OpenRaiser/Intern-Atlas huggingface dataset:
A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection
Venue: arXiv cs.CV 2025
Year: 2025

Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and reliability of deep learning have been raised. While only a few works have investigated the effects of randomly generated semantic perturbations, aka natural corruptions, on the multi-view BEV detection task, we develop a black-box robustness evaluation framework that adversarially optimises three common semantic perturbations: geometric transformation, colour shifting, and motion blur, to deceive BEV models, serving as the first approach in this emerging field. To address the challenge posed by optimising the semantic perturbation, we design a smoothed, distance-based surrogate function to replace the mAP metric and introduce SimpleDIRECT, a deterministic optimisation algorithm that utilises observed slopes to guide the optimisation process. By comparing with randomised perturbation and two optimisation baselines, we demonstrate the effectiveness of the proposed framework. Additionally, we provide a benchmark on the semantic robustness of ten recent BEV models. The results reveal that PolarFormer, which emphasises geometric information from multi-view images, exhibits the highest robustness, whereas BEVDet is fully compromised, with its precision reduced to zero.
CoRa: A Collision-Resistant LoRa Symbol Detector of Low Complexity
Venue: arXiv cs.NI 2025
Year: 2025

Long range communication with LoRa has become popular as it avoids the complexity of multi-hop communication at low cost and low energy consumption. LoRa is openly accessible, but its packets are particularly vulnerable to collisions due to long time on air in a shared band. This degrades communication performance. Existing techniques for demodulating LoRa symbols under collisions face challenges such as high computational complexity, reliance on accurate symbol boundary information, or error-prone peak detection methods. In this paper, we introduce CoRa , a symbol detector for demodulating LoRa symbols under severe collisions. CoRa employs a Bayesian classifier to accurately identify the true symbol amidst interference from other LoRa transmissions, leveraging empirically derived features from raw symbol data. Evaluations using real-world and simulated packet traces demonstrate that CoRa clearly outperforms the related state-of-the-art, i.e., up to 29% better decoding performance than TnB and 178% better than CIC. Compared to the LoRa baseline demodulator, CoRa magnifies the packet reception rate by up to 11.53x. CoRa offers a significant reduction in computational complexity compared to existing solutions by only adding a constant overhead to the baseline demodulator, while also eliminating the need for peak detection and accurately identifying colliding frames.
SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Surgical video generation can enhance medical education and research, but existing methods lack fine-grained motion control and realism. We introduce SurgSora, a framework that generates high-fidelity, motion-controllable surgical videos from a single input frame and user-specified motion cues. Unlike prior approaches that treat objects indiscriminately or rely on ground-truth segmentation masks, SurgSora leverages self-predicted object features and depth information to refine RGB appearance and optical flow for precise video synthesis. It consists of three key modules: (1) the Dual Semantic Injector, which extracts object-specific RGB-D features and segmentation cues to enhance spatial representations; (2) the Decoupled Flow Mapper, which fuses multi-scale optical flow with semantic features for realistic motion dynamics; and (3) the Trajectory Controller, which estimates sparse optical flow and enables user-guided object movement. By conditioning these enriched features within the Stable Video Diffusion, SurgSora achieves state-of-the-art visual authenticity and controllability in advancing surgical video synthesis, as demonstrated by extensive quantitative and qualitative comparisons. Our human evaluation in collaboration with expert surgeons further demonstrates the high realism of SurgSora-generated videos, highlighting the potential of our method for surgical training and education. Our project is available at https://surgsora.github.io/surgsora.github.io.
Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases
Venue: arXiv cs.AI 2025
Year: 2025

