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 =
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
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.
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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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.