OpenRaiser/Intern-Atlas huggingface dataset:
Using Fixed and Mobile Eye Tracking to Understand How Visitors View Art in a Museum: A Study at the Bowes Museum, County Durham, UK
Venue: arXiv cs.CV 2025
Year: 2025

The following paper describes a collaborative project involving researchers at Durham University, and professionals at the Bowes Museum, Barnard Castle, County Durham, UK, during which we used fixed and mobile eye tracking to understand how visitors view art. Our study took place during summer 2024 and builds on work presented at DH2017 (Bailey-Ross et al., 2017). Our interdisciplinary team included researchers from digital humanities, psychology, art history and computer science, working in collaboration with professionals from the museum. We used fixed and mobile eye tracking to understand how museum visitors view art in a physical gallery setting. This research will enable us to make recommendations about how the Museum's collections could be more effectively displayed, encouraging visitors to engage with them more fully.
Type-Based Unsourced Multiple Access over Fading Channels with Cell-Free Massive MIMO
Venue: arXiv cs.IT 2025
Year: 2025

Type-based unsourced multiple access (TUMA) is a recently proposed framework for type-based estimation in massive uncoordinated access networks. We extend the existing design of TUMA, developed for an additive white Gaussian channel, to a more realistic environment with fading and multiple antennas. Specifically, we consider a cell-free massive multiple-input multiple-output system and exploit spatial diversity to estimate the set of transmitted messages and the number of users transmitting each message. Our solution relies on a location-based codeword partition and on the use at the receiver of a multisource approximate message passing algorithm in both centralized and distributed implementations. The proposed TUMA framework results in a robust and scalable architecture for massive machine-type communications.
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning with heterogeneous data and personalization has received significant recent attention. Separately, robustness to corrupted data in the context of federated learning has also been studied. In this paper we explore combining personalization for heterogeneous data with robustness, where a constant fraction of the clients are corrupted. Motivated by this broad problem, we formulate a simple instantiation which captures some of its difficulty. We focus on the specific problem of personalized mean estimation where the data is drawn from a Gaussian mixture model. We give an algorithm whose error depends almost linearly on the ratio of corrupted to uncorrupted samples, and show a lower bound with the same behavior, albeit with a gap of a constant factor.
Revisiting Directed Disjoint Paths on tournaments (and relatives)
Venue: arXiv cs.DS 2025
Year: 2025

In the Directed Disjoint Paths problem ($k$-DDP), we are given a digraph $k$ pairs of terminals, and the goal is to find $k$ pairwise vertex-disjoint paths connecting each pair of terminals. Bang-Jensen and Thomassen [SIAM J. Discrete Math. 1992] claimed that $k$-DDP is NP-complete on tournaments, and this result triggered a very active line of research about the complexity of the problem on tournaments and natural superclasses. We identify a flaw in their proof, which has been acknowledged by the authors, and provide a new NP-completeness proof. From an algorithmic point of view, Fomin and Pilipczuk [J. Comb. Theory B 2019] provided an FPT algorithm for the edge-disjoint version of the problem on semicomplete digraphs, and showed that their technique cannot work for the vertex-disjoint version. We overcome this obstacle by showing that the version of $k$-DDP where we allow congestion $c$ on the vertices is FPT on semicomplete digraphs provided that $c$ is greater than $k/2$. This is based on a quite elaborate irrelevant vertex argument inspired by the edge-disjoint version, and we show that our choice of $c$ is best possible for this technique, with a counterexample with no irrelevant vertices when $c \leq k/2$. We also prove that $k$-DDP on digraphs that can be partitioned into $h$ semicomplete digraphs is $W[1]$-hard parameterized by $k+h$, which shows that the XP algorithm presented by Chudnovsky, Scott, and Seymour [J. Comb. Theory B 2019] is essentially optimal.
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets
Venue: arXiv cs.LG 2025
Year: 2025

Achieving both accuracy and diverse reasoning remains challenging for Large Language Models (LLMs) in complex domains like mathematics. A key bottleneck is evaluating intermediate reasoning steps to guide generation without costly human annotations. To address this, we first introduce a novel Process Reward Model (PRM) trained automatically using Monte Carlo Tree Search coupled with a similarity-based data augmentation technique, effectively capturing step-level reasoning quality. Leveraging this PRM, we then adapt Generative Flow Networks (GFlowNets) to operate at the reasoning step level. Unlike traditional reinforcement learning focused on maximizing a single reward, GFlowNets naturally sample diverse, high-quality solutions proportional to their rewards, as measured by our PRM. Empirical evaluation shows strong improvements in both accuracy and solution diversity on challenging mathematical benchmarks (e.g., +2.59% absolute accuracy on MATH Level 5 for Llama3.2-3B), with effective generalization to unseen datasets (+9.4\% absolute on SAT MATH). Furthermore, we benchmark our PRM against existing open-source reward models, demonstrating superior alignment with reasoning quality and more consistent guidance for downstream generation. Our work demonstrates the potential of PRM-guided, step-level GFlowNets for developing more robust and versatile mathematical reasoning in LLMs.
Mitigating Societal Cognitive Overload in the Age of AI: Challenges and Directions
Venue: arXiv cs.CY 2025
Year: 2025

