OpenRaiser/Intern-Atlas huggingface dataset:
Contextual Pattern Mining and Counting
Venue: arXiv cs.DS 2025
Year: 2025

Given a string $P$ of length $m$, a longer string $T$ of length $n>m$, and two integers $l\geq 0$ and $r\geq 0$, the context of $P$ in $T$ is the set of all string pairs $(L,R)$, with $|L|=l$ and $|R|=r$, such that the string $LPR$ occurs in $T$. We introduce two problems related to the notion of context: (1) the Contextual Pattern Mining (CPM) problem, which given $T$, $(m,l,r)$, and an integer $τ>0$, asks for outputting the context of each substring $P$ of length $m$ of $T$, provided that the size of the context of $P$ is at least $τ$; and (2) the Contextual Pattern Counting (CPC) problem, which asks for preprocessing $T$ so that the size of the context of a given query string $P$ of length $m$ can be found efficiently. For CPM, we propose a linear-work algorithm that either uses only internal memory, or a bounded amount of internal memory and external memory, which allows much larger datasets to be handled. For CPC, we propose an $\widetilde{\mathcal{O}}(n)$-space index that can be constructed in $\widetilde{\mathcal{O}}n)$ time and answers queries in $\mathcal{O}(m)+\widetilde{\mathcal{O}}(1)$ time. We further improve the practical performance of the CPC index by optimizations that exploit the LZ77 factorization of $T$ and an upper bound on the query length. Using billion-letter datasets from different domains, we show that the external memory version of our CPM algorithm can deal with very large datasets using a small amount of internal memory while its runtime is comparable to that of the internal memory version. Interestingly, we also show that our optimized index for CPC outperforms an approach based on the state of the art for the reporting version of CPC [Navarro, SPIRE 2020] in terms of query time, index size, construction time, and construction space, often by more than an order of magnitude.
Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation
Venue: arXiv cs.LG 2025
Year: 2025

Chronic diseases are long-term, manageable, yet typically incurable conditions, highlighting the need for effective preventive strategies. Machine learning has been widely used to assess individual risk for chronic diseases. However, many models rely on medical test data (e.g. blood results, glucose levels), which limits their utility for proactive self-assessment. Additionally, to gain public trust, machine learning models should be explainable and transparent. Although some research on self-assessment machine learning models includes explainability, their explanations are not validated against established medical literature, reducing confidence in their reliability. To address these issues, we develop deep learning models that predict the risk of developing 13 chronic diseases using only personal and lifestyle factors, enabling accessible, self-directed preventive care. Importantly, we use SHAP-based explainability to identify the most influential model features and validate them against established medical literature. Our results show a strong alignment between the models' most influential features and established medical literature, reinforcing the models' trustworthiness. Critically, we find that this observation holds across 13 distinct diseases, indicating that this machine learning approach can be broadly trusted for chronic disease prediction. This work lays the foundation for developing trustworthy machine learning tools for self-directed preventive care. Future research can explore other approaches for models' trustworthiness and discuss how the models can be used ethically and responsibly.
Imitation Learning for Active Neck Motion Enabling Robot Manipulation beyond the Field of View
Venue: arXiv cs.RO 2025
Year: 2025

Most prior research in deep imitation learning has predominantly utilized fixed cameras for image input, which constrains task performance to the predefined field of view. However, enabling a robot to actively maneuver its neck can significantly expand the scope of imitation learning to encompass a wider variety of tasks and expressive actions such as neck gestures. To facilitate imitation learning in robots capable of neck movement while simultaneously performing object manipulation, we propose a teaching system that systematically collects datasets incorporating neck movements while minimizing discomfort caused by dynamic viewpoints during teleoperation. In addition, we present a novel network model for learning manipulation tasks including active neck motion. Experimental results showed that our model can achieve a high success rate of around 90\%, regardless of the distraction from the viewpoint variations by active neck motion. Moreover, the proposed model proved particularly effective in challenging scenarios, such as when objects were situated at the periphery or beyond the standard field of view, where traditional models struggled. The proposed approach contributes to the efficiency of dataset collection and extends the applicability of imitation learning to more complex and dynamic scenarios.
Improving Compiler Bug Isolation by Leveraging Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Compilers play a foundational role in building reliable software systems, and bugs within them can lead to catastrophic consequences. The compilation process typically involves hundreds of files, making traditional automated bug isolation techniques inapplicable due to scalability or effectiveness issues. Current mainstream compiler bug localization techniques have limitations in test program mutation and resource consumption. Inspired by the recent advances of pre-trained Large Language Models (LLMs), we propose an innovative approach named AutoCBI, which (1) uses LLMs to summarize compiler file functions and (2) employs specialized prompts to guide LLM in reordering suspicious file rankings. This approach leverages four types of information: the failing test program, source file function summaries, lists of suspicious files identified through analyzing test coverage, as well as compilation configurations with related output messages, resulting in a refined ranking of suspicious files. Our evaluation of AutoCBI against state-of-the-art approaches (DiWi, RecBi and FuseFL) on 120 real-world bugs from the widely-used GCC and LLVM compilers demonstrates its effectiveness. Specifically, AutoCBI isolates 66.67%/69.23%, 300%/340%, and 100%/57.14% more bugs than RecBi, DiWi, and FuseFL, respectively, in the Top-1 ranked results for GCC/LLVM. Additionally, the ablation study underscores the significance of each component in our approach.
FaithfulSAE: Towards Capturing Faithful Features with Sparse Autoencoders without External Dataset Dependencies
Venue: arXiv cs.LG 2025
Year: 2025

