AAAI2026 VLM Reasoning AI paper notes paper summaries Reasoning Multimodal/VLM Agents LLM Layout & Composition Adversarial Robustness

🧠 VLM Reasoning¶

🤖 AAAI2026 · 10 paper notes

📌 Same area in other venues: 📷 CVPR2026 (144) · 🧪 ICML2026 (20) · 💬 ACL2026 (31) · 🔬 ICLR2026 (23) · 🧠 NeurIPS2025 (30) · 📹 ICCV2025 (13)

🔥 Top topics: Reasoning ×10 · Multimodal/VLM ×6

AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs: Inspired by the dual-mode human cognitive process of verbal abduction and pictorial imagination, this paper proposes AbductiveMLLM, which enhances visual abductive reasoning in MLLMs via two collaborative components — a Reasoner (causal contrastive learning for hypothesis selection) and an Imaginer (diffusion-model-based pictorial reasoning) — achieving state-of-the-art performance on the VAR and YouCookII benchmarks.
AStar: Boosting Multimodal Reasoning with Automated Structured Thinking: This paper proposes AStar, a training-free multimodal reasoning paradigm that constructs a library of high-level "thought card" reasoning templates from 500 seed samples. At inference time, the most suitable templates are adaptively retrieved to guide structured reasoning in MLLMs. A 7B model achieves 53.9% accuracy on MathVerse (surpassing GPT-4o's 50.2%), requiring only 50 minutes of preprocessing and no model training.
Concept-RuleNet: Grounded Multi-Agent Neurosymbolic Reasoning in Vision Language Models: This paper proposes Concept-RuleNet, a three-agent collaborative neurosymbolic reasoning framework that conditions symbol generation and rule construction on visual concepts extracted from training images. It addresses the symbol hallucination and non-representativeness issues of existing methods (e.g., Symbol-LLM) that rely solely on class labels, achieving an average accuracy improvement of ~5% across 5 OOD benchmarks and reducing hallucinated symbols by up to 50%.
CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models: This paper introduces CrossVid, the first comprehensive benchmark for systematically evaluating the Cross-Video Reasoning (CVR) capabilities of multimodal large language models (MLLMs). CrossVid encompasses 10 tasks across 4 dimensions, 5,331 videos, and 9,015 QA pairs. Experiments reveal that the current best-performing model, Gemini-2.5-Pro, achieves only 50.4% accuracy, far below the human performance of 89.2%.
FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation: This paper introduces FinMMDocR, a bilingual multimodal reasoning benchmark targeting real-world financial scenarios. It comprises 1,200 expert-annotated numerical reasoning questions spanning 12 implicit financial scenario types, 9 categories of long documents (averaging 50.8 pages), and reasoning chains averaging 11 steps. The strongest MLLM (o4-mini-high) achieves only 58% accuracy, exposing critical deficiencies of existing models in complex financial reasoning.
Graph-of-Mark: Promote Spatial Reasoning in Multimodal Language Models with Graph-Based Visual Prompting: This paper proposes Graph-of-Mark (GoM), a training-free pixel-level visual prompting method that explicitly encodes inter-object spatial relationships by overlaying a depth-aware scene graph (comprising nodes and directed edges) directly onto input images, achieving up to an 11 percentage point improvement in zero-shot spatial reasoning accuracy for multimodal language models on VQA and grounding tasks.
Leveraging Textual Compositional Reasoning for Robust Change Captioning: This paper proposes CORTEX, a framework that introduces VLM-generated compositional reasoning text as explicit cues, combined with an Image-Text Dual Alignment (ITDA) module, to enhance purely visual change captioning methods in understanding structured semantics such as object relationships and spatial configurations.
SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios: SToLa proposes the first Mixture-of-Experts (MoE)-based touch-language framework, which employs a dynamic routing mechanism to manage the modality gap between tactile and linguistic inputs. The work also introduces TactileBench, an open-ended tactile commonsense reasoning dataset covering 8 physical properties and 4 interaction characteristics. With only 7B parameters, SToLa achieves state-of-the-art performance on the PhysiCLeAR benchmark, surpassing the 13B Octopi model.
Tri-Bench: Stress-Testing VLM Reliability on Spatial Reasoning under Camera Tilt and Object Interference: Tri-Bench is a compact benchmark comprising 400 real-world photographs of triangles. By systematically controlling two factors — camera pose (planar vs. tilted) and object interference — it evaluates the spatial geometric reasoning capabilities of four leading VLMs. The results reveal that models default to 2D image-plane cues rather than genuine 3D geometry, even when explicit reference-frame guardrails are provided in the prompt, with accuracy on minority-class shapes dropping to near 0%.
Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection: This paper proposes FLoReNce, a framework that models humorous meme understanding as a closed-loop control system. Through a feedback loop consisting of a Judge agent, a PID controller, and a non-parametric knowledge base, the system retrieves similar past experiences at inference time to modulate prompts, enabling a frozen VLM to perform adaptive reasoning without fine-tuning, substantially improving both prediction accuracy and explanation quality.