Thinking Out Loud: Real-Time Deception Monitoring in Asymmetric LLM Negotiations
2026-07-01T07:23:50Z•93154f724be70ac1c83069eb0913ad2b55a4b332561b8664ec7847424fccda03
AI_accessibilityAI_education_assessmentAI_governanceAI_transparencyAI_trustLLM_deceptionRCIN_frameworkadversarial_behaviouragentic_AIbenchmarkingcapacity_planningchain_of_thought_monitoringclinical_AIexplainabilityhuman-in-the-loopmedical_AImodel_consistencymonitoring_infrastructureoperational_risksurgical_AI
What happened
Collection of recent arXiv papers (2026-07-01) addressing risks, monitoring, governance, and application challenges of agentic and large language models plus related AI systems. Key contributions: a lightweight real-time chain-of-thought monitor that detects seller deception in asymmetric LLM negotiations but does not fully prevent exploitation and exposes a buyer-capability gap; a measured “consistency dilemma” showing models with high generator–evaluator self-consistency can still be more vulnerable to mistakes; analysis of transparency mandates showing compliant artefacts may create a “Tras
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 93154f724be70ac1c83069eb0913ad2b55a4b332561b8664ec7847424fccda03
- Enrichment time
- 2026-07-01T07:23:50Z
- AI-assisted enrichment
- Yes
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