🧠 VLM Reasoning

🎞️ ECCV2026 · 19 paper notes

📌 Same area in other venues: 📷 CVPR2026 (150) · 🔬 ICLR2026 (112) · 💬 ACL2026 (32) · 🧪 ICML2026 (31) · 🤖 AAAI2026 (10) · 🧠 NeurIPS2025 (30)

🔥 Top topics: Reasoning ×15 · Multimodal/VLM ×7 · Reinforcement Learning ×2 · LLM ×2

3D-Layout-R1: Structured Reasoning for Language-Instructed Spatial Editing

3D-Layout-R1 trains models to update JSON 3D scene graphs while reasoning, then refines their outputs with IoU, collision, and format rewards through GRPO, raising Qwen3-8B's mean IoU on perfect-input sorting from 0.850 with Vanilla GRPO to 0.924, although gains and metric trade-offs depend substantially on the task.

A4-Agent: An Agentic Framework for Zero-Shot Affordance Reasoning

A4-Agent assigns interaction imagination, functional-part selection, and pixel-level localization to specialized pretrained models, reaching 70.52 gIoU on ReasonAff without affordance-specific fine-tuning, at approximately 32 seconds per inference for the full pipeline.

AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking

AdaThinking-E optimizes the first token that decides whether to think separately from the subsequent response, using high-entropy exploration followed by low-entropy convergence to reach 26.5 on OCR-Reasoning with its Qwen2.5-based model while matching approximately R-4B's CharXivRQ performance with only 72 output tokens on average.

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution

AnE retrieves real problems around a student's persistent failures, uses teacher hints to elicit the student's own successful reasoning trajectories, and removes external assistance through hint-free reinforcement learning, raising Qwen2.5-VL-7B's average accuracy across eight benchmarks from 51.6% to 61.9% after three evolution rounds.

Attention-based Vision-Language Memory for Spatial Reasoning

VLM² aligns semantic features, predicted geometry, and viewpoint information from monocular video before using sliding-window working memory and fixed-capacity episodic memory to retrieve, fuse, and update scene representations, reaching 68.8 on VSI-Bench with video-only inputs, 7.9 score points above VLM-3R.

Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning

PRP trains a small vision-language model to predict its own and a larger target model's solvability before lengthy reasoning begins; on MathVista, JRL keeps 60% of requests with the draft and achieves 74.2% accuracy at 26.9 seconds average latency, versus 74.1% and 64.8 seconds for target-only inference, with a reported 2.41x speedup.

Benchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset

DAR redefines video emotion understanding through event boundaries, 27 viewer-emotion categories, and contextual causal explanations, while DAR-R1, obtained by applying SFT and GRPO to Qwen2.5-VL-3B, achieves 41.5% segment-count accuracy, 52.3% temporal mIoU, and 28.6% emotion accuracy on its test set.

Beyond Final Answers: CRYSTAL Benchmark for Transparent Multimodal Reasoning Evaluation

CRYSTAL adds checkable reference reasoning chains to 6,372 visual questions, exposes incomplete reasoning behind correct answers through content coverage and relative-order evaluation, and raises Qwen2.5-VL-3B's Match F1 from 0.480 to 0.633 and accuracy from 39.85% to 47.52% with CPR-Curriculum.

Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

TGRL mixes a small number of complete expert reasoning trajectories into the student's RLVR rollout groups, using off-policy probability-ratio correction, adaptive token reweighting, and successful-trajectory filtering to connect observed facts to reasoning, improving Qwen2.5-VL-7B's five-benchmark DAPO average from 58.15 to 60.68 in the 39K-data setting.

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

BrepCoder aligns B-rep surface geometry and topology with Python-like CAD programs, learns shape-to-construction correspondences through reverse engineering, and adapts to completion, correction, and QA, reducing median Chamfer distance from CADCL's 0.972 to 0.464 on DeepCAD reverse engineering, both on the \(10^{-3}\) scale, without leading every task or metric.

CabinSI: Omni-Cabin Spatial Reasoning through Explicit Visual Cognitive Maps

CabinSI evaluates spatial relations and referring localization inside and outside vehicles with 3,758 real-world samples, and converts multi-camera observations into query-relevant top-down cognitive maps that raise Qwen2.5-VL-7B overall RelCabin accuracy from 25.31% to 41.17%, while perception errors and cross-cabin grounding remain substantial bottlenecks.

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

Instead of rewarding every medical reasoning trajectory that reaches the correct answer, CARE checks correctness, learnable difficulty, and whether the rationale independently reproduces the answer, then combines online exploration with difficult-experience replay to raise Hulu-Med-7B's average score on PMC-VQA, MedQA, and MMMU-Med from 63.9 to 65.4 and achieve 86.2% expert-rated valid reasoning on a MedQA subset answered correctly by both compared methods.

Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning

The paper couples three-class shadow segmentation with three-dimensional light-direction estimation through surface-normal-guided feedback, reducing attached-shadow BER from 19.49 for the strongest fine-tuned baseline to 12.92 after three passes, at the cost of richer geometry and class supervision and increased inference time.

Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens

CoVT teaches a VLM to generate continuous visual tokens supervised by segmentation, depth, and semantic experts before answering, replacing purely verbal intermediate observations with 16 latent perceptual states and raising Qwen2.5-VL-7B from 74.5 to 80.0 on CV-Bench, including a 14.0-percentage-point gain on its depth subtask.

Circuit-MLLM: Topological Logic-Guided Latent-Space Visual Reasoning for Circuit Schematic Understanding

Circuit-MLLM organizes training-time circuit knowledge from multiple vision experts into latent-vector supervision ordered by connectivity, enabling Qwen2.5-VL-7B to trace connections without invoking those external experts at inference and raising its average score from 48.28 to 76.20 on a custom benchmark, although exact match on hard connection identification remains only 28.52%.

CL4D: Contrastive Language–4D Pretraining for Vision-Language Reasoning in Dynamic Scenes

CL4D combines per-frame spatial encoding, cross-frame temporal encoding, and language-contrastive learning before connecting its representations to a frozen 7B language model as 4DVLM, achieving 70.32% batch text-to-motion R@1 on HumanOnly, 16.75 percentage points above the strongest reported baseline, while leaving substantial limitations in global retrieval and open-world reasoning evidence.

Clue Matters: Empower Video Reasoning with Brain-Inspired Latent Clue Learning

ClueNet converts video into temporally grounded entity-interaction clues, learns which clues are both relevant to the question and supported by the frames, and uses two-stage supervision to reach 77.6%, 84.3%, and 69.8% on STAR, NExT-QA, and MVBench; optional visual compression accelerates inference but is a different operating point from the highest-accuracy configuration.

CoLT: Teaching Multi-Modal Models to Think with Chain of Latent Thoughts

CoLT compresses multimodal textual reasoning into 3 continuous latent states by default using training-time forward text decoding, backward semantic alignment, and adjacent-state prediction, achieving a 79.1 average over eight benchmarks and approximately 10.1x end-to-end speedup over text CoT on MMStar using a single H200.

Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings

Embed-RL uses retrieval feedback from a frozen embedder to train a separate reasoner and supplies the embedder with image boxes, video keyframes, and text keywords from its chain-of-thought, allowing Embed-RL-4B to reach 68.1 overall on MMEB-V2, 3.6 points above UME-R1-7B.