OpenRaiser/Intern-Atlas huggingface dataset:
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning
Venue: arXiv cs.CV 2025
Year: 2025

In Generalized Zero-Shot Learning (GZSL), we aim to recognize both seen and unseen categories using a model trained only on seen categories. In computer vision, this translates into a classification problem, where knowledge from seen categories is transferred to unseen categories by exploiting the relationships between visual features and available semantic information, such as text corpora or manual annotations. However, learning this joint distribution is costly and requires one-to-one training with corresponding semantic information. We present a reversed conditional Diffusion-based model (RevCD) that mitigates this issue by generating semantic features synthesized from visual inputs by leveraging Diffusion models' conditional mechanisms. Our RevCD model consists of a cross Hadamard-Addition embedding of a sinusoidal time schedule and a multi-headed visual transformer for attention-guided embeddings. The proposed approach introduces three key innovations. First, we reverse the process of generating semantic space based on visual data, introducing a novel loss function that facilitates more efficient knowledge transfer. Second, we apply Diffusion models to zero-shot learning - a novel approach that exploits their strengths in capturing data complexity. Third, we demonstrate our model's performance through a comprehensive cross-dataset evaluation. The complete code will be available on GitHub.
Learning to Ask: When LLM Agents Meet Unclear Instruction
Venue: arXiv cs.CL 2025
Year: 2025

Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. However, the effective execution of these tools relies heavily not just on the advanced capabilities of LLMs but also on precise user instructions, which often cannot be ensured in the real world. To evaluate the performance of LLMs tool-use under imperfect instructions, we meticulously examine the real-world instructions queried from users, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench (NoisyToolBench). We find that due to the next-token prediction training objective, LLMs tend to arbitrarily generate the missed argument, which may lead to hallucinations and risks. To address this issue, we propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions. Moreover, to reduce the manual labor involved in user-LLM interaction and assess LLMs performance in tool utilization from both accuracy and efficiency perspectives, we design an automated evaluation tool named ToolEvaluator. Our experiments demonstrate that the AwN significantly outperforms existing frameworks for tool learning in the NoisyToolBench. We will release all related code and datasets to support future research.
Support + Belief = Decision Trust
Venue: arXiv cs.LO 2025
Year: 2025

We present SBTrust, a logical framework designed to formalize decision trust. Our logic integrates a doxastic modality with a novel non-monotonic conditional operator that establishes a positive support relation between statements, and is closely related to a known dyadic deontic modality. For SBTrust, we provide semantics, proof theory and complexity results, as well as motivating examples. Compared to existing approaches, our framework seamlessly accommodates the integration of multiple factors in the emergence of trust.
Fixing Function-Level Code Generation Errors for Foundation Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Function-level code generation leverages foundation Large Language Models (LLMs) to automatically produce source code with expected functionality. It has been widely investigated and applied in intelligent programming assistants, such as GitHub Copilot, to enhance software development productivity. Despite advancements in foundation LLMs, the generation involves many errors. Existing studies leverage static analysis tools (e.g., TBar) or add another fixing LLM (i.e., LDB) to post-process these errors. However, there are still many errors remaining to be solved because their root causes have not been investigated yet, making it challenging to design better fixing tools. In this paper, we first conducted an empirical study on the generation errors. Specifically, we reproduced 14 representative LLMs on the HumanEval dataset and verified their correctness. We obtained 12,837 code generation errors and conducted an analysis of their causes, leading to 19 categories of error causes. Our empirical analysis indicated that three of these causes can be directly fixed. Based on the findings, we proposed a fixing method called LlmFix, which addresses these three types of errors through a three-step process: filtering code for indentation correction, truncating redundant generated code, and importing missing modules. Evaluations of LlmFix are conducted from two perspectives: its performance on error-fixing tasks and its impact on improving function-level code generation tasks. For error fixing performance, we built an evaluation dataset LlmErrorEval. Experimental results show that LlmFix achieves a fix rate of 17.1% outperforming the best LDB by 8.9%. For code generation improvements, evaluations of LlmFix on both the HumanEval and MBPP datasets demonstrate its effectiveness, improving code generation accuracy by an average of 7.5% across 14 LLMs.
A Roadside Unit for Infrastructure Assisted Intersection Control of Autonomous Vehicles
Venue: arXiv cs.RO 2025
Year: 2025

