Rough Sets for Explainability of Spectral Graph Clustering
Rough set theory applied to explain results of spectral graph clustering algorithms for text document analysis.
Rough set theory applied to explain results of spectral graph clustering algorithms for text document analysis.
Neuroscience study of cortical neuron mechanisms underlying long-term working memory through spike-timing precision analysis.
Method for watermarking deep neural networks to protect intellectual property and verify model ownership using chaos-based approaches.
Geo-4D: training-free geometric approach for 4D LiDAR panoptic segmentation without deep networks or dedicated modules.
CARE: failure-centric post-training framework using contrastive learning for verifiable multimodal reasoning in reinforcement learning.
Multi-agent hybrid DRL approach for optimizing energy efficiency in multi-functional RIS-aided NOMA networks.
EgoGrasp: method for reconstructing world-space hand-object interactions from egocentric videos supporting open-vocabulary objects.
Agentic Retoucher: hierarchical agent for fixing distortions in text-to-image generation with spatial grounding.
WebCoderBench: benchmark for evaluating LLM-generated web applications with comprehensive metrics and interpretable results.
LAMB: LLM-based audio captioning framework bridging modality gap between audio and text using Cauchy-Schwarz divergence.
GeoMotionGPT: LLM framework for motion understanding using geometry-aligned discrete motion tokenization and embeddings.
Imagine-then-Plan framework for agent learning using world models and adaptive lookahead for complex task planning.
RAG-3DSG: retrieval-augmented generation approach for constructing 3D scene graphs with uncertainty estimation for robotics tasks.
Multi-agent LLM framework for generating research limitations by identifying methodological issues beyond superficial statements.
Research on decomposable inference in large models showing gradient updates are localized, reducing inference costs and complexity.
Jacobian Scopes: gradient-based methods for token-level causal attribution in LLMs to identify which prior tokens influence predictions across layers and attention heads.
VibeVoice-ASR framework for speech understanding in long-form audio using single-pass processing to handle context fragmentation and multi-speaker scenarios.
NaVIDA improves vision-language navigation agents by explicitly modeling action-grounded visual dynamics for better planning and generalization in embodied environments.
Benchmark for evaluating reasoning in baby language models trained on child-directed speech; developmentally-inspired evaluation methodology.
Expert-panel study on human detection of LLM-generated Korean text using rubric-based calibration framework for attribution.
Mixture-of-Experts approach for time-series forecasting transformers using segment-wise routing to improve scaling and temporal dynamics.
MDial framework for generating multi-dialectal dialogue data; addresses LLM performance gaps for non-standard English speakers.
Adversarial attacks against search-enabled LLM fact-checking systems; proposes DECEIVE-AFC for testing robustness of retrieval-augmented verification.
First labeled dataset of 98,380 malicious agent skills characterizing security threats in LLM-based agent registries and ecosystems.
Open-source singing voice synthesis system with zero-shot generalization and controllable generation capabilities.
Cross-domain image registration via inverse rendering with structure-appearance factorization; computer vision problem, not AI-focused.
System for natural language graph analytics over large property graphs using LLMs; enables querying complex heterogeneous datasets efficiently.
Token reduction method for multi-modal LLMs in autonomous driving to improve efficiency while maintaining human-vehicle interaction.
Physics-based tropical cyclone estimation using spline-parameterized KAN for efficient edge device deployment on satellite data.
Framework for on-policy supervised fine-tuning of LLMs using Distribution Discriminant Theory to improve generalization over standard SFT.
Generic object tracking method using joint-embedding predictive architecture with model adaptation and occlusion reasoning.
Research on identifying missing persona dimensions for user simulation in dialogue systems to improve validity of simulation results.
Geometric analysis of optimization dynamics in grokking; shows transformers train in low-dimensional subspaces with detailed PCA findings.
Research on early-warning signals for grokking phenomenon via loss-landscape geometry analysis across sequence learning benchmarks.
Investigation of text-to-image diffusion models' effectiveness as synthetic data generators, revealing performance regression when used for training data generation.
LESA method for accelerating diffusion models through learnable stage-aware predictors that adapt to stage-dependent dynamics with feature caching.
Survey of neural routing solvers that use deep learning to tackle vehicle routing problems by learning implicit heuristic rules from data.
Digital twin calibration methods for axial piston pumps to diagnose compound faults in fluid power systems with limited training data.
FINDS CoE program advancing digital forensic engineering education through HPC, software engineering, and adversarial analytics for cybersecurity workforce.
Study of LLM unlearning robustness in multi-turn interactive settings, addressing safety, privacy, and legal concerns in machine unlearning.
Discrete gauge-theoretic framework for understanding superposition in LLMs using sheaf theory and local semantic charts instead of global dictionaries.
arXiv paper introducing RMBench robotic manipulation benchmark emphasizing memory-dependent tasks and policy design insights.
arXiv paper proposing Causal Hamiltonian Learning Unit as deep learning primitive for temporal dynamics addressing LSTM/Neural ODE tradeoffs.
arXiv paper introducing Whisper-RIR-Mega benchmark dataset for evaluating speech recognition robustness to room acoustics.
arXiv paper analyzing Google's SynthID-Text watermarking system for LLM-generated text detection using tournament-based methods.
arXiv paper evaluating multi-vendor LLM agent teams for clinical diagnosis, testing whether diversity reduces correlated failure modes.
arXiv paper on time series forecasting with multi-dimensional exogenous integration for industrial applications like aviation.
arXiv paper proposing Latent-Mark watermarking framework for audio robust to neural resynthesis attacks using latent space embedding.
Study showing LLM-as-a-Judge evaluation frameworks are unreliable for safety assessment, demonstrating vulnerability to distribution shifts in red-teaming.
Mathematical framework for solving distributionally robust optimization problems using neurodynamic approaches with three uncertainty sets.