large language models

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

An exploratory, retrospective study of randomly selected ED visits to a multihospital health system having an ED revisit within 1-14 days to the same health system finds that the KGA may enable enhancing the scope and yield of screening without substantially increasing reviewer workload.

Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs

Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existing vision token pruning methods mitigate this overhead, they implicitly assume that a single fixed pruning strategy can be applied uniformly across all inputs. Our analys...

Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning

It is shown that preserving or reinjecting the weight offset fails to maintain protection, indicating that preventative steering relies on active adaptation rather than a static defense, and proposes Progressive Intensity Scheduling (PIS), which improves safety robustness over static-strength steering while reducing ha...

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

FedMVLA is proposed, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks that incorporates three mechanisms: modality-aware federated aggregation (MAFA), modality-aware privacy allocation (MAPA), and modality-aware communication compression (MACO), together with a modality-slic...

Beyond Surface Imitation: Contrastive Modeling for Reasoning Path Alignment in Multimodal In-Context Learning

A new multimodal ICL framework is proposed that combines contrastive demonstration modeling with the self-refinement capability of MLLMs and consistently improves MLLM performance, with particularly notable gains on visual question answering (VQA).

OntologyAligner: Ontology-Aligned Retrieval and Hierarchy-Guided Large Language Model Reranking for Biomedical Ontology Normalization

OntologyAligner is presented, a three-stage framework that combines ontology-aligned retrieval, large language model candidate reranking, and selective hierarchy-guided refinement that offers a generalizable framework for accurate mapping of biomedical text to structured ontology concepts.

Multi-Agent Agentic Graph Learning via Structural Signatures

A multi-agent agentic graph learning framework that partitions the graph into communities and assigns an independent agent to each community for region-specific specialization and shows that MAAGL outperforms SOTA AGL methods.

Can large language models replace human surveys in geospatial studies? A case of vague cognitive regions

Do large language models share the same culture? A psychometric framework for quantifying cross-model response divergence (Cross-Model Response Divergence, CMRD)

Synergistic integration of large language models and knowledge graphs for intelligent metabolic pathway design in food synthetic biology

Glossary

“Agreeableness Effect” (AI Sycophancy)The tendency for large language models, which are optimized to be cooperative, to affirm a user’s premise or framing

What Does a Missing Exclusion Clause Cost? Measuring the False-Positive Burden of LLM-Generated Detection Rules

Large language models now write detection rules, and the work evaluating them asks whether the rules catch attacks. It does not ask what they cost on a quiet day. That cost sits in the exclusion clause the "alert when X, except when Y" half of a rule, which encodes local knowledge of what is benign here. Prior work rep...