Towards Dependable Retrieval-Augmented Generation Using Factual Confidence Prediction
Confidence prediction mechanism for retrieval-augmented generation to assess factuality of retrieved context.
Confidence prediction mechanism for retrieval-augmented generation to assess factuality of retrieved context.
Theory of governed metaprogramming for AI systems that synthesize executable code at runtime with safety constraints.
LLM-based user simulator for evaluating conversational recommender systems with explicit human decision-making modeling.
Resume Tailor agentic system using multi-source RAG with career vault for personalized resume suggestions with provenance tracking.
Framework for selecting informative rollouts in tree search for tool-use agentic RL using submodular optimization under budget constraints.
Systematic review mapping training data quality issues to code generation defects in LLMs, linking generation failures to corpus problems.
Study on embedding hidden safeguards in manuscripts to detect when peer reviewers use commercial chatbots for full outsourcing.
Enterprise architecture for securing RAG and agentic AI systems with multi-tenant retrieval, access control, and compliance requirements.
Graph Normalization method as differentiable approximation for NP-hard Maximum Weight Independent Set problem.
Scaling Vision Transformer autoencoders to 5B parameters for improved image tokenization across native resolutions.
Empirical study evaluating privacy awareness of Vision-Language Models deployed as autonomous agents in physical environments.
Analysis of feature starvation in sparse autoencoders used to interpret LLM representations, proposing geometric stability perspective.
LLM-guided query embedding refinement system for open-vocabulary object retrieval in satellite imagery using vision-language models.
Investigation of how AI-generated draft quality affects human editors' workflow when creating audio descriptions for video accessibility.
Study evaluating how students use AI-generated counterarguments for critical thinking in writing, examining cheating and cognitive offloading risks.
Method to verify ownership of trained Graph Neural Networks by detecting if one GNN was trained to mimic another's embeddings.
Efficient 3D point cloud anomaly detection using consistency models instead of diffusion for resource-constrained deployment.
Method to automatically generate query keywords from query-free summarization datasets to enable query-focused summarization tasks.
Methodology for preparing context in AI coding agents using mise en place principles to reduce debugging and improve code quality.
Causal reasoning framework enabling robots to creatively use tools beyond their primary purpose via counterfactual simulation.
Information-theoretic adversarial training approach for improving LLM robustness to adversarial prompts at scale.
Method using semantic loss to prevent model collapse during transformer fine-tuning on causal reasoning tasks.
SLAM: white-box watermarking scheme for LLMs using sparse autoencoders to encode marks in structural geometry without quality loss.
Study on Graph Self-Supervised Learning robustness to noise in text-derived biomedical knowledge graphs.
Benchmark for evaluating knowledge graph construction methods and GNN robustness on noisy automatically-extracted graphs.
Unified mathematical framework for linear attribution methods (GradCAM, SHAP, LIME, Integrated Gradients) via Riesz representation theory for interpretability.
Method compiling LLM reasoning traces into symbolic program synthesizers for efficient program synthesis, achieving 91.3% accuracy on benchmarks without test-time LLM calls.
Causal representation learning framework for discovering interpretable modules in scientific time series using sparse additive identifiable methods.
Benchmark evaluating multimodal LLMs on specialized astronomical classification tasks, assessing reasoning accuracy and interpretability in scientific applications.
Analysis of LMO-based optimizers like Lion and Muon, proposing implicit gradient transport to accelerate convergence without additional gradient evaluations.
Multilingual zero-shot voice cloning model supporting 30 languages using IPA representation and two-stage training on 420K hours of audio.
Synthetic dataset and pipeline for training LLMs to determine appropriate speaking timing in multi-party conversations, addressing turn-taking calibration.
Defense mechanism against multi-turn dialogue attacks where harmful intent is distributed across benign-looking turns in deployed LLMs.
Study analyzing 10,000 student submissions to propose behavioral evaluation metrics for AI tutors beyond pedagogical feedback quality alone.
Multilingual benchmark of 5,500 test cases across 10 countries evaluating LLM safety and cultural sensitivity beyond English-centric approaches.
Adapter technique for frozen vision encoders in vector search that handles out-of-distribution queries without collapsing performance on unseen classes.
Method to reduce cross-task interference in multi-task instruction tuning of LLMs by decomposing basic abilities and mitigating conflicting gradients.
Red-teaming methodology for generative AI that incorporates persona-driven approaches to surface diverse risks based on human perspectives and backgrounds.
Framework for interpretable time-series forecasting using Kolmogorov-Arnold Networks with explainable edge functions instead of black-box MLPs.
Position-independent caching system for agentic LLM serving that addresses cache invalidation issues in multi-turn agent interactions using Multi-Head Latent Attention.
Research on budgeted attention allocation for transformers, enabling multiple cost-quality trade-offs in deployed systems via head-gating mechanisms.
int4 KV cache quantization technique for Apple Silicon that runs faster than fp16 via fused Metal kernels, implemented as HuggingFace Cache subclass.
Empirical study of proactive LLM-based coding assistants that infer developer intent from IDE interactions and repository context to reduce interaction overhead.
Active learning approach to optimize communication structure in multi-agent LLM systems, reducing token usage and improving performance with limited training data.
SafeHarbor: hierarchical memory-augmented safety guardrail for LLM agents that balances security with reducing over-refusal in tool-use scenarios.
Auto research system using specialist AI agents to autonomously develop training recipes through closed empirical loops with hypothesis testing and code generation.
CRAFT: continual learning framework for LLMs that avoids catastrophic forgetting by learning low-rank interventions on hidden representations instead of updating weights.
Framework for behavioral evaluation of agentic stock prediction systems using LLM judges and closed-loop RL feedback. Addresses hidden decision quality in aggregated metrics.
Methods for nonlinear causal discovery using Fourier feature approximations. Scales Gaussian process marginal likelihood scoring and kernel tests.
Theoretical research proving transformers can implement policy-improvement reinforcement learning in-context without parameter updates via explicit constructions.