OpenRaiser/Intern-Atlas huggingface dataset:
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts
Venue: arXiv cs.LG 2025
Year: 2025

Effective human-robot collaboration requires the ability to learn personalized concepts from a limited number of demonstrations, while exhibiting inductive generalization, hierarchical composition, and adaptability to novel constraints. Existing approaches that use code generation capabilities of pre-trained large (vision) language models as well as purely neural models show poor generalization to \emph{a-priori} unseen complex concepts. Neuro-symbolic methods (Grand et al., 2023) offer a promising alternative by searching in program space, but face challenges in large program spaces due to the inability to effectively guide the search using demonstrations. Our key insight is to factor inductive concept learning as: (i) {\it Sketch:} detecting and inferring a coarse signature of a new concept (ii) {\it Plan:} performing an MCTS search over grounded action sequences guided by human demonstrations (iii) {\it Generalize:} abstracting out grounded plans as inductive programs. Our pipeline facilitates generalization and modular re-use, enabling continual concept learning. Our approach combines the benefits of code generation ability of large language models (LLMs) along with grounded neural representations, resulting in neuro-symbolic programs that show stronger inductive generalization on the task of constructing complex structures vis-á-vis LLM-only and purely neural approaches. Further, we demonstrate reasoning and planning capabilities with learned concepts for embodied instruction following.
An efficient uniqueness theorem for overcomplete tensor decomposition
Venue: arXiv cs.DS 2025
Year: 2025

We give a new, constructive uniqueness theorem for tensor decomposition. It applies to order 3 tensors of format $n \times n \times p$ and can prove uniqueness of decomposition for generic tensors up to rank $r=4n/3$ as soon as $p \geq 4$. One major advantage over Kruskal's uniqueness theorem is that our theorem has an algorithmic proof, and the resulting algorithm is efficient. Like the uniqueness theorem, it applies in the range $n \leq r \leq 4n/3$. As a result, we obtain the first efficient algorithm for overcomplete decomposition of generic tensors of order 3. For instance, prior to this work it was not known how to efficiently decompose generic tensors of format $n \times n \times n$ and rank $r=1.01n$ (or rank $r \leq (1+ε) n$, for some constant $ε>0$). Efficient overcomplete decomposition of generic tensors of format $n \times n \times 3$ remains an open problem. Our results are based on the method of commuting extensions pioneered by Strassen for the proof of his $3n/2$ lower bound on tensor rank and border rank. In particular, we rely on an algorithm for the computation of commuting extensions recently proposed in a companion paper, and on the classical diagonalization-based "Jennrich algorithm" for undercomplete tensor decomposition. This is an updated version of a paper presented at SODA 2025. As a new result, we answer a question from that paper by giving a NP-hardness result for the computation of commuting extensions. The proof relies on a recent construction by Shitov. After the paper appearing in the SODA proceedings was written, another algorithm for the overcomplete decomposition of generic tensors of order~3 was proposed by Kothari, Moitra and Wein.
Generative Information Retrieval Evaluation
Venue: arXiv cs.IR 2025
Year: 2025

In this chapter, we consider generative information retrieval evaluation from two distinct but interrelated perspectives. First, large language models (LLMs) themselves are rapidly becoming tools for evaluation, with current research indicating that LLMs may be superior to crowdsource workers and other paid assessors on basic relevance judgement tasks. We review past and ongoing related research, including speculation on the future of shared task initiatives, such as TREC, and a discussion on the continuing need for human assessments. Second, we consider the evaluation of emerging LLM-based generative information retrieval (GenIR) systems, including retrieval augmented generation (RAG) systems. We consider approaches that focus both on the end-to-end evaluation of GenIR systems and on the evaluation of a retrieval component as an element in a RAG system. Going forward, we expect the evaluation of GenIR systems to be at least partially based on LLM-based assessment, creating an apparent circularity, with a system seemingly evaluating its own output. We resolve this apparent circularity in two ways: 1) by viewing LLM-based assessment as a form of "slow search", where a slower IR system is used for evaluation and training of a faster production IR system; and 2) by recognizing a continuing need to ground evaluation in human assessment, even if the characteristics of that human assessment must change.
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
Venue: arXiv cs.IR 2025
Year: 2025

Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular training frameworks typically learn from binary (positive/negative) relevance, making them ineffective at incorporating desired rankings. As a result, the poor ranking performance of these models forces systems to employ a re-ranker, which increases complexity, maintenance effort and inference time. To address this, we introduce Generalized Contrastive Learning (GCL), a training framework designed to learn from continuous ranking scores beyond binary relevance. GCL encodes both relevance and ranking information into a unified embedding space by applying ranking scores to the loss function. This enables a single-stage retrieval system. In addition, during our research, we identified a lack of public multi-modal datasets that benchmark both retrieval and ranking capabilities. To facilitate this and future research for ranked retrieval, we curated a large-scale MarqoGS-10M dataset using GPT-4 and Google Shopping, providing ranking scores for each of the 10 million query-document pairs. Our results show that GCL achieves a 29.3% increase in NDCG@10 for in-domain evaluations and 6.0% to 10.0% increases for cold-start evaluations compared to the finetuned CLIP baseline with MarqoGS-10M. Additionally, we evaluated GCL offline on a proprietary user interaction data. GCL shows an 11.2% gain for in-domain evaluations. The dataset and the method are available at: https://github.com/marqo-ai/GCL.
Measuring the Quality of Answers in Political Q&As with Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

This article proposes a new approach for assessing the quality of answers in political question-and-answer sessions. We measure the quality of an answer based on how easily and accurately it can be recognized in a random set of candidate answers given the question's text. This measure reflects the answer's relevance and depth of engagement with the question. Like semantic search, we can implement this approach by training a language model on the corpus of observed questions and answers without additional human-labeled data. We showcase and validate our methodology within the context of the Question Period in the Canadian House of Commons. Our analysis reveals that while some answers have a weak semantic connection to questions, hinting at some evasion or obfuscation, they are generally at least moderately relevant, far exceeding what we would expect from random replies. We also find a meaningful correlation between answer quality and the party affiliation of the members of Parliament asking the questions.
Blind Interference Alignment for MapReduce: Exploiting Side-information with Reconfigurable Antennas
Venue: arXiv cs.IT 2025
Year: 2025

In order to explore how blind interference alignment (BIA) schemes may take advantage of side-information in computation tasks, we study the degrees of freedom (DoF) of a $K$ user wireless network setting that arises in full-duplex wireless MapReduce applications. In this setting the receivers are assumed to have reconfigurable antennas and channel knowledge, while the transmitters have neither, i.e., the transmitters lack channel knowledge and are only equipped with conventional antennas. The central ingredient of the problem formulation is the message structure arising out of the Shuffle phase of MapReduce, whereby each transmitter has a subset of messages that need to be delivered to various receivers, and each receiver has a subset of messages available to it in advance as side-information. We approach this problem by decomposing it into distinctive stages that help identify key ingredients of the overall solution. The novel elements that emerge from the first stage, called broadcast with groupcast messages, include an outer maximum distance separable (MDS) code structure at the transmitter, and an algorithm for iteratively determining groupcast-optimal reconfigurable antenna switching patterns at the receiver to achieve intra-message (among the symbols of the same message) alignment. The next stage, called unicast with side-information, reveals optimal inter-message (among symbols of different messages) alignment patterns to exploit side-information, and by a relabeling of messages, connects to the desired MapReduce setting.
Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging
Venue: arXiv cs.SE 2025
Year: 2025

While existing machine learning (ML) frameworks focus on established platforms, like running CUDA on server-grade GPUs, there have been growing demands to enable emerging AI applications in a broader set of scenarios, such as running Large Language Models (LLMs) within browsers and mobile phones. However, deploying emerging models on new platforms (such as Metal and WebGPU) presents significant software engineering challenges due to rapid model evolution and limited tooling and practices for these platforms. Previous practice for ML model deployment often follows a bottom-up fashion, where engineers first implement individual required operators and then put them together. However, this traditional development approach fails to meet the productivity requirements when deploying emerging ML applications, with the testing and debugging part as a bottleneck. To this end, we introduce \textsc{TapML}, a top-down approach designed to streamline model deployment on diverse platforms. While the traditional bottom-up approach requires crafting manual tests, \textsc{TapML} automatically creates high-quality, realistic test data through operator-wise test carving. Furthermore, \textsc{TapML} uses a migration-based strategy to gradually offload model implementation from the mature source platform to the target platform, minimizing the debugging scope of compound errors. \textsc{TapML} has been used as the default development method in the MLC-LLM project to deploy emerging ML models. Within 2 years, \textsc{TapML} has accelerated the deployment of 105 emerging models in 27 model architectures across 5 emerging platforms. We show that \textsc{TapML} effectively boosts developer productivity while ensuring the quality of deployed models. Furthermore, we summarize comprehensive case studies from our real-world development, offering best practices for developing emerging ML systems.
Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
Venue: arXiv cs.CL 2025
Year: 2025

