Ontological foundations for contrastive explanatory narration of robot plans
Ontological framework for robots to explain competing plans through contrastive reasoning for human-robot interaction.
Ontological framework for robots to explain competing plans through contrastive reasoning for human-robot interaction.
Co-denoising framework for transferring manipulation skills from human videos to robots by bridging morphological differences.
Security research identifying backdoor vulnerabilities in AI agent supply chains through data poisoning across multiple pipeline stages.
Technique to prevent search-based linkage attacks on de-identified documents using perturbation methods.
Knowledge distillation method balancing exploration and guidance for training efficient small language models with reduced exposure bias.
Learning framework enabling robots to learn from constrained human demonstrations where demonstrators have fewer degrees of freedom.
Inference-time tree search guidance method for controllable graph generation using diffusion models across knowledge graphs and drug discovery.
Empirical study showing frontier LLMs trained on copyrighted books produce preferred literary text matching award-winning authors' styles.
Benchmark for open-vocabulary underwater instance segmentation with geometric and semantic alignment enhancements.
On-device object-goal navigation agent system using small LLMs with navigation map caching for zero-shot embodied AI.
Transformer-based model extending Prior-Data Fitted Networks for Bayesian clustering with uncertainty quantification on synthetic data.
Self-evolution framework for LLMs using ontology rules to reduce hallucinations in specialized domains like healthcare and law.
Training-free diffusion model enhancement for multi-layer text-to-image generation without fine-tuning.
Graph-based retrieval-augmented generation system addressing hallucination, reasoning transparency, and answer quality for LLMs.
Large-scale underwater segmentation dataset and open-vocabulary segmentation models for marine organism recognition.
Deep learning method for geometric representation learning on Grassmannian manifolds using multi-subspace fusion.
Federated learning approach addressing data distribution shifts using causal inference for out-of-distribution robustness.
Method for steering LLM behavior through activation injection to enhance empathy and negotiation capabilities using attribution patching.
Research on masked auto-regressive diffusion models optimizing inference speed for reinforcement learning applications through architectural improvements.
PRISM complex-valued encoder with phase-based spectral filtering shows semantic relationships correlate with phase angles in neural sequence models.
DiG differential grounding proxy task enhances fine-grained visual perception in MLLMs by learning differences between image pairs for precise spatial reasoning.
DexWM world model predicts latent environmental states for fine-grained dexterous hand-object interactions from human videos.
Programming abstraction for shared state between prompts and programs, enabling interoperability between natural language and traditional programming with LLMs.
AI4EOSC federated cloud platform for AI in scientific research with reproducible ML lifecycle management across distributed e-Infrastructures.
SentGraph hierarchical sentence graph enables multi-hop retrieval-augmented generation by constructing coherent evidence chains from multiple documents for complex QA.
Audits political alignment in 26 LLMs using psychometric inventories and news bias labeling across prompt variants to evaluate behavioral bias.
Vision-language reasoning for urban socio-semantic segmentation from satellite imagery, distinguishing socially-defined categories beyond physical attributes.
Ablates RLVR pipeline components for code verifiers: intermediate thinking traces, negative samples, and on-policy training to reduce adoption cost barriers.
CFM language-aligned concept foundation model decomposes vision model representations into human-interpretable concepts with spatial grounding for diverse downstream tasks.
LREAD rubric-based framework with three-phase expert study calibrates human detection of LLM-generated Korean text to improve attribution accuracy beyond surface-level assessment.
Time-annealed perturbation sampling for diffusion language models enables diverse generation across semantic and reasoning paths by controlling temporal denoising.
Bauplan code-first lakehouse with data contracts, versioning, and transactional pipelines for concurrent AI/analytics workloads supporting both human and agent workflows.
Predicts LLM success from pre-generation internal activations using linear probes to enable efficient inference routing on math and coding tasks.
Benchmark evaluating multimodal LLMs' ability to understand pedagogical reasoning and science instruction in K-12 classroom videos with model-based explanations.
Descent-guided policy gradient method addresses cross-agent noise scaling in multi-agent reinforcement learning, improving sample complexity from O(N/ε) to sublinear bounds.
KEEP system optimizes KV-cache memory management for memory-augmented LLMs in embodied planning, reducing prompt length and prefill latency for long-horizon tasks.
Experimental study of sycophancy in LLMs—tendency to favor user-affirming over critical responses—with controlled interventions to identify and prevent the alignment failure.
Evaluates 5 open-source small LLMs (Gemma, Phi, Llama, Mistral, Meditron) for clinical QA reliability and prompt sensitivity in low-resource healthcare settings.
AOI: Trainable multi-agent LLM framework for autonomous cloud diagnosis and SRE automation learning from failed trajectories.
SWE-CI: Benchmark evaluating LLM agent capabilities in continuous integration for long-term codebase maintenance and feature iterations.
Optimizes KV cache in transformers by reducing key dimensionality to log(N) while preserving value information for efficiency.
CRIMSON: Clinically-grounded LLM metric for evaluating chest X-ray report generation on diagnostic correctness and patient safety.
Systematic framework defining boundaries between AI models and AI systems for regulatory and policy compliance purposes.
Amnesia: Adversarial semantic activation steering technique for controlling harmful content generation in large language models.
DUCTILE: Agentic LLM orchestration framework for automating engineering analysis and tool coordination in product development.
ELISA: Interpretable AI agent combining scGPT embeddings with BioBERT for expression-grounded discovery in single-cell genomics.
FRAME: Systematic framework for real-world AI evaluation generating contextual evidence on model behavior in organizational environments.
APEX-Searcher: LLM agent with agentic planning for multi-hop retrieval-augmented generation to enhance complex question answering.
Audio-visual speech enhancement using RL with LLM-based interpretable reward model for perceptual quality optimization.
arXiv: Post-hoc model-agnostic explanation method using perturbation selection for uncertainty-aware surrogate model approximations.