mlsanityData to 7 Oct 2026

Meta-Modal Agent: Sequential Evidence Routing for Missing-Modality Candidate Reranking

arXiv 2605.25007 ↗First posted 24 May 2026cs.IRcited by 0

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

Missing-modality recommenders usually reconstruct absent representations, although the observed evidence may not determine the missing content. We formulate candidate reranking as budgeted sequential evidence acquisition. A policy queries text, image, and interaction-graph tools, incorporates textttNull returns into its observation history, and sparsely rescores a retrieved candidate pool. Our Meta-Modal Agent (MMA) uses PPO to optimize terminal NDCG and tool cost without explicit access to the route-availability mask or target identity. When only one evidence route is available, MMA-Auto improves NDCG@10 by 10.0% over the strongest completion baseline and by 9.5% over a fixed router with the same Llama scorer. It obtains the highest result in all nine reported combinations of dataset and available route against these comparators. MMA-Auto also reduces failed calls by 17.8 percentage points and uses 1.1 fewer turns than the fixed router. On the fixed candidate pools produced by full-catalog retrieval, MMA-Auto improves NDCG@10 by 19.7%. These results associate adaptive evidence routing with improved reranking under severe, constructed missingness. The code is available at: https://anonymous.4open.science/r/WSDM2027-MMA-C381.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 18 of its 29 references that are in the map. First posted on arXiv 24 May 2026.

Builds on

18 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems13 Oct

Other references · 17

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2018Self-Attentive Sequential Recommendationcited by 1,169
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  4. 2015Image-based Recommendations on Styles and Substitutescited by 702
  5. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  6. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  7. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  8. 2023LlamaRec: Two-Stage Recommendation using Large Language Models for Rankingcited by 52
  9. 2020Self-Supervised Reinforcement Learning for Recommender Systemscited by 49
  10. 2025Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresightcited by 11
  11. 2018LRMM: Learning to Recommend with Missing Modalitiescited by 8
  12. 2024Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?cited by 7
5 more
  1. 2023Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary citiescited by 6
  2. 2026DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendationcited by 5
  3. 2025Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarioscited by 4
  4. 2026TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendationcited by 3
  5. 2026Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completioncited by 2

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  1. 2026AdaM-Rec: Adaptive Modality Routing for Multimodal Recommendation ↗
  2. 2026Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation
  3. 2026Prism-Reranker: Beyond Relevance Scoring -- Jointly Producing Contributions and Evidence for Agentic Retrieval
  4. 2026RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
  5. 2026Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation
  6. 2026V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval
  7. 2026EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking
  8. 2026MM-VeriRec: Failure-Guided Fusion for Verifiable Agentic Multimodal Recommendation