Sparse Autoencoders (SAEs) have emerged as a promising solution for decomposing large language model representations into interpretable features. However, Paulo and Belrose (2025) have highlighted instability across different initialization seeds, and Heap et al. (2025) have pointed out that SAEs may not capture model-internal features. These problems likely stem from training SAEs on external datasets - either collected from the Web or generated by another model - which may contain out-of-distribution (OOD) data beyond the model's generalisation capabilities. This can result in hallucinated SAE features, which we term "Fake Features", that misrepresent the model's internal activations. To address these issues, we propose FaithfulSAE, a method that trains SAEs on the model's own synthetic dataset. Using FaithfulSAEs, we demonstrate that training SAEs on less-OOD instruction datasets results in SAEs being more stable across seeds. Notably, FaithfulSAEs outperform SAEs trained on web-based datasets in the SAE probing task and exhibit a lower Fake Feature Ratio in 5 out of 7 models. Overall, our approach eliminates the dependency on external datasets, advancing interpretability by better capturing model-internal features while highlighting the often neglected importance of SAE training datasets.
Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test
Venue: arXiv cs.LG 2025
Year: 2025

This paper introduces a novel deep-learning approach to predict true stress-strain curves of high-strength steels from small punch test (SPT) load-displacement data. The proposed approach uses Gramian Angular Field (GAF) to transform load-displacement sequences into images, capturing spatial-temporal features and employs a Sequence-to-Sequence (Seq2Seq) model with an LSTM-based encoder-decoder architecture, enhanced by multi-head cross-attention to improved accuracy. Experimental results demonstrate that the proposed approach achieves superior prediction accuracy, with minimum and maximum mean absolute errors of 0.15 MPa and 5.58 MPa, respectively. The proposed method offers a promising alternative to traditional experimental techniques in materials science, enhancing the accuracy and efficiency of true stress-strain relationship predictions.
Zero-Shot Conversational Stance Detection: Dataset and Approaches
Venue: arXiv cs.CL 2025
Year: 2025

Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the increasing number of online debates among social media users, conversational stance detection has become a crucial research area. However, existing conversational stance detection datasets are restricted to a limited set of specific targets, which constrains the effectiveness of stance detection models when encountering a large number of unseen targets in real-world applications. To bridge this gap, we manually curate a large-scale, high-quality zero-shot conversational stance detection dataset, named ZS-CSD, comprising 280 targets across two distinct target types. Leveraging the ZS-CSD dataset, we propose SITPCL, a speaker interaction and target-aware prototypical contrastive learning model, and establish the benchmark performance in the zero-shot setting. Experimental results demonstrate that our proposed SITPCL model achieves state-of-the-art performance in zero-shot conversational stance detection. Notably, the SITPCL model attains only an F1-macro score of 43.81%, highlighting the persistent challenges in zero-shot conversational stance detection.
Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages
Venue: arXiv cs.CL 2025
Year: 2025

Part-of-speech (POS) tagging remains a foundational component in natural language processing pipelines, particularly critical for historical text analysis at the intersection of computational linguistics and digital humanities. Despite significant advancements in modern large language models (LLMs) for ancient languages, their application to Medieval Romance languages presents distinctive challenges stemming from diachronic linguistic evolution, spelling variations, and labeled data scarcity. This study systematically investigates the central determinants of POS tagging performance across diverse corpora of Medieval Occitan, Medieval Spanish, and Medieval French texts, spanning biblical, hagiographical, medical, and dietary domains. Through rigorous experimentation, we evaluate how fine-tuning approaches, prompt engineering, model architectures, decoding strategies, and cross-lingual transfer learning techniques affect tagging accuracy. Our results reveal both notable limitations in LLMs' ability to process historical language variations and non-standardized spelling, as well as promising specialized techniques that effectively address the unique challenges presented by low-resource historical languages.
Physics-informed mixture of experts network for interpretable battery degradation trajectory computation amid second-life complexities
Venue: arXiv cs.LG 2025
Year: 2025

