OpenRaiser/Intern-Atlas huggingface dataset:
A simple proof of the coincidence of observational and labeled equivalence of processes in applied pi-calculus
Venue: arXiv cs.LO 2025
Year: 2025

This paper presents a new, significantly simpler proof of one of the main results of applied pi-calculus: the theorem that the concepts of observational and labeled equivalence of extended processes in applied pi-calculus coincide.
On the false election between regulation and innovation. Ideas for regulation through the responsible use of artificial intelligence in research and education.[Spanish version]
Venue: arXiv cs.CY 2025
Year: 2025

This short essay is a reworking of the answers offered by the author at the Debate Session of the AIHUB (CSIC) and EduCaixa Summer School, organized by Marta Garcia-Matos and Lissette Lemus, and coordinated by Albert Sabater (OEIAC, UG), with the participation of Vanina Martinez-Posse (IIIA-CSIC), Eulalia Soler (Eurecat) and Pompeu Casanovas (IIIA-CSIC) on July 4th 2025. Albert Sabater posed three questions: (1) How can regulatory frameworks priori-tise the protection of fundamental rights (privacy, non-discrimination, autonomy, etc.) in the development of AI, without falling into the false dichotomy between regulation and innova-tion? (2) Given the risks of AI (bias, mass surveillance, manipulation), what examples of regu-lations or policies have demonstrated that it is possible to foster responsible innovation, putting the public interest before profitability, without giving in to competitive pressure from actors such as China or the US? (3) In a scenario where the US prioritizes flexibility, what mecha-nisms could ensure that international cooperation in AI does not become a race to the bottom in rights, but rather a global standard of accountability? The article attempts to answer these three questions and concludes with some reflections on the relevance of the answers for education and research.
Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Venue: arXiv cs.AI 2025
Year: 2025

Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi-objective multi-agent path finding (MO-MAPF) algorithms typically produce conflict-free plans by computing Pareto frontiers. They do not explicitly optimize for user-defined preferences, even when the preferences are available, and scale poorly with the number of objectives. We propose a lexicographic framework for modeling MO-MAPF, along with an algorithm \textit{Lexicographic Conflict-Based Search} (LCBS) that directly computes a single solution aligned with a lexicographic preference over objectives. LCBS integrates a priority-aware low-level $A^*$ search with conflict-based search, avoiding Pareto frontier construction and enabling efficient planning guided by preference over objectives. We provide insights into optimality and scalability, and empirically demonstrate that LCBS computes optimal solutions while scaling to instances with up to ten objectives -- far beyond the limits of existing MO-MAPF methods. Evaluations on standard and randomized MAPF benchmarks show consistently higher success rates against state-of-the-art baselines, especially with increasing number of objectives.
Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection
Venue: arXiv cs.CV 2025
Year: 2025

Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically detecting this condition from fundus images, its application remains challenging due the limited availability of annotated data. Foundation Models (FM) have emerged as an alternative solution. However, it is unclear if they can cope with DME detection in particular. In this paper, we systematically compare different FM and standard transfer learning approaches for this task. Specifically, we compare the two most popular FM for retinal images--RETFound and FLAIR--and an EfficientNet-B0 backbone, across different training regimes and evaluation settings in IDRiD, MESSIDOR-2 and OCT-and-Eye-Fundus-Images (OEFI). Results show that despite their scale, FM do not consistently outperform fine-tuned CNNs in this task. In particular, an EfficientNet-B0 ranked first or second in terms of area under the ROC and precision/recall curves in most evaluation settings, with RETFound only showing promising results in OEFI. FLAIR, on the other hand, demonstrated competitive zero-shot performance, achieving notable AUC-PR scores when prompted appropriately. These findings reveal that FM might not be a good tool for fine-grained ophthalmic tasks such as DME detection even after fine-tuning, suggesting that lightweight CNNs remain strong baselines in data-scarce environments.
MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
Venue: arXiv cs.LG 2025
Year: 2025

Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook the rich molecular domain knowledge associated with submolecular instances (atoms and bonds). While molecular pre-training approaches incorporate such knowledge into their pre-training objectives, they typically employ designs tailored to a specific type of knowledge, lacking the flexibility to integrate diverse knowledge present in molecules. Hence, reusing widely available and well-validated pre-trained 2D encoders, while incorporating molecular domain knowledge during downstream adaptation, offers a more practical alternative. In this work, we propose MolGA, which adapts pre-trained 2D graph encoders to downstream molecular applications by flexibly incorporating diverse molecular domain knowledge. First, we propose a molecular alignment strategy that bridge the gap between pre-trained topological representations with domain-knowledge representations. Second, we introduce a conditional adaptation mechanism that generates instance-specific tokens to enable fine-grained integration of molecular domain knowledge for downstream tasks. Finally, we conduct extensive experiments on eleven public datasets, demonstrating the effectiveness of MolGA.
h1: Bootstrapping LLMs to Reason over Longer Horizons via Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Large language models excel at short-horizon reasoning tasks, but performance drops as reasoning horizon lengths increase. Existing approaches to combat this rely on inference-time scaffolding or costly step-level supervision, neither of which scales easily. In this work, we introduce a scalable method to bootstrap long-horizon reasoning capabilities using only existing, abundant short-horizon data. Our approach synthetically composes simple problems into complex, multi-step dependency chains of arbitrary length. We train models on this data using outcome-only rewards under a curriculum that automatically increases in complexity, allowing RL training to be scaled much further without saturating. Empirically, our method generalizes remarkably well: curriculum training on composed 6th-grade level math problems (GSM8K) boosts accuracy on longer, competition-level benchmarks (GSM-Symbolic, MATH-500, AIME) by up to 2.06x. It also transfers significantly to diverse out-of-distribution ReasoningGym domains and long-context benchmarks, indicating broader generalization. Importantly, our long-horizon improvements are significantly higher than baselines even at high pass@k, showing that models can learn new reasoning paths under RL. Theoretically, we show that curriculum RL with outcome rewards achieves an exponential improvement in sample complexity over full-horizon training, providing training signal comparable to dense supervision. h1 therefore introduces an efficient path towards scaling RL for long-horizon problems using only existing data.
Vibe Checker: Aligning Code Evaluation with Human Preference
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check. Vibe check is tied to real-world human preference and goes beyond functionality: the solution should feel right, read cleanly, preserve intent, and remain correct. However, current code evaluation remains anchored to pass@k and captures only functional correctness, overlooking the non-functional instructions that users routinely apply. In this paper, we hypothesize that instruction following is the missing piece underlying vibe check that represents human preference in coding besides functional correctness. To quantify models' code instruction following capabilities with measurable signals, we present VeriCode, a taxonomy of 30 verifiable code instructions together with corresponding deterministic verifiers. We use the taxonomy to augment established evaluation suites, resulting in Vibe Checker, a testbed to assess both code instruction following and functional correctness. Upon evaluating 31 leading LLMs, we show that even the strongest models struggle to comply with multiple instructions and exhibit clear functional regression. Most importantly, a composite score of functional correctness and instruction following correlates the best with human preference, with the latter emerging as the primary differentiator on real-world programming tasks. Our work identifies core factors of the vibe check, providing a concrete path for benchmarking and developing models that better align with user preferences in coding.
Temporal Prompting Matters: Rethinking Referring Video Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Referring Video Object Segmentation (RVOS) aims to segment the object referred to by the query sentence in the video. Most existing methods require end-to-end training with dense mask annotations, which could be computation-consuming and less scalable. In this work, we rethink the RVOS problem and aim to investigate the key to this task. Based on existing foundation segmentation models, we decompose the RVOS task into referring, video, and segmentation factors, and propose a Temporal Prompt Generation and Selection (Tenet) framework to address the referring and video factors while leaving the segmentation problem to foundation models. To efficiently adapt image-based foundation segmentation models to referring video object segmentation, we leverage off-the-shelf object detectors and trackers to produce temporal prompts associated with the referring sentence. While high-quality temporal prompts could be produced, they can not be easily identified from confidence scores. To tackle this issue, we propose Prompt Preference Learning to evaluate the quality of the produced temporal prompts. By taking such prompts to instruct image-based foundation segmentation models, we would be able to produce high-quality masks for the referred object, enabling efficient model adaptation to referring video object segmentation. Experiments on RVOS benchmarks demonstrate the effectiveness of the Tenet framework.
How human is the machine? Evidence from 66,000 Conversations with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

