OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Objective Task-Aware Predictor for Image-Text Alignment
Venue: arXiv cs.CV 2025
Year: 2025

Evaluating image-text alignment while reflecting human preferences across multiple aspects is a significant issue for the development of reliable vision-language applications. It becomes especially crucial in real-world scenarios where multiple valid descriptions exist depending on contexts or user needs. However, research progress is hindered by the lack of comprehensive benchmarks and existing evaluation predictors lacking at least one of these key properties: (1) Alignment with human judgments, (2) Long-sequence processing, (3) Inference efficiency, and (4) Applicability to multi-objective scoring. To address these challenges, we propose a plug-and-play architecture to build a robust predictor, MULTI-TAP (Multi-Objective Task-Aware Predictor), capable of both multi and single-objective scoring. MULTI-TAP can produce a single overall score, utilizing a reward head built on top of a large vision-language model (LVLMs). We show that MULTI-TAP is robust in terms of application to different LVLM architectures, achieving significantly higher performance than existing metrics and even on par with the GPT-4o-based predictor, G-VEval, with a smaller size (7-8B). By training a lightweight ridge regression layer on the frozen hidden states of a pre-trained LVLM, MULTI-TAP can produce fine-grained scores for multiple human-interpretable objectives. MULTI-TAP performs better than VisionREWARD, a high-performing multi-objective reward model, in both performance and efficiency on multi-objective benchmarks and our newly released text-image-to-text dataset, EYE4ALL. Our new dataset, consisting of chosen/rejected human preferences (EYE4ALLPref) and human-annotated fine-grained scores across seven dimensions (EYE4ALLMulti), can serve as a foundation for developing more accessible AI systems by capturing the underlying preferences of users, including blind and low-vision (BLV) individuals.
Solar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semantic com ponents. This study introduces SF-SPA (Semantic Facade Solar-PV Assessment), an automated framework that transforms street-view photographs into quantitative PV deployment assessments. The approach combines com puter vision and artificial intelligence techniques to address three key challenges: perspective distortion correction, semantic understanding of facade elements, and spatial reasoning for PV layout optimization. Our four-stage pipeline processes images through geometric rectification, zero-shot semantic segmentation, Large Language Model (LLM) guided spatial reasoning, and energy simulation. Validation across 80 buildings in four countries demonstrates ro bust performance with mean area estimation errors of 6.2% ± 2.8% compared to expert annotations. The auto mated assessment requires approximately 100 seconds per building, a substantial gain in efficiency over manual methods. Simulated energy yield predictions confirm the method's reliability and applicability for regional poten tial studies, urban energy planning, and building-integrated photovoltaic (BIPV) deployment. Code is available at: https:github.com/CodeAXu/Solar-PV-Installation
Learn to Guide Your Diffusion Model
Venue: arXiv cs.LG 2025
Year: 2025

Classifier-free guidance (CFG) is a widely used technique for improving the perceptual quality of samples from conditional diffusion models. It operates by linearly combining conditional and unconditional score estimates using a guidance weight $ω$. While a large, static weight can markedly improve visual results, this often comes at the cost of poorer distributional alignment. In order to better approximate the target conditional distribution, we instead learn guidance weights $ω_{c,(s,t)}$, which are continuous functions of the conditioning $c$, the time $t$ from which we denoise, and the time $s$ towards which we denoise. We achieve this by minimizing the distributional mismatch between noised samples from the true conditional distribution and samples from the guided diffusion process. We extend our framework to reward guided sampling, enabling the model to target distributions tilted by a reward function $R(x_0,c)$, defined on clean data and a conditioning $c$. We demonstrate the effectiveness of our methodology on low-dimensional toy examples and high-dimensional image settings, where we observe improvements in Fréchet inception distance (FID) for image generation. In text-to-image applications, we observe that employing a reward function given by the CLIP score leads to guidance weights that improve image-prompt alignment.
Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
Venue: arXiv cs.CL 2025
Year: 2025

