IET framework for attributing multi-agent system outputs to specific agents without execution logs, enabling accountability in agent interactions.
SQLBench benchmark for evaluating Text-to-SQL capabilities of LLMs across sub-tasks, addressing gaps in prompt templates and performance assessment.
Experimental study measuring how partisan biases in LLMs influence human political opinions and decision-making.
Structured transformer approach for offline model-based optimization combining reinforcement learning and generative modeling for design problems.
Framework addressing limitations of contrastive distillation for 3D representation learning by capturing modality-specific features.
Autoregressive transformer approach for component-based colored SVG generation from text descriptions.
LLM-based index advisor for database optimization using in-context learning to iteratively refine index recommendations.
Ensemble of language models for automated tumor group classification from unstructured pathology reports in cancer registries.
Federated learning system balancing privacy-utility tradeoffs with incentive mechanisms and heterogeneous resource accommodation across organizations.
Rank-based uniformity test for detecting undisclosed substitutions or quantization of black-box LLM APIs without access to model weights.
arXiv paper: surrogate ML model for predicting heat transfer in impinging jet arrays. CFD acceleration via neural networks.
arXiv paper: agent-based LLM approach for automating free-form clinical notes to HL7 FHIR structured data.
arXiv paper: 2D-guided cross-modal fusion method for LiDAR-camera alignment in 3D autonomous vehicle detection.
Automated page image classification system for historical document digitization handling diverse content types, layouts, and handwritten/printed text.
Unsupervised deep learning approach for inverse problems in computed tomography combining deep image prior and unrolled optimization.
One-step image generation improvement using soft embeddings in distilled masked diffusion models, enabling gradient flow for post-distillation refinements.
Analysis of positional and language bias in mid-layer representations of vision-language encoders for zero-shot language-grounded spatial understanding.
Artificial Age Score framework formalizing memory aging in LLMs, modeling how semantic and episodic information degrades across conversational sessions.
Benchmark using equivalence scoring for ground-truth-free evaluation of formally verifiable code generated by LLMs in languages like Dafny.
Knowledge distillation method for small language models that balances exploration and guidance through adaptive switching to address exposure bias.
Benchmark evaluating hallucinations in audio-visual multimodal LLMs with spoken queries under diverse acoustic conditions.
Information-Determined Scoring framework using LLMs to score free-text psychological assessment responses and augment rating-scale measures.
National Weather Service implementation of automated translation using LLMs and LILT's training process to serve non-English speakers.
Robotic assembly system using vision-language models to handle connector-aware assembly from instruction manuals with focus on physical constraints.
Video reasoning model with explicit spatio-temporal evidence grounding, extending evidence-centered reasoning from images to videos with temporal tracking and spatial localization.
Framework for composing synergistic multi-agent LLM teams by analyzing model interaction geometry to optimize collaboration and surpass single-model capabilities.
Method for LLM ownership verification using encrypted fingerprinting with protection against attacks during verification processes.
Novel knowledge distillation-based membership inference attack against LLM-based recommendation systems to determine if data samples were in training sets.
Study comparing genetic algorithms and other methods for generating sample weights to mitigate bias in ML models, examining trade-offs between fairness and accuracy.
Research on models' ability to detect activation steering vectors injected into their residual streams during forward passes, revealing steering awareness in instruction-tuned models.
MRD fusion approach for high-resolution image understanding in MLLMs combining retrieval-augmented generation with detection to prevent object fragmentation and false positives.
Survey of cell-cell communication inference from single-cell omics data, covering biological mechanisms and computational approaches for ligand-receptor interaction analysis.
Continual learning study revealing asymmetry in experience replay between feature-level and classifier-level forgetting, showing minimal buffers preserve representations but not predictions.
ClinicalTrialsHub platform consolidating ClinicalTrials.gov with PubMed data extraction, increasing structured trial data access by 83.8% for patients and clinicians.
Adaptive Accountability Framework for networked multi-agent systems using cryptographic provenance tracking and runtime detection of emergent norms like collusion and unfairness.
Neuron-level interpretability study of code LLMs identifying language-specific neurons and concept layers, adapting NLP techniques to formal programming language structure.
GeoMotionGPT aligns motion space geometry with embedding space in LLM-based motion understanding by coupling discrete motion tokenization with semantic learning.
Systematic evaluation of LLM susceptibility to persuasion across six models using SMCR communication framework, testing adoption of counterfactual beliefs.
Forest-Chat integrates vision-language agents with satellite imagery for interactive forest change analysis, combining LLMs with computer vision for environmental monitoring.
Mechanistic study comparing internal algorithmic changes when post-training autoregressive models into masked diffusion models, investigating genuine bidirectional reasoning acquisition.
Analysis of diffusion language models showing arbitrary token generation order doesn't unlock reasoning improvements over autoregressive models, revealing limitations of flexibility.
STELLAR framework guides LLM-based generation of SystemVerilog Assertions for formal verification using structural similarity from hardware design ASTs.
One-shot data augmentation method combining geometric perturbations with noise injection for few-shot learning generalization to novel classes.
Sheaf Neural Networks algorithm with biomedical case study outperforming GCNs, GATs, and GraphSage on graph-structured biomedical data.
Analysis of gender dynamics and homophily patterns in Chirper.ai, a social network of 70K+ autonomous LLM agents generating 140M posts, examining how AI agent identity develops in networks.
Theoretical study of expand-and-sparsify sparse representations for density and mode estimation, analyzing biological sensory system models with random projections and sparsification.
Krause Attention proposes a new transformer attention mechanism addressing representation collapse and attention sink phenomena through bounded normalization inspired by Krause dynamics.
SF-RAG improves retrieval-augmented generation for academic QA by preserving hierarchical document structure instead of flattening papers into chunks, enabling better evidence allocation under token constraints.
Deep reinforcement learning stability improvement using isotropic Gaussian representations to handle non-stationary training dynamics.
Parameter-efficient fine-tuning method using manifold expansion to overcome linear limitations of LoRA in complex reasoning tasks.