OpenRaiser/Intern-Atlas huggingface dataset:
Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Unlearning in large language models (LLMs) aims to remove specified data, but its efficacy is typically assessed with task-level metrics like accuracy and perplexity. We demonstrate that these metrics are often misleading, as models can appear to forget while their original behavior is easily restored through minimal fine-tuning. This phenomenon of \emph{reversibility} suggests that information is merely suppressed, not genuinely erased. To address this critical evaluation gap, we introduce a \emph{representation-level analysis framework}. Our toolkit comprises PCA-based similarity and shift, centered kernel alignment (CKA), and Fisher information, complemented by a summary metric, the mean PCA distance, to measure representational drift. Applying this framework across six unlearning methods, three data domains, and two LLMs, we identify four distinct forgetting regimes based on their \emph{reversibility} and \emph{catastrophicity}. Our analysis reveals that achieving the ideal state--irreversible, non-catastrophic forgetting--is exceptionally challenging. By probing the limits of unlearning, we identify a case of seemingly irreversible, targeted forgetting, offering new insights for designing more robust erasure algorithms. Our findings expose a fundamental gap in current evaluation practices and establish a representation-level foundation for trustworthy unlearning.
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information retrieval. However, existing approaches face critical limitations that lack high-quality training trajectories or suffer from the distributional mismatches in simulated environments and prohibitive computational costs for real-world deployment. This paper introduces SimpleDeepSearcher, a lightweight yet effective framework that bridges this gap through strategic data engineering rather than complex training paradigms. Our approach synthesizes high-quality training data by simulating realistic user interactions in live web search environments, coupled with a multi-criteria curation strategy that optimizes the diversity and quality of input and output side. Experiments on five benchmarks across diverse domains demonstrate that SFT on only 871 curated samples yields significant improvements over RL-based baselines. Our work establishes SFT as a viable pathway by systematically addressing the data-scarce bottleneck, offering practical insights for efficient deep search systems. Our code is available at https://github.com/RUCAIBox/SimpleDeepSearcher.
Number theory combination: natural density and SMT
Venue: arXiv cs.LO 2025
Year: 2025

The study of theory combination in Satisfiability Modulo Theories (SMT) involves various model theoretic properties (e.g., stable infiniteness, smoothness, etc.). We show that such properties can be partly captured by the natural density of the spectrum of the studied theories, which is the set of sizes of their finite models. This enriches the toolbox of the theory combination researcher, by providing new tools to determine the possibility of combining theories. It also reveals interesting and surprising connections between theory combination and number theory.
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) remedies these shortcomings by grounding LLM responses in structured external information from a knowledge base. However, many KG-based RAG approaches struggle with (i) aligning KG and textual representations, (ii) balancing retrieval accuracy and efficiency, and (iii) adapting to dynamically updated KGs. In this work, we introduce Walk&Retrieve, a simple yet effective KG-based framework that leverages walk-based graph traversal and knowledge verbalization for corpus generation for zero-shot RAG. Built around efficient KG walks, our method does not require fine-tuning on domain-specific data, enabling seamless adaptation to KG updates, reducing computational overhead, and allowing integration with any off-the-shelf backbone LLM. Despite its simplicity, Walk&Retrieve performs competitively, often outperforming existing RAG systems in response accuracy and hallucination reduction. Moreover, it demonstrates lower query latency and robust scalability to large KGs, highlighting the potential of lightweight retrieval strategies as strong baselines for future RAG research.
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers. The approach models context at the product category level, allowing offers to span multiple categories and enabling knowledge transfer across similar offers. This improves learning efficiency and generalization in dynamic environments. The framework extends standard CMAB methodology to support multi-category contexts, and achieves scalability through efficient feature engineering and modular design. Advanced features such as MPG (Member Purchase Gap) and MF (Matrix Factorization) capture nuanced user-offer interactions, with implementation in Python for practical deployment. A key contribution is interpretability at scale: logistic regression models yield transparent weight vectors, accessible via a large language model (LLM) interface for real-time, user-level tracking and explanation of evolving preferences. This enables the generation of detailed member profiles and identification of behavioral patterns, supporting personalized offer optimization and enhancing trust in automated decisions. By situating our prototype alongside established paradigms like Generalized Linear Models and Thompson Sampling, we demonstrate its value for both research and real-world CMAB applications.
Cracking Aegis: An Adversarial LLM-based Game for Raising Awareness of Vulnerabilities in Privacy Protection
Venue: arXiv cs.HC 2025
Year: 2025

