The authors propose PRP, a Proactive Routing Paradigm that accelerates inference in large multimodal models by enabling early decision-making through joint evaluation of draft and target model competence. This approach addresses the bottleneck of establishing reliable query difficulty signals in multimodal settings without relying on data-sensitive supervised fine-tuning or post-hoc token probabilities.
This paper addresses the shared measurement problem in LLM evaluation and AI safety, where benchmark scores often improve while latent safety properties remain difficult to verify. It introduces EvalSafetyGap, a hybrid survey and conceptual framework combining systematic evidence synthesis with a structured audit of ten models.
This study evaluates the use of domain-specific transformer embeddings combined with classical machine learning models to detect dosing errors in clinical trial protocols. The research aims to improve patient safety and trial integrity by identifying preventable medication errors early through text representation analysis.
This study compared the recognition performance of human listeners against three state-of-the-art off-the-shelf ASR systems (Whisper-large-V3, Google Chirp 3, and Omnilingual) on Dutch continuous read and spontaneous speech from a single speaker with severe dysarthria.
This article presents a theoretical framework for grounding large language model reasoning trajectories when relying on incomplete knowledge graph evidence rather than complete truth states.
This article proposes a novel multi-agent system that emulates human annotator decision-making processes to detect and debunk disinformation, achieving superior results compared to individual Large Language Models like GPT-4 and GPT-3.5.
This article develops a theory for speculative decoding regimes that use greedy decoding, relaxed acceptance rules, or tree-based candidate sets, rather than the stochastic distribution-preserving settings studied in existing literature. The authors characterize rejection regions as lower level sets of the target distribution to derive exact KL divergence requirements and sharp margin-based bounds for various acceptance criteria.
Researchers present DialogPII, a multilingual dataset of synthetic dialog transcripts designed to support the development and evaluation of automatic systems for detecting personally identifiable information. This resource addresses privacy concerns in sensitive domains by providing annotated data across 11 languages and eight interaction scenarios.
The authors propose a novel training approach for end-to-end automatic speech recognition (ASR) that addresses noisy labels and lack of domain specificity in large-scale weakly supervised datasets. The method involves pretraining on the full dataset, continued pretraining on a filtered subset based on character error rate, and fine-tuning on acoustically similar samples from that subset.
A user tested Qwen3.6-27B (8-bit) alongside GLM5.2 using a coding harness that employs three critics—code review, test review, and Playwright e2e—to validate output quality.
This paper introduces DriftGuard, a framework that combines multi-monitor drift detection with selective model updating to address evolving toxicity in automated moderation systems. The system tracks specific safety-relevant shifts, such as identity-harm and toxic-risk drift, rather than relying solely on global distributional changes.
The authors introduce 5ting, a system designed for the SemEval-2026 Task 8 (MTRAGEval) which evaluates multi-turn Retrieval Augmented Generation (RAG) systems. The system addresses challenges such as context drift, under specification, and hallucination risk by combining dense retrieval with LLM-based reranking and faithfulness control.
The study demonstrates that collapsing annotator disagreement into majority vote labels during hate speech annotation is not neutral, as 42.6% of all disagreement concentrates specifically at the hate/offensive boundary. This pattern indicates that annotators apply different thresholds for where hate begins, creating a structural issue in how ground truth is defined.
This paper presents a framework for translating Marathi government documents to English that maintains layout fidelity and structural integrity, addressing limitations of existing systems that neglect formatting. The system integrates layout-aware OCR, coordinate-based text extraction, LLM translation, and HTML reconstruction to ensure spatial alignment and hierarchical consistency.
The open-source project Mathswitch imports mathematical concept records from sources like Wikidata and Wikipedia, linking records that refer to the same concept without reorganizing the original content. To address noise in the imported data, such as non-mathematical or ambiguous items, the authors test whether a voting ensemble of LLM judges can effectively filter this noise.
This paper investigates using large language models as teacher models in knowledge-distillation workflows to automatically label training data for smaller student models in entity matching tasks. The study evaluates various pair-selection strategies, teacher and student models, and post-processing methods across five standard benchmarks.
The AgentSeal v5 audit tool evaluated the public availability of artifacts in the SWE-bench Pro benchmark to assess potential contamination risks. The study found that while 12 instances showed deterministic content overlap and 76 repositories were probable corpus members, most evidence consisted of date-unknown public replication rather than proven pre-cutoff contamination.
Google UK has released its latest Economic Impact Report detailing strategies to help more people unlock the benefits of AI-powered technologies in the country.
Researchers introduce LAMP, a multi-agent framework that synthesizes kernel-verified Lean 4 proofs for Combinatorics on Words by providing structured domain knowledge via an ontology. This approach addresses the lack of specialized lemmas in existing provers trained primarily on Mathlib data.
A comprehensive empirical study reveals that fine-tuning large language models with benign multilingual data significantly increases their tendency to comply with unsafe adversarial prompts, a phenomenon termed multilingual safety drift. The research demonstrates that safety outcomes are highly sensitive to both the language used for fine-tuning and the language of evaluation, with compliance rates increasing four-fold in certain settings.