Traditional causal discovery methods often rely on strong, untestable assumptions, which makes them unreliable in real applications. In this context, Large Language Models (LLMs) have emerged as a promising alternative for extracting causal knowledge from text-based metadata, which consolidates domain expertise. However, LLMs tend to be unreliable and prone to hallucinations, necessitating strategies that account for their limitations. One effective strategy is to use a consistency measure to assess reliability. Additionally, most text metadata does not clearly distinguish direct causal relationships from indirect ones, further complicating the discovery of a causal DAG. As a result, focusing on causal orders, rather than causal DAGs, emerges as a more practical and robust approach. We present a new method to derive a class of acyclic tournaments, which represent plausible causal orders, maximizing a consistency score derived from an LLM. Our approach starts by calculating pairwise consistency scores between variables, resulting in a semi-complete partially directed graph that consolidates these scores into an abstraction of the maximally consistent causal orders. Using this structure, we identify optimal acyclic tournaments, focusing on those that maximize consistency across all configurations. We subsequently show how both the abstraction and the class of causal orders can be used to estimate causal effects. We tested our method on both well-established benchmarks, as well as, real-world datasets from epidemiology and public health. Our results demonstrate the effectiveness of our approach in recovering the correct causal order.
SEKE: Specialised Experts for Keyword Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Keyword extraction involves identifying the most descriptive words in a document, allowing automatic categorisation and summarisation of large quantities of diverse textual data. Relying on the insight that real-world keyword detection often requires handling of diverse content, we propose a novel supervised keyword extraction approach based on the mixture of experts (MoE) technique. MoE uses a learnable routing sub-network to direct information to specialised experts, allowing them to specialise in distinct regions of the input space. SEKE, a mixture of Specialised Experts for supervised Keyword Extraction, uses DeBERTa as the backbone model and builds on the MoE framework, where experts attend to each token, by integrating it with a bidirectional Long short-term memory (BiLSTM) network, to allow successful extraction even on smaller corpora, where specialisation is harder due to lack of training data. The MoE framework also provides an insight into inner workings of individual experts, enhancing the explainability of the approach. We benchmark SEKE on multiple English datasets, achieving state-of-the-art performance compared to strong supervised and unsupervised baselines. Our analysis reveals that depending on data size and type, experts specialise in distinct syntactic and semantic components, such as punctuation, stopwords, parts-of-speech, or named entities. Code is available at https://github.com/matejMartinc/SEKE_keyword_extraction
Revolutionizing QoE-Driven Network Management with Digital Agents in 6G
Venue: arXiv cs.NI 2025
Year: 2025

In this article, we present a digital agent (DA)-assisted network management framework for future sixth generation (6G) networks considering user quality of experience (QoE). A novel QoE metric is defined by incorporating the impact of user behavioral dynamics and environmental complexity on quality of service (QoS). A two-level DA architecture is proposed to assist the QoE-driven network slicing and orchestration. Three potential solutions are presented from the perspectives of DA data collection, resource scheduling, and DA deployment. A case study demonstrates that the proposed framework can effectively improve user QoE compared with benchmark schemes.
The "Huh?" Button: Improving Understanding in Educational Videos with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

We propose a simple way to use large language models (LLMs) in education. Specifically, our method aims to improve individual comprehension by adding a novel feature to online videos. We combine the low threshold for interactivity in digital experiences with the benefits of rephrased and elaborated explanations typical of face-to-face interactions, thereby supporting to close knowledge gaps at scale. To demonstrate the technical feasibility of our approach, we conducted a proof-of-concept experiment and implemented a prototype which is available for testing online. Through the use case, we also show how caching can be applied in LLM-powered applications to reduce their carbon footprint.
Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE
Venue: arXiv cs.SE 2025
Year: 2025

This paper presents the first empirical study of a vulnerability detection and fix tool with professional software developers on real projects that they own. We implemented DeepVulGuard, an IDE-integrated tool based on state-of-the-art detection and fix models, and show that it has promising performance on benchmarks of historic vulnerability data. DeepVulGuard scans code for vulnerabilities (including identifying the vulnerability type and vulnerable region of code), suggests fixes, provides natural-language explanations for alerts and fixes, leveraging chat interfaces. We recruited 17 professional software developers at Microsoft, observed their usage of the tool on their code, and conducted interviews to assess the tool's usefulness, speed, trust, relevance, and workflow integration. We also gathered detailed qualitative feedback on users' perceptions and their desired features. Study participants scanned a total of 24 projects, 6.9k files, and over 1.7 million lines of source code, and generated 170 alerts and 50 fix suggestions. We find that although state-of-the-art AI-powered detection and fix tools show promise, they are not yet practical for real-world use due to a high rate of false positives and non-applicable fixes. User feedback reveals several actionable pain points, ranging from incomplete context to lack of customization for the user's codebase. Additionally, we explore how AI features, including confidence scores, explanations, and chat interaction, can apply to vulnerability detection and fixing. Based on these insights, we offer practical recommendations for evaluating and deploying AI detection and fix models. Our code and data are available at https://doi.org/10.6084/m9.figshare.26367139.
The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation
Venue: arXiv cs.CL 2025
Year: 2025

Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in English translation. By manually inserting the omitted particle X ('DE'). In news article titles from the Penn Chinese Discourse Treebank, we developed a targeted dataset to fine-tune Hugging Face Chinese to English translation models, specifically improving how this critical function word is handled. This focused approach not only complements the broader strategies suggested by previous studies but also offers a practical enhancement by specifically addressing a common error type in Chinese-English translation.
Do Large Language Models Advocate for Inferentialism?
Venue: arXiv cs.CL 2025
Year: 2025