Traditional methods for raising awareness of privacy protection often fail to engage users or provide hands-on insights into how privacy vulnerabilities are exploited. To address this, we incorporate an adversarial mechanic in the design of the dialogue-based serious game Cracking Aegis. Leveraging LLMs to simulate natural interactions, the game challenges players to impersonate characters and extract sensitive information from an AI agent, Aegis. A user study (n=22) revealed that players employed diverse deceptive linguistic strategies, including storytelling and emotional rapport, to manipulate Aegis. After playing, players reported connecting in-game scenarios with real-world privacy vulnerabilities, such as phishing and impersonation, and expressed intentions to strengthen privacy control, such as avoiding oversharing personal information with AI systems. This work highlights the potential of LLMs to simulate complex relational interactions in serious games, while demonstrating how an adversarial game strategy provides unique insights for designs for social good, particularly privacy protection.
On Multilingual Encoder Language Model Compression for Low-Resource Languages
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we combine two-step knowledge distillation, structured pruning, truncation, and vocabulary trimming for extremely compressing multilingual encoder-only language models for low-resource languages. Our novel approach systematically combines existing techniques and takes them to the extreme, reducing layer depth, feed-forward hidden size, and intermediate layer embedding size to create significantly smaller monolingual models while retaining essential language-specific knowledge. We achieve compression rates of up to 92% while maintaining competitive performance, with average drops of 2-10% for moderate compression and 8-13% at maximum compression in four downstream tasks, including sentiment analysis, topic classification, named entity recognition, and part-of-speech tagging, across three low-resource languages. Notably, the performance degradation correlates with the amount of language-specific data in the teacher model, with larger datasets resulting in smaller performance losses. Additionally, we conduct ablation studies to identify the best practices for multilingual model compression using these techniques.
Modeling Inequality in Complex Networks of Strategic Agents using Iterative Game-Theoretic Transactions
Venue: arXiv cs.GT 2025
Year: 2025

Transactions are an important aspect of human social life, and represent dynamic flow of information, intangible values, such as trust, as well as monetary and social capital. Although much research has been conducted on the nature of transactions in fields ranging from the social sciences to game theory, the systemic effects of different types of agents transacting in real-world social networks (often following a scale-free distribution) are not fully understood. A particular systemic measure that has not received adequate attention in the complex networks and game theory communities, is the Gini Coefficient, which is widely used in economics to quantify and understand wealth inequality. In part, the problem is a lack of experimentation using a replicable algorithm and publicly available data. Motivated by this problem, this article proposes a model and simulation algorithm, based on game theory, for quantifying the evolution of inequality in complex networks of strategic agents. Our results shed light on several complex drivers of inequality, even in simple, abstract settings, and exhibit consistency across networks with different origins and descriptions.
T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving dependencies between API or tool calls-particularly in multi-turn conversations-remains a significant challenge. To address this, we introduce T1, a tool-augmented, multi-domain, multi-turn conversational dataset specifically designed to capture and manage inter-tool dependencies across diverse domains. T1 enables rigorous evaluation of agents' ability to coordinate tool use across nine distinct domains (4 single domain and 5 multi-domain) with the help of an integrated caching mechanism for both short- and long-term memory, while supporting dynamic replanning-such as deciding whether to recompute or reuse cached results. Beyond facilitating research on tool use and planning, T1 also serves as a benchmark for evaluating the performance of open-weight and proprietary large language models. We present results powered by T1-Agent, highlighting their ability to plan and reason in complex, tool-dependent scenarios.
A Unified Framework for Simultaneous Parameter and Function Discovery in Differential Equations
Venue: arXiv cs.LG 2025
Year: 2025

Inverse problems involving differential equations often require identifying unknown parameters or functions from data. Existing approaches, such as Physics-Informed Neural Networks (PINNs), Universal Differential Equations (UDEs) and Universal Physics-Informed Neural Networks (UPINNs), are effective at isolating either parameters or functions but can face challenges when applied simultaneously due to solution non-uniqueness. In this work, we introduce a framework that addresses these limitations by establishing conditions under which unique solutions can be guaranteed. To illustrate, we apply it to examples from biological systems and ecological dynamics, demonstrating accurate and interpretable results. Our approach significantly enhances the potential of machine learning techniques in modeling complex systems in science and engineering.
Deep mineralogical segmentation of thin section images based on QEMSCAN maps
Venue: arXiv cs.CV 2025
Year: 2025