Societal cognitive overload, driven by the deluge of information and complexity in the AI age, poses a critical challenge to human well-being and societal resilience. This paper argues that mitigating cognitive overload is not only essential for improving present-day life but also a crucial prerequisite for navigating the potential risks of advanced AI, including existential threats. We examine how AI exacerbates cognitive overload through various mechanisms, including information proliferation, algorithmic manipulation, automation anxieties, deregulation, and the erosion of meaning. The paper reframes the AI safety debate to center on cognitive overload, highlighting its role as a bridge between near-term harms and long-term risks. It concludes by discussing potential institutional adaptations, research directions, and policy considerations that arise from adopting an overload-resilient perspective on human-AI alignment, suggesting pathways for future exploration rather than prescribing definitive solutions.
Chatbot Arena Meets Nuggets: Towards Explanations and Diagnostics in the Evaluation of LLM Responses
Venue: arXiv cs.IR 2025
Year: 2025

Battles, or side-by-side comparisons in so-called arenas that elicit human preferences, have emerged as a popular approach for assessing the output quality of LLMs. Recently, this idea has been extended to retrieval-augmented generation (RAG) systems. While undoubtedly representing an advance in evaluation, battles have at least two drawbacks, particularly in the context of complex information-seeking queries: they are neither explanatory nor diagnostic. Recently, the nugget evaluation methodology has emerged as a promising approach to evaluate the quality of RAG answers. Nuggets decompose long-form LLM-generated answers into atomic facts, highlighting important pieces of information necessary in a "good" response. In this work, we apply our AutoNuggetizer framework to analyze data from roughly 7K Search Arena battles provided by LMArena in a fully automatic manner. Our results show a significant correlation between nugget scores and human preferences, showcasing promise in our approach to explainable and diagnostic system evaluations. All the code necessary to reproduce results in our work is available in https://github.com/castorini/lmsys_nuggetize.
All-Subsets Important Separators with Applications to Sample Sets, Balanced Separators and Vertex Sparsifiers in Directed Graphs
Venue: arXiv cs.DS 2025
Year: 2025

Given a directed graph $G$ with $n$ vertices and $m$ edges, a parameter $k$ and two disjoint subsets $S,T \subseteq V(G)$, we show that the number of all-subsets important separators, which is the number of $A$-$B$ important vertex separators of size at most $k$ over all $A \subseteq S$ and $B \subseteq T$, is at most $β(|S|, |T|, k) = 4^k {|S| \choose \leq k} {|T| \choose \leq 2k}$, where ${x \choose \leq c} = \sum_{i = 1}^c {x \choose i}$, and that they can be enumerated in time $O(β(|S|,|T|,k)k^2(m+n))$. This is a generalization of the folklore result stating that the number of $A$-$B$ important separators for two fixed sets $A$ and $B$ is at most $4^k$ (first implicitly shown by Chen, Liu and Lu Algorithmica '09). From this result, we obtain the following applications: We give a construction for detection sets and sample sets in directed graphs, generalizing the results of Kleinberg (Internet Mathematics' 03) and Feige and Mahdian (STOC' 06) to directed graphs. Via our new sample sets, we give the first FPT algorithm for finding balanced separators in directed graphs parameterized by $k$, the size of the separator. Our algorithm runs in time $2^{O(k)} (m + n)$. We also give a $O({\sqrt{\log k}})$ approximation algorithm for the same problem. Finally, we present new results on vertex sparsifiers for preserving small cuts.
Marmot: Object-Level Self-Correction via Multi-Agent Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