Retired electric vehicle batteries offer immense potential to support low-carbon energy systems, but uncertainties in their degradation behavior and data inaccessibilities under second-life use pose major barriers to safe and scalable deployment. This work proposes a Physics-Informed Mixture of Experts (PIMOE) network that computes battery degradation trajectories using partial, field-accessible signals in a single cycle. PIMOE leverages an adaptive multi-degradation prediction module to classify degradation modes using expert weight synthesis underpinned by capacity-voltage and relaxation data, producing latent degradation trend embeddings. These are input to a use-dependent recurrent network for long-term trajectory prediction. Validated on 207 batteries across 77 use conditions and 67,902 cycles, PIMOE achieves an average mean absolute percentage (MAPE) errors of 0.88% with a 0.43 ms inference time. Compared to the state-of-the-art Informer and PatchTST, it reduces computational time and MAPE by 50%, respectively. Compatible with random state of charge region sampling, PIMOE supports 150-cycle forecasts with 1.50% average and 6.26% maximum MAPE, and operates effectively even with pruned 5MB training data. Broadly, PIMOE framework offers a deployable, history-free solution for battery degradation trajectory computation, redefining how second-life energy storage systems are assessed, optimized, and integrated into the sustainable energy landscape.
Maintaining a Bounded Degree Expander in Dynamic Peer-to-Peer Networks
Venue: arXiv cs.DC 2025
Year: 2025

We study the problem of maintaining robust and sparse overlay networks in fully distributed settings where nodes continuously join and leave the system. This scenario closely models real-world unstructured peer-to-peer networks, where maintaining a well-connected yet low-degree communication graph is crucial. We generalize a recent protocol by Becchetti et al. [SODA 2020] that relies on a simple randomized connection strategy to build an expander topology with high probability to a dynamic networks with churn setting. In this work, the network dynamism is governed by an oblivious adversary that controls which nodes join and leave the system in each round. The adversary has full knowledge of the system and unbounded computational power, but cannot see the random choices made by the protocol. Our analysis builds on the framework of Augustine et al. [FOCS 2015], and shows that our distributed algorithm maintains a constant-degree expander graph with high probability, despite a continuous adversarial churn with a rate of up to $\mathcal{O}(n/polylog(n))$ per round, where $n$ is the stable network size. The protocol and proof techniques are not new, but together they resolve a specific open problem raised in prior work. The result is a simple, fully distributed, and churn-resilient protocol with provable guarantees that align with observed empirical behavior.
PAGENT: Learning to Patch Software Engineering Agents
Venue: arXiv cs.SE 2025
Year: 2025

LLM Agents produce patches automatically to resolve an issue. However, they can generate inaccurate patches. Little is known about the root causes behind those failed patches or how those could be fixed. This paper reports an empirical study of the failed patches generated by seven top LLM code agents. We collected 114 issues from the SWE-bench Lite dataset that remained unresolved across the agents. The seven agents produced a total of 769 failed patches for those issues, which we checked with a combination of GPT-4o and manual analysis. We present a taxonomy of the failure reasons across the patches. The taxonomy contains six categories, with several sub-categories under each category. For example, a frequently observed category is the inability of an LLM to correctly infer/produce the appropriate variable type in the produced patch. As a first step towards addressing such type-related errors, we designed PAGENT (Patch Agent). PAGENT utilizes program analysis techniques like CFG creation and exploration to infer the type of information of a patch. PAGENT does this by applying repository-level static code analysis techniques. Then, PAGENT refines the inferred type by further utilizing an LLM-based inference technique. We tested PAGENT on all 127 type-related failed patches from the top three agents in our study. PAGENT could fix 29 of the 127 failed patches.
Beyond instruction-conditioning, MoTE: Mixture of Task Experts for Multi-task Embedding Models
Venue: arXiv cs.LG 2025
Year: 2025

Dense embeddings are fundamental to modern machine learning systems, powering Retrieval-Augmented Generation (RAG), information retrieval, and representation learning. While instruction-conditioning has become the dominant approach for embedding specialization, its direct application to low-capacity models imposes fundamental representational constraints that limit the performance gains derived from specialization. In this paper, we analyze these limitations and introduce the Mixture of Task Experts (MoTE) transformer block, which leverages task-specialized parameters trained with Task-Aware Contrastive Learning (\tacl) to enhance the model ability to generate specialized embeddings. Empirical results show that MoTE achieves $64\%$ higher performance gains in retrieval datasets ($+3.27 \rightarrow +5.21$) and $43\%$ higher performance gains across all datasets ($+1.81 \rightarrow +2.60$). Critically, these gains are achieved without altering instructions, training data, inference time, or number of active parameters.
Efficient Strategy Synthesis for MDPs via Hierarchical Block Decomposition
Venue: arXiv cs.AI 2025
Year: 2025