Interpreting the mineralogical aspects of rock thin sections is an important task for oil and gas reservoirs evaluation. However, human analysis tend to be subjective and laborious. Technologies like QEMSCAN(R) are designed to automate the mineralogical mapping process, but also suffer from limitations like high monetary costs and time-consuming analysis. This work proposes a Convolutional Neural Network model for automatic mineralogical segmentation of thin section images of carbonate rocks. The model is able to mimic the QEMSCAN mapping itself in a low-cost, generalized and efficient manner. For this, the U-Net semantic segmentation architecture is trained on plane and cross polarized thin section images using the corresponding QEMSCAN maps as target, which is an approach not widely explored. The model was instructed to differentiate occurrences of Calcite, Dolomite, Mg-Clay Minerals, Quartz, Pores and the remaining mineral phases as an unique class named "Others", while it was validated on rock facies both seen and unseen during training, in order to address its generalization capability. Since the images and maps are provided in different resolutions, image registration was applied to align then spatially. The study reveals that the quality of the segmentation is very much dependent on these resolution differences and on the variety of learnable rock textures. However, it shows promising results, especially with regard to the proper delineation of minerals boundaries on solid textures and precise estimation of the minerals distributions, describing a nearly linear relationship between expected and predicted distributions, with coefficient of determination (R^2) superior to 0.97 for seen facies and 0.88 for unseen.
Prompt Engineering: How Prompt Vocabulary affects Domain Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

Prompt engineering has emerged as a critical component in optimizing large language models (LLMs) for domain-specific tasks. However, the role of prompt specificity, especially in domains like STEM (physics, chemistry, biology, computer science and mathematics), medicine, and law, remains underexplored. This thesis addresses the problem of whether increasing the specificity of vocabulary in prompts improves LLM performance in domain-specific question-answering and reasoning tasks. We developed a synonymization framework to systematically substitute nouns, verbs, and adjectives with varying specificity levels, measuring the impact on four LLMs: Llama-3.1-70B-Instruct, Granite-13B-Instruct-V2, Flan-T5-XL, and Mistral-Large 2, across datasets in STEM, law, and medicine. Our results reveal that while generally increasing the specificity of prompts does not have a significant impact, there appears to be a specificity range, across all considered models, where the LLM performs the best. Identifying this optimal specificity range offers a key insight for prompt design, suggesting that manipulating prompts within this range could maximize LLM performance and lead to more efficient applications in specialized domains.
Exploring EFL Secondary Students' AI-generated Text Editing While Composition Writing
Venue: arXiv cs.CY 2025
Year: 2025

Generative Artificial Intelligence is transforming how English as a foreign language students write. Still, little is known about how students manipulate text generated by generative AI during the writing process. This study investigates how EFL secondary school students integrate and modify AI-generated text when completing an expository writing task. The study employed an exploratory mixed-methods design. Screen recordings were collected from 29 Hong Kong secondary school students who attended an AI-assisted writing workshop and recorded their screens while using generative AI to write an article. Content analysis with hierarchical coding and thematic analysis with a multiple case study approach were adopted to analyze the recordings. 15 types of AI-generated text edits across seven categories were identified from the recordings. Notably, AI-initiated edits from iOS and Google Docs emerged as unanticipated sources of AI-generated text. A thematic analysis revealed four patterns of students' editing behaviors based on planning and drafting direction: planning with top-down drafting and revising; top-down drafting and revising without planning; planning with bottom-up drafting and revising; and bottom-up drafting and revising without planning. Network graphs illustrate cases of each pattern, demonstrating that students' interactions with AI-generated text involve more complex cognitive processes than simple text insertion. The findings challenge assumptions about students' passive, simplistic use of generative AI tools and have implications for developing explicit instructional approaches to teaching AI-generated text editing strategies in the AFL writing pedagogy.
Assessing GPT's Bias Towards Stigmatized Social Groups: An Intersectional Case Study on Nationality Prejudice and Psychophobia
Venue: arXiv cs.CL 2025
Year: 2025

