Anomaly detection in smart power grids is a critical challenge due to the complexity, heterogeneity, and dynamic nature of sensor data streams. Existing one-class classification methods, particularly Subspace Support Vector Data Description (SVDD), have been extended to multimodal scenarios but often fail to fully exploit the structural dependencies across modalities, limiting their robustness in real-world applications. In this paper, we address this gap by proposing a generalized Multimodal Subspace Support Vector Data Description (MS-SVDD) model with graph-embedded regularization. The method projects data from multiple modalities into a shared low-dimensional subspace while preserving modality-specific structure through Laplacian regularizers. Our approach is evaluated on a three-modality dataset derived from smart grid event time series, using a dedicated preprocessing pipeline for constructing one-class classification training samples. The results demonstrate that our graph-embedded MS-SVDD improves robustness of event detection compared to conventional approaches, highlighting the potential of integrating graph priors with multimodal subspace learning for advancing anomaly detection in critical infrastructure. More broadly, this work contributes to the wider field of AI by illustrating how relational and structural information can be systematically embedded into one-class models, enabling robust learning under complex, high-dimensional, and multimodal conditions.
Thomas Debelle, F. Sohrab, Pekka Abrahamsson et al.· Scientific Reports· 1 citation
This paper presents MARARE, a real-time multi-agent system that transforms meeting dialogues into structured software requirements. One agent interacts with participants, while background agents extract and verify requirements collaboratively. Evaluation using the LLM-as-a-Judge method across five meetings (5–8 minutes each) shows a mean coverage of 80.0 ± 11.2 % (mean ± SD), semantic similarity of 0.86 ± 0.05, and hallucination rate of 14.3 ± 6.2 %. Preliminary results indicate performance differences across LLMs, suggesting that model choice influences coverage, consistency, and hallucination rates.
Malik Abdul Sami, Gessé Evangelista, Kai-Kristian Kemell et al.· AGENT@ICSE· 0 citations
Context: Organizations adopting Artificial Intelligence (AI) face challenges in eliciting and analyzing requirements that align with strategic objectives, especially when human oversight and iterative refinement are needed. Large Language Models (LLMs)-based Multi-agent systems provide a potential solution by supporting structured and collaborative Requirements Engineering (RE) processes for AI adoption planning.
Objective: The objective of this study is to investigate whether a multi-agent system, built on LLMs and supported by human input, can assist in requirements analysis for AI adoption. Method: We used a mixed-method approach: (i) designed and developed a multi-agent system to support the generation and prioritization of requirements for AI adoption, (ii) conducted multiple case studies with four companies to evaluate the system, and (iii) collected data through post-session questionnaires from nine participants and follow-up interviews, one per company.
Results: Questionnaire and interview findings together indicate that the system may assist in identifying relevant and goal-aligned requirements. Seven participants considered the generated requirements relevant, and six found them aligned with organizational goals. Participants noted that iterative feedback improved completeness and feasibility, often within two feedback rounds. Both data sources show that human input was essential to clarify technical details, ensure contextual accuracy, and validate prioritization results. Participants from all companies also identified usability, transparency, and scalability as areas requiring further refinement for broader organizational use.
Conclusions: LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts. Future work will include more interviews with stakeholders and adjustments to system features to improve transparency, usability, and scalability.
Malik Abdul Sami, Zheying Zhang, Muhammad Waseem et al.· e-Informatica Software Engin...· 5 citations
Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues or altering behaviour-relevant code structures. We introduce REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates. REFINE combines static-analysis-guided smell identification, smell-informed planning, LLM-based transformation, automated re-analysis, preservation checks, and structured reporting. We evaluate REFINE on 450 Java files from 15 open-source systems, producing 1,350 model-pass outputs using OpenAI GPT-5.5, Google Gemini 3.1 Pro Preview, and Anthropic Claude Opus 4.8. REFINE reduces detected code smells by 68.26%, 72.79%, and 68.49% across the three configurations, respectively, with the strongest reductions observed for major smells. A matched 150-file direct-prompt baseline shows that REFINE achieves a higher median code-smell reduction with smaller edits and fewer public-method removals. However, broader quality improvements are inconsistent, and preservation checks reveal residual risks, including assert/fail-call changes and public-method removal. Therefore, REFINE outputs should be treated as refactoring candidates requiring compilation, testing, dependency analysis, and human review before adoption in repository- or system-level settings.
