TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
On the map: LLM reasoning → Multimodal reasoning
also in 1 more
Abstract (arXiv)
The frontier of visual reasoning is shifting toward models like OpenAI o3, which can intelligently create and operate tools to transform images for problem-solving, also known as thinking-textitwith-images in chain-of-thought. Yet existing benchmarks fail to fully capture this advanced capability. Even Visual Search, the most common benchmark for current thinking-textitwith-images methods, tests only basic operations such as localization and cropping, offering little insight into more complex, dynamic, and tool-dependent reasoning. We introduce TIR-Bench, a comprehensive benchmark for evaluating agentic thinking-with-images across 13 diverse tasks, each requiring novel tool use for image processing and manipulation in chain-of-thought. We evaluate 22 multimodal large language models (MLLMs), from leading open-sourced and proprietary models to those with explicit tool-use augmentation. Results show that TIR-Bench is universally challenging, and strong performance requires genuine thinking-with-images capabilities. Finally, we present a pilot study comparing direct versus agentic fine-tuning.
Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 31 of its 33 references that are in the map. First posted on arXiv 3 Nov 2025.
Builds on
31 of 33 references.
Closest ancestors · 13
Same direction, newest first
- 2025Reinforced Visual Perception with Tools1 Sep
- 2025Thyme: Think Beyond Images15 Aug
- 2025Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers30 Jun
- 2025Play to Generalize: Learning to Reason Through Game Play9 Jun
- 2025Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning21 May
- 2025Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs21 May
- 2025ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness10 Apr
- 2024We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?1 Jul
- 2024Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models13 Jun
- 2024CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning6 Feb
- 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts3 Oct
- 2023MMBench: Is Your Multi-modal Model an All-around Player?12 Jul
1 more
Other references · 18
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2025Qwen2.5-VL Technical Reportcited by 5,418
- 2024GPT-4o System Cardcited by 4,467
- 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
- 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,669
- 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
- 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
- 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMscited by 822
- 2024MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?cited by 573
- 2023V*: Guided Visual Search as a Core Mechanism in Multimodal LLMscited by 459
- 2021Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoningcited by 376
6 more
- 2023SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Modelscited by 246
- 2024CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMscited by 229
- 2024Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Modelscited by 151
- 2025Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Searchcited by 116
- 2025PyVision: Agentic Vision with Dynamic Toolingcited by 71
- 2024EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmarkcited by 14
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Citing papers per year: 2025: 3; 2026: 31.
34 papers.
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- 2026VC-Tooler: Learning Compositional and Adaptive Visual Tool Use3 Aug 2026
- 2026Beacon: Knowing When and How to Perform Agentic Visual Reasoning30 Jul 2026
- 2026LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger30 Jul 2026
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- 2026GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces5 Apr 2026
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- 2026LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledgercited by 3
- 2026Thinking With Tools, Not With Pixels: Tool Calls as Text Scaffolds for Visual Reasoningcited by 2
- 2026Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agentscited by 1
- 2026Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarioscited by 1
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- 2026V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoningcited by 0
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- 2026LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger30 Jul 2026
- 2026InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward26 May 2026
- 2026V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning11 May 2026
- 2026Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios15 Apr 2026
- 2026GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces5 Apr 2026
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- 2026VistaHop: Benchmarking Long-Horizon Visual DeepSearchcited by 1
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- 2026VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes25 May 2026
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- 2026AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarioscited by 22
- 2026Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gainscited by 4
- 2026SenseNova-U1.5: Towards Native Unified Visual Intelligencecited by 3
- 2026Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolutioncited by 2
- 2026SPyCE: Skill-Policy Co-evolution for Multimodal Agentscited by 2
- 2026From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Modelscited by 0
- 2026SkillRubric: Co-Evolving Actor Guidance and Evaluator Rubrics for Multimodal Agentscited by 0
- 2026Visual Parallel Search: Learning to Search High-Resolution Images with Parallel Tile Inspection and Adaptive Zoomcited by 0
- 2026Visual Parallel Search: Learning to Search High-Resolution Images with Parallel Tile Inspection and Adaptive Zoom29 Sep 2026
- 2026SkillRubric: Co-Evolving Actor Guidance and Evaluator Rubrics for Multimodal Agents28 Sep 2026
- 2026SenseNova-U1.5: Towards Native Unified Visual Intelligence10 Sep 2026
- 2026From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models10 Sep 2026
- 2026SPyCE: Skill-Policy Co-evolution for Multimodal Agents15 Jul 2026
- 2026Do Multimodal Agents Really Benefit from Tool Use? A Systematic Study of Capability Gains1 Jun 2026
- 2026Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution9 May 2026
- 2026XSkill: Continual Learning from Experience and Skills in Multimodal Agents12 Mar 2026
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