OpenRaiser/Intern-Atlas huggingface dataset:
Tracing the Invisible: Understanding Students' Judgment in AI-Supported Design Work
Venue: arXiv cs.HC 2025
Year: 2025

As generative AI tools become integrated into design workflows, students increasingly engage with these tools not just as aids, but as collaborators. This study analyzes reflections from 33 student teams in an HCI design course to examine the kinds of judgments students make when using AI tools. We found both established forms of design judgment (e.g., instrumental, appreciative, quality) and emergent types: agency-distribution judgment and reliability judgment. These new forms capture how students negotiate creative responsibility with AI and assess the trustworthiness of its outputs. Our findings suggest that generative AI introduces new layers of complexity into design reasoning, prompting students to reflect not only on what AI produces, but also on how and when to rely on it. By foregrounding these judgments, we offer a conceptual lens for understanding how students engage in co-creative sensemaking with AI in design contexts.
Even Faster Algorithm for the Chamfer Distance
Venue: arXiv cs.CG 2025
Year: 2025

For two d-dimensional point sets A, B of size up to n, the Chamfer distance from A to B is defined as CH(A,B) = \sum_{a \in A} \min_{b \in B} \|a-b\|. The Chamfer distance is a widely used measure for quantifying dissimilarity between sets of points, used in many machine learning and computer vision applications. A recent work of Bakshi et al, NeuriPS'23, gave the first near-linear time (1+eps)-approximate algorithm, with a running time of O(ndlog(n)/eps^2). In this paper we improve the running time further, to O(nd(loglog(n)+log(1/eps))/eps^2). When eps is a constant, this reduces the gap between the upper bound and the trivial Omega(dn) lower bound significantly, from O(log n) to O(loglog n).
GPML: Graph Processing for Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML (Graph Processing for Machine Learning) library addresses this need by transforming raw network traffic traces into graph representations, enabling advanced insights into network behaviors. The library provides tools to detect anomalies in interaction and community shifts in dynamic networks. GPML supports community and spectral metrics extraction, enhancing both real-time detection and historical forensics analysis. This library supports modern cybersecurity challenges with a robust, graph-based approach.
ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and strict hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end--to--end hardware--software co-design for this task. An independently actuated dual-jaw pneumatic gripper clamps both chicken legs, while a conditional diffusion-policy controller, trained from only 50 multi--view teleoperation demonstrations (RGB + proprioception), plans 5 DoF end--effector motion, which includes jaw commands in one shot. On individually presented raw broiler carcasses, our system achieves a 40.6\% grasp--and--lift success rate and completes the pick to shackle cycle in 38 s, whereas state--of--the--art implicit behaviour cloning (IBC) and LSTM-GMM baselines fail entirely. All CAD, code, and datasets will be open-source. ChicGrasp shows that imitation learning can bridge the gap between rigid hardware and variable bio--products, offering a reproducible benchmark and a public dataset for researchers in agricultural engineering and robot learning.
Neural BRDF Importance Sampling by Reparameterization
Venue: arXiv cs.GR 2025
Year: 2025

Neural bidirectional reflectance distribution functions (BRDFs) have emerged as popular material representations for enhancing realism in physically-based rendering. Yet their importance sampling remains a significant challenge. In this paper, we introduce a reparameterization-based formulation of neural BRDF importance sampling that seamlessly integrates into the standard rendering pipeline with precise generation of BRDF samples. The reparameterization-based formulation transfers the distribution learning task to a problem of identifying BRDF integral substitutions. In contrast to previous methods that rely on invertible networks and multi-step inference to reconstruct BRDF distributions, our model removes these constraints, which offers greater flexibility and efficiency. Our variance and performance analysis demonstrates that our reparameterization method achieves the best variance reduction in neural BRDF renderings while maintaining high inference speeds compared to existing baselines.
For GPT-4 as with Humans: Information Structure Predicts Acceptability of Long-Distance Dependencies
Venue: arXiv cs.CL 2025
Year: 2025

It remains debated how well any LM understands natural language or generates reliable metalinguistic judgments. Moreover, relatively little work has demonstrated that LMs can represent and respect subtle relationships between form and function proposed by linguists. We here focus on a particular such relationship established in recent work: English speakers' judgments about the information structure of canonical sentences predicts independently collected acceptability ratings on corresponding 'long distance dependency' [LDD] constructions, across a wide array of base constructions and multiple types of LDDs. To determine whether any LM captures this relationship, we probe GPT-4 on the same tasks used with humans and new extensions.Results reveal reliable metalinguistic skill on the information structure and acceptability tasks, replicating a striking interaction between the two, despite the zero-shot, explicit nature of the tasks, and little to no chance of contamination [Studies 1a, 1b]. Study 2 manipulates the information structure of base sentences and confirms a causal relationship: increasing the prominence of a constituent in a context sentence increases the subsequent acceptability ratings on an LDD construction. The findings suggest a tight relationship between natural and GPT-4 generated English, and between information structure and syntax, which begs for further exploration.
FocusE: A semantic extension of FocusST
Venue: arXiv cs.FL 2025
Year: 2025