The emergence of large language models (LLMs) such as ChatGPT and Claude presents new challenges for philosophy of language, particularly regarding the nature of linguistic meaning and representation. While LLMs have traditionally been understood through distributional semantics, this paper explores Robert Brandom's inferential semantics as an alternative foundational framework for understanding these systems. We examine how key features of inferential semantics -- including its anti-representationalist stance, logical expressivism, and quasi-compositional approach -- align with the architectural and functional characteristics of Transformer-based LLMs. Through analysis of the ISA (Inference, Substitution, Anaphora) approach, we demonstrate that LLMs exhibit fundamentally anti-representationalist properties in their processing of language. We further develop a consensus theory of truth appropriate for LLMs, grounded in their interactive and normative dimensions through mechanisms like RLHF. While acknowledging significant tensions between inferentialism's philosophical commitments and LLMs' sub-symbolic processing, this paper argues that inferential semantics provides valuable insights into how LLMs generate meaning without reference to external world representations. Our analysis suggests that LLMs may challenge traditional assumptions in philosophy of language, including strict compositionality and semantic externalism, though further empirical investigation is needed to fully substantiate these theoretical claims.
Theoretical Proof that Auto-regressive Language Models Collapse when Real-world Data is a Finite Set
Venue: arXiv cs.CL 2025
Year: 2025

Auto-regressive language models (LMs) have been widely used to generate data in data-scarce domains to train new LMs, compensating for the scarcity of real-world data. Previous work experimentally found that LMs collapse when trained on recursively generated data. This paper presents a theoretical proof: once a corpus (such as a subset of the World Wide Web) begins to incorporate generated data and no new real-world data is added to the corpus, then no matter how small the amount of data each LM generates and contributes to the corpus, LM collapse is inevitable after sufficient time. This finding suggests that attempts to mitigate collapse by limiting the quantity of synthetic data in the corpus are fundamentally insufficient. Instead, avoiding collapse hinges on ensuring the quality of synthetic data.
Efficient Knowledge Injection in LLMs via Self-Distillation
Venue: arXiv cs.CL 2025
Year: 2025

In many practical applications, large language models (LLMs) need to acquire new knowledge not present in their pre-training data. Efficiently leveraging this knowledge usually relies on supervised fine-tuning or retrieval-augmented generation (RAG). Although RAG has emerged as the industry standard for knowledge injection, fine-tuning has not yet achieved comparable success. This paper proposes utilizing prompt distillation, a self-distillation-based method previously explored primarily for style alignment and instruction tuning, to internalize new factual knowledge from free-form documents. Unlike prior methods, our approach requires neither larger teacher models nor structured knowledge formats. Across multiple LLM sizes and model families, we show that prompt distillation outperforms standard supervised fine-tuning and can even surpass RAG. We analyze the key factors contributing to prompt distillation's effectiveness and examine how it scales.
Minimizing speculation overhead in a parallel recognizer for regular texts
Venue: arXiv cs.DC 2025
Year: 2025

Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finite-state automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived from regular expressions (RE). Such an algorithm cuts the input string into chunks, independently recognizes each chunk in parallel by means of identical FAs, and at last joins the chunk results and checks overall consistency. In chunk recognition, it is necessary to speculatively start the FAs in any state, thus causing an overhead that reduces the speedup compared to a serial algorithm. Existing data-parallel DFA-based recognizers suffer from the excessive number of starting states, and the NFA-based ones suffer from the number of nondeterministic transitions. Our data-parallel algorithm is based on the new FA type called reduced interface DFA (RI-DFA), which minimizes the speculation overhead without incurring in the penalty of nondeterministic transitions or of impractically enlarged DFA machines. The algorithm is proved to be correct and theoretically efficient, because it combines the state-reduction of an NFA with the speed of deterministic transitions, thus improving on both DFA-based and NFA-based existing implementations. The practical applicability of the RI-DFA approach is confirmed by a quantitative comparison of the number of starting states for a large public benchmark of complex FAs. On multi-core computing architectures, the RI-DFA recognizer is much faster than the NFA-based one on all benchmarks, while it matches the DFA-based one on some benchmarks and performs much better on some others. The extra time cost needed to construct an RI-DFA compared to a DFA is moderate and is compatible with a practical use.
FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment
Venue: arXiv cs.SD 2025
Year: 2025

