OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
Venue: arXiv cs.IR 2025
Year: 2025

This study presents a comprehensive reproducibility and extension analysis of the Setwise prompting methodology for zero-shot ranking with Large Language Models (LLMs), as proposed by Zhuang et al. We evaluate its effectiveness and efficiency compared to traditional Pointwise, Pairwise, and Listwise approaches in document ranking tasks. Our reproduction confirms the findings of Zhuang et al., highlighting the trade-offs between computational efficiency and ranking effectiveness in Setwise methods. Building on these insights, we introduce Setwise Insertion, a novel approach that leverages the initial document ranking as prior knowledge, reducing unnecessary comparisons and uncertainty by focusing on candidates more likely to improve the ranking results. Experimental results across multiple LLM architectures (Flan-T5, Vicuna, and Llama) show that Setwise Insertion yields a 31% reduction in query time, a 23% reduction in model inferences, and a slight improvement in reranking effectiveness compared to the original Setwise method. These findings highlight the practical advantage of incorporating prior ranking knowledge into Setwise prompting for efficient and accurate zero-shot document reranking.
Finding Pathways in Reaction Networks guided by Energy Barriers using Integer Linear Programming
Venue: arXiv cs.CE 2025
Year: 2025

Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This work presents a computational methodology for searching for pathways in reaction networks which is based on integer linear programming and the modeling of reaction networks by directed hypergraphs. Often multiple pathways fit the given search criteria. To rank them, we develop an objective function based on physical arguments maximizing the probability of the pathway. We furthermore develop an automated pipeline to estimate the energy barriers of individual reactions in reaction networks. Combined, the methodology facilitates flexible and kinetically informed pathway investigations on large reaction networks by computational means, even for networks coming without kinetic annotation, such as those created via generative approaches for expanding molecular spaces.
Beyond Coordinates: Meta-Equivariance in Statistical Inference
Venue: arXiv cs.OH 2025
Year: 2025

Optimal statistical decisions should transcend the language used to describe them. Yet, how do we guarantee that the choice of coordinates - the parameterisation of an optimisation problem - does not subtly dictate the solution? This paper reveals a fundamental geometric invariance principle. We first analyse the optimal combination of two asymptotically normal estimators under a strictly convex trace-AMSE risk. While methods for finding optimal weights are known, we prove that the resulting optimal estimator is invariant under direct affine reparameterisations of the weighting scheme. This exemplifies a broader principle we term meta-equivariance: the unique minimiser of any strictly convex, differentiable scalar objective over a matrix space transforms covariantly under any invertible affine reparameterisation of that space. Distinct from classical statistical equivariance tied to data symmetries, meta-equivariance arises from the immutable geometry of convex optimisation itself. It guarantees that optimality, in these settings, is not an artefact of representation but an intrinsic, coordinate-free truth.
Token Sliding Reconfiguration on DAGs
Venue: arXiv cs.DM 2025
Year: 2025

Given a graph $G$ and two independent sets of same size, the Independent Set Reconfiguration Problem under token sliding ask whether one can, in a step by step manner, transform the first independent set into the second one. In each step we must preserve the condition of independence. Further, referring to solution vertices as tokens, we are only permitted to slide a token along an edge. Until the recent work of Ito et al. [Ito et al. MFCS 2022] this problem was only considered on undirected graphs. In this work, we study reconfiguration under token sliding focusing on DAGs. We present a complete dichotomy of intractability in regard to the depth of the DAG, by proving that this problem is NP-complete for DAGs of depth 3 and $\textrm{W}[1]$-hard for depth 4 when parameterized by the number of tokens $k$, and that these bounds are tight. Further, we prove that it is fixed parameter tractable on DAGs parameterized by the combination of treewidth and $k$. We show that this result applies to undirected graphs, when the number of times a token can visit a vertex is restricted.
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Venue: arXiv cs.CV 2025
Year: 2025

Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction with NTIRE 2025, we organized the 1st CD-FSOD Challenge, aiming to advance the performance of current object detectors on entirely novel target domains with only limited labeled data. The challenge attracted 152 registered participants, received submissions from 42 teams, and concluded with 13 teams making valid final submissions. Participants approached the task from diverse perspectives, proposing novel models that achieved new state-of-the-art (SOTA) results under both open-source and closed-source settings. In this report, we present an overview of the 1st NTIRE 2025 CD-FSOD Challenge, highlighting the proposed solutions and summarizing the results submitted by the participants.
FuzzSense: Towards A Modular Fuzzing Framework for Autonomous Driving Software
Venue: arXiv cs.CR 2025
Year: 2025