Recent advances in autonomous vehicle technologies and cellular network speeds motivate developments in vehicle-to-everything (V2X) communications. Enhanced road safety features and improved fuel efficiency are some of the motivations behind V2X for future transportation systems. Adaptive intersection control systems have considerable potential to achieve these goals by minimizing idle times and predicting short-term future traffic conditions. Integrating V2X into traffic management systems introduces the infrastructure necessary to make roads safer for all users and initiates the shift towards more intelligent and connected cities. To demonstrate our control algorithm, we implement both a simulated and real-world representation of a 4-way intersection and crosswalk scenario with 2 self-driving electric vehicles, a roadside unit (RSU), and a traffic light. Our architecture reduces acceleration and braking through intersections by up to 75.35%, which has been shown to minimize fuel consumption in gas vehicles. We propose a cost-effective solution to intelligent and connected intersection control to serve as a proof-of-concept model suitable as the basis for continued research and development. Code for this project is available at https://github.com/MMachado05/REU-2024.
A Framework for Synthetic Audio Conversations Generation using Large Language Models
Venue: arXiv cs.SD 2025
Year: 2025

In this paper, we introduce ConversaSynth, a framework designed to generate synthetic conversation audio using large language models (LLMs) with multiple persona settings. The framework first creates diverse and coherent text-based dialogues across various topics, which are then converted into audio using text-to-speech (TTS) systems. Our experiments demonstrate that ConversaSynth effectively generates highquality synthetic audio datasets, which can significantly enhance the training and evaluation of models for audio tagging, audio classification, and multi-speaker speech recognition. The results indicate that the synthetic datasets generated by ConversaSynth exhibit substantial diversity and realism, making them suitable for developing robust, adaptable audio-based AI systems.
Free-DyGS: Camera-Pose-Free Scene Reconstruction for Dynamic Surgical Videos with Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

High-fidelity reconstruction of surgical scene is a fundamentally crucial task to support many applications, such as intra-operative navigation and surgical education. However, most existing methods assume the ideal surgical scenarios - either focus on dynamic reconstruction with deforming tissue yet assuming a given fixed camera pose, or allow endoscope movement yet reconstructing the static scenes. In this paper, we target at a more realistic yet challenging setup - free-pose reconstruction with a moving camera for highly dynamic surgical scenes. Meanwhile, we take the first step to introduce Gaussian Splitting (GS) technique to tackle this challenging setting and propose a novel GS-based framework for fast reconstruction, termed \textit{Free-DyGS}. Concretely, our model embraces a novel scene initialization in which a pre-trained Sparse Gaussian Regressor (SGR) can efficiently parameterize the initial attributes. For each subsequent frame, we propose to jointly optimize the deformation model and 6D camera poses in a frame-by-frame manner, easing training given the limited deformation differences between consecutive frames. A Scene Expansion scheme is followed to expand the GS model for the unseen regions introduced by the moving camera. Moreover, the framework is equipped with a novel Retrospective Deformation Recapitulation (RDR) strategy to preserve the entire-clip deformations throughout the frame-by-frame training scheme. The efficacy of the proposed Free-DyGS is substantiated through extensive experiments on two datasets: StereoMIS and Hamlyn datasets. The experimental outcomes underscore that Free-DyGS surpasses other advanced methods in both rendering accuracy and efficiency. Code will be available.
Time series classification with random convolution kernels: pooling operators and input representations matter
Venue: arXiv cs.LG 2025
Year: 2025

This article presents a new approach based on MiniRocket, called SelF-Rocket, for fast time series classification (TSC). Unlike existing approaches based on random convolution kernels, it dynamically selects the best couple of input representations and pooling operator during the training process. SelF-Rocket achieves state-of-the-art accuracy on the University of California Riverside (UCR) TSC benchmark datasets.
Coverage and metadata completeness and accuracy of African research publications in OpenAlex: A comparative analysis
Venue: arXiv cs.DL 2025
Year: 2025