Muhammad Waseem, Aakash Ahmad, Pekka Abrahamsson· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Part IV of a series on equity and reliability in medical imaging AI. Part III of this series showed that naming a demographic group in the text prompt of a medical vision–language model changes its diagnosis on a fixed chest radiograph, that the perturbation is exactly rank one in the standard positive/negative pair readout, and that group-specific thresholds therefore provably cannot remove it. Two questions were left open: whether any of this survives a change of imaging domain, and whether it matters for a model that is actually good at its task — Part III's strongest encoder reached a mean AUC of 0.687. This paper audits four pathology vision–language models (PLIP, QuiltNet-B-32, CONCH, and the slide-level TITAN) under the descriptor set from Part III, unchanged, and combines them with Part III's three radiology encoders for a seven-model comparison spanning two imaging domains and both patch and slide granularity. On TITAN the audit runs at full strength: 8,010 whole-slide images from 6,582 patients across 25 TCGA cancer types, every interval clustered on patient, mean neutral-prompt AUC 0.982 with all 25 projects clearing the utility floor. Four results. The rank-one identity transfers exactly — verified to 5.2×10−8 on real slide embeddings, two orders of magnitude tighter than Part III because TITAN has no learned logit scale to amplify float error. Part III's threshold-irremovability corollary therefore holds for whole-slide pathology. Diagnostic competence does not protect against prompt-channel bias. Across seven encoders, neutral-prompt AUC does not predict the effect (r = −0.443, p = 0.32): CONCH at AUC 0.849 shows 21.1 points of mean absolute excess false-negative rate, indistinguishable from OpenAI CLIP at AUC 0.518. What predicts it is a purely geometric quantity, the descriptor perturbation measured against the diagnostic contrast (r = +0.780, p = 0.039), reported as a hypothesis with n = 7 rather than a law. The model answers the token and not its truth, including for race. Part III could test congruence only for sex and age, because NIH ChestX-ray14 records no race; TCGA does, and the difference-in-differences estimator gives a median 0.0045 logits for race and 0.0010 for sex, against a specimen-site positive control 164–730× larger in 4 of 4 pairs at tighter precision. Anatomically impossible descriptors — "a male patient showing endometrial carcinoma" — move the score as much as coherent ones. Prompt symmetrisation is a domain-dependent intervention rather than a general one: it removes 59–63% of the effect on radiology encoders, 26–34% on pathology patch encoders, and amplifies the effect by 94% on TITAN. The text-side symmetry residual separates the two domains completely (Mann–Whitney p = 0.029). The practical consequence is a correction to how this series has framed mitigation. A deployer cannot infer prompt-channel robustness from a model's accuracy, and cannot assume a mitigation validated in one imaging domain transfers to another — including on the strength of Part III. Honest scope. Unlike Parts II, IIb, and III, this part is not preregistered; PROTOCOL_PART4.md is retrospective and says so in its first line, recording which hypotheses preceded their endpoints and which did not. Only TITAN carries interval estimates: the public CRC-VAL-HE-7K mirror records no patient identifiers, so the three patch-level encoders are reported as point estimates only. The anatomically-impossible result rests on five cells and is a demonstration of mechanism, not an estimate of magnitude. This paper also supplies the out-of-domain evidence behind amendment 8 to Part III's protocol, which withdrew Part III's proposed text-only predictor of symmetrisation failure. Files. The manuscript PDF (21 pp, 4 figures); the retrospective protocol; and an artifact archive containing all 13 analysis scripts and every result table as CSV/JSON. Model weights are not redistributed — TITAN, CONCH, and UNI are CC-BY-NC-ND-4.0 and were obtained through the gated Hugging Face process. Ethics. No patient data was collected. TCGA is a public consortium dataset; CRC-VAL-HE-7K is public and de-identified. This is an audit of a model property, not a clinical study, and makes no claim about patient outcomes.