Fuzz testing to find semantic control vulnerabilities is an essential activity to evaluate the robustness of autonomous driving (AD) software. Whilst there is a preponderance of disparate fuzzing tools that target different parts of the test environment, such as the scenario, sensors, and vehicle dynamics, there is a lack of fuzzing strategies that ensemble these fuzzers to enable concurrent fuzzing, utilizing diverse techniques and targets. This research proposes FuzzSense, a modular, black-box, mutation-based fuzzing framework that is architected to ensemble diverse AD fuzzing tools. To validate the utility of FuzzSense, a LiDAR sensor fuzzer was developed as a plug-in, and the fuzzer was implemented in the new AD simulation platform AWSIM and Autoware.Universe AD software platform. The results demonstrated that FuzzSense was able to find vulnerabilities in the new Autoware.Universe software. We contribute to FuzzSense open-source with the aim of initiating a conversation in the community on the design of AD-specific fuzzers and the establishment of a community fuzzing framework to better target the diverse technology base of autonomous vehicles.
Iterative Recommendations based on Monte Carlo Sampling and Trust Estimation in Multi-Stage Vehicular Traffic Routing Games
Venue: arXiv cs.GT 2025
Year: 2025

The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to the misalignment in motives, drivers exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of ITS based on counterfactual and observed game outcomes. Specifically, DVS and ITS are respectively modeled as a travel-time minimizer and network congestion minimizer, each having nonidentical prior beliefs about the network state. A novel approximate algorithm to compute the Bayesian Nash equilibrium, called ROSTER(Recommendation Outcome Sampling with Trust Estimation and Re-evaluation), is proposed based on Monte Carlo sampling with trust belief updating to determine the best response route recommendations of the ITS at each stage of the game. Simulation results demonstrate that the trust prediction error in the proposed algorithm converges to zero with a growing number of multi-stage DVS-ITS interactions and is effectively able to both mitigate congestion and reduce driver travel times when compared to alternative route recommendation strategies.
PQ-CAN: A Framework for Simulating Post-Quantum Cryptography in Embedded Systems
Venue: arXiv cs.CR 2025
Year: 2025

The rapid development of quantum computers threatens traditional cryptographic schemes, prompting the need for Post-Quantum Cryptography (PQC). Although the NIST standardization process has accelerated the development of such algorithms, their application in resource-constrained environments such as embedded systems remains a challenge. Automotive systems relying on the Controller Area Network (CAN) bus for communication are particularly vulnerable due to their limited computational capabilities, high traffic, and need for real-time response. These constraints raise concerns about the feasibility of implementing PQC in automotive environments, where legacy hardware and bit rate limitations must also be considered. In this paper, we introduce PQ-CAN, a modular framework for simulating the performance and overhead of PQC algorithms in embedded systems. We consider the automotive domain as our case study, testing a variety of PQC schemes under different scenarios. Our simulation enables the adjustment of embedded system computational capabilities and CAN bus bit rate constraints. We also provide insights into the trade-offs involved by analyzing each algorithm's security level and overhead for key encapsulation and digital signature. By evaluating the performance of these algorithms, we provide insights into their feasibility and identify the strengths and limitations of PQC in securing automotive communications in the post-quantum era.
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
Venue: arXiv cs.HC 2025
Year: 2025

Explanations for computer vision models are important tools for interpreting how the underlying models work. However, they are often presented in static formats, which pose challenges for users, including information overload, a gap between semantic and pixel-level information, and limited opportunities for exploration. We investigate interactivity as a mechanism for tackling these issues in three common explanation types: heatmap-based, concept-based, and prototype-based explanations. We conducted a study (N=24), using a bird identification task, involving participants with diverse technical and domain expertise. We found that while interactivity enhances user control, facilitates rapid convergence to relevant information, and allows users to expand their understanding of the model and explanation, it also introduces new challenges. To address these, we provide design recommendations for interactive computer vision explanations, including carefully selected default views, independent input controls, and constrained output spaces.
An Improved Fully Dynamic Algorithm for Counting 4-Cycles in General Graphs using Fast Matrix Multiplication
Venue: arXiv cs.DS 2025
Year: 2025