Traditional sound design workflows rely on manual alignment of audio events to visual cues, as in Foley sound design, where everyday actions like footsteps or object interactions are recreated to match the on-screen motion. This process is time-consuming, difficult to scale, and lacks automation tools that preserve creative intent. Despite recent advances in vision-to-audio generation, producing temporally coherent and semantically controllable sound effects from video remains a major challenge. To address these limitations, we introduce FolAI, a two-stage generative framework that decouples the when and the what of sound synthesis, i.e., the temporal structure extraction and the semantically guided generation, respectively. In the first stage, we estimate a smooth control signal from the video that captures the motion intensity and rhythmic structure over time, serving as a temporal scaffold for the audio. In the second stage, a diffusion-based generative model produces sound effects conditioned both on this temporal envelope and on high-level semantic embeddings, provided by the user, that define the desired auditory content (e.g., material or action type). This modular design enables precise control over both timing and timbre, streamlining repetitive tasks while preserving creative flexibility in professional Foley workflows. Results on diverse visual contexts, such as footstep generation and action-specific sonorization, demonstrate that our model reliably produces audio that is temporally aligned with visual motion, semantically consistent with user intent, and perceptually realistic. These findings highlight the potential of FolAI as a controllable and modular solution for scalable, high-quality Foley sound synthesis in professional and interactive settings. Supplementary materials are accessible on our dedicated demo page at https://ispamm.github.io/FolAI.
Fully Dynamic Approximate Minimum Cut in Subpolynomial Time per Operation
Venue: arXiv cs.DS 2025
Year: 2025

Dynamically maintaining the minimum cut in a graph $G$ under edge insertions and deletions is a fundamental problem in dynamic graph algorithms for which no conditional lower bound on the time per operation exists. In an $n$-node graph the best known $(1+o(1))$-approximate algorithm takes $\tilde O(\sqrt{n})$ update time [Thorup 2007]. If the minimum cut is guaranteed to be $(\log n)^{o(1)}$, a deterministic exact algorithm with $n^{o(1)}$ update time exists [Jin, Sun, Thorup 2024]. We present the first fully dynamic algorithm for $(1+o(1))$-approximate minimum cut with $n^{o(1)}$ update time. Our main technical contribution is to show that it suffices to consider small-volume cuts in suitably contracted graphs.
Efficient Ranking, Order Statistics, and Sorting under CKKS
Venue: arXiv cs.CR 2025
Year: 2025

Fully Homomorphic Encryption (FHE) enables operations on encrypted data, making it extremely useful for privacy-preserving applications, especially in cloud computing environments. In such contexts, operations like ranking, order statistics, and sorting are fundamental functionalities often required for database queries or as building blocks of larger protocols. However, the high computational overhead and limited native operations of FHE pose significant challenges for an efficient implementation of these tasks. These challenges are exacerbated by the fact that all these functionalities are based on comparing elements, which is a severely expensive operation under encryption. Previous solutions have typically based their designs on swap-based techniques, where two elements are conditionally swapped based on the results of their comparison. These methods aim to reduce the primary computational bottleneck: the comparison depth, which is the number of non-parallelizable homomorphic comparisons in the algorithm. The current state of the art solution for sorting by Hong et al. (IEEE TIFS 2021), for instance, achieves a comparison depth of k log_k^2 N. In this paper, we address the challenge of reducing the comparison depth by shifting away from the swap-based paradigm. We present solutions for ranking, order statistics, and sorting, that achieve a comparison depth of up to 2 (constant), making our approach highly parallelizable and suitable for hardware acceleration. Leveraging the SIMD capabilities of the CKKS FHE scheme, our approach re-encodes the input vector under encryption to allow for simultaneous comparisons of all elements with each other. Experimental results show that our approach ranks a 128-element vector in approximately 5.76s, computes its argmin/argmax in 12.83s, and sorts it in 78.64s.
On the structure of the Schur squares of Twisted Generalized Reed-Solomon codes and application to cryptanalysis
Venue: arXiv cs.IT 2025
Year: 2025

Twisted generalized Reed-Solomon (TGRS) codes constitute an interesting family of evaluation codes, containing a large class of maximum distance separable codes non-equivalent to generalized Reed-Solomon (GRS) ones. Moreover, the Schur squares of TGRS codes may be much larger than those of GRS codes with same dimension. Exploiting these structural differences, in 2018, Beelen, Bossert, Puchinger and Rosenkilde proposed a subfamily of Maximum Distance Separable (MDS) Twisted Reed-Solomon (TRS) codes over $\mathbb{F}_q$ with $\ell$ twists $q \approx n^{2^{\ell}}$ for McEliece encryption, claiming their resistance to both Sidelnikov Shestakov attack and Schur products--based attacks. In short, they claimed these codes to resist to classical key recovery attacks on McEliece encryption scheme instantiated with Reed-Solomon (RS) or GRS codes. In 2020, Lavauzelle and Renner presented an original attack on this system based on the computation of the subfield subcode of the public TRS code. In this paper, we show that the original claim on the resistance of TRS and TGRS codes to Schur products based--attacks is wrong. We identify a broad class of codes including TRS and TGRS ones that is distinguishable from random by computing the Schur square of some shortening of the code. Then, we focus on the case of single twist (i.e., $\ell = 1$), which is the most efficient one in terms of decryption complexity, to derive an attack. The technique is similar to the distinguisher-based attacks of RS code-based systems given by Couvreur, Gaborit, Gauthier-Umaña, Otmani, Tillich in 2014.