Unlike traditional proprietary data sources such as Scopus and the Web of Science (WoS), OpenAlex emphasizes its comprehensiveness. This study analyzes OpenAlex coverage and metadata completeness and accuracy of African research publications. To achieve this, OpenAlex is compared with Scopus, WoS, and African Journals Online (AJOL). First, we examine the coverage of African research publications in OpenAlex relative to Scopus, WoS, and AJOL. Then, we assess and compare the availability and accuracy of metadata in OpenAlex, Scopus, and WoS. The findings indicate that OpenAlex offers the most extensive publication coverage. In terms of metadata, OpenAlex provides high coverage for publication and author information, though its coverage of affiliations, references, and funder information is comparatively lower. Metadata accuracy is similarly high for publication and author fields, while affiliation, reference, and funding information show higher rates of missing or incomplete data. Notably, the results demonstrate that both metadata availability and accuracy in OpenAlex improve significantly for publications also indexed in Scopus and WoS. These findings suggest that OpenAlex has the potential to replace proprietary data sources for certain types of analyses. However, for some metadata fields, there remains a trade-off between extensiveness and accuracy.
FMRFT: Fusion Mamba and DETR for Query Time Sequence Intersection Fish Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Early detection of abnormal fish behavior caused by disease or hunger can be achieved through fish tracking using deep learning techniques, which holds significant value for industrial aquaculture. However, underwater reflections and some reasons with fish, such as the high similarity, rapid swimming caused by stimuli and mutual occlusion bring challenges to multi-target tracking of fish. To address these challenges, this paper establishes a complex multi-scenario sturgeon tracking dataset and introduces the FMRFT model, a real-time end-to-end fish tracking solution. The model incorporates the low video memory consumption Mamba In Mamba (MIM) architecture, which facilitates multi-frame temporal memory and feature extraction, thereby addressing the challenges to track multiple fish across frames. Additionally, the FMRFT model with the Query Time Sequence Intersection (QTSI) module effectively manages occluded objects and reduces redundant tracking frames using the superior feature interaction and prior frame processing capabilities of RT-DETR. This combination significantly enhances the accuracy and stability of fish tracking. Trained and tested on the dataset, the model achieves an IDF1 score of 90.3% and a MOTA accuracy of 94.3%. Experimental results show that the proposed FMRFT model effectively addresses the challenges of high similarity and mutual occlusion in fish populations, enabling accurate tracking in factory farming environments.
Logit Scaling for Out-of-Distribution Detection
Venue: arXiv cs.LG 2025
Year: 2025

The safe deployment of machine learning and AI models in open-world settings hinges critically on the ability to detect out-of-distribution (OOD) data accurately, data samples that contrast vastly from what the model was trained with. Current approaches to OOD detection often require further training the model, and/or statistics about the training data which may no longer be accessible. Additionally, many existing OOD detection methods struggle to maintain performance when transferred across different architectures. Our research tackles these issues by proposing a simple, post-hoc method that does not require access to the training data distribution, keeps a trained network intact, and holds strong performance across a variety of architectures. Our method, Logit Scaling (LTS), as the name suggests, simply scales the logits in a manner that effectively distinguishes between in-distribution (ID) and OOD samples. We tested our method on benchmarks across various scales, including CIFAR-10, CIFAR-100, ImageNet and OpenOOD. The experiments cover 3 ID and 14 OOD datasets, as well as 9 model architectures. Overall, we demonstrate state-of-the-art performance, robustness and adaptability across different architectures, paving the way towards a universally applicable solution for advanced OOD detection.
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Sequential recommendation methods are crucial in modern recommender systems for their remarkable capability to understand a user's changing interests based on past interactions. However, a significant challenge faced by current methods (e.g., RNN- or Transformer-based models) is to effectively and efficiently capture users' preferences by modeling long behavior sequences, which impedes their various applications like short video platforms where user interactions are numerous. Recently, an emerging architecture named Mamba, built on state space models (SSM) with efficient hardware-aware designs, has showcased the tremendous potential for sequence modeling, presenting a compelling avenue for addressing the challenge effectively. Inspired by this, we propose a novel generic and efficient sequential recommendation backbone, SSD4Rec, which explores the seamless adaptation of Mamba for sequential recommendations. Specifically, SSD4Rec marks the variable- and long-length item sequences with sequence registers and processes the item representations with bidirectional Structured State Space Duality (SSD) blocks. This not only allows for hardware-aware matrix multiplication but also empowers outstanding capabilities in variable-length and long-range sequence modeling. Extensive evaluations on four benchmark datasets demonstrate that the proposed model achieves state-of-the-art performance while maintaining near-linear scalability with user sequence length. Our code is publicly available at https://github.com/ZhangYifeng1995/SSD4Rec.
CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classification
Venue: arXiv cs.NE 2025
Year: 2025

In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified object description, correct class label and SNN decision) have spiking nature. The distinctive feature of this architecture is a combination of prototypical network structures corresponding to different classes and significantly distinctive instances of one class (=columns) and functionally differing populations of neurons inside columns (=layers). The other distinctive feature is a novel combination of anti-Hebbian and dopamine-modulated plasticity. The plasticity rules are local and do not use the backpropagation principle. Besides that, as in my previous studies, I was guided by the requirement that the all neuron/plasticity models should be easily implemented on modern neurochips. I illustrate the high performance of my network on a task related to model-based reinforcement learning, namely, evaluation of proximity of an external world state to the target state.
Language Models Benefit from Preparation with Elicited Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

