OpenRaiser/Intern-Atlas huggingface dataset:
Time to Revist Exact Match
Venue: arXiv cs.CL 2025
Year: 2025

Temporal question answering is an established method for evaluating temporal reasoning in large language models. Expected answers are often numeric (e.g., dates or durations), yet model responses are evaluated like regular text with exact match (EM), unable to distinguish small from large errors. In this investigative work, we frame temporal question answering as a numerical estimation task to assess the shortcomings of EM. We introduce TempAnswerQA, a benchmark distilled from Test of Time and TempTabQA, where all questions require a numerical, temporal answer, allowing us to evaluate models beyond EM. We use the forecasting metrics symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE). With sMAPE, we find that error size and EM are decoupled. Models with low EM still have low sMAPE (both ~20%), and some models have high sMAPE despite high EM. Scaling errors by the deviation of the ground truth data with MASE reshuffles model rankings compared to EM, revealing gaps in models' understanding of temporal domain knowledge, especially when trained with synthetic data. Lastly, the models' most frequent error is to deviate by only $\pm1$ from the ground truth. sMAPE and MASE, unlike EM, adequately weight these errors. Our findings underscore the need for specialised metrics for temporal QA tasks. Code and data are available on https://github.com/aauss/temporal-answer-qa.
Discrepancy And Fair Division For Non-Additive Valuations
Venue: arXiv cs.GT 2025
Year: 2025

We extend the notion of combinatorial discrepancy to \emph{non-additive} functions. Our main result is an upper bound of $O(\sqrt{n \log(nk)})$ on the non-additive $k$-color discrepancy when $k$ is a prime power. We demonstrate two applications of this result to problems in fair division. First, we establish a bound for a consensus halving problem, where fairness is measured by the minimum number of items that must be transferred between the two parts to eliminate envy. Second, we improve the upper bound on the total subsidy required to achieve an envy-free allocation when the number of agents is a prime power, obtaining an $O(n \sqrt{n \log n})$ bound. This constitutes the first known subquadratic guarantee in this setting.
Survivors, Complainers, and Borderliners: Upward Bias in Online Discussions of Academic Conference Reviews
Venue: arXiv cs.SI 2025
Year: 2025

Online discussion platforms, such as community Q&A sites and forums, have become important hubs where academic conference authors share and seek information about the peer review process and outcomes. However, these discussions involve only a subset of all submissions, raising concerns about the representativeness of the self-reported review scores. In this paper, we conduct a systematic study comparing the review score distributions of self-reported submissions in online discussions (based on data collected from Zhihu and Reddit) with those of all submissions. We reveal a consistent upward bias: the score distribution of self-reported samples is shifted upward relative to the population score distribution, with this difference statistically significant in most cases. Our analysis identifies three distinct contributors to this bias: (1) survivors, authors of accepted papers who are more likely to share good results than those of rejected papers who tend to conceal bad ones; (2) complainers, authors of high-scoring rejected papers who are more likely to voice complaints about the peer review process or outcomes than those of low scores; and (3) borderliners, authors with borderline scores who face greater uncertainty prior to decision announcements and are more likely to seek advice during the rebuttal period. These findings have important implications for how information seekers should interpret online discussions of academic conference reviews.
seqBench: A Tunable Benchmark to Quantify Sequential Reasoning Limits of LLMs
Venue: arXiv cs.AI 2025
Year: 2025

We introduce seqBench, a parametrized benchmark for probing sequential reasoning limits in Large Language Models (LLMs) through precise, multi-dimensional control over several key complexity dimensions. seqBench allows systematic variation of (1) the logical depth, defined as the number of sequential actions required to solve the task; (2) the number of backtracking steps along the optimal path, quantifying how often the agent must revisit prior states to satisfy deferred preconditions (e.g., retrieving a key after encountering a locked door); and (3) the noise ratio, defined as the ratio between supporting and distracting facts about the environment. Our evaluations on state-of-the-art LLMs reveal a universal failure pattern: accuracy collapses exponentially beyond a model-specific logical depth. Unlike existing benchmarks, seqBench's fine-grained control facilitates targeted analyses of these reasoning failures, illuminating universal scaling laws and statistical limits, as detailed in this paper alongside its generation methodology and evaluation metrics. We find that even top-performing models systematically fail on seqBench's structured reasoning tasks despite minimal search complexity, underscoring key limitations in their commonsense reasoning capabilities. Designed for future evolution to keep pace with advancing models, the seqBench datasets are publicly released to spur deeper scientific inquiry into LLM reasoning, aiming to establish a clearer understanding of their true potential and current boundaries for robust real-world application.
SAM-DCE: Addressing Token Uniformity and Semantic Over-Smoothing in Medical Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

