OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging large language models for structured information extraction from pathology reports
Venue: arXiv cs.CL 2025
Year: 2025

Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-consuming and expensive, limiting scalability. Large language models (LLMs) offer efficient automated extraction through zero-shot prompting, requiring only natural language instructions without labeled data or training. We evaluate LLMs' accuracy in extracting structured information from breast cancer histopathology reports, compared to manual extraction by a trained human annotator. Methods: We developed the Medical Report Information Extractor, a web application leveraging LLMs for automated extraction. We developed a gold standard extraction dataset to evaluate the human annotator alongside five LLMs including GPT-4o, a leading proprietary model, and the Llama 3 model family, which allows self-hosting for data privacy. Our assessment involved 111 histopathology reports from the Breast Cancer Now (BCN) Generations Study, extracting 51 pathology features specified in the study's data dictionary. Results: Evaluation against the gold standard dataset showed that both Llama 3.1 405B (94.7% accuracy) and GPT-4o (96.1%) achieved extraction accuracy comparable to the human annotator (95.4%; p = 0.146 and p = 0.106, respectively). While Llama 3.1 70B (91.6%) performed below human accuracy (p <0.001), its reduced computational requirements make it a viable option for self-hosting. Conclusion: We developed an open-source tool for structured information extraction that can be customized by non-programmers using natural language. Its modular design enables reuse for various extraction tasks, producing standardized, structured data from unstructured text reports to facilitate analytics through improved accessibility and interoperability.
Self-supervised Attribute-aware Dynamic Preference Ranking Alignment
Venue: arXiv cs.CL 2025
Year: 2025

Reinforcement Learning from Human Feedback and its variants excel in aligning with human intentions to generate helpful, harmless, and honest responses. However, most of them rely on costly human-annotated pairwise comparisons for supervised alignment, which is not suitable for list-level scenarios, such as community question answering. Additionally, human preferences are influenced by multiple intrinsic factors in responses, leading to decision-making inconsistencies. Therefore, we propose \textbf{Se}lf-supervised \textbf{A}ttribute-aware \textbf{d}ynamic \textbf{p}reference \textbf{ra}nking, called \shortname. \ It quantifies preference differences between responses based on Attribute-Perceptual Distance Factors (APDF) and dynamically determines the list-wise alignment order. Furthermore, it achieves fine-grained preference difference learning and enables precise alignment with the optimal one. We specifically constructed a challenging code preference dataset named StaCoCoQA, and introduced more cost-effective and scalable preference evaluation metrics: PrefHit and PrefRecall. Extensive experimental results show that SeAdpra exhibits superior performance and generalizability on both StaCoCoQA and preference datasets from eight popular domains.
AI and the Law: Evaluating ChatGPT's Performance in Legal Classification
Venue: arXiv cs.CL 2025
Year: 2025

The use of ChatGPT to analyze and classify evidence in criminal proceedings has been a topic of ongoing discussion. However, to the best of our knowledge, this issue has not been studied in the context of the Polish language. This study addresses this research gap by evaluating the effectiveness of ChatGPT in classifying legal cases under the Polish Penal Code. The results show excellent binary classification accuracy, with all positive and negative cases correctly categorized. In addition, a qualitative evaluation confirms that the legal basis provided for each case, along with the relevant legal content, was appropriate. The results obtained suggest that ChatGPT can effectively analyze and classify evidence while applying the appropriate legal rules. In conclusion, ChatGPT has the potential to assist interested parties in the analysis of evidence and serve as a valuable legal resource for individuals with less experience or knowledge in this area.
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Reasoning Models (LRMs) are designed to solve complex tasks by generating explicit reasoning traces before producing final answers. However, we reveal a critical vulnerability in LRMs -- termed Unthinking Vulnerability -- wherein the thinking process can be bypassed by manipulating special delimiter tokens. It is empirically demonstrated to be widespread across mainstream LRMs, posing both a significant risk and potential utility, depending on how it is exploited. In this paper, we systematically investigate this vulnerability from both malicious and beneficial perspectives. On the malicious side, we introduce Breaking of Thought (BoT), a novel attack that enables adversaries to bypass the thinking process of LRMs, thereby compromising their reliability and availability. We present two variants of BoT: a training-based version that injects backdoor during the fine-tuning stage, and a training-free version based on adversarial attack during the inference stage. As a potential defense, we propose thinking recovery alignment to partially mitigate the vulnerability. On the beneficial side, we introduce Monitoring of Thought (MoT), a plug-and-play framework that allows model owners to enhance efficiency and safety. It is implemented by leveraging the same vulnerability to dynamically terminate redundant or risky reasoning through external monitoring. Extensive experiments show that BoT poses a significant threat to reasoning reliability, while MoT provides a practical solution for preventing overthinking and jailbreaking. Our findings expose an inherent flaw in current LRM architectures and underscore the need for more robust reasoning systems in the future.
GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation
Venue: arXiv cs.CL 2025
Year: 2025

