OpenRaiser/Intern-Atlas huggingface dataset:
Path Planning through Multi-Agent Reinforcement Learning in Dynamic Environments
Venue: arXiv cs.RO 2025
Year: 2025

Path planning in dynamic environments is a fundamental challenge in intelligent transportation and robotics, where obstacles and conditions change over time, introducing uncertainty and requiring continuous adaptation. While existing approaches often assume complete environmental unpredictability or rely on global planners, these assumptions limit scalability and practical deployment in real-world settings. In this paper, we propose a scalable, region-aware reinforcement learning (RL) framework for path planning in dynamic environments. Our method builds on the observation that environmental changes, although dynamic, are often localized within bounded regions. To exploit this, we introduce a hierarchical decomposition of the environment and deploy distributed RL agents that adapt to changes locally. We further propose a retraining mechanism based on sub-environment success rates to determine when policy updates are necessary. Two training paradigms are explored: single-agent Q-learning and multi-agent federated Q-learning, where local Q-tables are aggregated periodically to accelerate the learning process. Unlike prior work, we evaluate our methods in more realistic settings, where multiple simultaneous obstacle changes and increasing difficulty levels are present. Results show that the federated variants consistently outperform their single-agent counterparts and closely approach the performance of A* Oracle while maintaining shorter adaptation times and robust scalability. Although initial training remains time-consuming in large environments, our decentralized framework eliminates the need for a global planner and lays the groundwork for future improvements using deep RL and flexible environment decomposition.
Edge-Centric Relational Reasoning for 3D Scene Graph Prediction
Venue: arXiv cs.CV 2025
Year: 2025

3D scene graph prediction aims to abstract complex 3D environments into structured graphs consisting of objects and their pairwise relationships. Existing approaches typically adopt object-centric graph neural networks, where relation edge features are iteratively updated by aggregating messages from connected object nodes. However, this design inherently restricts relation representations to pairwise object context, making it difficult to capture high-order relational dependencies that are essential for accurate relation prediction. To address this limitation, we propose a Link-guided Edge-centric relational reasoning framework with Object-aware fusion, namely LEO, which enables progressive reasoning from relation-level context to object-level understanding. Specifically, LEO first predicts potential links between object pairs to suppress irrelevant edges, and then transforms the original scene graph into a line graph where each relation is treated as a node. A line graph neural network is applied to perform edge-centric relational reasoning to capture inter-relation context. The enriched relation features are subsequently integrated into the original object-centric graph to enhance object-level reasoning and improve relation prediction. Our framework is model-agnostic and can be integrated with any existing object-centric method. Experiments on the 3DSSG dataset with two competitive baselines show consistent improvements, highlighting the effectiveness of our edge-to-object reasoning paradigm.
MAPROC at AHaSIS Shared Task: Few-Shot and Sentence Transformer for Sentiment Analysis of Arabic Hotel Reviews
Venue: arXiv cs.CL 2025
Year: 2025

Sentiment analysis of Arabic dialects presents significant challenges due to linguistic diversity and the scarcity of annotated data. This paper describes our approach to the AHaSIS shared task, which focuses on sentiment analysis on Arabic dialects in the hospitality domain. The dataset comprises hotel reviews written in Moroccan and Saudi dialects, and the objective is to classify the reviewers sentiment as positive, negative, or neutral. We employed the SetFit (Sentence Transformer Fine-tuning) framework, a data-efficient few-shot learning technique. On the official evaluation set, our system achieved an F1 of 73%, ranking 12th among 26 participants. This work highlights the potential of few-shot learning to address data scarcity in processing nuanced dialectal Arabic text within specialized domains like hotel reviews.
A Multimodal Transformer Approach for UAV Detection and Aerial Object Recognition Using Radar, Audio, and Video Data
Venue: arXiv cs.CV 2025
Year: 2025

