OpenRaiser/Intern-Atlas huggingface dataset:
Symmetry-Breaking Descent for Invariant Cost Functionals
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of reducing a task cost functional $W : H^s(M) \to \mathbb{R}$, not assumed continuous or differentiable, defined over Sobolev-class signals $S \in H^s(M) $, in the presence of a global symmetry group $G \subset \mathrm{Diff}(M)$. The group acts on signals by pullback, and the cost $W$ is invariant under this action. Such scenarios arise in machine learning and related optimization tasks, where performance metrics may be discontinuous or model-internal. We propose a variational method that exploits the symmetry structure to construct explicit deformations of the input signal. A deformation control field $ ϕ: M \to \mathbb R^d$, obtained by minimizing an auxiliary energy functional, induces a flow that generically lies in the normal space (with respect to the $L^2$ inner product) to the $G$-orbit of $S$, and hence is a natural candidate to cross the decision boundary of the $G $-invariant cost. We analyze two variants of the coupling term: (1) purely geometric, independent of $W$, and (2) weakly coupled to $W$. Under mild conditions, we show that symmetry-breaking deformations of the signal can reduce the cost. Our approach requires no gradient backpropagation or training labels and operates entirely at test time. It provides a principled tool for optimizing discontinuous invariant cost functionals via Lie-algebraic variational flows.
Unsplittable Multicommodity Flows in Outerplanar Graphs
Venue: arXiv cs.DS 2025
Year: 2025

We consider the problem of multicommodity flows in outerplanar graphs. Okamura and Seymour showed that the cut-condition is sufficient for routing demands in outerplanar graphs. We consider the unsplittable version of the problem and prove that if the cut-condition is satisfied, then we can route each demand along a single path by exceeding the capacity of an edge by no more than $\frac{18}{5} \cdot d_{max}$, where $d_{max}$ is the value of the maximum demand.
An Alignment Between the CRA's Essential Requirements and the ATT&CK's Mitigations
Venue: arXiv cs.CR 2025
Year: 2025

The paper presents an alignment evaluation between the mitigations present in the MITRE's ATT&CK framework and the essential cyber security requirements of the recently introduced Cyber Resilience Act (CRA) in the European Union. In overall, the two align well with each other. With respect to the CRA, there are notable gaps only in terms of data minimization, data erasure, and vulnerability coordination. In terms of the ATT&CK framework, gaps are present only in terms of threat intelligence, training, out-of-band communication channels, and residual risks. The evaluation presented contributes to narrowing of a common disparity between law and technical frameworks.
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to adapt using only test samples. However, existing TTA methods in FL face challenges such as computational overhead, privacy risks from feature sharing, and scalability concerns due to memory constraints. To address these limitations, we propose Federated Continual Test-Time Adaptation (FedCTTA), a privacy-preserving and computationally efficient framework for federated adaptation. Unlike prior methods that rely on sharing local feature statistics, FedCTTA avoids direct feature exchange by leveraging similarity-aware aggregation based on model output distributions over randomly generated noise samples. This approach ensures adaptive knowledge sharing while preserving data privacy. Furthermore, FedCTTA minimizes the entropy at each client for continual adaptation, enhancing the model's confidence in evolving target distributions. Our method eliminates the need for server-side training during adaptation and maintains a constant memory footprint, making it scalable even as the number of clients or training rounds increases. Extensive experiments show that FedCTTA surpasses existing methods across diverse temporal and spatial heterogeneity scenarios.
MAFA: A multi-agent framework for annotation
Venue: arXiv cs.AI 2025
Year: 2025

Modern consumer banking applications require accurate and efficient retrieval of information in response to user queries. Mapping user utterances to the most relevant Frequently Asked Questions (FAQs) is a crucial component of these systems. Traditional approaches often rely on a single model or technique, which may not capture the nuances of diverse user inquiries. In this paper, we introduce a multi-agent framework for FAQ annotation that combines multiple specialized agents with different approaches and a judge agent that reranks candidates to produce optimal results. Our agents utilize a structured reasoning approach inspired by Attentive Reasoning Queries (ARQs), which guides them through systematic reasoning steps using targeted, task-specific JSON queries. Our framework features a few-shot example strategy, where each agent receives different few-shots, enhancing ensemble diversity and coverage of the query space. We evaluate our framework on a real-world major bank dataset as well as public benchmark datasets (LCQMC and FiQA), demonstrating significant improvements over single-agent approaches across multiple metrics, including a 14% increase in Top-1 accuracy, an 18% increase in Top-5 accuracy, and a 12% improvement in Mean Reciprocal Rank on our dataset, and similar gains on public benchmarks when compared with traditional and single-agent annotation techniques. Our framework is particularly effective at handling ambiguous queries, making it well-suited for deployment in production banking applications while showing strong generalization capabilities across different domains and languages.
GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching
Venue: arXiv cs.CV 2025
Year: 2025

