CoRM-RAG uses a cognitive perturbation protocol to simulate biases and trains an Evidence Critic to retrieve documents that support correct decisions even under adversarial query changes.
Reg4rec: Reasoning-enhanced generative model for large-scale recommendation systems
5 Pith papers cite this work. Polarity classification is still indexing.
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LWGR applies personalized soft instructions for LLM knowledge extraction and Lagrangian primal-dual optimization to selectively fuse beneficial world knowledge into generative recommendation while bounding degradation.
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
AutoModel uses three core agents (AutoTrain, AutoFeature, AutoPerf) connected by a shared coordination layer to automate model design, feature evolution, performance management, and paper-driven reproduction in large-scale recommender systems.
citing papers explorer
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Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation
CoRM-RAG uses a cognitive perturbation protocol to simulate biases and trains an Evidence Critic to retrieve documents that support correct decisions even under adversarial query changes.
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LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation
LWGR applies personalized soft instructions for LLM knowledge extraction and Lagrangian primal-dual optimization to selectively fuse beneficial world knowledge into generative recommendation while bounding degradation.
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Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
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AgenticRS-Architecture: System Design for Agentic Recommender Systems
AutoModel uses three core agents (AutoTrain, AutoFeature, AutoPerf) connected by a shared coordination layer to automate model design, feature evolution, performance management, and paper-driven reproduction in large-scale recommender systems.
- Chain of Evidence: Pixel-Level Visual Attribution for Iterative Retrieval-Augmented Generation