The zero-shot chain of thought (CoT) approach is often used in question answering (QA) by language models (LMs) for tasks that require multiple reasoning steps. However, some QA tasks hinge more on accessing relevant knowledge than on chaining reasoning steps. We introduce a simple prompting technique, called PREP, that involves using two instances of LMs: the first (LM1) generates relevant information, and the second (LM2) receives the information from the user and answers the question. This design is intended to make better use of the LM's instruction-following capability. PREP is applicable across various QA tasks without domain-specific prompt engineering. PREP is developed on a dataset of 100 QA questions, derived from an extensive schematic dataset specifying artifact parts and material composition. These questions ask which of two artifacts is less likely to share materials with another artifact. Such questions probe the LM's knowledge of shared materials in the part structure of different artifacts. We test our method on our parts-and-materials dataset and three published commonsense reasoning datasets. The average accuracy of our method is consistently higher than that of all the other tested methods across all the tested datasets.
A Unified, Practical, and Understandable Model of Non-transactional Consistency Levels in Distributed Replication
Venue: arXiv cs.DC 2025
Year: 2025

We present a practical model of non-transactional consistency levels in the context of distributed data replication. Unlike prior work, our simple Shared Object Pool (SOP) model defines common consistency levels in a unified framework centered around the single concept of ordering. This naturally reflects modern cloud object storage services and is thus easy to understand. We show that a consistency level can be intuitively defined by specifying two types of constraints on the validity of orderings allowed by the level: convergence, which bounds the lineage shape of the ordering, and relationship, which bounds the relative positions between operations. We give examples of representative protocols and systems, and discuss their availability upper bound. To further demonstrate the expressiveness and practical relevance of our model, we use it to implement a Jepsen-integrated consistency checker for the four most common levels (linearizable, sequential, causal+, and eventual); the checker analyzes consistency conformity for small-scale histories of real system runs (etcd, ZooKeeper, and RabbitMQ).
Empirical evidence of Large Language Model's influence on human spoken communication
Venue: arXiv cs.CY 2025
Year: 2025

From the invention of writing and the printing press, to television and social media, human history is punctuated by major innovations in communication technology, which fundamentally altered how ideas spread and reshaped our culture. Recent chatbots powered by generative artificial intelligence constitute a novel medium that encodes cultural patterns in their neural representations and disseminates them in conversations with hundreds of millions of people. Understanding whether these patterns transmit into human language, and ultimately shape human culture, is a fundamental question. While fully quantifying the causal impact of a chatbot like ChatGPT on human culture is very challenging, lexicographic shift in human spoken communication may offer an early indicator of such broad phenomenon. Here, we apply econometric causal inference techniques to 740,249 hours of human discourse from 360,445 YouTube academic talks and 771,591 conversational podcast episodes across multiple disciplines. We detect a measurable and abrupt increase in the use of words preferentially generated by ChatGPT, such as delve, comprehend, boast, swift, and meticulous, after its release. These findings suggest a scenario where machines, originally trained on human data and subsequently exhibiting their own cultural traits, can, in turn, measurably reshape human culture. This marks the beginning of a closed cultural feedback loop in which cultural traits circulate bidirectionally between humans and machines. Our results motivate further research into the evolution of human-machine culture, and raise concerns over the erosion of linguistic and cultural diversity, and the risks of scalable manipulation.
CyberHost: Taming Audio-driven Avatar Diffusion Model with Region Codebook Attention
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion-based video generation technology has advanced significantly, catalyzing a proliferation of research in human animation. However, the majority of these studies are confined to same-modality driving settings, with cross-modality human body animation remaining relatively underexplored. In this paper, we introduce, an end-to-end audio-driven human animation framework that ensures hand integrity, identity consistency, and natural motion. The key design of CyberHost is the Region Codebook Attention mechanism, which improves the generation quality of facial and hand animations by integrating fine-grained local features with learned motion pattern priors. Furthermore, we have developed a suite of human-prior-guided training strategies, including body movement map, hand clarity score, pose-aligned reference feature, and local enhancement supervision, to improve synthesis results. To our knowledge, CyberHost is the first end-to-end audio-driven human diffusion model capable of facilitating zero-shot video generation within the scope of human body. Extensive experiments demonstrate that CyberHost surpasses previous works in both quantitative and qualitative aspects.