LLM reliability techniques are unified as communication channel operators, with a new cost-aware router achieving superior quality-cost tradeoffs on hard tasks.
arXiv preprint arXiv:2510.24476 , year =
5 Pith papers cite this work. Polarity classification is still indexing.
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SmartVector augments embeddings with time, confidence, and relation signals plus a consolidation process, raising top-1 accuracy on versioned queries from 31% to 62% on a synthetic benchmark while cutting stale answers and calibration error.
A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
Presents an end-to-end system using LLM agents to add behavioral anomalies to simulated trajectories, then applies map routing and noise to generate realistic annotated anomaly datasets for mobility research.
PassiveQA trains models via supervised finetuning to decide Answer, Ask, or Abstain using structured information-state representations and knowledge-graph context, yielding better abstention and lower hallucination on QA datasets.
citing papers explorer
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A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability
LLM reliability techniques are unified as communication channel operators, with a new cost-aware router achieving superior quality-cost tradeoffs on hard tasks.
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Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge
SmartVector augments embeddings with time, confidence, and relation signals plus a consolidation process, raising top-1 accuracy on versioned queries from 31% to 62% on a synthetic benchmark while cutting stale answers and calibration error.
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Qiskit Code Migration with LLMs
A taxonomy-guided RAG system with LLMs reduces hallucinations and improves migration suggestions for Qiskit code compared to unconstrained retrieval.
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Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints
Presents an end-to-end system using LLM agents to add behavioral anomalies to simulated trajectories, then applies map routing and noise to generate realistic annotated anomaly datasets for mobility research.
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PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning
PassiveQA trains models via supervised finetuning to decide Answer, Ask, or Abstain using structured information-state representations and knowledge-graph context, yielding better abstention and lower hallucination on QA datasets.