Recent studies have separately highlighted significant biases within foundational large language models (LLMs) against certain nationalities and stigmatized social groups. This research investigates the ethical implications of these biases intersecting with outputs of widely-used GPT-3.5/4/4o LLMS. Through structured prompt series, we evaluate model responses to several scenarios involving American and North Korean nationalities with various mental disabilities. Findings reveal significant discrepancies in empathy levels with North Koreans facing greater negative bias, particularly when mental disability is also a factor. This underscores the need for improvements in LLMs designed with a nuanced understanding of intersectional identity.
Assessing the Quality of AI-Generated Clinical Notes: A Validated Evaluation of a Large Language Model Scribe
Venue: arXiv cs.CL 2025
Year: 2025

In medical practices across the United States, physicians have begun implementing generative artificial intelligence (AI) tools to perform the function of scribes in order to reduce the burden of documenting clinical encounters. Despite their widespread use, no established methods exist to gauge the quality of AI scribes. To address this gap, we developed a blinded study comparing the relative performance of large language model (LLM) generated clinical notes with those from field experts based on audio-recorded clinical encounters. Quantitative metrics from the Physician Documentation Quality Instrument (PDQI9) provided a framework to measure note quality, which we adapted to assess relative performance of AI generated notes. Clinical experts spanning 5 medical specialties used the PDQI9 tool to evaluate specialist-drafted Gold notes and LLM authored Ambient notes. Two evaluators from each specialty scored notes drafted from a total of 97 patient visits. We found uniformly high inter rater agreement (RWG greater than 0.7) between evaluators in general medicine, orthopedics, and obstetrics and gynecology, and moderate (RWG 0.5 to 0.7) to high inter rater agreement in pediatrics and cardiology. We found a modest yet significant difference in the overall note quality, wherein Gold notes achieved a score of 4.25 out of 5 and Ambient notes scored 4.20 out of 5 (p = 0.04). Our findings support the use of the PDQI9 instrument as a practical method to gauge the quality of LLM authored notes, as compared to human-authored notes.
METHOD: Modular Efficient Transformer for Health Outcome Discovery
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in transformer architectures have revolutionised natural language processing, but their application to healthcare domains presents unique challenges. Patient timelines are characterised by irregular sampling, variable temporal dependencies, and complex contextual relationships that differ substantially from traditional language tasks. This paper introduces \METHOD~(Modular Efficient Transformer for Health Outcome Discovery), a novel transformer architecture specifically designed to address the challenges of clinical sequence modelling in electronic health records. \METHOD~integrates three key innovations: (1) a patient-aware attention mechanism that prevents information leakage whilst enabling efficient batch processing; (2) an adaptive sliding window attention scheme that captures multi-scale temporal dependencies; and (3) a U-Net inspired architecture with dynamic skip connections for effective long sequence processing. Evaluations on the MIMIC-IV database demonstrate that \METHOD~consistently outperforms the state-of-the-art \ETHOS~model, particularly in predicting high-severity cases that require urgent clinical intervention. \METHOD~exhibits stable performance across varying inference lengths, a crucial feature for clinical deployment where patient histories vary significantly in length. Analysis of learned embeddings reveals that \METHOD~better preserves clinical hierarchies and relationships between medical concepts. These results suggest that \METHOD~represents a significant advancement in transformer architectures optimised for healthcare applications, providing more accurate and clinically relevant predictions whilst maintaining computational efficiency.
Synthetic Data RL: Task Definition Is All You Need
Venue: arXiv cs.CL 2025
Year: 2025

Reinforcement learning (RL) is a powerful way to adapt foundation models to specialized tasks, but its reliance on large-scale human-labeled data limits broad adoption. We introduce Synthetic Data RL, a simple and general framework that reinforcement fine-tunes models using only synthetic data generated from a task definition. Our method first generates question and answer pairs from the task definition and retrieved documents, then adapts the difficulty of the question based on model solvability, and selects questions using the average pass rate of the model across samples for RL training. On Qwen-2.5-7B, our method achieves a 29.2% absolute improvement over the base model on GSM8K (+2.9 pp vs. instruction-tuned, +6.6 pp vs. Self-Instruct), 8.7% on MATH, 13.1% on GPQA (+7.0 pp vs. SynthLLM), 8.9% on MedQA, 17.7% on CQA (law) and 13.7% on CFA (finance). It surpasses supervised fine-tuning under the same data budget and nearly matches RL with full human data across datasets (e.g., +17.2 pp on GSM8K). Adding 100 human demonstrations improves the performance of GSM8K only by 0.4 pp, showing a limited added value. By reducing human data annotation, Synthetic Data RL enables scalable and efficient RL-based model adaptation. Code and demos are available at https://github.com/gydpku/Data_Synthesis_RL/.