To analyse and verify the safety and security properties of interactive systems, a formal specification might be necessary. There are many types of formal languages and frameworks. The decision regarding what type of formal specification should be applied in each particular case depends on many factors. One of the approaches to specify interactive systems formally is to present them as a composition of components processing data and control streams. In this short paper, we present FocusE, a formal approach for modelling event-based streams. The proposed approach is based on a formal language FocusST, and can be seen as its semantic extension.
Atomic Consistency Preference Optimization for Long-Form Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) often produce factoid hallucinations - plausible yet incorrect answers. A common mitigation strategy is model alignment, which improves factual accuracy by training on curated (factual, non-factual) pairs. However, this approach often relies on a stronger model (e.g., GPT-4) or an external knowledge base to assess factual correctness that may not always be accessible. Addressing this, we propose Atomic Consistency Preference Optimization (ACPO), a self-supervised preference-tuning method that enhances factual accuracy without external supervision. ACPO leverages atomic consistency signals (i.e., the agreement of individual facts across multiple stochastic responses) to identify high- and low-quality data pairs for model alignment. Despite being fully self-supervised, ACPO outperforms the strong supervised alignment baseline by 1.95 points averaged across Phi-3 and Llama3 on the LongFact and BioGen datasets, demonstrating its effectiveness in improving factual reliability without relying on external models or knowledge bases.
Approximating the Directed Hausdorff Distance
Venue: arXiv cs.CG 2025
Year: 2025

The Hausdorff distance is a metric commonly used to compute the set similarity of geometric sets. For sets containing a total of $n$ points, the exact distance can be computed naïvely in $O(n^2)$ time. In this paper, we show how to preprocess point sets individually so that the Hausdorff distance of any pair can then be approximated in linear time. We assume that the metric is doubling. The preprocessing time for each set is $O(n\log Δ)$ where $Δ$ is the ratio of the largest to smallest pairwise distances of the input. In theory, this can be reduced to $O(n\log n)$ time using a much more complicated algorithm. We compute $(1+\varepsilon)$-approximate Hausdorff distance in $(2 + \frac{1}{\varepsilon})^{O(d)}n$ time in a metric space with doubling dimension $d$. The $k$-partial Hausdorff distance ignores $k$ outliers to increase stability. Additionally, we give a linear-time algorithm to compute directed $k$-partial Hausdorff distance for all values of $k$ at once with no change to the preprocessing.
Modeling Interdependent Cybersecurity Threats Using Bayesian Networks: A Case Study on In-Vehicle Infotainment Systems
Venue: arXiv cs.CR 2025
Year: 2025

Cybersecurity threats are increasingly marked by interdependence, uncertainty, and evolving complexity challenges that traditional assessment methods such as CVSS, STRIDE, and attack trees fail to adequately capture. This paper reviews the application of Bayesian Networks (BNs) in cybersecurity risk modeling, highlighting their capacity to represent probabilistic dependencies, integrate diverse threat indicators, and support reasoning under uncertainty. A structured case study is presented in which a STRIDE-based attack tree for an automotive In-Vehicle Infotainment (IVI) system is transformed into a Bayesian Network. Logical relationships are encoded using Conditional Probability Tables (CPTs), and threat likelihoods are derived from normalized DREAD scores. The model enables not only probabilistic inference of system compromise likelihood but also supports causal analysis using do-calculus and local sensitivity analysis to identify high-impact vulnerabilities. These analyses provide insight into the most influential nodes within the threat propagation chain, informing targeted mitigation strategies. While demonstrating the potential of BNs for dynamic and context-aware risk assessment, the study also outlines limitations related to scalability, reliance on expert input, static structure assumptions, and limited temporal modeling. The paper concludes by advocating for future enhancements through Dynamic Bayesian Networks, structure learning, and adaptive inference to better support real-time cybersecurity decision-making in complex environments.
EcoSphere: A Decision-Support Tool for Automated Carbon Emission and Cost Optimization in Sustainable Urban Development
Venue: arXiv cs.HC 2025
Year: 2025