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community.
Gem5-AcceSys: Enabling System-Level Exploration of Standard Interconnects for Novel Accelerators
Venue: arXiv cs.AR 2025
Year: 2025

The growing demand for efficient, high-performance processing in machine learning (ML) and image processing has made hardware accelerators, such as GPUs and Data Streaming Accelerators (DSAs), increasingly essential. These accelerators enhance ML and image processing tasks by offloading computation from the CPU to dedicated hardware. These accelerators rely on interconnects for efficient data transfer, making interconnect design crucial for system-level performance. This paper introduces Gem5-AcceSys, an innovative framework for system-level exploration of standard interconnects and configurable memory hierarchies. Using a matrix multiplication accelerator tailored for transformer workloads as a case study, we evaluate PCIe performance across diverse memory types (DDR4, DDR5, GDDR6, HBM2) and configurations, including host-side and device-side memory. Our findings demonstrate that optimized interconnects can achieve up to 80% of device-side memory performance and, in some scenarios, even surpass it. These results offer actionable insights for system architects, enabling a balanced approach to performance and cost in next-generation accelerator design.
Integrating Expert Knowledge into Logical Programs via LLMs
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces ExKLoP, a novel framework designed to evaluate how effectively Large Language Models (LLMs) integrate expert knowledge into logical reasoning systems. This capability is especially valuable in engineering, where expert knowledge-such as manufacturer-recommended operational ranges-can be directly embedded into automated monitoring systems. By mirroring expert verification steps, tasks like range checking and constraint validation help ensure system safety and reliability. Our approach systematically evaluates LLM-generated logical rules, assessing both syntactic fluency and logical correctness in these critical validation tasks. We also explore the models' capacity for self-correction via an iterative feedback loop based on code execution outcomes. ExKLoP presents an extensible dataset comprising 130 engineering premises, 950 prompts, and corresponding validation points. It enables comprehensive benchmarking while allowing control over task complexity and scalability of experiments. We leverage the synthetic data creation methodology to conduct extensive empirical evaluation on a diverse set of LLMs including Llama3, Gemma3, Codestral and QwenCoder. The results reveal that most models generate nearly perfect syntactically correct code and exhibit strong performance in translating expert knowledge into correct code. At the same time, while most LLMs produce nearly flawless syntactic output, their ability to correctly implement logical rules varies, as does their capacity for self-improvement. Overall, ExKLoP serves as a robust evaluation platform that streamlines the selection of effective models for self-correcting systems while clearly delineating the types of errors encountered.
Healthcare cost prediction for heterogeneous patient profiles using deep learning models with administrative claims data
Venue: arXiv cs.LG 2025
Year: 2025

Problem: How can we design patient cost prediction models that effectively address the challenges of heterogeneity in administrative claims (AC) data to ensure accurate, fair, and generalizable predictions, especially for high-need (HN) patients with complex chronic conditions? Relevance: Accurate and equitable patient cost predictions are vital for developing health management policies and optimizing resource allocation, which can lead to significant cost savings for healthcare payers, including government agencies and private insurers. Addressing disparities in prediction outcomes for HN patients ensures better economic and clinical decision-making, benefiting both patients and payers. Methodology: This study is grounded in socio-technical considerations that emphasize the interplay between technical systems (e.g., deep learning models) and humanistic outcomes (e.g., fairness in healthcare decisions). It incorporates representation learning and entropy measurement to address heterogeneity and complexity in data and patient profiles, particularly for HN patients. We propose a channel-wise deep learning framework that mitigates data heterogeneity by segmenting AC data into separate channels based on types of codes (e.g., diagnosis, procedures) and costs. This approach is paired with a flexible evaluation design that uses multi-channel entropy measurement to assess patient heterogeneity. Results: The proposed channel-wise models reduce prediction errors by 23% compared to single-channel models, leading to 16.4% and 19.3% reductions in overpayments and underpayments, respectively. Notably, the reduction in prediction bias is significantly higher for HN patients, demonstrating effectiveness in handling heterogeneity and complexity in data and patient profiles. This demonstrates the potential for applying channel-wise modeling to domains with similar heterogeneity challenges.
Data-Efficient Limited-Angle CT Using Deep Priors and Regularization
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing an image from its Radon transform is a fundamental computed tomography (CT) task arising in applications such as X-ray scans. In many practical scenarios, a full 180-degree scan is not feasible, or there is a desire to reduce radiation exposure. In these limited-angle settings, the problem becomes ill-posed, and methods designed for full-view data often leave significant artifacts. We propose a very low-data approach to reconstruct the original image from its Radon transform under severe angle limitations. Because the inverse problem is ill-posed, we combine multiple regularization methods, including Total Variation, a sinogram filter, Deep Image Prior, and a patch-level autoencoder. We use a differentiable implementation of the Radon transform, which allows us to use gradient-based techniques to solve the inverse problem. Our method is evaluated on a dataset from the Helsinki Tomography Challenge 2022, where the goal is to reconstruct a binary disk from its limited-angle sinogram. We only use a total of 12 data points--eight for learning a prior and four for hyperparameter selection--and achieve results comparable to the best synthetic data-driven approaches.
On the Computational Tractability of the (Many) Shapley Values
Venue: arXiv cs.LG 2025
Year: 2025