We study subgraph counting over fully dynamic graphs, which undergo edge insertions and deletions. Counting subgraphs is a fundamental problem in graph theory with numerous applications across various fields, including database theory, social network analysis, and computational biology. Maintaining the number of triangles in fully dynamic graphs is very well studied and has an upper bound of O(m^{1/2}) for the update time by Kara, Ngo, Nikolic, Olteanu, and Zhang (TODS 20). There is also a conditional lower bound of approximately Omega(m^{1/2}) for the update time by Henzinger, Krinninger, Nanongkai, and Saranurak (STOC 15) under the OMv conjecture implying that O(m^{1/2}) is the ``right answer'' for the update time of counting triangles. More recently, Hanauer, Henzinger, and Hua (SAND 22) studied the problem of maintaining the number of 4-cycles in fully dynamic graphs and designed an algorithm with O(m^{2/3}) update time which is a natural generalization of the approach for counting triangles. Thus, it seems natural that O(m^{2/3}) might be the correct answer for the complexity of the update time for counting 4-cycles. In this work, we present an improved algorithm for maintaining the number of 4-cycles in fully dynamic graphs. Our algorithm achieves a worst-case update time of O(m^{2/3-eps}) for some constant eps>0. Our approach crucially uses fast matrix multiplication and leverages recent developments therein to get an improved runtime. Using the current best value of the matrix multiplication exponent omega=2.371339 we get eps=0.009811 and if we assume the best possible exponent i.e. omega=2 then we get eps=1/24. The lower bound for the update time is Omega(m^{1/2}), so there is still a big gap between the best-known upper and lower bounds. The key message of our paper is demonstrating that O(m^{2/3}) is not the correct answer for the complexity of the update time.
Following Is All You Need: Robot Crowd Navigation Using People As Planners
Venue: arXiv cs.RO 2025
Year: 2025

Navigating in crowded environments requires the robot to be equipped with high-level reasoning and planning techniques. Existing works focus on developing complex and heavyweight planners while ignoring the role of human intelligence. Since humans are highly capable agents who are also widely available in a crowd navigation setting, we propose an alternative scheme where the robot utilises people as planners to benefit from their effective planning decisions and social behaviours. Through a set of rule-based evaluations, we identify suitable human leaders who exhibit the potential to guide the robot towards its goal. Using a simple base planner, the robot follows the selected leader through shorthorizon subgoals that are designed to be straightforward to achieve. We demonstrate through both simulated and real-world experiments that our novel framework generates safe and efficient robot plans compared to existing planners, even without predictive or data-driven modules. Our method also brings human-like robot behaviours without explicitly defining traffic rules and social norms. Code will be available at https://github.com/centiLinda/PeopleAsPlanner.git.
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control
Venue: arXiv cs.AI 2025
Year: 2025

This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable unmanned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics.
Ichiyo: Fragile and Transient Interaction in Neighborhood
Venue: arXiv cs.HC 2025
Year: 2025

As the Internet develops, social networking and other communication tools have transformed people's relationships into something fast, visible, and geographically huge. However, these communication tools have not expanded opportunities for acquainting oneself with neighbors outside one's social network; rather, they have comparatively diminished occasions for interacting with unfamiliar neighbors by prioritizing communication with existing friends. Therefore, we invented the medium Ichiyo to increase the opportunities to think of neighbors walking along the same street or in the same neighborhood and to expand the imagination of those who pass by and those who used to be there. Thus, users can engage in indirect interaction. We used commercially available laser cutters to engrave QR codes on leaves that are naturally found in our living space to prevent environmental invasion. The QR codes lead to a communal space on the web where users can freely leave messages. By engraving QR codes, information can be virtually expanded to be presented. To get the feedback of Ichiyo, we let a total of several thousand people experience a new way of communication as a part of the exhibition ''iii Exhibition 2022'', an art exhibition at the University of Tokyo. A total of more than 1,000 leaves engraved with QR codes were prepared and scattered at the exhibition site and along the road from the nearest station to the venue.
A Sublinear Algorithm for Path Feasibility Among Rectangular Obstacles
Venue: arXiv cs.CG 2025
Year: 2025

The problem of finding a path between two points while avoiding obstacles is critical in robotic path planning. We focus on the feasibility problem: determining whether such a path exists. We model the robot as a query-specific rectangular object capable of moving parallel to its sides. The obstacles are axis-aligned, rectangular, and may overlap. Most previous works only consider nondisjoint rectangular objects and point-sized or statically sized robots. Our approach introduces a novel technique leveraging generalized Gabriel graphs and constructs a data structure to facilitate online queries regarding path feasibility with varying robot sizes in sublinear time. To efficiently handle feasibility queries, we propose an online algorithm utilizing sweep line to construct a generalized Gabriel graph under the $L_\infty$ norm, capturing key gap constraints between obstacles. We utilize a persistent disjoint-set union data structure to efficiently determine feasibility queries in $\mathcal{O}(\log n)$ time and $\mathcal{O}(n)$ total space.
A Pseudorandom Generator for Functions of Low-Degree Polynomial Threshold Functions
Venue: arXiv cs.CC 2025
Year: 2025