Cross-view geo-localisation identifies coarse geographical position of an automated vehicle by matching a ground-level image to a geo-tagged satellite image from a database. Despite the advancements in Cross-view geo-localisation, significant challenges still persist such as similar looking scenes which makes it challenging to find the correct match as the top match. Existing approaches reach high recall rates but they still fail to rank the correct image as the top match. To address this challenge, this paper proposes GeoVLM, a novel approach which uses the zero-shot capabilities of vision language models to enable cross-view geo-localisation using interpretable cross-view language descriptions. GeoVLM is a trainable reranking approach which improves the best match accuracy of cross-view geo-localisation. GeoVLM is evaluated on standard benchmark VIGOR and University-1652 and also through real-life driving environments using Cross-View United Kingdom, a new benchmark dataset introduced in this paper. The results of the paper show that GeoVLM improves retrieval performance of cross-view geo-localisation compared to the state-of-the-art methods with the help of explainable natural language descriptions. The code is available at https://github.com/CAV-Research-Lab/GeoVLM
ResQue Greedy: Rewiring Sequential Greedy for Improved Submodular Maximization
Venue: arXiv cs.DM 2025
Year: 2025

This paper introduces Rewired Sequential Greedy (ResQue Greedy), an enhanced approach for submodular maximization under cardinality constraints. By integrating a novel set curvature metric within a lattice-based framework, ResQue Greedy identifies and corrects suboptimal decisions made by the standard sequential greedy algorithm. Specifically, a curvature-aware rewiring strategy is employed to dynamically redirect the solution path, leading to improved approximation performance over the conventional sequential greedy algorithm without significantly increasing computational complexity. Numerical experiments demonstrate that ResQue Greedy achieves tighter near-optimality bounds compared to the traditional sequential greedy method.
Revenue-Optimal Efficient Mechanism Design with General Type Spaces
Venue: arXiv cs.GT 2025
Year: 2025

We derive the revenue-optimal efficient (welfare-maximizing) mechanism in a general multidimensional mechanism design setting when type spaces -- that is, the underlying domains from which agents' values come from -- can capture arbitrarily complex informational constraints about the agents. Type spaces can encode information about agents representing, for example, machine learning predictions of agent behavior, institutional knowledge about feasible market outcomes (such as item substitutability or complementarity in auctions), and correlations between multiple agents. Prior work has only dealt with connected type spaces, which are not expressive enough to capture many natural kinds of constraints such as disjunctive constraints. We provide two characterizations of the optimal mechanism based on allocations and connected components; both make use of an underlying network flow structure to the mechanism design. Our results significantly generalize and improve the prior state of the art in revenue-optimal efficient mechanism design. They also considerably expand the scope of what forms of agent information can be expressed and used to improve revenue.
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Large Language models (LLMs) have demonstrated significant potential in text-to-SQL reasoning tasks, yet a substantial performance gap persists between existing open-source models and their closed-source counterparts. In this paper, we introduce SQLForge, a novel approach for synthesizing reliable and diverse data to enhance text-to-SQL reasoning in LLMs. We improve data reliability through SQL syntax constraints and SQL-to-question reverse translation, ensuring data logic at both structural and semantic levels. We also propose an SQL template enrichment and iterative data domain exploration mechanism to boost data diversity. Building on the augmented data, we fine-tune a variety of open-source models with different architectures and parameter sizes, resulting in a family of models termed SQLForge-LM. SQLForge-LM achieves the state-of-the-art performance on the widely recognized Spider and BIRD benchmarks among the open-source models. Specifically, SQLForge-LM achieves EX accuracy of 85.7% on Spider Dev and 59.8% on BIRD Dev, significantly narrowing the performance gap with closed-source methods.
A Complexity Dichotomy for Semilinear Target Sets in Automata with One Counter
Venue: arXiv cs.FL 2025
Year: 2025

