GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis

2026-07-20T08:52:11Zeac4508ddfc60a5f618000e8dac485e70b12a6245d98b46d84d9fccc1285b0fd
LLMsMIMIC-IVMedQAPrologbenchmarkscausal-inferenceconstruction-drawingsevaluationexplainable-AIhealthcareknowledge-graphslocal-voice-assistantmedical-diagnosismulti-agent-systemsmultimodalprivacyreinforcement-learning

What happened

Collection of recent arXiv ML/AI papers (20 Jul 2026) covering medical diagnosis LLM systems, causal/graph-based reasoning, healthcare-specialized LLM training, local voice assistants, multi-agent reasoning behavior, multimodal engineering benchmarks, agentic game-solving components, meme harm detection, and explainable RL via Prolog. Highlights include GraphDx (knowledge-enhanced MDKG + cost-aware multi-agent pipeline improving diagnostic success and lowering test costs on MedQA/MIMIC‑IV), Causal-Audit (target-aware causal-graph construction and auditable path aggregation), Cura 1T (human-g-g

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_ai
Record identifier
eac4508ddfc60a5f618000e8dac485e70b12a6245d98b46d84d9fccc1285b0fd
Enrichment time
2026-07-20T08:52:11Z
AI-assisted enrichment
Yes

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