Developing explicit pseudorandom generators (PRGs) for prominent categories of Boolean functions is a key focus in computational complexity theory. In this paper, we investigate the PRGs against the functions of degree-$d$ polynomial threshold functions (PTFs) over Gaussian space. Our main result is an explicit construction of PRG with seed length $\mathrm{poly}(k,d,1/ε)\cdot\log n$ that can fool any function of $k$ degree-$d$ PTFs with probability at least $1-\varepsilon$. More specifically, we show that the summation of $L$ independent $R$-moment-matching Gaussian vectors $ε$-fools functions of $k$ degree-$d$ PTFs, where $L=\mathrm{poly}( k, d, \frac{1}ε)$ and $R = O({\log \frac{kd}ε})$. The PRG is then obtained by applying an appropriate discretization to Gaussian vectors with bounded independence.
Towards Efficient Partially Relevant Video Retrieval with Active Moment Discovering
Venue: arXiv cs.CV 2025
Year: 2025

Partially relevant video retrieval (PRVR) is a practical yet challenging task in text-to-video retrieval, where videos are untrimmed and contain much background content. The pursuit here is of both effective and efficient solutions to capture the partial correspondence between text queries and untrimmed videos. Existing PRVR methods, which typically focus on modeling multi-scale clip representations, however, suffer from content independence and information redundancy, impairing retrieval performance. To overcome these limitations, we propose a simple yet effective approach with active moment discovering (AMDNet). We are committed to discovering video moments that are semantically consistent with their queries. By using learnable span anchors to capture distinct moments and applying masked multi-moment attention to emphasize salient moments while suppressing redundant backgrounds, we achieve more compact and informative video representations. To further enhance moment modeling, we introduce a moment diversity loss to encourage different moments of distinct regions and a moment relevance loss to promote semantically query-relevant moments, which cooperate with a partially relevant retrieval loss for end-to-end optimization. Extensive experiments on two large-scale video datasets (\ie, TVR and ActivityNet Captions) demonstrate the superiority and efficiency of our AMDNet. In particular, AMDNet is about 15.5 times smaller (\#parameters) while 6.0 points higher (SumR) than the up-to-date method GMMFormer on TVR.
MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by interacting with users through conversations. Most existing studies of CRS focus on extracting user preferences from conversational contexts. However, due to the short and sparse nature of conversational contexts, it is difficult to fully capture user preferences by conversational contexts only. We argue that multi-modal semantic information can enrich user preference expressions from diverse dimensions (e.g., a user preference for a certain movie may stem from its magnificent visual effects and compelling storyline). In this paper, we propose a multi-modal semantic graph prompt learning framework for CRS, named MSCRS. First, we extract textual and image features of items mentioned in the conversational contexts. Second, we capture higher-order semantic associations within different semantic modalities (collaborative, textual, and image) by constructing modality-specific graph structures. Finally, we propose an innovative integration of multi-modal semantic graphs with prompt learning, harnessing the power of large language models to comprehensively explore high-dimensional semantic relationships. Experimental results demonstrate that our proposed method significantly improves accuracy in item recommendation, as well as generates more natural and contextually relevant content in response generation.
The Effectiveness of Business Process Visualisations: a Systematic Literature Review
Venue: arXiv cs.HC 2025
Year: 2025

Business Process Visualisations (BPVs) have become indispensable tools for organisations seeking to enhance their operational efficiency, decision-making capabilities, and overall performance. The burgeoning interest in process modeling and tool development, coupled with the rise of data visualisation field, underscores the significant role of visual tools in leveraging human cognition. Unlike traditional models, data visualisation approaches graphics from a novel angle, emphasising the potency of visual representations. This review aims to integrate the domains of BPV and data visualisation to assess their combined influence on organisational effectiveness comprehensively. Through a meticulous analysis of existing literature, this study aims to amalgamate insights on BPVs impact from a data visualisation standpoint, advocating for a design philosophy that prioritises user engagement to bolster organisational outcomes. Additionally, our systematic review has unveiled promising avenues for future research, identifying underexplored variables that influence the efficacy of BPVs, thereby charting a path for forthcoming scholarly inquiries.