Actualizado diariamente1519 historias verificadas
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A new preprint proposes triggering bias corrections only when evidence of stereotyping accumulates during an LLM’s reasoning, reporting fewer interventions than fixed-interval methods but important accuracy tradeoffs across model types.
A paper accepted to Findings of EMNLP 2026 introduces OmniPhys, a benchmark of 15,246 physics questions and 19,850 images drawn from Chinese educational corpora. It evaluates multimodal AI systems on both physics reasoning and the generation of structured physics diagrams.
A new arXiv preprint introduces DCGC, a masked-diffusion framework that uses an upstream draft to revise reasoning globally instead of allowing early mistakes to propagate.
A new arXiv paper introduces VietAIDetector, an open-source zero-shot tool designed to identify AI-generated Vietnamese text without domain-specific training data. The authors say it supports long and scanned documents and outperforms methods developed primarily for English on out-of-domain evaluations.
An arXiv preprint introduces ReliableRAG, a retrieval-augmented generation framework that scores individual evidence triples for relevance and credibility before constructing multi-step answers.
A new arXiv paper introduces TOPAS, a scheduler that jointly manages cached LLM prefixes and request execution in multi-stage agent workflows. The authors report lower job-completion times than their tested baselines on synthetic workflows and two MetaGPT software-development workloads.
A new arXiv paper describes EmoVec, an inference-time method that injects emotion-specific vectors into language models to adjust emotional intensity while preserving the response’s meaning, fluency and coherence, according to experiments reported by the authors.
A preprint reports that targeting 32 attention heads in LLaVA-1.5-7B reduced hallucinated objects in captions on 400 held-out COCO images.
A new arXiv preprint presents JIT-Agent, a model designed to generate, repair and improve the software harnesses that guide AI agents. The authors report sizable benchmark gains across several language-model families, but the work remains an unreviewed preprint without independent validation in the source.
An arXiv preprint introduces $R^3$, a post-training method that uses free-form language reasoning to guide robotic manipulation policies. The authors report gains on two controlled benchmarks, while leaving real-world performance and the size of those gains unspecified.
A new arXiv preprint reports that several physical-AI benchmarks measure overlapping information, potentially changing how researchers rank models and choose evaluation suites.
A new preprint reports that medical vision-language models can appear reliable on familiar data while failing cross-dataset transfer, multimodal alignment and shortcut tests.
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