Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks.
VOGUE: A multimodal dataset for conversational recommendation in fashion
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TRACE is a new benchmark dataset and evaluation suite for conversational tourism recommenders that requires systems to suggest POIs, cite verifiable review spans, and recover from rejections, revealing a Three-Competency Gap across baselines.
Existing methods for selecting in-situ labels in immersive recommendation scenes often show redundant or incomplete information and fail to anticipate users' proactive information needs.
A multimodal CNN on 87,547 Vogue images classifies fashion houses at 78.2% top-1 accuracy, decades at 88.6%, and years at 58.3% with 2.2-year mean error, and shows texture and luminance carry most of the house-identity signal.
citing papers explorer
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Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems
Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks.
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TRACE: Tourism Recommendation with Accountable Citation Evidence
TRACE is a new benchmark dataset and evaluation suite for conversational tourism recommenders that requires systems to suggest POIs, cite verifiable review spans, and recover from rejections, revealing a Three-Competency Gap across baselines.
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Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation
Existing methods for selecting in-situ labels in immersive recommendation scenes often show redundant or incomplete information and fail to anticipate users' proactive information needs.
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FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing
A multimodal CNN on 87,547 Vogue images classifies fashion houses at 78.2% top-1 accuracy, decades at 88.6%, and years at 58.3% with 2.2-year mean error, and shows texture and luminance carry most of the house-identity signal.