In many kinds of infinite-state systems, the coverability problem has significantly lower complexity than the reachability problem. In order to delineate the border of computational hardness between coverability and reachability, we propose to place these problems in a more general context, which makes it possible to prove complexity dichotomies. The more general setting arises as follows. We note that for coverability, we are given a vector $t$ and are asked if there is a reachable vector $x$ satisfying the relation $x\ge t$. For reachability, we want to satisfy the relation $x=t$. In the more general setting, there is a Presburger formula $φ(t,x)$, and we are given $t$ and are asked if there is a reachable $x$ with $φ(t,x)$. We study this setting for systems with one counter and binary updates: (i) integer VASS, (ii) Parikh automata, and (i) standard (non-negative) VASS. In each of these cases, reachability is NP-complete, but coverability is known to be in polynomial time. Our main results are three dichotomy theorems, one for each of the cases (i)--(iii). In each case, we show that for every $φ$, the problem is either NP-complete or belongs to $\mathsf{AC}^1$, a circuit complexity class within polynomial time. We also show that it is decidable on which side of the dichotomy a given formula falls.
Krikri: Advancing Open Large Language Models for Greek
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Llama-Krikri-8B, a cutting-edge Large Language Model tailored for the Greek language, built on Meta's Llama 3.1-8B. Llama-Krikri-8B has been extensively trained on high-quality Greek data to ensure superior adaptation to linguistic nuances. With 8 billion parameters, it offers advanced capabilities while maintaining efficient computational performance. Llama-Krikri-8B supports both Modern Greek and English, and is also equipped to handle polytonic text and Ancient Greek. The chat version of Llama-Krikri-8B features a multi-stage post-training pipeline, utilizing both human and synthetic instruction and preference data, by applying techniques such as MAGPIE. In addition, for evaluation, we propose three novel public benchmarks for Greek. Our evaluation on existing as well as the proposed benchmarks shows notable improvements over comparable Greek and multilingual LLMs in both natural language understanding and generation as well as code generation.
CoIn: Counting the Invisible Reasoning Tokens in Commercial Opaque LLM APIs
Venue: arXiv cs.AI 2025
Year: 2025

As post-training techniques evolve, large language models (LLMs) are increasingly augmented with structured multi-step reasoning abilities, often optimized through reinforcement learning. These reasoning-enhanced models outperform standard LLMs on complex tasks and now underpin many commercial LLM APIs. However, to protect proprietary behavior and reduce verbosity, providers typically conceal the reasoning traces while returning only the final answer. This opacity introduces a critical transparency gap: users are billed for invisible reasoning tokens, which often account for the majority of the cost, yet have no means to verify their authenticity. This opens the door to token count inflation, where providers may overreport token usage or inject synthetic, low-effort tokens to inflate charges. To address this issue, we propose CoIn, a verification framework that audits both the quantity and semantic validity of hidden tokens. CoIn constructs a verifiable hash tree from token embedding fingerprints to check token counts, and uses embedding-based relevance matching to detect fabricated reasoning content. Experiments demonstrate that CoIn, when deployed as a trusted third-party auditor, can effectively detect token count inflation with a success rate reaching up to 94.7%, showing the strong ability to restore billing transparency in opaque LLM services. The dataset and code are available at https://github.com/CASE-Lab-UMD/LLM-Auditing-CoIn.
LLM-based Evaluation Policy Extraction for Ecological Modeling
Venue: arXiv cs.AI 2025
Year: 2025

Evaluating ecological time series is critical for benchmarking model performance in many important applications, including predicting greenhouse gas fluxes, capturing carbon-nitrogen dynamics, and monitoring hydrological cycles. Traditional numerical metrics (e.g., R-squared, root mean square error) have been widely used to quantify the similarity between modeled and observed ecosystem variables, but they often fail to capture domain-specific temporal patterns critical to ecological processes. As a result, these methods are often accompanied by expert visual inspection, which requires substantial human labor and limits the applicability to large-scale evaluation. To address these challenges, we propose a novel framework that integrates metric learning with large language model (LLM)-based natural language policy extraction to develop interpretable evaluation criteria. The proposed method processes pairwise annotations and implements a policy optimization mechanism to generate and combine different assessment metrics. The results obtained on multiple datasets for evaluating the predictions of crop gross primary production and carbon dioxide flux have confirmed the effectiveness of the proposed method in capturing target assessment preferences, including both synthetically generated and expert-annotated model comparisons. The proposed framework bridges the gap between numerical metrics and expert knowledge while providing interpretable evaluation policies that accommodate the diverse needs of different ecosystem modeling studies.
Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
Venue: arXiv cs.RO 2025
Year: 2025

Robots navigating complex environments must manage uncertainty from sensor noise, environmental changes, and incomplete information, with different tasks requiring varying levels of precision in different areas. For example, precise localization may be crucial near obstacles but less critical in open spaces. We present GUIDE (Generalized Uncertainty Integration for Decision-Making and Execution), a framework that integrates these task-specific requirements into navigation policies via Task-Specific Uncertainty Maps (TSUMs). By assigning acceptable uncertainty levels to different locations, TSUMs enable robots to adapt uncertainty management based on context. When combined with reinforcement learning, GUIDE learns policies that balance task completion and uncertainty management without extensive reward engineering. Real-world tests show significant performance gains over methods lacking task-specific uncertainty awareness.
Provable Execution in Real-Time Embedded Systems
Venue: arXiv cs.CR 2025
Year: 2025

