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arXiv — cs.AIMedHopQA benchmark tests LLM reasoning in biomedical Q&A◆arXiv — cs.CLRouters learn geometry of sparse mixture-of-experts◆arXiv — cs.AIStudy audits how LLMs generate political discourse during crises◆arXiv — cs.AIClassifier Context Rot: Monitor Performance Degrades with Context◆arXiv — cs.AIExecutable Agentic Memory for GUI Agent◆arXiv — cs.CLLongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced◆arXiv — cs.AISparse-to-dense rewards improve language model post-training◆arXiv — cs.AIAI-native mobility dataset advances 6G handover and beam management◆arXiv — cs.AIMedHopQA benchmark tests LLM reasoning in biomedical Q&A◆arXiv — cs.CLRouters learn geometry of sparse mixture-of-experts◆arXiv — cs.AIStudy audits how LLMs generate political discourse during crises◆arXiv — cs.AIClassifier Context Rot: Monitor Performance Degrades with Context◆arXiv — cs.AIExecutable Agentic Memory for GUI Agent◆arXiv — cs.CLLongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced◆arXiv — cs.AISparse-to-dense rewards improve language model post-training◆arXiv — cs.AIAI-native mobility dataset advances 6G handover and beam management◆
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SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests

May 11 · · primary fetch1 sourcecluster 6cd40aafupdated May 11 ·

Microsoft Research introduced SocialReasoning-Bench, a benchmark evaluating AI agents' social reasoning in calendar coordination and marketplace negotiation, testing outcome optimality and due diligence.

read full article on microsoft.com ↗
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  1. microsoft.comSocialReasoning-Bench: Measuring whether AI agents act in users’ best interestsprimary
SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests · shipfeed