Signal or Noise? Modality Contribution and Cooperation in Multimodal GraphRAG

Antonios Georgakopoulos; Paul Groth; Lise Stork

Home/Archive/arXiv

arXiv · 2609.35304

Signal or Noise? Modality Contribution and Cooperation in Multimodal GraphRAG

Antonios Georgakopoulos·Paul Groth·Lise Stork

View original source ↗Read source document ↗Export metadata ↓

Abstract

Multimodal knowledge graphs (KGs) integrate information from text, figures, tables, and other modalities into a unified structured representation, with the promise that richer evidence enables better inference. In GraphRAG systems built over such graphs, it is commonly assumed that retrieving evidence from more modalities at inference time improves downstream performance. Yet, redundant or overlapping multimodal evidence may distract language models in question answering (QA), and whether each modality contributes equally across questions, models, and tasks remains poorly understood. In this work, we study how modality-aware retrieval affects downstream inference in a multimodal GraphRAG pipeline, using document visual question answering (DocVQA) as a testbed. We extend an existing KG-based QA framework to be modality-aware, leveraging the graph structure to track which modality supports which facts and to selectively filter evidence at the edge level. This enables us to investigate whether providing all available multimodal evidence at inference time benefits QA, and to evaluate the contribution and cooperation of modalities across question, task, and model characteristics. Through a controlled analysis within a state-of-the-art multimodal GraphRAG pipeline, five multimodal LLMs and two DocVQA benchmarks, we find that tables and text provide the strongest contributions, and that combining modalities frequently produces redundancy rather than synergy, particularly for pairs involving textual information. Positive cooperation appears mainly between non-text modalities and depends on question intent and task type. Our findings argue for selective, modality-aware retrieval in the design of more effective GraphRAG systems, where modalities are filtered according to the downstream task rather than retrieved uniformly.

Explore related subjects

cs.IR

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Antonios Georgakopoulos, Paul Groth, Lise Stork. 2026-09-28. Signal or Noise? Modality Contribution and Cooperation in Multimodal GraphRAG. https://arxiv.org/abs/2609.35304

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval augmented generation (RAG) tries to reduce factual errors by identifying information in a knowledge corpus and putting it in the context window of the model. While this approach is well-established for document-structured data, it is non-trivial to adapt it for Knowledge Graphs (KGs), especially for queries that require multi-node/multi-hop reasoning on graphs. We introduce UltRAG, a training-free KG-RAG recipe that combines LLM query generation, a fully inductive neural query executor, and LLM arbitration. This off-the-shelf composition achieves state-of-the-art results on Knowledge Graph Question Answering (KGQA) tasks without retraining the LLM or executor, while enabling language models to interface with Wikidata-scale graphs (116M entities, 1.6B relations) at comparable or lower costs. Our ablation studies indicate that these gains come from the full system design rather than from any single component.

cs.IR↗

arXiv2026

SIREN (Luring LLMs onto the Rocks): PAIR-Driven Preference Manipulation in Web-RAG Recommenders

This paper investigates the adversarial manipulation of the ranked recommendations produced by web-augmented large language models (LLMs). When an LLM answers a recommendation query by retrieving and reading live webpages, it acts as a recommender, and each retrieved page becomes a potential attack surface. Prior work has examined fabricated products, retrieval poisoning, and rank promotion. However, these studies do not compare how different edits to an already retrieved page change the model's final ranking while the surrounding source set remains unchanged. To address this gap, we propose SIREN, an automated attacker--judge method that adapts the PAIR jailbreaking loop to competitive rank manipulation, with the goal of moving a chosen entity to rank~1 in an LLM-generated recommendation. SIREN retrieves and captures webpages using Anthropic's web tools, then iteratively edits a retrieved source using an interpretable taxonomy of 23 content-poisoning techniques. The custom-RAG replay platform keeps the same sources in the same order, so changes in the model's ranking can be linked to changes in the supplied content rather than to differences in retrieval. Across two production Claude models, SIREN reaches rank~1 in 62 of 124 technique trials nested within eight query--model contexts. The payloads that reached rank~1 were then tested in fresh sessions, where they reproduced the result with a mean success rate of 0.805. Across the evaluated settings, declarative ranking claims and seeded lists were generally more effective than directive-form injections, although the strength of this difference depended on the target model. To the best of our knowledge, this is among the first controlled studies of competitive rank manipulation in production LLMs where the supplied source context is kept fixed.

cs.IR↗

arXiv2026

OneLatent: Latent Reasoning for Efficient Foundation Recommendation Models

Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their use as the backbone of foundation recommendation models (FRMs). Existing methods enhance recommendations through explicit Chain-of-Thought (CoT) reasoning under a Think-then-Answer paradigm. However, explicit CoT incurs substantial inference overhead by generating lengthy reasoning traces and relies on manually designed templates that struggle to capture diverse, dynamic user interests. We propose OneLatent, an efficient latent reasoning framework that compresses explicit reasoning traces into several learnable latent tokens, enabling Latent-Reason-then-Answer inference without generating verbose traces. OneLatent first introduces Multi-View Adaptive CoT (MV-ACoT), which creates diverse, high-quality teacher-generated supervision by exploring user interests from multiple perspectives and automatically adapting reasoning complexity to each instance. Building on pretrained FRMs, it then uses a three-stage latent-token alignment paradigm to progressively internalize CoT traces into learnable latent tokens. Finally, a multistage curriculum-based post-training strategy activates latent-token reasoning for downstream recommendation tasks. Experiments on an industrial-scale Kuaishou dataset and the public Kuaishou LLM-Rec benchmark show that OneLatent consistently outperforms explicit CoT-based methods and traditional baselines. Compared with the Think and No-Think variants of FRMs, OneLatent improves SID@64 by 17.44% and 9.33%, respectively, while achieving over 17x higher online inference throughput. We further develop a production serving system for scalable, real-time FRM inference. An online A/B test in Kuaishou's local-services advertising scenario shows that deploying OneLatent with this system yields an estimated 9.6% revenue lift over strong online baselines, including OneRec and OneReason.

cs.IR↗