The construction industry is a major contributor to global greenhouse gas emissions, with embodied carbon being a key component. This study develops EcoSphere, an innovative software designed to evaluate and balance embodied and operational carbon emissions with construction and environmental costs in urban planning. Using high-resolution data from the National Structure Inventory, combined with computer vision and natural language processing applied to Google Street View and satellite imagery, EcoSphere categorizes buildings by structural and material characteristics with a bottom-up approach, creating a baseline emissions dataset. By simulating policy scenarios and mitigation strategies, EcoSphere provides policymakers and non-experts with actionable insights for sustainable development in cities and provide them with a vision of the environmental and financial results of their decisions. Case studies in Chicago and Indianapolis showcase how EcoSphere aids in assessing policy impacts on carbon emissions and costs, supporting data-driven progress toward carbon neutrality.
Human-like Cognitive Generalization for Large Models via Brain-in-the-loop Supervision
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in deep neural networks (DNNs), particularly large-scale language models, have demonstrated remarkable capabilities in image and natural language understanding. Although scaling up model parameters with increasing volume of training data has progressively improved DNN capabilities, achieving complex cognitive abilities - such as understanding abstract concepts, reasoning, and adapting to novel scenarios, which are intrinsic to human cognition - remains a major challenge. In this study, we show that brain-in-the-loop supervised learning, utilizing a small set of brain signals, can effectively transfer human conceptual structures to DNNs, significantly enhancing their comprehension of abstract and even unseen concepts. Experimental results further indicate that the enhanced cognitive capabilities lead to substantial performance gains in challenging tasks, including few-shot/zero-shot learning and out-of-distribution recognition, while also yielding highly interpretable concept representations. These findings highlight that human-in-the-loop supervision can effectively augment the complex cognitive abilities of large models, offering a promising pathway toward developing more human-like cognitive abilities in artificial systems.
Seeing Beyond the Scene: Enhancing Vision-Language Models with Interactional Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Traditional scene graphs primarily focus on spatial relationships, limiting vision-language models' (VLMs) ability to reason about complex interactions in visual scenes. This paper addresses two key challenges: (1) conventional detection-to-construction methods produce unfocused, contextually irrelevant relationship sets, and (2) existing approaches fail to form persistent memories for generalizing interaction reasoning to new scenes. We propose Interaction-augmented Scene Graph Reasoning (ISGR), a framework that enhances VLMs' interactional reasoning through three complementary components. First, our dual-stream graph constructor combines SAM-powered spatial relation extraction with interaction-aware captioning to generate functionally salient scene graphs with spatial grounding. Second, we employ targeted interaction queries to activate VLMs' latent knowledge of object functionalities, converting passive recognition into active reasoning about how objects work together. Finally, we introduce a lone-term memory reinforcement learning strategy with a specialized interaction-focused reward function that transforms transient patterns into long-term reasoning heuristics. Extensive experiments demonstrate that our approach significantly outperforms baseline methods on interaction-heavy reasoning benchmarks, with particularly strong improvements on complex scene understanding tasks. The source code can be accessed at https://github.com/open_upon_acceptance.
Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Big model has emerged as a new research paradigm that can be applied to various down-stream tasks with only minor effort for domain adaption. Correspondingly, this study tackles Camouflaged Object Detection (COD) leveraging the Segment Anything Model (SAM). The previous studies declared that SAM is not workable for COD but this study reveals that SAM works if promoted properly, for which we devise a new framework to render point promotions: First, we develop the Promotion Point Targeting Network (PPT-net) to leverage multi-scale features in predicting the probabilities of camouflaged objects' presences at given candidate points over the image. Then, we develop a key point selection (KPS) algorithm to deploy both positive and negative point promotions contrastively to SAM to guide the segmentation. It is the first work to facilitate big model for COD and achieves plausible results experimentally over the existing methods on 3 data sets under 6 metrics. This study demonstrates an off-the-shelf methodology for COD by leveraging SAM, which gains advantage over designing professional models from scratch, not only in performance, but also in turning the problem to a less challenging task, that is, seeking informative but not exactly precise promotions.
Initial Algebra Correspondence under Reachability Conditions
Venue: arXiv cs.LO 2025
Year: 2025

Suitable reachability conditions can make two different fixed point semantics of a transition system coincide. For instance, the total and partial expected reward semantics on Markov chains (MCs) coincide whenever the MC at hand is almost surely reachable. In this paper, we present a unifying framework for such reachability conditions that ensures the correspondence of two different semantics. Our categorical framework naturally induces an abstract reachability condition via a suitable adjunction, which allows us to prove coincidences of fixed points, and more generally of initial algebras. We demonstrate the generality of our approach by instantiating several examples, including the almost surely reachability condition for MCs, and the unambiguity condition of automata. We further study a canonical construction of our instance for Markov decision processes by pointwise Kan extensions.
ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor
Venue: arXiv cs.DC 2025
Year: 2025

