Digi Turbine is a synthetic benchmark that combines a simplified beam model with Winkler soil foundation in its training objective. It uses Bayesian inverse identification and First Order Reliability Method screening to enable reliable state estimation from sparse sensor data. Validation is based on synthetic configurations derived from the NREL 5MW turbine.
Agon is an autonomous research system that uses prompt economy to validate checkable claims in workflows, leaving judgment to human scientists. It operates across 444 iterations with minimal prompts and no human-written code, revealing a taxonomy of failures by severity, fixability, visibility, and capability locus. The system demonstrates scalability and advances research toward a paradigm where machines handle scale and humans guide judgment.
A rigorous N-qubit theory proves that depolarising noise in stochastic quantum neural networks contracts Pauli read-outs exponentially, enabling robust anomaly detection. On the NSL-KDD dataset, such noise achieves significant adversarial resilience without catastrophic collapse, outperforming noiseless models and classical detectors under FGSM and PGD attacks, with reduced robustness variance and a train-test gap reduction of approximately 0.01.
SURGELLM introduces a unified transformer framework with surgical feature gating, task-conditioned prefix tokens, and Instance-Weighted Normalization to address inductive bias mismatches, class imbalance, and lack of lexical knowledge integration. The IWN variant achieves macro-F1 of 0.940 across four tasks, outperforming baselines by 0.036 overall and 0.130 on authorship detection, with gains confirmed as lexical rather than parametric.
PDT-C 2.0 presents a uniformly annotated, genre-diversified Czech corpus of nearly 4 million tokens. It includes meaning representations and inter-sentential phenomena like coreference and discourse relations, and is accompanied by fully compatible lexicons. The resource is available under a CC BY-NC-SA licence.
This review presents a taxonomy of transformer-based language models across domain verticals, covering encoder-only, decoder-only, encoder-decoder, long-context, permutation-based, and generator-discriminator variants. It evaluates post-2023 advancements like instruction tuning and mixture-of-experts scaling, and assesses model deployments in healthcare, finance, legal, education, customer service, creative writing, and scientific work, linking each to specific capabilities. The paper critically analyzes model architectures on four key deployment axes, quantifies parameter count versus energy cost, and examines how alignment methods, data provenance, and benchmark saturation define 'state of the art'.
PETRA transforms public web text into a curated petroleum engineering corpus with synthetic supervision for dense retrieval and reranking. It improves in-domain nDCG from 0.703 to 0.763 and boosts Earth Science benchmark performance by 44% and a six-task reasoning panel by 23%.
The paper presents an algorithm that transfers English part-of-speech tags from Princeton WordNet to Arabic-English dictionary senses after disambiguation. This enables linking bilingual dictionaries to WordNet and standardizing them into WordNet-LMF format, where synsets are the fundamental unit, with high accuracy at low cost.
MorfFlex is a morphological dictionary architecture designed for languages with complex inflection and derivation. MorfFlex CZ, its primary implementation, contains over 100 million wordforms and more than 1 million lemmas, reduced through encoded inflectional and derivational patterns. It supports annotation consistency in the Prague Dependency Treebanks and powers tools like MorphoDiTa.
Prompt rankings in large language model evaluation are often unstable under minor variations like random seeds and limited subsets. A stability-aware selection strategy using lower confidence bounds improves robustness by accounting for both performance and variance, while maintaining competitiveness in stable settings.
AutoSpecNER is a dataset of 659 car advertisements with over 10,000 entities annotated across 15 categories. It achieves 91.5% inter-annotator agreement and shows that DeBERTa outperforms both rule-based methods and large language models in vehicle specification extraction, reaching a 90% micro-F1 score.
A lightweight GPT-2-style Transformer enables hierarchical feature extraction from vibration signals. The framework achieves 92.61% average accuracy using only 10% labeled data, outperforming state-of-the-art methods by 17.24 percentage points in cross-domain bearing fault diagnosis.
UOL@IDEM presents a closed-track submission to BEA 2026, modeling vocabulary difficulty prediction as regression for Spanish, German, and Chinese. The system integrates multilingual contextual embeddings with engineered features like frequency and cognate similarity, achieving lower RMSE scores than baselines, with feature analysis highlighting frequency as the most stable predictor and contextual predictability as a key L1-sensitive signal.
RaDaR, a compact reasoning large language model, outperformed other open-source models in rare disease diagnosis. In a randomized trial, RaDaR improved physicians' diagnostic accuracy by 21.44 percentage points over internet search alone.
This research introduces the first public multi-modal dataset of 100 aligned audio-transcript pairs for Turkish scam and benign calls. It evaluates seven large language models under raw audio, automatic, and human-corrected transcript inputs, finding that transcript-based inputs outperform direct audio processing, with human correction having minimal impact.
AdversaBench introduces an end-to-end red-teaming pipeline that generates adversarial prompts via five structured operators, evaluates target models, and confirms failures through a three-judge panel with meta-judge tiebreaker. Experiments on 45 seed prompts across reasoning, instruction-following, and tool use show every seed produces a confirmed failure, with operator effectiveness, failure iteration counts, judge agreement, and cross-model transferability revealing key patterns in LLM vulnerability.
Qwen-AgentWorld-35B-A3B and Qwen-AgentWorld-397B-A17B are the first language world models that simulate agentic environments across seven domains using long chain-of-thought reasoning. Trained via a three-stage pipeline—CPT, SFT, and RL—these models outperform existing frontier models on AgentWorldBench, a benchmark derived from real-world interactions of five models on nine established tasks.
SIFT introduces claim-conditioned re-scoring of evidence spans to better align with full claims, recovering up to 27.6 points in accuracy on FEVER, SciFact, 5PILS, and DP. WSP, an automatic NLI check, achieves AUC 0.92 and precision 0.98 when calibrating against human gold evidence.
MedLayXPlain introduces the first large-scale benchmark for medical lay language generation, featuring 122,789 region-grounded samples across eight imaging modalities. It evaluates medical vision-language models on expert-lay alignment using a hierarchical ontology system and a lightweight evaluator, revealing a systematic gap: expert-level performance in captioning coexists with significant degradation in lay language, while general-purpose models lack clinical precision.
QBioFusion-QSAR integrates a quantum fidelity kernel with Morgan/Tanimoto fingerprints to improve ligand classification. On the PsychLight-A benchmark, QMKL increased accuracy and MCC compared to Morgan/Tanimoto alone, with improvements attributed to better predictions of molecules with activity cliffs, such as N-Me-5-HT and N-Me-tryptamine. Auditable analysis confirms localized quantum-kernel contributions in small-data settings.