The Segment Anything Model (SAM) demonstrates impressive zero-shot segmentation ability on natural images but encounters difficulties in medical imaging due to domain shifts, anatomical variability, and its reliance on user-provided prompts. Recent prompt-free adaptations alleviate the need for expert intervention, yet still suffer from limited robustness and adaptability, often overlooking the issues of semantic over-smoothing and token uniformity. We propose SAM-DCE, which balances local discrimination and global semantics while mitigating token uniformity, enhancing inter-class separability, and enriching mask decoding with fine-grained, consistent representations. Extensive experiments on diverse medical benchmarks validate its effectiveness.
Analysis of an Architecture for Integrated Sensing and Communication in 5G OpenRAN
Venue: arXiv cs.NI 2025
Year: 2025

This paper analyzes the functional requirements and architectural considerations for Integrated Sensing and Communication ( ISAC) in a 5G Open Radio Access Network (OpenRAN) environment, with emphasis on secure and modular deployment. Focusing on a mono-static, half-duplex sensing approach, it evaluates radar setup options, signal types, and processing placement within the Radio Access Network ( RAN), considering performance and security implications. The proposed architecture minimizes hardware modifications by leveraging sniffer Radio Units (RU s) and existing OpenRAN fronthaul interfaces, while protecting sensitive In-phase and Quadrature (I/Q) data and control traffic against potential attacks. Security threats, such as passive sensing, spoofing, and privacy violations, are mapped to mitigation strategies within the OpenRAN framework. The result is a deployment blueprint applicable to both Public Land Mobile Networks ( PLMNs) and Non-Public Networks (NPNs), supporting future 6G ISAC capabilities in a standards-compliant manner.
Equip Pre-ranking with Target Attention by Residual Quantization
Venue: arXiv cs.IR 2025
Year: 2025

The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel at capturing complex feature interactions in the ranking stage, their high computational cost makes them infeasible for pre-ranking, which often relies on simplistic vector-product models. This disparity creates a significant performance bottleneck for the entire system. To bridge this gap, we propose TARQ, a novel pre-ranking framework. Inspired by generative models, TARQ's key innovation is to equip pre-ranking with an architecture approximate to TA by Residual Quantization. This allows us to bring the modeling power of TA into the latency-critical pre-ranking stage for the first time, establishing a new state-of-the-art trade-off between accuracy and efficiency. Extensive offline experiments and large-scale online A/B tests at Taobao demonstrate TARQ's significant improvements in ranking performance. Consequently, our model has been fully deployed in production, serving tens of millions of daily active users and yielding substantial business improvements.
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Venue: arXiv cs.SE 2025
Year: 2025

We introduce SWE-Bench Pro, a substantially more challenging benchmark that builds upon the best practices of SWE-BENCH [25], but is explicitly designed to capture realistic, complex, enterprise-level problems beyond the scope of SWE-BENCH. SWE-BENCH PRO contains 1,865 problems sourced from a diverse set of 41 actively maintained repositories spanning business applications, B2B services, and developer tools. The benchmark is partitioned into a public set with open access to problems sourced from 11 repositories, a held-out set of 12 repositories and a commercial set of 18 proprietary repositories where we have formal partnership agreements with early-stage startups. Problems in the held-out and the commercial set are not publicly accessible, but we release results on the commercial set. Our benchmark features long-horizon tasks that may require hours to days for a professional software engineer to complete, often involving patches across multiple files and substantial code modifications. All tasks are human-verified and augmented with sufficient context to ensure resolvability. To better understand these limitations, we cluster the failure modes observed in the collected agent trajectories for a clearer characterization of the error patterns exhibited by current models. Overall, SWE-BENCH PRO provides a contamination-resistant testbed that more faithfully captures the complexity and diversity of real-world software development, advancing the pursuit of truly autonomous software engineering agents at a professional level.
Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners
Venue: arXiv cs.SD 2025
Year: 2025