Media Storms, dramatic outbursts of attention to a story, are central components of media dynamics and the attention landscape. Despite their significance, there has been little systematic and empirical research on this concept due to issues of measurement and operationalization. We introduce an iterative human-in-the-loop method to identify media storms in a large-scale corpus of news articles. The text is first transformed into signals of dispersion based on several textual characteristics. In each iteration, we apply unsupervised anomaly detection to these signals; each anomaly is then validated by an expert to confirm the presence of a storm, and those results are then used to tune the anomaly detection in the next iteration. We demonstrate the applicability of this method in two scenarios: first, supplementing an initial list of media storms within a specific time frame; and second, detecting media storms in new time periods. We make available a media storm dataset compiled using both scenarios. Both the method and dataset offer the basis for comprehensive empirical research into the concept of media storms, including characterizing them and predicting their outbursts and durations, in mainstream media or social media platforms.
Generative transformations and patterns in LLM-native approaches for software verification and falsification
Venue: arXiv cs.SE 2025
Year: 2025

The emergence of prompting as the dominant paradigm for leveraging Large Language Models (LLMs) has led to a proliferation of LLM-native software, where application behavior arises from complex, stochastic data transformations. However, the engineering of such systems remains largely exploratory and ad-hoc, hampered by the absence of conceptual frameworks, ex-ante methodologies, design guidelines, and specialized benchmarks. We argue that a foundational step towards a more disciplined engineering practice is a systematic understanding of the core functional units--generative transformations--and their compositional patterns within LLM-native applications. Focusing on the rich domain of software verification and falsification, we conduct a secondary study of over 100 research proposals to address this gap. We first present a fine-grained taxonomy of generative transformations, abstracting prompt-based interactions into conceptual signatures. This taxonomy serves as a scaffolding to identify recurrent transformation relationship patterns--analogous to software design patterns--that characterize solution approaches in the literature. Our analysis not only validates the utility of the taxonomy but also surfaces strategic gaps and cross-dimensional relationships, offering a structured foundation for future research in modular and compositional LLM application design, benchmarking, and the development of reliable LLM-native systems.
Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
Venue: arXiv cs.CV 2025
Year: 2025

Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential to provide a safer driving solution. Despite extensive researches in transportation and communication to support V2X-AD, the actual utilization of these infrastructures and communication resources in enhancing driving performances remains largely unexplored. This highlights the necessity of collaborative autonomous driving: a machine learning approach that optimizes the information sharing strategy to improve the driving performance of each vehicle. This effort necessitates two key foundations: a platform capable of generating data to facilitate the training and testing of V2X-AD, and a comprehensive system that integrates full driving-related functionalities with mechanisms for information sharing. From the platform perspective, we present V2Xverse, a comprehensive simulation platform for collaborative autonomous driving. This platform provides a complete pipeline for collaborative driving. From the system perspective, we introduce CoDriving, a novel end-to-end collaborative driving system that properly integrates V2X communication over the entire autonomous pipeline, promoting driving with shared perceptual information. The core idea is a novel driving-oriented communication strategy. Leveraging this strategy, CoDriving improves driving performance while optimizing communication efficiency. We make comprehensive benchmarks with V2Xverse, analyzing both modular performance and closed-loop driving performance. Experimental results show that CoDriving: i) significantly improves the driving score by 62.49% and drastically reduces the pedestrian collision rate by 53.50% compared to the SOTA end-to-end driving method, and ii) achieves sustaining driving performance superiority over dynamic constraint communication conditions.
FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal image fusion aims to integrate information from different imaging techniques to produce a comprehensive, detail-rich single image for downstream vision tasks. Existing methods based on local convolutional neural networks (CNNs) struggle to capture global features efficiently, while Transformer-based models are computationally expensive, although they excel at global modeling. Mamba addresses these limitations by leveraging selective structured state space models (S4) to effectively handle long-range dependencies while maintaining linear complexity. In this paper, we propose FusionMamba, a novel dynamic feature enhancement framework that aims to overcome the challenges faced by CNNs and Vision Transformers (ViTs) in computer vision tasks. The framework improves the visual state-space model Mamba by integrating dynamic convolution and channel attention mechanisms, which not only retains its powerful global feature modeling capability, but also greatly reduces redundancy and enhances the expressiveness of local features. In addition, we have developed a new module called the dynamic feature fusion module (DFFM). It combines the dynamic feature enhancement module (DFEM) for texture enhancement and disparity perception with the cross-modal fusion Mamba module (CMFM), which focuses on enhancing the inter-modal correlation while suppressing redundant information. Experiments show that FusionMamba achieves state-of-the-art performance in a variety of multimodal image fusion tasks as well as downstream experiments, demonstrating its broad applicability and superiority.
HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation
Venue: arXiv cs.AI 2025
Year: 2025