When Artificial Intelligence (AI) is used to replace consumers (e.g., synthetic data), it is often assumed that AI emulates established consumers, and more generally human behaviors. Ten experiments with Large Language Models (LLMs) investigate if this is true in the domain of well-documented biases and heuristics. Across studies we observe four distinct types of deviations from human-like behavior. First, in some cases, LLMs reduce or correct biases observed in humans. Second, in other cases, LLMs amplify these same biases. Third, and perhaps most intriguingly, LLMs sometimes exhibit biases opposite to those found in humans. Fourth, LLMs' responses to the same (or similar) prompts tend to be inconsistent (a) within the same model after a time delay, (b) across models, and (c) among independent research studies. Such inconsistencies can be uncharacteristic of humans and suggest that, at least at one point, LLMs' responses differed from humans. Overall, unhuman-like responses are problematic when LLMs are used to mimic or predict consumer behavior. These findings complement research on synthetic consumer data by showing that sources of bias are not necessarily human-centric. They also contribute to the debate about the tasks for which consumers, and more generally humans, can be replaced by AI.
Truth-Aware Decoding: A Program-Logic Approach to Factual Language Generation
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces Truth-Aware Decoding (TAD), a verification-oriented decoding scheme that aligns neural language generation with knowledge bases. Situated in the tradition of probabilistic program semantics for sequence models, TAD augments modern instruction-tuned systems with a lattice of semantic guards that operate at decode time. Our contributions are fourfold: (i) a constraint-based semantics that renders oracle filtering as a program-logic judgment, (ii) a proof that greedy selection enjoys local likelihood dominance under sound and complete guards (Theorem 2.7), (iii) an entropy-style invariant that quantifies factual risk via knowledge-aware safe mass, and (iv) a multi-agent operational calculus with verified Lean artefacts to certify implementation behaviour. Numerical and algorithmic case studies confirm that the resulting guardrails reduce hallucinations without sacrificing throughput, yielding a pragmatic bridge between large-scale empirical models and formal verification.
Enhancing Maritime Object Detection in Real-Time with RT-DETR and Data Augmentation
Venue: arXiv cs.CV 2025
Year: 2025

Maritime object detection faces essential challenges due to the small target size and limitations of labeled real RGB data. This paper will present a real-time object detection system based on RT-DETR, enhanced by employing augmented synthetic images while strictly evaluating on real data. This study employs RT-DETR for the maritime environment by combining multi-scale feature fusion, uncertainty-minimizing query selection, and smart weight between synthetic and real training samples. The fusion module in DETR enhances the detection of small, low-contrast vessels, query selection focuses on the most reliable proposals, and the weighting strategy helps reduce the visual gap between synthetic and real domains. This design preserves DETR's refined end-to-end set prediction while allowing users to adjust between speed and accuracy at inference time. Data augmentation techniques were also used to balance the different classes of the dataset to improve the robustness and accuracy of the model. Regarding this study, a full Python robust maritime detection pipeline is delivered that maintains real-time performance even under practical limits. It also verifies how each module contributes, and how the system handles failures in extreme lighting or sea conditions. This study also includes a component analysis to quantify the contribution of each architectural module and explore its interactions.
Out-of-Distribution Generalization in Climate-Aware Yield Prediction with Earth Observation Data
Venue: arXiv cs.LG 2025
Year: 2025

Climate change is increasingly disrupting agricultural systems, making accurate crop yield forecasting essential for food security. While deep learning models have shown promise in yield prediction using satellite and weather data, their ability to generalize across geographic regions and years - critical for real-world deployment - remains largely untested. We benchmark two state-of-the-art models, GNN-RNN and MMST-ViT, under realistic out-of-distribution (OOD) conditions using the large-scale CropNet dataset spanning 1,200+ U.S. counties from 2017-2022. Through leave-one-cluster-out cross-validation across seven USDA Farm Resource Regions and year-ahead prediction scenarios, we identify substantial variability in cross-region transferability. GNN-RNN demonstrates superior generalization with positive correlations under geographic shifts, while MMST-ViT performs well in-domain but degrades sharply under OOD conditions. Regions like Heartland and Northern Great Plains show stable transfer dynamics (RMSE less than 10 bu/acre for soybean), whereas Prairie Gateway exhibits persistent underperformance (RMSE greater than 20 bu/acre) across both models and crops, revealing structural dissimilarities likely driven by semi-arid climate, irrigation patterns, and incomplete spectral coverage. Beyond accuracy differences, GNN-RNN achieves 135x faster training than MMST-ViT (14 minutes vs. 31.5 hours), making it more viable for sustainable deployment. Our findings underscore that spatial-temporal alignment - not merely model complexity or data scale - is key to robust generalization, and highlight the need for transparent OOD evaluation protocols to ensure equitable and reliable climate-aware agricultural forecasting.
Homomorphism Problems in Graph Databases and Automatic Structures
Venue: arXiv cs.LO 2025
Year: 2025