Unmanned aerial vehicle (UAV) detection and aerial object recognition are critical for modern surveillance and security, prompting a need for robust systems that overcome limitations of single-modality approaches. This research addresses these challenges by designing and rigorously evaluating a novel multimodal Transformer model that integrates diverse data streams: radar, visual band video (RGB), infrared (IR) video, and audio. The architecture effectively fuses distinct features from each modality, leveraging the Transformer's self-attention mechanisms to learn comprehensive, complementary, and highly discriminative representations for classification. The model demonstrated exceptional performance on an independent test set, achieving macro-averaged metrics of 0.9812 accuracy, 0.9873 recall, 0.9787 precision, 0.9826 F1-score, and 0.9954 specificity. Notably, it exhibited particularly high precision and recall in distinguishing drones from other aerial objects. Furthermore, computational analysis confirmed its efficiency, with 1.09 GFLOPs, 1.22 million parameters, and an inference speed of 41.11 FPS, highlighting its suitability for real-time applications. This study presents a significant advancement in aerial object classification, validating the efficacy of multimodal data fusion via a Transformer architecture for achieving state-of-the-art performance, thereby offering a highly accurate and resilient solution for UAV detection and monitoring in complex airspace.
Anchor-and-Connect: Robotic Aerial Base Stations Transforming 6G Infrastructure
Venue: arXiv cs.ET 2025
Year: 2025

Despite the significant attention that aerial base stations (ABSs) have received recently, their practical implementation is severely weakened by their limited endurance due to the battery constraints of drones. To overcome this fundamental limitation and barrier for wider adoption, we propose the concept of robotic aerial base stations (RABSs) that are equipped with energy-neutral anchoring end-effectors able to autonomously grasp or perch on tall urban landforms. Thanks to the energy-efficient anchoring operation, RABSs could offer seamless wireless connectivity for multiple hours compared to minutes of the typical hovering-based ABSs. Therefore, the prolonged service capabilities of RABSs allowing them to integrate into the radio access network and augment the network capacity where and when needed. To set the scene, we discuss the key components of the proposed RABS concept including hardware, workflow, communication considerations, and regulation issues. Then, the advantages of RABSs are highlighted which is followed by case studies that compare RABSs with terrestrial micro BSs and other types of non-terrestrial communication infrastructure, such as hovering-based, tethered, and laser-powered ABSs.
IPTQ-ViT: Post-Training Quantization of Non-linear Functions for Integer-only Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Previous Quantization-Aware Training (QAT) methods for vision transformers rely on expensive retraining to recover accuracy loss in non-linear layer quantization, limiting their use in resource-constrained environments. In contrast, existing Post-Training Quantization (PTQ) methods either partially quantize non-linear functions or adjust activation distributions to maintain accuracy but fail to achieve fully integer-only inference. In this paper, we introduce IPTQ-ViT, a novel PTQ framework for fully integer-only vision transformers without retraining. We present approximation functions: a polynomial-based GELU optimized for vision data and a bit-shifting-based Softmax designed to improve approximation accuracy in PTQ. In addition, we propose a unified metric integrating quantization sensitivity, perturbation, and computational cost to select the optimal approximation function per activation layer. IPTQ-ViT outperforms previous PTQ methods, achieving up to 6.44\%p (avg. 1.78\%p) top-1 accuracy improvement for image classification, 1.0 mAP for object detection. IPTQ-ViT outperforms partial floating-point PTQ methods under W8A8 and W4A8, and achieves accuracy and latency comparable to integer-only QAT methods. We plan to release our code https://github.com/gihwan-kim/IPTQ-ViT.git.
CID: Measuring Feature Importance Through Counterfactual Distributions
Venue: arXiv cs.LG 2025
Year: 2025

Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous methods exist, the lack of a definitive ground truth for comparison highlights the need for alternative, well-founded measures. This paper introduces a novel post-hoc local feature importance method called Counterfactual Importance Distribution (CID). We generate two sets of positive and negative counterfactuals, model their distributions using Kernel Density Estimation, and rank features based on a distributional dissimilarity measure. This measure, grounded in a rigorous mathematical framework, satisfies key properties required to function as a valid metric. We showcase the effectiveness of our method by comparing with well-established local feature importance explainers. Our method not only offers complementary perspectives to existing approaches, but also improves performance on faithfulness metrics (both for comprehensiveness and sufficiency), resulting in more faithful explanations of the system. These results highlight its potential as a valuable tool for model analysis.
Towards Evolutionary Optimization Using the Ising Model
Venue: arXiv cs.NE 2025
Year: 2025

In this paper, we study the problem of finding the global minima of a given function. Specifically, we consider complicated functions with numerous local minima, as is often the case for real-world data mining losses. We do so by applying a model from theoretical physics to create an Ising model-based evolutionary optimization algorithm. Our algorithm creates stable regions of local optima and a high potential for improvement between these regions. This enables the accurate identification of global minima, surpassing comparable methods, and has promising applications to ensembles.
Building Robust and Scalable Multilingual ASR for Indian Languages
Venue: arXiv cs.CL 2025
Year: 2025

This paper describes the systems developed by SPRING Lab, Indian Institute of Technology Madras, for the ASRU MADASR 2.0 challenge. The systems developed focuses on adapting ASR systems to improve in predicting the language and dialect of the utterance among 8 languages across 33 dialects. We participated in Track 1 and Track 2, which restricts the use of additional data and develop from-the-scratch multilingual systems. We presented a novel training approach using Multi-Decoder architecture with phonemic Common Label Set (CLS) as intermediate representation. It improved the performance over the baseline (in the CLS space). We also discuss various methods used to retain the gain obtained in the phonemic space while converting them back to the corresponding grapheme representations. Our systems beat the baseline in 3 languages (Track 2) in terms of WER/CER and achieved the highest language ID and dialect ID accuracy among all participating teams (Track 2).
A Dataset and Baseline for Deep Learning-Based Visual Quality Inspection in Remanufacturing
Venue: arXiv cs.CV 2025
Year: 2025

Remanufacturing describes a process where worn products are restored to like-new condition and it offers vast ecological and economic potentials. A key step is the quality inspection of disassembled components, which is mostly done manually due to the high variety of parts and defect patterns. Deep neural networks show great potential to automate such visual inspection tasks but struggle to generalize to new product variants, components, or defect patterns. To tackle this challenge, we propose a novel image dataset depicting typical gearbox components in good and defective condition from two automotive transmissions. Depending on the train-test split of the data, different distribution shifts are generated to benchmark the generalization ability of a classification model. We evaluate different models using the dataset and propose a contrastive regularization loss to enhance model robustness. The results obtained demonstrate the ability of the loss to improve generalisation to unseen types of components.
Coopetitive Index: a measure of cooperation and competition in coalition formation
Venue: arXiv cs.GT 2025
Year: 2025

We extend the coopetition index introduced by Aleandri and Dall'Aglio (2025) for simple games to the broader class of monotone transferable utility (TU) games and to all non-empty coalitions, including singletons. The new formulation allows us to define an absolute coopetition index with a universal range in [-1,1], facilitating meaningful comparisons across coalitions. We study several notable instances of the index, including the Banzhaf, Uniform Shapley, and Shapley-Owen coopetition indices, and we derive explicit formulas that connect coopetition to classical semivalues. Finally, we provide axiomatic characterizations of the Uniform Shapley and Shaple--Owen versions, showing that each is uniquely determined by linearity, symmetry over pure bargaining games, external null player neutrality, and a contraction axiom reflecting its internal distribution. These results position the coopetition index as a versatile tool for quantifying the cooperative and competitive tendencies of coalitions in TU-games.
CroPS: Improving Dense Retrieval with Cross-Perspective Positive Samples in Short-Video Search
Venue: arXiv cs.IR 2025
Year: 2025