Speech intelligibility evaluation for hearing-impaired (HI) listeners is essential for assessing hearing aid performance, traditionally relying on listening tests or intrusive methods like HASPI. However, these methods require clean reference signals, which are often unavailable in real-world conditions, creating a gap between lab-based and real-world assessments. To address this, we propose a non-intrusive intelligibility prediction framework that leverages speech enhancers to provide a parallel enhanced-signal pathway, enabling robust predictions without reference signals. We evaluate three state-of-the-art enhancers and demonstrate that prediction performance depends on the choice of enhancer, with ensembles of strong enhancers yielding the best results. To improve cross-dataset generalization, we introduce a 2-clips augmentation strategy that enhances listener-specific variability, boosting robustness on unseen datasets. Our approach consistently outperforms the non-intrusive baseline, CPC2 Champion across multiple datasets, highlighting the potential of enhancer-guided non-intrusive intelligibility prediction for real-world applications.
Static Security Vulnerability Scanning of Proprietary and Open-Source Software: An Adaptable Process with Variants and Results
Venue: arXiv cs.SE 2025
Year: 2025

Software vulnerabilities remain a significant risk factor in achieving security objectives within software development organizations. This is especially true where either proprietary or open-source software (OSS) is included in the technological environment. In this paper an end-to-end process with supporting methods and tools is presented. This industry proven generic process allows for the custom instantiation, configuration, and execution of routinized code scanning for software vulnerabilities and their prioritized remediation. A select set of tools are described for this key DevSecOps function and placed into an iterative process. Examples of both industrial proprietary applications and open-source applications are provided including specific vulnerability instances and a discussion of their treatment. The benefits of each selected tool are considered, and alternative tools are also introduced. Application of this method in a comprehensive SDLC model is also reviewed along with prospective enhancements from automation and the application of advanced technologies including AI. Adoption of this method can be achieved with minimal adjustments and with maximum flexibility for results in reducing source code vulnerabilities, reducing supply chain risk, and improving the security profile of new or legacy solutions.
A Cross-Hierarchical Difference Feature Fusion Network Based on Multiscale Encoder-Decoder for Hyperspectral Change Detection
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral change detection (HCD) is one of the core applications of remote sensing images, holding significant research value in fields like environmental monitoring and disaster assessment. However, existing methods often suffer from incomplete capture of multiscale spatial-spectral features and insufficient fusion of differential feature information. To address these challenges, this paper proposes a Cross-Hierarchical Differential Feature Fusion Network (CHDFFN) based on a multiscale encoder-decoder. Firstly, a multiscale feature extraction subnetwork is designed, taking the customized encoder-decoder as the backbone, combined with residual connections and the proposed dual-core channel-spatial attention module to achieve multi-level extraction and initial integration of spatial-spectral features. The encoder embeds convolutional blocks with different receptive field sizes to capture multiscale representations from shallow details to deep semantics. The decoder fuses the encoder's output via skip connections to gradually restore spatial resolution while suppressing background noise and redundancy. To enhance the model's ability to capture differential features between bi-temporal hyperspectral images, a spatial-spectral change feature learning module is designed to learn hierarchical change representations. Additionally, an adaptive high-level feature fusion module is proposed, dynamically balancing the contribution of hierarchical differential features by adaptively assigning weights, which effectively strengthens the model's capability to characterize complex change patterns. Finally, experiments on four public hyperspectral datasets show that compared with some state-of-the-art methods, the average maximum improvements of OA, KC, and F1 are 4.61%, 19.79%, and 18.90% respectively, verifying the model's effectiveness.
When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration
Venue: arXiv cs.CV 2025
Year: 2025

Adverse Weather Image Restoration (AWIR) is a highly challenging task due to the unpredictable and dynamic nature of weather-related degradations. Traditional task-specific methods often fail to generalize to unseen or complex degradation types, while recent prompt-learning approaches depend heavily on the degradation estimation capabilities of vision-language models, resulting in inconsistent restorations. In this paper, we propose \textbf{LCDiff}, a novel framework comprising two key components: \textit{Lumina-Chroma Decomposition Network} (LCDN) and \textit{Lumina-Guided Diffusion Model} (LGDM). LCDN processes degraded images in the YCbCr color space, separately handling degradation-related luminance and degradation-invariant chrominance components. This decomposition effectively mitigates weather-induced degradation while preserving color fidelity. To further enhance restoration quality, LGDM leverages degradation-related luminance information as a guiding condition, eliminating the need for explicit degradation prompts. Additionally, LGDM incorporates a \textit{Dynamic Time Step Loss} to optimize the denoising network, ensuring a balanced recovery of both low- and high-frequency features in the image. Finally, we present DriveWeather, a comprehensive all-weather driving dataset designed to enable robust evaluation. Extensive experiments demonstrate that our approach surpasses state-of-the-art methods, setting a new benchmark in AWIR. The dataset and code are available at: https://github.com/fiwy0527/LCDiff.
The Transfer Neurons Hypothesis: An Underlying Mechanism for Language Latent Space Transitions in Multilingual LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Recent studies have suggested a processing framework for multilingual inputs in decoder-based LLMs: early layers convert inputs into English-centric and language-agnostic representations; middle layers perform reasoning within an English-centric latent space; and final layers generate outputs by transforming these representations back into language-specific latent spaces. However, the internal dynamics of such transformation and the underlying mechanism remain underexplored. Towards a deeper understanding of this framework, we propose and empirically validate The Transfer Neurons Hypothesis: certain neurons in the MLP module are responsible for transferring representations between language-specific latent spaces and a shared semantic latent space. Furthermore, we show that one function of language-specific neurons, as identified in recent studies, is to facilitate movement between latent spaces. Finally, we show that transfer neurons are critical for reasoning in multilingual LLMs.
KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration
Venue: arXiv cs.AI 2025
Year: 2025