This thesis investigates the central role of homomorphism problems (structure-preserving maps) in two complementary domains: database querying over finite, graph-shaped data, and constraint solving over (potentially infinite) structures. Building on the well-known equivalence between conjunctive query evaluation and homomorphism existence, the first part focuses on conjunctive regular path queries, a standard extension of conjunctive queries that incorporates regular-path predicates. We study the fundamental problem of query minimization under two measures: the number of atoms (constraints) and the tree-width of the query graph. In both cases, we prove the problem to be decidable, and provide efficient algorithms for a large fragment of queries used in practice. The second part of the thesis lifts homomorphism problems to automatic structures, which are infinite structures describable by finite automata. We highlight a dichotomy, between homomorphism problems over automatic structures that are decidable in non-deterministic logarithmic space, and those that are undecidable (proving to be the more common case). In contrast to this prevalence of undecidability, we then focus on the language-theoretic properties of these structures, and show, relying on a novel algebraic language theory, that for any well-behaved logic (a pseudovariety), whether an automatic structure can be described in this logic is decidable.
LASER: An LLM-based ASR Scoring and Evaluation Rubric
Venue: arXiv cs.CL 2025
Year: 2025

Standard ASR evaluation metrics like Word Error Rate (WER) tend to unfairly penalize morphological and syntactic nuances that do not significantly alter sentence semantics. We introduce an LLM-based scoring rubric LASER that leverages state-of-the-art LLMs' in-context learning abilities to learn from prompts with detailed examples. Hindi LASER scores using Gemini 2.5 Pro achieved a very high correlation score of 94% with human annotations. Hindi examples in the prompt were also effective in analyzing errors in other Indian languages such as Marathi, Kannada and Malayalam. We also demonstrate how a smaller LLM like Llama 3 can be finetuned on word-pair examples derived from reference and ASR predictions to predict what kind of penalty should be applied with close to 89% accuracy.
VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
Venue: arXiv cs.RO 2025
Year: 2025

Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicles; therefore, vehicle models are developed under conditions of information scarcity. This paper proposes a lightweight encoder-decoder model based on Gate Recurrent Unit layers to correlate the vehicle's future state with its past states, measured onboard, and control actions the driver performs. The results demonstrate that the model achieves a maximum mean relative error below 2.6% in extreme dynamic conditions. It also shows good robustness when subject to noisy input data across the interested frequency components. Furthermore, being entirely data-driven and free from physical constraints, the model exhibits physical consistency in the output signals, such as longitudinal and lateral accelerations, yaw rate, and the vehicle's longitudinal velocity.
Populism Meets AI: Advancing Populism Research with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Measuring the ideational content of populism remains a challenge. Traditional strategies based on textual analysis have been critical for building the field's foundations and providing a valid, objective indicator of populist framing. Yet these approaches are costly, time consuming, and difficult to scale across languages, contexts, and large corpora. Here we present the results from a rubric and anchor guided chain of thought (CoT) prompting approach that mirrors human coder training. By leveraging the Global Populism Database (GPD), a comprehensive dataset of global leaders' speeches annotated for degrees of populism, we replicate the process used to train human coders by prompting the LLM with an adapted version of the same documentation to guide the model's reasoning. We then test multiple proprietary and open weight models by replicating scores in the GPD. Our findings reveal that this domain specific prompting strategy enables the LLM to achieve classification accuracy on par with expert human coders, demonstrating its ability to navigate the nuanced, context sensitive aspects of populism.
DRACO: Data Replication and Collection Framework for Enhanced Data Availability and Robustness in IoT Networks
Venue: arXiv cs.NI 2025
Year: 2025

The Internet of Things (IoT) bridges the gap between the physical and digital worlds, enabling seamless interaction with real-world objects via the Internet. However, IoT systems face significant challenges in ensuring efficient data generation, collection, and management, particularly due to the resource-constrained and unreliable nature of connected devices, which can lead to data loss. This paper presents DRACO (Data Replication and Collection), a framework that integrates a distributed hop-by-hop data replication approach with an overhead-free mobile sink-based data collection strategy. DRACO enhances data availability, optimizes replica placement, and ensures efficient data retrieval even under node failures and varying network densities. Extensive ns-3 simulations demonstrate that DRACO outperforms state-of-the-art techniques, improving data availability by up to 15% and 34%, and replica creation by up to 18% and 40%, compared to greedy and random replication techniques, respectively. DRACO also ensures efficient data dissemination through optimized replica distribution and achieves superior data collection efficiency under varying node densities and failure scenarios as compared to commonly used uncontrolled sink mobility approaches namely random walk and self-avoiding random walk. By addressing key IoT data management challenges, DRACO offers a scalable and resilient solution well-suited for emerging use cases.