Recent studies have examined the computational complexity of computing Shapley additive explanations (also known as SHAP) across various models and distributions, revealing their tractability or intractability in different settings. However, these studies primarily focused on a specific variant called Conditional SHAP, though many other variants exist and address different limitations. In this work, we analyze the complexity of computing a much broader range of such variants, including Conditional, Interventional, and Baseline SHAP, while exploring both local and global computations. We show that both local and global Interventional and Baseline SHAP can be computed in polynomial time for various ML models under Hidden Markov Model distributions, extending popular algorithms such as TreeSHAP beyond empirical distributions. On the downside, we prove intractability results for these variants over a wide range of neural networks and tree ensembles. We believe that our results emphasize the intricate diversity of computing Shapley values, demonstrating how their complexity is substantially shaped by both the specific SHAP variant, the model type, and the distribution.
SMOL: Professionally translated parallel data for 115 under-represented languages
Venue: arXiv cs.CL 2025
Year: 2025

We open-source SMOL (Set of Maximal Overall Leverage), a suite of training data to unlock machine translation for low-resource languages. SMOL has been translated into 124 (and growing) under-resourced languages (125 language pairs), including many for which there exist no previous public resources, for a total of 6.1M translated tokens. SMOL comprises two sub-datasets, each carefully chosen for maximum impact given its size: SMOLSENT, a set of sentences chosen for broad unique token coverage, and SMOLDOC, a document-level resource focusing on a broad topic coverage. They join the already released GATITOS for a trifecta of paragraph, sentence, and token-level content. We demonstrate that using SMOL to prompt or fine-tune Large Language Models yields robust chrF improvements. In addition to translation, we provide factuality ratings and rationales for all documents in SMOLDOC, yielding the first factuality datasets for most of these languages.
The Agafonov and Schnorr-Stimm theorems for probabilistic automata
Venue: arXiv cs.FL 2025
Year: 2025

For a fixed alphabet $A$, an infinite sequence $X$ is said to be normal if every word $w$ over $A$ appears in $X$ with the same frequency as any other word of the same length. A classical result of Agafonov (1966) relates normality to finite automata as follows: a sequence $X$ is normal if and only if any subsequence of $X$ selected by a finite automaton is itself normal. Another theorem of Schnorr and Stimm (1972) gives an alternative characterization: a sequence $X$ is normal if and only if no gambler can win large amounts of money by betting on the sequence $X$ using a strategy that can be described by a finite automaton. Both of these theorems are established in the setting of deterministic finite automata. This raises the question as to whether they can be extended to the setting of probabilistic finite automata. In the case of the Agafonov theorem, this question was positively answered by Léchine et al.\ (2024) in a restricted case of probabilistic automata with rational transition probabilities. In this paper, we settle the full conjecture by proving that both the Agafonov and the Schnorr-Stimm theorems hold true for arbitrary probabilistic automata. Specifically, we show that a sequence $X$ is normal if and only if any probabilistic automaton selects a normal subsequence of $X$ with probability $1$. We also show that a sequence $X$ is normal if and only if a probabilistic finite-state gambler fails to win on $X$ with probability $1$.
VIC: Evasive Video Game Cheating via Virtual Machine Introspection
Venue: arXiv cs.CR 2025
Year: 2025