While diffusion models excel at generating high-quality images, they often struggle with accurate counting, attributes, and spatial relationships in complex multi-object scenes. One potential solution involves employing Multimodal Large Language Model (MLLM) as an AI agent to construct a self-correction framework. However, these approaches heavily rely on the capabilities of the MLLMs used, often fail to account for all objects within the image, and suffer from cumulative distortions during multi-round editing processes. To address these challenges, we propose Marmot, a novel and generalizable framework that leverages Multi-Agent Reasoning for Multi-Object Self-Correcting to enhance image-text alignment. First, we employ a large language model as an Object-Aware Agent to perform object-level divide-and-conquer, automatically decomposing self-correction tasks into object-centric subtasks based on image descriptions. For each subtask, we construct an Object Correction System featuring a decision-execution-verification mechanism that operates exclusively on a single object's segmentation mask or the bounding boxes of object pairs, effectively mitigating inter-object interference and enhancing editing reliability. To efficiently integrate correction results from subtasks while avoiding cumulative distortions from multi-stage editing, we propose a Pixel-Domain Stitching Smoother, which employs mask-guided two-stage latent space optimization. This innovation enables parallel processing of subtasks, significantly improving runtime efficiency while preventing distortion accumulation. Extensive experiments demonstrate that Marmot significantly improves accuracy in object counting, attribute assignment, and spatial relationships for image generation tasks.
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors
Venue: arXiv cs.LG 2025
Year: 2025

The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that are both faithful to the model's inner workings and intelligible to human end-users. In this paper, we describe a novel approach to creating a neural-network-based behavior detection model that is interpretable by design. Our model is fully interpretable, meaning that the parameters we extract for our explanations have a clear interpretation, fully capture the model's learned knowledge about the learner behavior of interest, and can be used to create explanations that are both faithful and intelligible. We achieve this by implementing a series of constraints to the model that both simplify its inference process and bring it closer to a human conception of the task at hand. We train the model to detect gaming-the-system behavior, evaluate its performance on this task, and compare its learned patterns to those identified by human experts. Our results show that the model is successfully able to learn patterns indicative of gaming-the-system behavior while providing evidence for fully interpretable explanations. We discuss the implications of our approach and suggest ways to evaluate explainability using a human-grounded approach.
A Computational Analysis and Visualization of In-Text Reference Networks Across Philosophical Texts
Venue: arXiv cs.DL 2025
Year: 2025

We applied computational methods to analyze references across 2,245 philosophical texts, spanning from approximately 550 BCE to 1940 AD, in order to measure patterns in how philosophical ideas have spread over time. Using natural language processing and network analysis, we mapped over 294,970 references between authors, classifying each reference into subdisciplines of philosophy based on its surrounding context. We then constructed a graph, with authors as nodes and textual references as edges, to empirically validate, visualize, and quantify intellectual lineages as they are understood within philosophical scholarship. For instance, we find that Plato and Aristotle alone account for nearly 10% of all references from authors in our dataset, suggesting that their influence may still be underestimated. As another example, we support the view that St. Thomas Aquinas served as a synthesizer between Aristotelian and Christian philosophy by analyzing the network structures of Aquinas, Aristotle, and Christian theologians. Our results are presented through an interactive visualization tool, allowing users to dynamically explore these networks, alongside a mathematical analysis of the network's structure. Our methodology demonstrates the value of applying network analysis with textual references to study a large collection of historical works.
Improving Deep Knowledge Tracing via Gated Architectures and Adaptive Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Deep Knowledge Tracing (DKT) models student learning behavior by using Recurrent Neural Networks (RNNs) to predict future performance based on historical interaction data. However, the original implementation relied on standard RNNs in the Lua-based Torch framework, which limited extensibility and reproducibility. In this work, we revisit the DKT model from two perspectives: architectural improvements and optimization efficiency. First, we enhance the model using gated recurrent units, specifically Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), which better capture long-term dependencies and help mitigate vanishing gradient issues. Second, we re-implement DKT using the PyTorch framework, enabling a modular and accessible infrastructure compatible with modern deep learning workflows. We also benchmark several optimization algorithms SGD, RMSProp, Adagrad, Adam, and AdamW to evaluate their impact on convergence speed and predictive accuracy in educational modeling tasks. Experiments on the Synthetic-5 and Khan Academy datasets show that GRUs and LSTMs achieve higher accuracy and improved training stability compared to basic RNNs, while adaptive optimizers such as Adam and AdamW consistently outperform SGD in both early-stage learning and final model performance. Our open-source PyTorch implementation provides a reproducible and extensible foundation for future research in neural knowledge tracing and personalized learning systems.
Low-Rank Matrix Approximation for Neural Network Compression
Venue: arXiv cs.LG 2025
Year: 2025

Deep Neural Networks (DNNs) have encountered an emerging deployment challenge due to large and expensive memory and computation requirements. In this paper, we present a new Adaptive-Rank Singular Value Decomposition (ARSVD) method that approximates the optimal rank for compressing weight matrices in neural networks using spectral entropy. Unlike conventional SVD-based methods that apply a fixed-rank truncation across all layers, ARSVD uses an adaptive selection of the rank per layer through the entropy distribution of its singular values. This approach ensures that each layer will retain a certain amount of its informational content, thereby reducing redundancy. Our method enables efficient, layer-wise compression, yielding improved performance with reduced space and time complexity compared to static-rank reduction techniques.
Billions at Stake: How Self-Citation Adjusted Metrics Can Transform Equitable Research Funding
Venue: arXiv cs.DL 2025
Year: 2025