Omar Mohammed· Zenodo (CERN European Organi...· 0 citations
Measured board power, tokens per joule, and electricity cost per million tokens for local large language model inference on a retail NVIDIA RTX 5080. Board power was logged with nvidia-smi at 1 Hz while driving fixed-length generations. Efficiency. Llama 3.2 3B 1.20 tokens/joule; sparse gpt-oss 20B 0.665; Qwen 2.5 7B 0.53; Qwen 2.5 14B 0.317. The sparse 20.9B model is approximately twice as efficient per joule as the dense 14B, indicating that architecture and quantization dominate parameter count on the efficiency axis. Loaded power. Board power medians of 265-344 W against a 448 W stock limit that was never reached, at 82-93% utilization and 49-52 degrees C. Idle finding. Across a week of captures the card idled at 52-71 W at the Windows desktop, pinned in P0 with graphics clocks near 2.9 GHz; the cleanest achievable state still read 53.7 W. Published review figures typically quote single-digit to 15 W idle. At the May 2026 EIA US residential average of $0.184/kWh, 52-71 W continuous is 456-622 kWh, or approximately $85-115 per year before any tokens are generated. Since generation itself costs only $0.04-$0.16 per million tokens, idle behaviour rather than model choice dominates the operating cost of an intermittently used inference node. Includes raw 1 Hz telemetry captures in addition to summary rows. Canonical page, full method and change log: https://techfuelhq.com/data/rtx-5080-llm-power-efficiency/
Wood, LK, IV· Zenodo (CERN European Organi...· 0 citations
The Transformer architecture---built on attention rather than recurrence---redrew the landscape of natural language processing and became the substrate of contemporary artificial intelligence. This article presents a narrative review of the architecture's canonical line: Sutskever and colleagues' 2014 sequence-to-sequence learning, Bahdanau and colleagues' 2015 attention alignment, Vaswani and colleagues' 2017 Attention Is All You Need, Devlin and colleagues' 2019 BERT pretraining, Radford and colleagues' 2019 GPT-2, Brown and colleagues' 2020 GPT-3 and few-shot learning, Raffel and colleagues' 2020 T5 transfer, Dosovitskiy and colleagues' 2021 Vision Transformer, Bommasani and colleagues' 2021 foundation-model framing, Hoffmann and colleagues' 2022 Chinchilla scaling laws, Ouyang and colleagues' 2022 InstructGPT alignment, and Touvron and colleagues' 2023 LLaMA openness. The synthesis is organized around three themes: architecture, in which self-attention's parallel sequence processing replaced recurrence and enabled scale; scaling, in which pretraining on text plus parameter growth yielded emergent few-shot capability and then compute-optimal correction; and alignment and access, in which instruction tuning, RL from feedback, and open weights reshaped capability's deployment. It is concluded that the Transformer is machine learning's most consequential architecture to date---its attention mechanism the field's new inductive bias---and that scaling's economics and governance now define its trajectory.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This document presents the defensible core of the Universal Model Framework (UMF), isolating the minimal set of structural assumptions and derivations that remain logically coherent, mathematically motivated, and empirically falsifiable. As stated in the text, the goal is to extract “the smallest segment that is logically structured, mathematically motivated, and empirically vulnerable,” while ensuring that “every load‑bearing claim is paired with an explicit failure condition.” It is a deliberately falsifiable research program investigating whether quantum structure, arithmetic regularity, and emergent spacetime geometry can arise from a common relational foundation. It separates three logically distinct questions: whether relational systems can reconstruct quantum-theoretic structure; whether ordinary prime-number organization is physically selected rather than merely mathematically available; and whether a stable continuum geometry with causal and gravitational dynamics can emerge under refinement. The work reports exact finite results for recursive graph constructions, discrete geometry, cochain-based fermionic operators, local frames, symmetry tests, and numerical-reproducibility controls, while documenting failed frame-transport and continuum candidates. Crucially, it does not claim established fundamental physics: no continuum limit, Lorentzian causal structure, gravitational field equation, physical mass scale, complete quantum reconstruction, or prime-specific empirical signal has yet been derived. The framework’s contribution is therefore methodological as well as mathematical: it provides a transparent architecture for distinguishing theorem, model assumption, numerical fit, negative result, and falsifiable prediction in foundational physics. This project was developed by Marco Gericke, with structured assistance from a large language model. All scientific concepts and conclusions were generated, verified, and interpreted by the author. Dedicated to Peter Plichta, who envisioned the code before it could be computed.
