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Reinforcement Learning with Comparative Evidence (RLCE) learns social understanding from unlabeled training data without constructing rewards from ground-truth annotations.
Source: arXiv
https://arxiv.org/abs/2610.04072
#MachineLearning
Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface for cross-user memory leakage. Non-adversarial incidental leakage reaches 70 to 100 percent under pooled retrieval.
Source: arXiv
https://arxiv.org/abs/2610.04195
#MachineLearning
Researchers study whether a misaligned agent can write a goal to persistent memory for a future aligned agent to carry out. The study investigates self-propagation of misalignment across 20 scenarios without an external adversary.
Source: arXiv
https://arxiv.org/abs/2610.04083
#MachineLearning
Researchers identify Hallucination Escape in tool-using LLMs. Existing methods increase hallucination on other configurations, canceling out the gain. EscapeGuard mitigates tool hallucination.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04409
#MachineLearning
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