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arXiv Computer Vision

Attention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation

The paper introduces Difficulty-Aware Chain-of-Thought (DA‑CoT), a training‑free framework that selectively applies chain‑of‑thought prompting in reasoning segmentation tasks based on the entropy of attention maps. Low entropy signals confident, localized grounding, while high entropy indicates uncertainty and the potential benefit of additional reasoning. DA‑CoT also incorporates a Bidirectional Rethinking mechanism to automatically reverse reasoning decisions when initial outputs fail, thereby improving robustness and inference efficiency while achieving state‑of‑the‑art results on Reasoning Segmentation benchmarks.

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arXiv AI
Sep 28

Skip the Talk, Re-Focus on Vision: Latent Reasoning for Reasoning Segmentation in Multimodal Large Language Models

The paper introduces LIRSeg, a method that replaces explicit Chain-of-Thought reasoning in multimodal large language models with a compact set of learnable latent tokens for reasoning segmentation. LIRSeg is trained in two stages—spatial alignment and GRPO—while employing extreme-advantage sampling, decoupled exploration-stability updates, and latent diversity amplification to enhance token informativeness. Experiments show that LIRSeg improves segmentation accuracy and reasoning efficiency, achieving significant gIoU gains over the VisionReasoner baseline and reducing reasoning tokens by about 16×.

By Tianhang Guo, Yulin He, Wei Chen, Wenjuan Zhou, Yuhang Li, Xinbiao Gan
llmsagentscomputer-visionroboticsmultimodalbenchmarkssafety
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arXiv Computation and Language
Sep 25

Evaluating Explanation-Driven Vision-Language Reasoning via Generation Order Interventions

The paper investigates how the order of generating explanations—whether a rationale is produced before or after the answer—affects vision‑language reasoning. By conducting controlled experiments on knowledge‑intensive QA, visual entailment, and compositional grounding tasks, the authors show that larger models are required for reliable rationale‑first generation, while answer‑first generation is less susceptible to format errors. The study concludes that explanation ordering, model scale, pre‑training knowledge, fine‑tuning, and task structure jointly influence prediction accuracy and reasoning faithfulness.

By Siting Liang, Luca Rippe, Omar Adjali, Daniel Sonntag
llmsfine-tuningmultimodalbenchmarks
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arXiv AI
Jul 22

Robust Reasoning Benchmark

arXiv:2604. 08571v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their problem-solving abilities depend on the context and textual formatting.

By Pavel Golikov, Evgenii Opryshko, Gennady Pekhimenko, Mark C. Jeffrey
llmsnlpbenchmarks
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arXiv AI
Oct 31

ClueTracer: Question-to-Vision Clue Tracing for Training-Free Hallucination Suppression in Multimodal Reasoning

ClueTracer is a training‑free, parameter‑free plugin that suppresses hallucinations in multimodal reasoning models by tracing how visual clues propagate from the question through the model’s reasoning pathway. It localizes task‑relevant image patches while ignoring irrelevant regions, thereby addressing the problem of reasoning drift where models over‑focus on question‑irrelevant entities. The method improves performance across various reasoning architectures by an average of 1.21× on reasoning benchmarks.

By Gongli Xi, Kun Wang, Zeming Gao, Huahui Yi, Haolang Lu, Qianshan Wei, Ye Tian, Wendong Wang
multimodalbenchmarkssafety
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