Video game cheats modify a video game behaviour to give unfair advantages to some players while bypassing the methods game developers use to detect them. This destroys the experience of online gaming and can result in financial losses for game developers. In this work, we present a new type of game cheat, Virtual machine Introspection Cheat (VIC), that takes advantage of virtual machines to stealthy execute game cheats. VIC employees a hypervisor with introspection enabled to lower the bar of cheating against legacy and modern anti-cheat systems. We demonstrate the feasibility and stealthiness of VIC against three popular games (Fortnite, BlackSquad and Team Fortress 2) that include five different anti-cheats. In particular, we use VIC to implement a cheat radar, a wall-hack cheat and a trigger-bot. To support our claim that this type of cheats can be effectively used, we present the performance impact VICs have on gameplay by monitoring the frames per second (fps) while the cheats are activated. Our experimentation also shows how these cheats are currently undetected by the most popular anti-cheat systems, enabling a new paradigm that can take advantage of cloud infrastructure to offer cheating-as-a-service.
A Novel Unified Parametric Assumption for Nonconvex Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Nonconvex optimization is central to modern machine learning, but the general framework of nonconvex optimization yields weak convergence guarantees that are too pessimistic compared to practice. On the other hand, while convexity enables efficient optimization, it is of limited applicability to many practical problems. To bridge this gap and better understand the practical success of optimization algorithms in nonconvex settings, we introduce a novel unified parametric assumption. Our assumption is general enough to encompass a broad class of nonconvex functions while also being specific enough to enable the derivation of a unified convergence theorem for gradient-based methods. Notably, by tuning the parameters of our assumption, we demonstrate its versatility in recovering several existing function classes as special cases and in identifying functions amenable to efficient optimization. We derive our convergence theorem for both deterministic and stochastic optimization, and conduct experiments to verify that our assumption can hold practically over optimization trajectories.
Stability-based Generalization Bounds for Variational Inference
Venue: arXiv cs.LG 2025
Year: 2025

Variational inference (VI) is widely used for approximate inference in Bayesian machine learning. In addition to this practical success, generalization bounds for variational inference and related algorithms have been developed, mostly through the connection to PAC-Bayes analysis. A second line of work has provided algorithm-specific generalization bounds through stability arguments or using mutual information bounds, and has shown that the bounds are tight in practice, but unfortunately these bounds do not directly apply to approximate Bayesian algorithms. This paper fills this gap by developing algorithm-specific stability based generalization bounds for a class of approximate Bayesian algorithms that includes VI, specifically when using stochastic gradient descent to optimize their objective. As in the non-Bayesian case, the generalization error is bounded by by expected parameter differences on a perturbed dataset. The new approach complements PAC-Bayes analysis and can provide tighter bounds in some cases. An experimental illustration shows that the new approach yields non-vacuous bounds on modern neural network architectures and datasets and that it can shed light on performance differences between variant approximate Bayesian algorithms.
On the Performance of Uplink Pinching Antenna Systems (PASS)
Venue: arXiv cs.IT 2025
Year: 2025

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, three scenarios, multiple PAs for a single user (MPSU), a single PA for a single user (SPSU), and a single PA for multiple users (SPMU) are considered. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. As the further advance, closed-form expressions of approximated ergodic rate is derived when a single PA is fixed in the SPMU scenario. Our results demonstrate the following key insights: i) The proposed PASS significantly outperforms conventional Multiple-input Single-output networks by exploiting the flexibility of PAs; ii) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; iii) Optimizing PA positions significantly enhances the ergodic sum rate performance.
Soft Robotics for Search and Rescue: Advancements, Challenges, and Future Directions
Venue: arXiv cs.RO 2025
Year: 2025

Soft robotics has emerged as a transformative technology in Search and Rescue (SAR) operations, addressing challenges in navigating complex, hazardous environments that often limit traditional rigid robots. This paper critically examines advancements in soft robotic technologies tailored for SAR applications, focusing on their unique capabilities in adaptability, safety, and efficiency. By leveraging bio-inspired designs, flexible materials, and advanced locomotion mechanisms, such as crawling, rolling, and shape morphing, soft robots demonstrate exceptional potential in disaster scenarios. However, significant barriers persist, including material durability, power inefficiency, sensor integration, and control complexity. This comprehensive review highlights the current state of soft robotics in SAR, discusses simulation methodologies and hardware validations, and introduces performance metrics essential for their evaluation. By bridging the gap between theoretical advancements and practical deployment, this study underscores the potential of soft robotic systems to revolutionize SAR missions and advocates for continued interdisciplinary innovation to overcome existing limitations.