDH, to define the underlying hard relation. We, furthermore, provide a formal security proof for our proposed scheme.