We propose KAHAN, a knowledge-augmented hierarchical framework that systematically extracts insights from raw tabular data at entity, pairwise, group, and system levels. KAHAN uniquely leverages LLMs as domain experts to drive the analysis. On DataTales financial reporting benchmark, KAHAN outperforms existing approaches by over 20% on narrative quality (GPT-4o), maintains 98.2% factuality, and demonstrates practical utility in human evaluation. Our results reveal that knowledge quality drives model performance through distillation, hierarchical analysis benefits vary with market complexity, and the framework transfers effectively to healthcare domains. The data and code are available at https://github.com/yajingyang/kahan.
AgriDoctor: A Multimodal Intelligent Assistant for Agriculture
Venue: arXiv cs.CV 2025
Year: 2025

Accurate crop disease diagnosis is essential for sustainable agriculture and global food security. Existing methods, which primarily rely on unimodal models such as image-based classifiers and object detectors, are limited in their ability to incorporate domain-specific agricultural knowledge and lack support for interactive, language-based understanding. Recent advances in large language models (LLMs) and large vision-language models (LVLMs) have opened new avenues for multimodal reasoning. However, their performance in agricultural contexts remains limited due to the absence of specialized datasets and insufficient domain adaptation. In this work, we propose AgriDoctor, a modular and extensible multimodal framework designed for intelligent crop disease diagnosis and agricultural knowledge interaction. As a pioneering effort to introduce agent-based multimodal reasoning into the agricultural domain, AgriDoctor offers a novel paradigm for building interactive and domain-adaptive crop health solutions. It integrates five core components: a router, classifier, detector, knowledge retriever and LLMs. To facilitate effective training and evaluation, we construct AgriMM, a comprehensive benchmark comprising 400000 annotated disease images, 831 expert-curated knowledge entries, and 300000 bilingual prompts for intent-driven tool selection. Extensive experiments demonstrate that AgriDoctor, trained on AgriMM, significantly outperforms state-of-the-art LVLMs on fine-grained agricultural tasks, establishing a new paradigm for intelligent and sustainable farming applications.
RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulated Environments
Venue: arXiv cs.RO 2025
Year: 2025

RoboManipBaselines is an open framework for robot imitation learning that unifies data collection, training, and evaluation across simulation and real robots. We introduce it as a platform enabling systematic benchmarking of diverse tasks, robots, and multimodal policies with emphasis on integration, generality, extensibility, and reproducibility.
TSGym: Design Choices for Deep Multivariate Time-Series Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Recently, deep learning has driven significant advancements in multivariate time series forecasting (MTSF) tasks. However, much of the current research in MTSF tends to evaluate models from a holistic perspective, which obscures the individual contributions and leaves critical issues unaddressed. Adhering to the current modeling paradigms, this work bridges these gaps by systematically decomposing deep MTSF methods into their core, fine-grained components like series-patching tokenization, channel-independent strategy, attention modules, or even Large Language Models and Time-series Foundation Models. Through extensive experiments and component-level analysis, our work offers more profound insights than previous benchmarks that typically discuss models as a whole. Furthermore, we propose a novel automated solution called TSGym for MTSF tasks. Unlike traditional hyperparameter tuning, neural architecture searching or fixed model selection, TSGym performs fine-grained component selection and automated model construction, which enables the creation of more effective solutions tailored to diverse time series data, therefore enhancing model transferability across different data sources and robustness against distribution shifts. Extensive experiments indicate that TSGym significantly outperforms existing state-of-the-art MTSF and AutoML methods. All code is publicly available on https://github.com/SUFE-AILAB/TSGym.