Citation metrics serve as the cornerstone of scholarly impact evaluation despite their well-documented vulnerability to inflation through self-citation practices. This paper introduces the Self-Citation Adjusted Index (SCAI), a sophisticated metric designed to recalibrate citation counts by accounting for discipline-specific self-citation patterns. Through comprehensive analysis of 5,000 researcher profiles across diverse disciplines, we demonstrate that excessive self-citation inflates traditional metrics by 10-20%, potentially misdirecting billions in research funding. Recent studies confirm that self-citation patterns exhibit significant gender disparities, with men self-citing up to 70% more frequently than women, exacerbating existing inequalities in academic recognition. Our open-source implementation provides comprehensive tools for calculating SCAI and related metrics, offering a more equitable assessment of research impact that reduces the gender citation gap by approximately 8.5%. This work contributes to the paradigm shift toward transparent, nuanced, and equitable research evaluation methodologies in academia, with direct implications for funding allocation decisions that collectively amount to over $100 billion annually in the United States alone.
A model and package for German ColBERT
Venue: arXiv cs.IR 2025
Year: 2025

In this work, we introduce a German version for ColBERT, a late interaction multi-dense vector retrieval method, with a focus on RAG applications. We also present the main features of our package for ColBERT models, supporting both retrieval and fine-tuning workflows.
Understanding and Mitigating Risks of Generative AI in Financial Services
Venue: arXiv cs.CL 2025
Year: 2025

To responsibly develop Generative AI (GenAI) products, it is critical to define the scope of acceptable inputs and outputs. What constitutes a "safe" response is an actively debated question. Academic work puts an outsized focus on evaluating models by themselves for general purpose aspects such as toxicity, bias, and fairness, especially in conversational applications being used by a broad audience. In contrast, less focus is put on considering sociotechnical systems in specialized domains. Yet, those specialized systems can be subject to extensive and well-understood legal and regulatory scrutiny. These product-specific considerations need to be set in industry-specific laws, regulations, and corporate governance requirements. In this paper, we aim to highlight AI content safety considerations specific to the financial services domain and outline an associated AI content risk taxonomy. We compare this taxonomy to existing work in this space and discuss implications of risk category violations on various stakeholders. We evaluate how existing open-source technical guardrail solutions cover this taxonomy by assessing them on data collected via red-teaming activities. Our results demonstrate that these guardrails fail to detect most of the content risks we discuss.
Spark: A System for Scientifically Creative Idea Generation
Venue: arXiv cs.AI 2025
Year: 2025

Recently, large language models (LLMs) have shown promising abilities to generate novel research ideas in science, a direction which coincides with many foundational principles in computational creativity (CC). In light of these developments, we present an idea generation system named Spark that couples retrieval-augmented idea generation using LLMs with a reviewer model named Judge trained on 600K scientific reviews from OpenReview. Our work is both a system demonstration and intended to inspire other CC researchers to explore grounding the generation and evaluation of scientific ideas within foundational CC principles. To this end, we release the annotated dataset used to train Judge, inviting other researchers to explore the use of LLMs for idea generation and creative evaluations.
Long-Distance Field Demonstration of Imaging-Free Drone Identification in Intracity Environments
Venue: arXiv cs.CV 2025
Year: 2025

Detecting small objects, such as drones, over long distances presents a significant challenge with broad implications for security, surveillance, environmental monitoring, and autonomous systems. Traditional imaging-based methods rely on high-resolution image acquisition, but are often constrained by range, power consumption, and cost. In contrast, data-driven single-photon-single-pixel light detection and ranging (\text{D\textsuperscript{2}SP\textsuperscript{2}-LiDAR}) provides an imaging-free alternative, directly enabling target identification while reducing system complexity and cost. However, its detection range has been limited to a few hundred meters. Here, we introduce a novel integration of residual neural networks (ResNet) with \text{D\textsuperscript{2}SP\textsuperscript{2}-LiDAR}, incorporating a refined observation model to extend the detection range to 5~\si{\kilo\meter} in an intracity environment while enabling high-accuracy identification of drone poses and types. Experimental results demonstrate that our approach not only outperforms conventional imaging-based recognition systems, but also achieves 94.93\% pose identification accuracy and 97.99\% type classification accuracy, even under weak signal conditions with long distances and low signal-to-noise ratios (SNRs). These findings highlight the potential of imaging-free methods for robust long-range detection of small targets in real-world scenarios.