In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple associated with attribute-value qualifiers, which express additional factual knowledge. The hyper-relational knowledge graph completion (HKGC) task aims at inferring plausible missing links in a HKG. Most existing approaches to HKGC focus on enhancing the communication between qualifier pairs and main triples, while overlooking two important properties that emerge from the monotonicity of the hyper-relational graphs representation regime. Stage Reasoning allows for a two-step reasoning process, facilitating the integration of coarse-grained inference results derived solely from main triples and fine-grained inference results obtained from hyper-relational facts with qualifiers. In the initial stage, coarse-grained results provide an upper bound for correct predictions, which are subsequently refined in the fine-grained step. More generally, Qualifier Monotonicity implies that by attaching more qualifier pairs to a main triple, we may only narrow down the answer set, but never enlarge it. This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity. To implement qualifier monotonicity HyperMono resorts to cone embeddings. Experiments on three real-world datasets with three different scenario conditions demonstrate the strong performance of HyperMono when compared to the SoTA.
How fair are we? From conceptualization to automated assessment of fairness definitions
Venue: arXiv cs.SE 2025
Year: 2025

Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques to automatically assess the fairness of software systems. Nonetheless, a significant proportion of these techniques rely upon pre-established fairness definitions, metrics, and criteria, which may fail to encompass the wide-ranging needs and preferences of users and stakeholders. To overcome this limitation, we propose a novel approach, called MODNESS, that enables users to customize and define their fairness concepts using a dedicated modeling environment. Our approach guides the user through the definition of new fairness concepts also in emerging domains, and the specification and composition of metrics for its evaluation. Ultimately, MODNESS generates the source code to implement fair assessment based on these custom definitions. In addition, we elucidate the process we followed to collect and analyze relevant literature on fairness assessment in software engineering (SE). We compare MODNESS with the selected approaches and evaluate how they support the distinguishing features identified by our study. Our findings reveal that i) most of the current approaches do not support user-defined fairness concepts; ii) our approach can cover two additional application domains not addressed by currently available tools, i.e., mitigating bias in recommender systems for software engineering and Arduino software component recommendations; iii) MODNESS demonstrates the capability to overcome the limitations of the only two other Model-Driven Engineering-based approaches for fairness assessment.
Parameterized Algorithms for Editing to Uniform Cluster Graph
Venue: arXiv cs.DS 2025
Year: 2025

We study the parameterized complexity of transforming graphs into Uniform Cluster graphs, where each component is an equal-sized clique. We consider Uniform Cluster Vertex Deletion (UCVD), Uniform Cluster Edge Deletion (UCED), Uniform Cluster Edge Addition (UCEA), Uniform Cluster Edge Editing (UCEE), Uniform Cluster Exclusive Vertex Splitting (UCEVS), and Uniform Cluster Inclusive Vertex Splitting (UCIVS). For UCVD, we provide a vertex kernel of size $\mathcal{O}(k^{3})$ and an FPT algorithm with running time $2^{k} \cdot n^{\mathcal{O}(1)}$, improving the known $3^{k} \cdot n^{\mathcal{O}(1)}$ algorithm. For edge-based variants, we obtain a $\mathcal{O}(k^{2})$ vertex kernel for UCEE and linear vertex kernels for UCED and UCEA, improving the best-known results. Additionally, we present a $1.47^{k} \cdot n^{\mathcal{O}(1)}$ algorithm for UCED, improving upon the previous $2^{k} \cdot n^{\mathcal{O}(1)}$ bound. We develop a sub-exponential algorithm for UCED on everywhere dense graphs by reducing it to $d$-Way Cut. Lastly, we study vertex splitting operations and provide vertex kernels of size $4k$ for both UCIVS and UCEVS.