Embedded devices are increasingly ubiquitous and vital, often supporting safety-critical functions. However, due to strict cost and energy constraints, they are typically implemented with Micro-Controller Units (MCUs) that lack advanced architectural security features. Within this space, recent efforts have created low-cost architectures capable of generating Proofs of Execution (PoX) of software on potentially compromised MCUs. This capability can ensure the integrity of sensor data from the outset, by binding sensed results to an unforgeable cryptographic proof of execution on edge sensor MCUs. However, the security of existing PoX requires the proven execution to occur atomically. This requirement precludes the application of PoX to (1) time-shared systems, and (2) applications with real-time constraints, creating a direct conflict between execution integrity and the real-time availability needs of several embedded system uses. In this paper, we formulate a new security goal called Real-Time Proof of Execution (RT-PoX) that retains the integrity guarantees of classic PoX while enabling its application to existing real-time systems. This is achieved by relaxing the atomicity requirement of PoX while dispatching interference attempts from other potentially malicious tasks (or compromised operating systems) executing on the same device. To realize the RT-PoX goal, we develop Provable Execution Architecture for Real-Time Systems (PEARTS). To the best of our knowledge, PEARTS is the first PoX system that can be directly deployed alongside a commodity embedded real-time operating system (FreeRTOS). This enables both real-time scheduling and execution integrity guarantees on commodity MCUs. To showcase this capability, we develop a PEARTS open-source prototype atop FreeRTOS on a single-core ARM Cortex-M33 processor. We evaluate and report on PEARTS security and (modest) overheads.
Forensic deepfake audio detection using segmental speech features
Venue: arXiv cs.SD 2025
Year: 2025

This study explores the potential of using acoustic features of segmental speech sounds to detect deepfake audio. These features are highly interpretable because of their close relationship with human articulatory processes and are expected to be more difficult for deepfake models to replicate. The results demonstrate that certain segmental features commonly used in forensic voice comparison (FVC) are effective in identifying deep-fakes, whereas some global features provide little value. These findings underscore the need to approach audio deepfake detection using methods that are distinct from those employed in traditional FVC, and offer a new perspective on leveraging segmental features for this purpose. In addition, the present study proposes a speaker-specific framework for deepfake detection, which differs fundamentally from the speaker-independent systems that dominate current benchmarks. While speaker-independent frameworks aim at broad generalization, the speaker-specific approach offers advantages in forensic contexts where case-by-case interpretability and sensitivity to individual phonetic realization are essential.
Domain Adaptation of VLM for Soccer Video Understanding
Venue: arXiv cs.CV 2025
Year: 2025

Vision Language Models (VLMs) have demonstrated strong performance in multi-modal tasks by effectively aligning visual and textual representations. However, most video understanding VLM research has been domain-agnostic, leaving the understanding of their transfer learning capability to specialized domains under-explored. In this work, we address this by exploring the adaptability of open-source VLMs to specific domains, and focusing on soccer as an initial case study. Our approach uses large-scale soccer datasets and LLM to create instruction-following data, and use them to iteratively fine-tune the general-domain VLM in a curriculum learning fashion (first teaching the model key soccer concepts to then question answering tasks). The final adapted model, trained using a curated dataset of 20k video clips, exhibits significant improvement in soccer-specific tasks compared to the base model, with a 37.5% relative improvement for the visual question-answering task and an accuracy improvement from 11.8% to 63.5% for the downstream soccer action classification task.
EEG-to-Text Translation: A Model for Deciphering Human Brain Activity
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid advancement of large language models like Gemini, GPT, and others, bridging the gap between the human brain and language processing has become an important area of focus. To address this challenge, researchers have developed various models to decode EEG signals into text. However, these models still face significant performance limitations. To overcome these shortcomings, we propose a new model, R1 Translator, which aims to improve the performance of EEG-to-text decoding. The R1 Translator model combines a bidirectional LSTM encoder with a pretrained transformer-based decoder, utilizing EEG features to produce high-quality text outputs. The model processes EEG embeddings through the LSTM to capture sequential dependencies, which are then fed into the transformer decoder for effective text generation. The R1 Translator excels in ROUGE metrics, outperforming both T5 (previous research) and Brain Translator. Specifically, R1 achieves a ROUGE-1 score of 38.00% (P), which is up to 9% higher than T5 (34.89%) and 3% better than Brain (35.69%). It also leads in ROUGE-L, with a F1 score of 32.51%, outperforming T5 by 3% (29.67%) and Brain by 2% (30.38%). In terms of CER, R1 achieves a CER of 0.5795, which is 2% lower than T5 (0.5917) and 4% lower than Brain (0.6001). Additionally, R1 performs better in WER with a score of 0.7280, outperforming T5 by 4.3% (0.7610) and Brain by 3.6% (0.7553). Code is available at https://github.com/Mmurrad/EEG-To-text.