\textbf{RE}trieval-\textbf{A}ugmented \textbf{L}LM-based \textbf{M}achine \textbf{T}ranslation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but its reliability under noisy retrieval contexts remains poorly understood despite this being a common challenge in real-world deployment. To address this gap, we propose a noise synthesis framework and new metrics to evaluate the robustness of REAL-MT systematically. Using this framework, we instantiate REAL-MT with Qwen-series models, including standard LLMs and large reasoning models (LRMs) with enhanced reasoning, and evaluate their performance on idiomatic translation across high-, medium-, and low-resource language pairs under synthesized noise. Our results show that low-resource language pairs, which rely more heavily on retrieved context, degrade more severely under noise than high-resource ones and often produce nonsensical translations. Although LRMs possess enhanced reasoning capabilities, they show no improvement in error correction and are even more susceptible to noise, tending to rationalize incorrect contexts. We find that this stems from an attention shift away from the source idiom to noisy content, while confidence increases despite declining accuracy, indicating poor calibration. To mitigate these issues, we investigate training-free and fine-tuning strategies, which improve robustness at the cost of performance in clean contexts, revealing a fundamental trade-off. Our findings highlight the limitations of current approaches, underscoring the need for self-verifying integration mechanisms.
LLM Routing with Dueling Feedback
Venue: arXiv cs.LG 2025
Year: 2025

We study LLM routing, the problem of selecting the best model for each query while balancing user satisfaction, model expertise, and inference cost. We formulate routing as contextual dueling bandits, learning from pairwise preference feedback rather than absolute scores, thereby yielding label-efficient and dynamic adaptation. Building on this formulation, we introduce Category-Calibrated Fine-Tuning (CCFT), a representation-learning method that derives model embeddings from offline data using contrastive fine-tuning with categorical weighting. These embeddings enable the practical instantiation of Feel-Good Thompson Sampling for Contextual Dueling Bandits (FGTS.CDB), a theoretically grounded posterior-sampling algorithm. We propose four variants of the categorical weighting that explicitly integrate model quality and cost, and we empirically evaluate the proposed methods on the RouterBench and MixInstruct datasets. Across both benchmarks, our methods achieve lower cumulative regret and faster convergence, with better robustness and performance-cost balance than strong baselines built with a general-purpose OpenAI embedding model.
Opal: A Modular Framework for Optimizing Performance using Analytics and LLMs
Venue: arXiv cs.PF 2025
Year: 2025

Large Language Models (LLMs) show promise for automated code optimization but struggle without performance context. This work introduces Opal, a modular framework that connects performance analytics insights with the vast body of published by guiding LLMs to generate informed, trustworthy optimizations. Unlike traditional performance tools that identify bottlenecks but stop short of actionable suggestions, Opal bridges this long-standing gap by linking dynamic insights from hardware counters and Roofline analysis to stall events to optimization decisions. We evaluate Opal across 1640 experiments on real-world GPU kernels and find that in over 98.5% of cases, even a single insight source yields speedups, ranging on average from 19.34% to 52.3%. Our prompt template produced correct code in all but one case, where a vague diagnostic caused an unsafe suggestion. By automatically optimizing GPU kernels using performance analytics and LLMs, Opal marks a leap toward democratizing expert-level performance engineering for all.
Enhancing Urban VANETs Stability: A Single-Hop Clustering Strategy in Metropolitan Environments
Venue: arXiv cs.NI 2025
Year: 2025

Vehicular Ad-hoc Networks (VANETs), a subclass of Mobile Ad-hoc Networks (MANETs), are expected to play a crucial role in the future of intelligent transportation systems (ITSs). A key objective of VANETs is to enable efficient and cost-effective communication among vehicles while supporting a large number of network participants and minimizing infrastructure dependency. However, the highly dynamic nature of vehicular networks poses significant challenges to their deployment. Clustering techniques are employed to address these challenges, with a strong emphasis on stability, as they directly influence the routing process and enhance the quality of service (QoS). This paper explores the feasibility of reducing reliance on roadside units (RSUs) in metropolitan areas while improving cluster stability. We propose an efficient clustering algorithm tailored for urban environments, leveraging existing metropolitan infrastructure to compensate for the absence of RSUs. Our approach designates public transportation buses as primary cluster heads (CHs), minimizing reliance on additional infrastructure, while stand-alone vehicles (SAVs) dynamically select additional CHs. Through comprehensive case studies and comparative analysis with existing algorithms, our results demonstrate the superior performance of the proposed method across different transmission ranges (TRs).
Non-submodular Visual Attention for Robot Navigation
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a task-oriented computational framework to enhance Visual-Inertial Navigation (VIN) in robots, addressing challenges such as limited time and energy resources. The framework strategically selects visual features using a Mean Squared Error (MSE)-based, non-submodular objective function and a simplified dynamic anticipation model. To address the NP-hardness of this problem, we introduce four polynomial-time approximation algorithms: a classic greedy method with constant-factor guarantees; a low-rank greedy variant that significantly reduces computational complexity; a randomized greedy sampler that balances efficiency and solution quality; and a linearization-based selector based on a first-order Taylor expansion for near-constant-time execution. We establish rigorous performance bounds by leveraging submodularity ratios, curvature, and element-wise curvature analyses. Extensive experiments on both standardized benchmarks and a custom control-aware platform validate our theoretical results, demonstrating that these methods achieve strong approximation guarantees while enabling real-time deployment.
Modeling Market States with Clustering and State Machines
Venue: arXiv cs.CE 2025
Year: 2025