We propose ELIS, a serving system for Large Language Models (LLMs) featuring an Iterative Shortest Remaining Time First (ISRTF) scheduler designed to efficiently manage inference tasks with the shortest remaining tokens. Current LLM serving systems often employ a first-come-first-served scheduling strategy, which can lead to the "head-of-line blocking" problem. To overcome this limitation, it is necessary to predict LLM inference times and apply a shortest job first scheduling strategy. However, due to the auto-regressive nature of LLMs, predicting the inference latency is challenging. ELIS addresses this challenge by training a response length predictor for LLMs using the BGE model, an encoder-based state-of-the-art model. Additionally, we have devised the ISRTF scheduling strategy, an optimization of shortest remaining time first tailored to existing LLM iteration batching. To evaluate our work in an industrial setting, we simulate streams of requests based on our study of real-world user LLM serving trace records. Furthermore, we implemented ELIS as a cloud-native scheduler system on Kubernetes to evaluate its performance in production environments. Our experimental results demonstrate that ISRTF reduces the average job completion time by up to 19.6%.
Adaptive Migration Decision for Multi-Tenant Memory Systems
Venue: arXiv cs.OS 2025
Year: 2025

Tiered memory systems consisting of fast small memory and slow large memory have emerged to provide high capacity memory in a cost-effective way. The effectiveness of tiered memory systems relies on how many memory accesses can be absorbed by the fast first-tier memory by page migration. The recent studies proposed several different ways of detecting hot pages and migrating them efficiently. However, our investigation shows that page migration is not always beneficial as it has the associated cost of detecting and migrating hot pages. When an application is unfriendly to migration, it is often better not to migrate pages at all. Based on the observation on migration friendliness, this paper proposes a migration control framework for multi-tenant tiered memory systems. First, it proposes a detection mechanism for migration friendliness, using per-page ping-pong status. Ping-pong pages which are promoted and demoted repeatedly in a short period of time tells migration effectiveness. Based on their change behaviors, migration is stopped or continued. After the page migration is stopped, the second mechanism detects changes of memory access patterns in a low cost way to determine whether migration needs to be resumed. Finally, as each application has a different behavior, our framework provides per-process migration control to selectively stop and start migration depending on application characteristics. We implement the framework in the Linux kernel. The evaluation with a commercial CXL-based tiered memory system shows that it effectively controls migration in single and multi-tenant environments.
BusOut is NP-complete
Venue: arXiv cs.CC 2025
Year: 2025

This study examines the computational complexity of the decision problem modeled on the smartphone game Bus Out. The objective of the game is to load all the passengers in a queue onto appropriate buses using a limited number of bus parking spots by selecting and dispatching the buses on a map. We show that the problem is NP-complete, even for highly restricted instances. We also show that it is hard to approximate the minimum number of parking spots needed to solve a given instance.
Some Computational Tools for Solving a Selection of Problems in Control Theory
Venue: arXiv cs.SC 2025
Year: 2025

This paper demonstrates how certified computational tools can be used to address various problems in control theory. In particular, we introduce PACE.jl, a Julia package that implements symbolic elimination techniques, including (among others) discriminant varieties and Rational Univariate Representation, while also supporting multi-precision interval computations. We showcase its applications to key control theory problems, including identification, stability analysis, and optimization, for both parameter-dependent and parameter-free systems.
Predicting butterfly species presence from satellite imagery using soft contrastive regularisation
Venue: arXiv cs.CV 2025
Year: 2025

The growing demand for scalable biodiversity monitoring methods has fuelled interest in remote sensing data, due to its widespread availability and extensive coverage. Traditionally, the application of remote sensing to biodiversity research has focused on mapping and monitoring habitats, but with increasing availability of large-scale citizen-science wildlife observation data, recent methods have started to explore predicting multi-species presence directly from satellite images. This paper presents a new data set for predicting butterfly species presence from satellite data in the United Kingdom. We experimentally optimise a Resnet-based model to predict multi-species presence from 4-band satellite images, and find that this model especially outperforms the mean rate baseline for locations with high species biodiversity. To improve performance, we develop a soft, supervised contrastive regularisation loss that is tailored to probabilistic labels (such as species-presence data), and demonstrate that this improves prediction accuracy. In summary, our new data set and contrastive regularisation method contribute to the open challenge of accurately predicting species biodiversity from remote sensing data, which is key for efficient biodiversity monitoring.