Software-intensive systems, such as software product lines and robotics, utilise Markov decision processes (MDPs) to capture uncertainty and analyse sequential decision-making problems. Despite the usefulness of conventional policy synthesis methods, they fail to scale to large state spaces. Our approach addresses this issue and accelerates policy synthesis in large MDPs by dynamically refining the MDP and iteratively selecting the most fragile MDP regions for refinement. This iterative procedure offers a balance between accuracy and efficiency, as refinement occurs only when necessary. Through a comprehensive empirical evaluation comprising diverse case studies and MDPs up to 1M states, we demonstrate significant performance improvements yielded by our approach compared to the leading probabilistic model checker PRISM (up to 2x), thus offering a very competitive solution for real-world policy synthesis tasks in larger MDPs.
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) enables collaborative learning without exposing clients' data. While clients only share model updates with the aggregator, studies reveal that aggregators can infer sensitive information from these updates. Secure Aggregation (SA) protects individual updates during transmission; however, recent work demonstrates a critical vulnerability where adversarial aggregators manipulate client selection to bypass SA protections, constituting a Biased Selection Attack (BSA). Although verifiable random selection prevents BSA, it precludes informed client selection essential for FL performance. We propose Adversarial Robust Federated Learning (AdRo-FL), which simultaneously enables: informed client selection based on client utility, and robust defense against BSA maintaining privacy-preserving aggregation. AdRo-FL implements two client selection frameworks tailored for distinct settings. The first framework assumes clients are grouped into clusters based on mutual trust, such as different branches of an organization. The second framework handles distributed clients where no trust relationships exist between them. For the cluster-oriented setting, we propose a novel defense against BSA by (1) enforcing a minimum client selection quota from each cluster, supervised by a cluster-head in every round, and (2) introducing a client utility function to prioritize efficient clients. For the distributed setting, we design a two-phase selection protocol: first, the aggregator selects the top clients based on our utility-driven ranking; then, a verifiable random function (VRF) ensures a BSA-resistant final selection. AdRo-FL also applies quantization to reduce communication overhead and sets strict transmission deadlines to improve energy efficiency. AdRo-FL achieves up to $1.85\times$ faster time-to-accuracy and up to $1.06\times$ higher final accuracy compared to insecure baselines.
The value of human and machine in machine-generated creative contents
Venue: arXiv cs.CY 2025
Year: 2025

The seemingly "imagination" and "creativity" from machine-generated contents should not be misattributed to the accomplishment of machine. They are accomplishments of both human and machine. Without human interpretation, the machine-generated contents remain in the imaginary space of the large language models, and cannot automatically establish grounding in the reality and human experience.
Flatness After All?
Venue: arXiv cs.LG 2025
Year: 2025

Recent literature generalization in deep learning has examined the relationship between the curvature of the loss function at minima and generalization, mainly in the context of overparameterized neural networks. A key observation is that "flat" minima tend to generalize better than "sharp" minima. While this idea is supported by empirical evidence, it has also been shown that deep networks can generalize even with arbitrary sharpness, as measured by either the trace or the spectral norm of the Hessian. In this paper, we argue that generalization could be assessed by measuring flatness using a soft rank measure of the Hessian. We show that when an exponential family neural network model is exactly calibrated, and its prediction error and its confidence on the prediction are not correlated with the first and the second derivative of the network's output, our measure accurately captures the asymptotic expected generalization gap. For non-calibrated models, we connect a soft rank based flatness measure to the well-known Takeuchi Information Criterion and show that it still provides reliable estimates of generalization gaps for models that are not overly confident. Experimental results indicate that our approach offers a robust estimate of the generalization gap compared to baselines.
SLAP: Siamese Language-Audio Pretraining Without Negative Samples for Music Understanding
Venue: arXiv cs.SD 2025
Year: 2025

Joint embedding spaces have significantly advanced music understanding and generation by linking text and audio through multimodal contrastive learning. However, these approaches face large memory requirement limitations due to relying on large batch sizes to effectively utilize negative samples. Further, multimodal joint embedding spaces suffer from a modality gap wherein embeddings from different modalities lie in different manifolds of the embedding space. To address these challenges, we propose Siamese Language-Audio Pretraining (SLAP), a novel multimodal pretraining framework that allows learning powerful representations without negative samples. SLAP adapts the Bootstrap Your Own Latent (BYOL) paradigm for multimodal audio-text training, promoting scalability in training multimodal embedding spaces. We illustrate the ability of our model to learn meaningful relationships between music and text -- specifically, we show that SLAP outperforms CLAP on tasks such as text-music retrieval and zero-shot classification. We also observe competitive downstream performance on several MIR tasks, including with larger or supervised models (genre and instrument classification, auto-tagging). Additionally, our approach has attractive properties, such as a quantifiably reduced modality gap and improved robustness to batch size variations on retrieval performance. Finally, its novel formulation unlocks large-scale training on a single GPU through gradient accumulation.