CMR-SPB: Cross-Modal Multi-Hop Reasoning over Text, Image, and Speech with Path Balance

• URL: http://arxiv.org/abs/2508.16198v1
• Date: Fri, 22 Aug 2025 08:17:31 GMT
• Title: CMR-SPB: Cross-Modal Multi-Hop Reasoning over Text, Image, and Speech with Path Balance
• Authors: Seunghee Kim, Ingyu Bang, Seokgyu Jang, Changhyeon Kim, Sanghwan Bae, Jihun Choi, Richeng Xuan, Taeuk Kim,
• Abstract summary: Cross-modal multi-hop reasoning (CMR) is a valuable yet underexplored capability of multimodal large language models (MLLMs)<n>We argue that existing benchmarks for evaluating this ability have critical shortcomings.<n>We introduce a novel benchmark -- Cross-Modal Multi-Hop Reasoning over Text, Image and Speech with Path Balance (CMR-SPB)
• Score: 10.843417240658992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-modal multi-hop reasoning (CMR) is a valuable yet underexplored capability of multimodal large language models (MLLMs), entailing the integration of information from multiple modalities to produce a coherent output for a given context. We argue that existing benchmarks for evaluating this ability have critical shortcomings: (1) they largely overlook the speech modality, and (2) they exhibit heavily biased reasoning path distributions, which can severely undermine fair evaluation. To address these limitations, we introduce a novel benchmark -- Cross-Modal Multi-Hop Reasoning over Text, Image and Speech with Path Balance (CMR-SPB) -- designed to assess tri-modal multi-hop reasoning while ensuring both unbiased and diverse reasoning paths. Our experiments with the new dataset reveal consistent model failures in specific reasoning sequences and show that biased benchmarks risk misrepresenting model performance. Finally, based on our extensive analysis, we propose a new ECV (Extract, Connect, Verify) prompting technique that effectively mitigates the performance gap across different reasoning paths. Overall, we call for more careful evaluation in CMR to advance the development of robust multimodal AI.

