Introduces the Grounded Observer framework that applies robotics-inspired formal constructs for runtime constraint enforcement on foundation model interaction trajectories in socially sensitive domains.
Lynx: An open source hallucination evaluation model
9 Pith papers cite this work. Polarity classification is still indexing.
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RAGognizer adds a detection head to LLMs for joint training on generation and token-level hallucination detection, yielding SOTA detection and fewer hallucinations in RAG while preserving output quality.
LegalHalluLens provides typed hallucination profiles over CUAD, a Risk Direction Index, and a calibrated debate pipeline that reveals 38-40 pp category gaps hidden by aggregate 52% error rates and reduces fabricated detections by 45%.
CuraView detects sentence-level faithfulness hallucinations in medical discharge summaries via GraphRAG knowledge graphs and multi-agent evidence grading, achieving 0.831 F1 on critical contradictions with a fine-tuned Qwen3-14B model and 50% relative improvement over baselines.
KYA provides a framework-agnostic trust layer using inbound pipelines, policy composition, unified trust scoring, interaction multipliers, and delegation attribution to ensure authorized, conforming, and verifiable actions in autonomous systems.
FinGround reduces financial hallucinations by 68% over baselines in retrieval-equalized tests through atomic claim verification and grounding, with an 8B model retaining 91.4% F1 at low cost.
Probing experiments indicate that hallucination patterns in LLMs are not fully reducible to knowledge conflict representations.
A literature survey that taxonomizes hallucination phenomena in LLMs, reviews evaluation benchmarks, and analyzes approaches for their detection, explanation, and mitigation.
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.
citing papers explorer
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Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains
Introduces the Grounded Observer framework that applies robotics-inspired formal constructs for runtime constraint enforcement on foundation model interaction trajectories in socially sensitive domains.
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RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
RAGognizer adds a detection head to LLMs for joint training on generation and token-level hallucination detection, yielding SOTA detection and fewer hallucinations in RAG while preserving output quality.
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LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI
LegalHalluLens provides typed hallucination profiles over CUAD, a Risk Direction Index, and a calibrated debate pipeline that reveals 38-40 pp category gaps hidden by aggregate 52% error rates and reduces fabricated detections by 45%.
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CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification
CuraView detects sentence-level faithfulness hallucinations in medical discharge summaries via GraphRAG knowledge graphs and multi-agent evidence grading, achieving 0.831 F1 on critical contradictions with a fine-tuned Qwen3-14B model and 50% relative improvement over baselines.
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KYA: A Framework-Agnostic Trust Layer for Autonomous Systems with Verifiable Provenance and Hierarchical Policy Composition
KYA provides a framework-agnostic trust layer using inbound pipelines, policy composition, unified trust scoring, interaction multipliers, and delegation attribution to ensure authorized, conforming, and verifiable actions in autonomous systems.
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FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification
FinGround reduces financial hallucinations by 68% over baselines in retrieval-equalized tests through atomic claim verification and grounding, with an 8B model retaining 91.4% F1 at low cost.
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Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models
Probing experiments indicate that hallucination patterns in LLMs are not fully reducible to knowledge conflict representations.
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Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
A literature survey that taxonomizes hallucination phenomena in LLMs, reviews evaluation benchmarks, and analyzes approaches for their detection, explanation, and mitigation.
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Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
A framework detects LLM anomalies including hallucinations, jailbreaks, and backdoors by forensic inspection of layer-wise hidden state patterns, reporting over 95% accuracy with minimal computational overhead.