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Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a signif...
Results show that self-evolving agents should place experience inside the control flow, rather than only store it as text, and show how learned conventions must reach the right execution step on long tool-use environments where learned conventions must reach the right execution step.
PersonaMem-v3 is introduced, a real-world-grounded benchmark and evaluation harness for omni-platform personal intelligence that evaluates whether AI agents can infer holistic user understanding from cross-platform evidence, personalize responses, rerank recommendations on social media, follow user steering through nat...
A hypergraph-based paired failure attribution (HPFA) framework that attributes the failure root cause by comparing the hyperedges of the targeted failure reasoning path against a reference successful path is proposed, and the trained attributor consistently improves reasoning accuracy at test time.
MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.
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