Related papers

• Multimodal Fact-Level Attribution for Verifiable Reasoning [80.60864342985748]
  Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation.<n>Existing multimodal grounding benchmarks and evaluation methods fail to assess attribution in complex multimodal reasoning.<n>We introduce MuRGAt, a benchmark for evaluating fact-level multimodal attribution in settings that require reasoning beyond direct observation.
  arXiv  Detail & Related papers  (2026-02-12T03:10:02Z)
• MMhops-R1: Multimodal Multi-hop Reasoning [89.68086555694084]
  We introduce MMhops, a novel benchmark designed to evaluate and foster multi-modal multi-hop reasoning.<n> MMhops dataset comprises two challenging task formats, Bridging and Comparison.<n>We propose MMhops-R1, a novel multi-modal Retrieval-Augmented Generation framework for dynamic reasoning.
  arXiv  Detail & Related papers  (2025-12-15T17:29:02Z)
• Multi-Path Collaborative Reasoning via Reinforcement Learning [54.8518809800168]
  Chain-of-Thought (CoT) reasoning has significantly advanced the problem-solving capabilities of Large Language Models (LLMs)<n>Recent methods attempt to address this by generating soft abstract tokens to enable reasoning in a continuous semantic space.<n>We propose Multi-Path Perception Policy Optimization (M3PO), a novel reinforcement learning framework that explicitly injects collective insights into the reasoning process.
  arXiv  Detail & Related papers  (2025-12-01T10:05:46Z)
• RPTS: Tree-Structured Reasoning Process Scoring for Faithful Multimodal Evaluation [5.080252830507515]
  Reasoning Process Tree Score (RPTS) is a tree structure-based metric to assess reasoning processes.<n>To validate RPTS in real-world multimodal scenarios, we construct a new benchmark, RPTS-Eval, comprising 374 images and 390 reasoning instances.
  arXiv  Detail & Related papers  (2025-11-10T09:48:07Z)
• KnowDR-REC: A Benchmark for Referring Expression Comprehension with Real-World Knowledge [1.5833270109954136]
  We propose KnowDR-REC, built upon real-world knowledge, requiring fine-grained multimodal reasoning across text and image.<n>We evaluate 16 state-of-the-art multimodal models on KnowDR-REC, with experimental results showing that existing MLLMs still struggle with knowledge-driven visual grounding tasks.
  arXiv  Detail & Related papers  (2025-08-12T19:43:44Z)
• Coherent Multimodal Reasoning with Iterative Self-Evaluation for Vision-Language Models [4.064135211977999]
  Large language models (LLMs) and vision-language models (LVLMs) struggle with complex, multi-step, cross-modal common sense reasoning tasks.<n>We propose the Coherent Multimodal Reasoning Framework (CMRF), a novel approach that enhances LVLMs' common sense reasoning capabilities.<n>CMRF mimics human problem-solving by decomposing complex queries, generating step-by-step inferences, and self-correcting errors.
  arXiv  Detail & Related papers  (2025-08-04T20:33:58Z)
• Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning [71.3533541927459]
  We propose a novel data selection paradigm termed Activation Reasoning Potential (RAP)<n>RAP identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning.<n>Our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%.
  arXiv  Detail & Related papers  (2025-06-05T08:40:24Z)
• Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models [45.15161506154318]
  Infi-MMR is a framework to systematically unlock the reasoning potential of Multimodal Small Language Models.<n>The first phase, Foundational Reasoning Activation, leverages high-quality textual reasoning datasets to activate and strengthen the model's logical reasoning capabilities.<n>The second phase, Cross-Modal Reasoning Adaptation, utilizes caption-augmented multimodal data to facilitate the progressive transfer of reasoning skills to multimodal contexts.<n>The third phase, Multimodal Reasoning Enhancement, employs curated, caption-free multimodal data to mitigate linguistic biases and promote robust cross-modal reasoning.
  arXiv  Detail & Related papers  (2025-05-29T04:51:56Z)
• MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation [56.87891213797931]
  We present MTR-Bench for Large Language Models' Multi-Turn Reasoning evaluation.<n>Comprising 4 classes, 40 tasks, and 3600 instances, MTR-Bench covers diverse reasoning capabilities.<n>MTR-Bench features fully-automated framework spanning both dataset constructions and model evaluations.
  arXiv  Detail & Related papers  (2025-05-21T17:59:12Z)
• RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning [60.84707424369494]
  Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models (LLMs) on complex tasks.<n>We introduce the Reasoning Boundary Framework++ (RBF++), a framework for evaluating and optimizing measurable boundaries of CoT capability.
  arXiv  Detail & Related papers  (2025-05-19T16:25:55Z)
• R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization [26.757458496178437]
  We introduce R1-Onevision, a multimodal reasoning model designed to bridge the gap between visual perception and deep reasoning.<n>We construct the R1-Onevision dataset which provides detailed, step-by-step multimodal reasoning annotations across diverse domains.<n>We further develop the R1-Onevision model through supervised fine-tuning and reinforcement learning to cultivate advanced reasoning.<n> Experimental results show that R1-Onevision achieves state-of-the-art performance, outperforming models such as GPT-4o and Qwen2.5-VL.
  arXiv  Detail & Related papers  (2025-03-13T17:56:05Z)
• Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models [26.17300490736624]
  Multimodal Large Language Models (MLLMs) are predominantly trained and tested on consistent visual-textual inputs.<n>We propose the Multimodal Inconsistency Reasoning benchmark to assess MLLMs' ability to detect and reason about semantic mismatches.<n>We evaluate six state-of-the-art MLLMs, showing that models with dedicated multimodal reasoning capabilities, such as o1, substantially outperform their counterparts.
  arXiv  Detail & Related papers  (2025-02-22T01:52:37Z)
• Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark [73.27104042215207]
  We introduce EMMA, a benchmark targeting organic multimodal reasoning across mathematics, physics, chemistry, and coding.<n>EMMA tasks demand advanced cross-modal reasoning that cannot be addressed by reasoning independently in each modality.<n>Our evaluation of state-of-the-art MLLMs on EMMA reveals significant limitations in handling complex multimodal and multi-step reasoning tasks.
  arXiv  Detail & Related papers  (2025-01-09T18:55:52Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.