On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
Analysis of generative models (VAE, GAN, diffusion) for anomaly detection in federated IoT predictive maintenance systems.
Analysis of generative models (VAE, GAN, diffusion) for anomaly detection in federated IoT predictive maintenance systems.
Framework for decentralized multi-agent optimization using Gaussian Process surrogates and compact knowledge tokens.
Stabilized on-policy distillation method for LLM reasoning using control variate baseline to reduce gradient variance.
Federated learning approach using quadruplet learning with stochastic client selection for heterogeneous data.
Multi-distribution learning analysis showing both loss landscape flatness and gradient alignment are necessary for generalization improvement.
Sparse autoencoder method learning hierarchical feature structures without relying on activation coverage assumptions.
Discrete flow matching distillation for few-step text generation, using energy navigation to improve student model training trajectories.
Optimization algorithm addressing utility imbalance under individualized differential privacy where data owners set heterogeneous privacy requirements.
Theoretical study of Slowly Annealed Langevin Dynamics sampler with applications to training-free guided generation using pretrained score models.
Theoretical analysis of attention layer trainability with LoRA low-rank adaptation, establishing convergence guarantees under stochastic training.
Privacy-preserving federated fine-tuning of LLMs using graph representation learning to detect and mitigate adversarial model manipulation attacks.
Federated learning evaluation framework addressing metric aggregation challenges when assessing global model performance across distributed participants.
Theoretical analysis of why diffusion models efficiently sample high-dimensional data using entropy-based convergence bounds beyond ambient dimension.
Neural posterior estimation for amortized inference on set-structured observations with shared factors, addressing high-dimensional conditioning problems.
Feedback-based LLM fine-tuning system using advantage-weighted self-play in federated online settings without requiring ground-truth labels.
Theoretical framework interpreting neural networks through susceptibilities and Bayesian learning, connecting data perturbations to posterior covariances.
Mechanistic study identifying where planning representations form in LLMs using linear probing and activation patching across multiple model scales.
Susceptibilities technique for deep reinforcement learning interpretability studying how agent behaviors respond to loss perturbations.
Position paper critiquing mechanistic interpretability papers for making causal claims without explicit identification assumptions.
Extension of DPO showing language models optimize preference graphs; proposes method to exploit rich preference structure beyond pairwise comparisons.
Value-based RL algorithms for exponential-utility optimization in discounted MDPs with convergence analysis and Bellman-type equations.
MASPO: joint prompt optimization method for LLM-based multi-agent systems addressing misalignment between agent and system objectives.
Evaluation methodology for prompt-injection defenses in educational LLM tutors examining security-usability-latency trade-offs.
Analysis of position bias in reasoning-tuned LLMs showing bias scales with reasoning trajectory length, not reduced by chain-of-thought.
Study of 33 frontier LLMs showing domain-level variation in metacognitive monitoring across MMLU benchmarks using confidence calibration.
Framework for representing reasoning state as epistemic graph and determining termination conditions in recursive reasoning systems.
Method for detecting hidden coalitions in multi-agent AI systems through spectral analysis of internal representations for safety alignment.
Framework for enabling LLMs to continually adapt and learn during deployment through case-based examples and retrieval.
Analysis of when language models stabilize answer preferences during reasoning generation using finite-answer projection.
Security analysis of unintended long-term state poisoning in personalized LLM agents through routine interactions.
Framework improving reliability of multimodal LLMs in inferring spatial relations for 3D layout generation.
Framework for constructing scalable, reproducible web environments for training visual web agents with realistic diversity.
Comprehensive benchmark for evaluating LLM intent understanding capabilities across 12 domains with 49 corpora.
Model-based learning framework for agents in environments with compositional action prerequisites, using affordance-grounded world models.
Cooperative game theory approach for assigning credit to creators whose IP appears in LLM context windows, using least core solution concept.
PIC-Flow generative neural surrogate combining physics-based learning to predict electromagnetic fields for photonic devices.
Inverse reinforcement learning approach for multi-objective constraint inference from heterogeneous expert demonstrations.
Piecewise linear regression via Adaptive Block Gradient Descent using difference of max-affine functions parametrization.
Differentiable Bayesian method for inferring latent partial orders from linearized agent and workflow traces.
Theoretical equivalence between PQ and TDS learning models for algorithms handling distribution shift in training and test data.
TRUSTEE method to identify artifact reliance in ML-based static malware classifiers, revealing which features are semantic vs. spurious.
POMDP framework for LLM agents to iteratively search and gather relevant context from large environments exceeding context window limits.
Interpretable and scalable LLM evaluation framework using Item Response Theory to model latent abilities and item characteristics beyond average accuracy.
Bayesian framework for physics-informed neural networks using functional priors for PDE-constrained inverse problems.
Self-Consolidating language models that write current context into model weights while incorporating new information and limiting catastrophic forgetting.
Visual feature-based world models using residual latent actions to predict future visual features instead of pixels for more efficient predictions.
Framework separating channel importance in vision networks into task relevance and local replaceability, proposing two-axis evaluation method.
Develops TRACE method for constructing valid conformal prediction regions using diffusion and flow matching models.
Proposes classification fields for infinite-depth recursive hierarchical clustering with fine-scale refinements from few examples.
Introduces AdaTKG with adaptive entity-level memory for temporal knowledge graph reasoning over evolving events.