# HANIA: Planner-Guided Multimodal Graph Evidence Selection for Grounded Question Answering

arXiv: https://arxiv.org/abs/2608.29088 · HTML: https://mlsanity.com/paper/2608.29088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 23 of 23)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
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- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text](https://mlsanity.com/paper/2210.02928/) (2022)
- [UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph](https://mlsanity.com/paper/2212.00959/) (2022)
- [KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning](https://mlsanity.com/paper/2401.12863/) (2024)
- [VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents](https://mlsanity.com/paper/2504.09795/) (2025)
- [Knowledge Graph-Guided Retrieval Augmented Generation](https://mlsanity.com/paper/2502.06864/) (2025)
- [Multimodal Reasoning with Multimodal Knowledge Graph](https://mlsanity.com/paper/2406.02030/) (2024)
- [VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.10704/) (2024)
- [Cantor: Inspiring Multimodal Chain-of-Thought of MLLM](https://mlsanity.com/paper/2404.16033/) (2024)
- [LogiCoT: Logical Chain-of-Thought Instruction-Tuning](https://mlsanity.com/paper/2305.12147/) (2023)
- [SCITUNE: Aligning Large Language Models with Human-Curated Scientific Multimodal Instructions](https://mlsanity.com/paper/2307.01139/) (2023)
- [Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning](https://mlsanity.com/paper/2503.12972/) (2025)
- [GLiREL -- Generalist Model for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2501.03172/) (2025)
- [Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering](https://mlsanity.com/paper/2403.19167/) (2024)
- [EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models](https://mlsanity.com/paper/2503.15369/) (2025)
- [KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering](https://mlsanity.com/paper/2509.04716/) (2025)
- [Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering](https://mlsanity.com/paper/2506.00806/) (2025)
- [Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset](https://mlsanity.com/paper/2412.02788/) (2024)
- [Leveraging LLM-GNN Integration for Open-World Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2604.13979/) (2026)
- [GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG](https://mlsanity.com/paper/2608.15056/) (2026)

## Abstract (arXiv)

Multimodal question answering remains sensitive to noisy, incomplete, and weakly grounded evidence. Long unstructured contexts can introduce redundancy and encourage unsupported generation, while flat retrieval may overlook relations needed for multi-step reasoning. We present HANIA, a planner-guided multimodal graph framework for evidence-grounded question answering. HANIA processes the supplied image and text using a frozen vision-language model to extract concise question-relevant visual evidence with explicit abstention. It then constructs an input-grounded multimodal graph and applies a two-group finite-state planner to coordinate descriptive and relational evidence. Coverage-aware pruning retains a compact evidence set based on relevance, graph confidence, concept coverage, and modality diversity. The selected passages, visual statements, and graph triples are provided to a frozen instruction-tuned decoder. We evaluate HANIA on ScienceQA using answer accuracy, evidence-filtering quality, evidence-budget sensitivity, and efficiency. The results show that structured evidence planning and compact graph-guided retrieval can support competitive multimodal question answering without target-dataset fine-tuning or iterative retrieval. The code is available at https://github.com/Zafar-southeast/HANIA.