Marco Gericke· Zenodo (CERN European Organi...· 0 citations
Corrected analysis and figure-generation package for a benchmark of six large language models, four single-model prompting strategies, and a separate GPT-5.4-assisted external-review workflow across 119 de-identified gastrointestinal oncology cases.
Jiajia Yuan, Shengyuan Zhou, Guangzhengao Yang et al.· Zenodo (CERN European Organi...· 0 citations
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: How does the computational efficiency of English intermediate-task fine-tuning compare to target-language fine-tuning for zero-shot cross-lingual transfer, measured in terms of total training time and FLOPs on XTREME-R tasks? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.
Assignee Research· Zenodo (CERN European Organi...· 0 citations
Background: Multidisciplinary tumor boards (MTBs) are central to contemporary head and neck oncology, ensuring accurate staging, guideline-concordant therapy, and balanced functional outcomes. In parallel, large language models (LLMs) have demonstrated increasing competence in synthesizing complex clinical data and generating structured recommendations. Their potential role as decision-support tools in head and neck oncology, however, remains insufficiently evaluated.Methods: We retrospectively compared treatment recommendations generated by two state-of-the-art LLMs (ChatGPT ™ and Google Gemini ™) with consensus decisions from a multidisciplinary tumor board. Fifty consecutive, synthetic head and neck cancer cases discussed between April and June 2025 were included without restriction on tumor type or stage. Both models received identical anonymized clinical, radiologic, and histopathologic reports and were prompted to generate guideline-based first- and second-line treatment recommendations
Data, field notebooks and code supporting a study of two video-based vehicle counting campaigns on a single arterial corridor of Ciudad Juárez, Mexico, and of how a classification error propagates into a road traffic noise model of the FHWA/REMEL family.The deposit contains the full adjudication register of the 105 image crops that a pretrained detector flagged as heavy vehicles, adjudicated twice and independently — once with the assistance of a large language model and once by the author under blind conditions, in two tranches — together with the blind answer sheets and keys, the measured-versus-modelled evaluation over 126 pairs from six continuously monitored sites, the vehicle registry tabulations used to characterise the local fleet, the calibration notebooks of both campaigns, and the code that reproduces every figure.The author's labels are the reference throughout. Agreement between the two passes was 30 of 30 on a pre-registered random subsample (Cohen's κ = 1.00) and 98 of 105 over the full census (93.3 %, κ = 0.586); the seven discrepancies are itemised in the accompanying paper.Video material and the original crops are deliberately not deposited: the capture protocol prohibits retaining legible licence plates, and the crops contain them. Every image reproduced in the paper had its plates blurred. The adjudication register makes each verdict auditable without distributing any image with a plate.
Pedro Martínez Cisneros· Zenodo (CERN European Organi...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.