This work introduces a new framework for modeling financial markets through an interpretable probabilistic state machine. By clustering historical returns based on momentum and risk features across multiple time horizons, we identify distinct market states that capture underlying regimes, such as expansion phase, contraction, crisis, or recovery. From a transition matrix representing the dynamics between these states, we construct a probabilistic state machine that models the temporal evolution of the market. This state machine enables the generation of a custom distribution of returns based on a mixture of Gaussian components weighted by state frequencies. We show that the proposed benchmark significantly outperforms the traditional approach in capturing key statistical properties of asset returns, including skewness and kurtosis, and our experiments across random assets and time periods confirm its robustness.
Enhancing Software Testing Education: Understanding Where Students Struggle
Venue: arXiv cs.SE 2025
Year: 2025

Effective software testing is critical for producing reliable and secure software, yet many computer science students struggle to master the foundational concepts required to construct comprehensive test suites. While automated feedback tools are widely used to support student learning, it remains unclear which testing concepts are most frequently misunderstood and how these misunderstandings are reflected in students' test suite revisions. This study examines the specific testing concepts that lead students to make ineffective changes, those that fail to improve code coverage, during test suite development. Leveraging an automated feedback tool in a senior-level software testing course, we analyzed student submissions from two assignments to identify prevalent conceptual gaps and patterns of unproductive modification. Our results reveal that decision coverage and exception handling are persistent challenges, and that students most often make superficial or method-level changes that do not enhance coverage. These findings provide actionable insights for educators, researchers, and tool designers. By pinpointing the concepts that most often contribute to poor testing outcomes, we can refine feedback systems, target instruction to address persistent misconceptions, and more effectively support students in developing robust, maintainable test suites.
Test-Time Search in Neural Graph Coarsening Procedures for the Capacitated Vehicle Routing Problem
Venue: arXiv cs.AI 2025
Year: 2025

The identification of valid inequalities, such as the rounded capacity inequalities (RCIs), is a key component of cutting plane methods for the Capacitated Vehicle Routing Problem (CVRP). While a deep learning-based separation method can learn to find high-quality cuts, our analysis reveals that the model produces fewer cuts than expected because it is insufficiently sensitive to generate a diverse set of generated subsets. This paper proposes an alternative: enhancing the performance of a trained model at inference time through a new test-time search with stochasticity. First, we introduce stochastic edge selection into the graph coarsening procedure, replacing the previously proposed greedy approach. Second, we propose the Graph Coarsening History-based Partitioning (GraphCHiP) algorithm, which leverages coarsening history to identify not only RCIs but also, for the first time, the Framed capacity inequalities (FCIs). Experiments on randomly generated CVRP instances demonstrate the effectiveness of our approach in reducing the dual gap compared to the existing neural separation method. Additionally, our method discovers effective FCIs on a specific instance, despite the challenging nature of identifying such cuts.
Random Feature Spiking Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Spiking Neural Networks (SNNs) as Machine Learning (ML) models have recently received a lot of attention as a potentially more energy-efficient alternative to conventional Artificial Neural Networks. The non-differentiability and sparsity of the spiking mechanism can make these models very difficult to train with algorithms based on propagating gradients through the spiking non-linearity. We address this problem by adapting the paradigm of Random Feature Methods (RFMs) from Artificial Neural Networks (ANNs) to Spike Response Model (SRM) SNNs. This approach allows training of SNNs without approximation of the spike function gradient. Concretely, we propose a novel data-driven, fast, high-performance, and interpretable algorithm for end-to-end training of SNNs inspired by the SWIM algorithm for RFM-ANNs, which we coin S-SWIM. We provide a thorough theoretical discussion and supplementary numerical experiments showing that S-SWIM can reach high accuracies on time series forecasting as a standalone strategy and serve as an effective initialisation strategy before gradient-based training. Additional ablation studies show that our proposed method performs better than random sampling of network weights.
Prometheus: Universal, Open-Source Mocap-Based Teleoperation System with Force Feedback for Dataset Collection in Robot Learning
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a novel teleoperation system with force feedback, utilizing consumer-grade HTC Vive Trackers 2.0. The system integrates a custom-built controller, a UR3 robotic arm, and a Robotiq gripper equipped with custom-designed fingers to ensure uniform pressure distribution on an embedded force sensor. Real-time compression force data is transmitted to the controller, enabling operators to perceive the gripping force applied to objects. Experimental results demonstrate that the system enhances task success rates and provides a low-cost solution for large-scale imitation learning data collection without compromising affordability.
Syntax-Guided Diffusion Language Models with User-Integrated Personalization
Venue: arXiv cs.CL 2025
Year: 2025

Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insufficient structural diversity, which limits personalized expression. Recent advances in diffusion models have opened new opportunities for improving language generation beyond the limitations of autoregressive paradigms. In this work, we propose a syntax-guided diffusion language model that integrates structural supervision and personalized conditioning to enhance text quality, diversity, and controllability. We introduce a cascaded framework that generates syntactic guidance before conditional text generation, and further generalize it to a novel noncascaded architecture for better alignment between structure and content. By incorporating syntactic information in the generating process, the proposed model better captures the lexical and structural characteristics of stylistic sentence construction. To enable fine-grained personalization, we develop a shared representation mechanism that facilitates information integration across users, supporting both faithful stylistic generation and generalizable zero-shot inference. Extensive experiments on multiple tasks demonstrate the superiority of our approach in fluency, diversity, and stylistic fidelity. Further qualitative analyses highlight its interpretability and flexibility in learning personalized patterns.
Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI
Venue: arXiv cs.AI 2025
Year: 2025

Black-box explainability methods are popular tools for explaining the decisions of image classifiers. A major drawback of these tools is their reliance on mutants obtained by occluding parts of the input, leading to out-of-distribution images. This raises doubts about the quality of the explanations. Moreover, choosing an appropriate occlusion value often requires domain knowledge. In this paper we introduce a novel forward-pass paradigm Activation-Deactivation (AD), which removes the effects of occluded input features from the model's decision-making by switching off the parts of the model that correspond to the occlusions. We introduce ConvAD, a drop-in mechanism that can be easily added to any trained Convolutional Neural Network (CNN), and which implements the AD paradigm. This leads to more robust explanations without any additional training or fine-tuning. We prove that the ConvAD mechanism does not change the decision-making process of the network. We provide experimental evaluation across several datasets and model architectures. We compare the quality of AD-explanations with explanations achieved using a set of masking values, using the proxies of robustness, size, and confidence drop-off. We observe a consistent improvement in robustness of AD explanations (up to 62.5%) compared to explanations obtained with occlusions, demonstrating that ConvAD extracts more robust explanations without the need for domain knowledge.
Solving decision problems by distributed consensus with one-dimensional, binary, radius-$2$ cellular automata over cyclic configurations
Venue: arXiv cs.DM 2025
Year: 2025

Probing the ability of automata networks to solve decision problems has received a continuous attention in the literature, and specially with the automata reaching the answer by distributed consensus, i.e., their all taking on a same state, out of two. In the case of binary automata networks, regardless of the kind of update employed, the networks should display only two possible attractors, the fixed points $0^L$ and $1^L$, for all cyclic configurations of size $L$. A previous investigation into the space of one-dimensional, binary, radius-2 cellular automata identified a restricted subset of rules as potential solvers of decision problems, but the reported results were incomplete and lacked sufficient detail for replication. To address this gap, we conducted a comprehensive reevaluation of the entire radius-2 rule space, by filtering it with all configuration sizes from 5 to 20, according to their basins of attraction being formed by only the two expected fixed points. A set of over fifty-four thousand potential decision problem solvers were then obtained. Among these, more than forty-five thousand were associated with 3 well-defined decision problems, and precise formal explanations were provided for over forty thousand of them. The remaining candidate rules suggest additional problem classes yet to be fully characterised. Overall, this work substantially extends the understanding of radius-2 cellular automata, offering a more complete picture of their capacity to solve decision problems by consensus.
Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Genomic sequence analysis plays a crucial role in various scientific and medical domains. Traditional machine-learning approaches often struggle to capture the complex relationships and hierarchical structures of sequence data when working in high-dimensional Euclidean spaces. This limitation hinders accurate sequence classification and similarity measurement. To address these challenges, this research proposes a method to transform the feature representation of biological sequences into the hyperboloid space. By applying a transformation, the sequences are mapped onto the hyperboloid, preserving their inherent structural information. Once the sequences are represented in the hyperboloid space, a kernel matrix is computed based on the hyperboloid features. The kernel matrix captures the pairwise similarities between sequences, enabling more effective analysis of biological sequence relationships. This approach leverages the inner product of the hyperboloid feature vectors to measure the similarity between pairs of sequences. The experimental evaluation of the proposed approach demonstrates its efficacy in capturing important sequence correlations and improving classification accuracy.