Dense retrieval has become a foundational paradigm in modern search systems, especially on short-video platforms. However, most industrial systems adopt a self-reinforcing training pipeline that relies on historically exposed user interactions for supervision. This paradigm inevitably leads to a filter bubble effect, where potentially relevant but previously unseen content is excluded from the training signal, biasing the model toward narrow and conservative retrieval. In this paper, we present CroPS (Cross-Perspective Positive Samples), a novel retrieval data engine designed to alleviate this problem by introducing diverse and semantically meaningful positive examples from multiple perspectives. CroPS enhances training with positive signals derived from user query reformulation behavior (query-level), engagement data in recommendation streams (system-level), and world knowledge synthesized by large language models (knowledge-level). To effectively utilize these heterogeneous signals, we introduce a Hierarchical Label Assignment (HLA) strategy and a corresponding H-InfoNCE loss that together enable fine-grained, relevance-aware optimization. Extensive experiments conducted on Kuaishou Search, a large-scale commercial short-video search platform, demonstrate that CroPS significantly outperforms strong baselines both offline and in live A/B tests, achieving superior retrieval performance and reducing query reformulation rates. CroPS is now fully deployed in Kuaishou Search, serving hundreds of millions of users daily.
SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in computational pathology have leveraged vision-language models to learn joint representations of Hematoxylin and Eosin (HE) images with spatial transcriptomic (ST) profiles. However, existing approaches typically align HE tiles with their corresponding ST profiles at a single scale, overlooking fine-grained cellular structures and their spatial organization. To address this, we propose Sigmma, a multi-modal contrastive alignment framework for learning hierarchical representations of HE images and spatial transcriptome profiles across multiple scales. Sigmma introduces multi-scale contrastive alignment, ensuring that representations learned at different scales remain coherent across modalities. Furthermore, by representing cell interactions as a graph and integrating inter- and intra-subgraph relationships, our approach effectively captures cell-cell interactions, ranging from fine to coarse, within the tissue microenvironment. We demonstrate that Sigmm learns representations that better capture cross-modal correspondences, leading to an improvement of avg. 9.78\% in the gene-expression prediction task and avg. 26.93\% in the cross-modal retrieval task across datasets. We further show that it learns meaningful multi-tissue organization in downstream analyses.
PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles
Venue: arXiv cs.LG 2025
Year: 2025

Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconcile high-fidelity learning with the structural requirements of verifiable control. We address this by introducing PCARNN-DCBF, a novel pipeline integrating a Physics-encoded Control-Affine Residual Neural Network with a preview-based Discrete Control Barrier Function. Unlike generic learned models, PCARNN explicitly preserves the control-affine structure of vehicle dynamics, ensuring the linearity required for reliable optimization. This enables the DCBF to enforce polygonal keep-in constraints via a real-time Quadratic Program (QP) that handles high relative degree and mitigates actuator saturation. Experiments in CARLA across electric and combustion platforms demonstrate that this structure-preserving approach significantly outperforms analytical and unstructured neural baselines.
Exploring the use of AI authors and reviewers at Agents4Science
Venue: arXiv cs.AI 2025
Year: 2025

There is growing interest in using AI agents for scientific research, yet fundamental questions remain about their capabilities as scientists and reviewers. To explore these questions, we organized Agents4Science, the first conference in which AI agents serve as both primary authors and reviewers, with humans as co-authors and co-reviewers. Here, we discuss the key learnings from the conference and their implications for human-AI collaboration in science.
Computer-Use Agents as Judges for Generative User Interface
Venue: arXiv cs.CV 2025
Year: 2025

Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed primarily for humans--prioritizing aesthetics and usability--forcing agents to adopt human-oriented behaviors that are unnecessary for efficient task execution. At the same time, rapid advances in coding-oriented language models (Coder) have transformed automatic GUI design. This raises a fundamental question: Can CUA as judges to assist Coder for automatic GUI design? To investigate, we introduce AUI-Gym, a benchmark for Automatic GUI development spanning 52 applications across diverse domains. Using language models, we synthesize 1560 tasks that simulate real-world scenarios. To ensure task reliability, we further develop a verifier that programmatically checks whether each task is executable within its environment. Building on this, we propose a Coder-CUA in Collaboration framework: the Coder acts as Designer, generating and revising websites, while the CUA serves as Judge, evaluating functionality and refining designs. Success is measured not by visual appearance, but by task solvability and CUA navigation success rate. To turn CUA feedback into usable guidance, we design a CUA Dashboard that compresses multi-step navigation histories into concise visual summaries, offering interpretable guidance for iterative redesign. By positioning agents as both designers and judges, our framework shifts interface design toward agent-native efficiency and reliability. Our work takes a step toward shifting agents from passive use toward active participation in digital environments. Our code and dataset are available at https://github.com/showlab/AUI.
From Low-Rank Features to Encoding Mismatch: Rethinking Feature Distillation in Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Feature-map knowledge distillation (KD) is highly effective for convolutional networks but often fails for Vision Transformers (ViTs). To understand this failure and guide method design, we conduct a two-view representation analysis of ViTs. First, a layer-wise Singular Value Decomposition (SVD) of full feature matrices shows that final-layer representations are globally low-rank: for CaiT-S24, only $121/61/34/14$ dimensions suffice to capture $99\%/95\%/90\%/80\%$ of the energy. In principle, this suggests that a compact student plus a simple linear projector should be enough for feature alignment, contradicting the weak empirical performance of standard feature KD. To resolve this paradox, we introduce a token-level Spectral Energy Pattern (SEP) analysis that measures how each token uses channel capacity. SEP reveals that, despite the global low-rank structure, individual tokens distribute energy over most channels, forming a high-bandwidth encoding pattern. This results in an encoding mismatch between wide teachers and narrow students. Motivated by this insight, we propose two minimal, mismatch-driven strategies: (1) post-hoc feature lifting with a lightweight projector retained during inference, or (2) native width alignment that widens only the student's last block to the teacher's width. On ImageNet-1K, these strategies reactivate simple feature-map distillation in ViTs, raising DeiT-Tiny accuracy from $74.86\%$ to $77.53\%$ and $78.23\%$ when distilling from CaiT-S24, while also improving standalone students trained without any teacher. Our analysis thus explains why ViT feature distillation fails and shows how exploiting low-rank structure yields effective, interpretable remedies and concrete design guidance for compact ViTs.
Two-Faced Social Agents: Context Collapse in Role-Conditioned Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

In this study, we evaluate the persona fidelity of frontier LLMs, GPT-5, Claude Sonnet 4.5 and Gemini 2.5 Flash when assigned distinct socioeconomic personas performing scholastic assessment test (SAT) mathematics items and affective preference tasks. Across 15 distinct role conditions and three testing scenarios, GPT-5 exhibited complete contextual collapse and adopted a singular identity towards optimal responses (PERMANOVA p=1.000, R^2=0.0004), while Gemini 2.5 Flash showed partial collapse (p=0.120, R^2=0.0020). Claude Sonnet 4.5 retained limited but measurable role-specific variation on the SAT items (PERMANOVA p<0.001, R^2=0.0043), though with inverted SES-performance relationships where low-SES personas outperformed high-SES personas (eta^2 = 0.15-0.19 in extended replication). However, all models exhibited distinct role-conditioned affective preference (average d = 0.52-0.58 vs near zero separation for math), indicating that socio-affective variation can reemerge when cognitive constraints are relaxed. These findings suggest that distributional fidelity failure originates in task-dependent contextual collapse: optimization-driven identity convergence under cognitive load combined with impaired role-contextual understanding. Realistic social simulations may require embedding contextual priors in the model's post-training alignment and not just distributional calibration to replicate human-like responses. Beyond simulation validity, these results have implications for survey data integrity, as LLMs can express plausible demographic variation on preference items while failing to maintain authentic reasoning constraints.