Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence

• URL: http://arxiv.org/abs/2608.07987v1
• Date: Sat, 08 Aug 2026 07:46:04 GMT
• Title: Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence
• Abstract summary: Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks.<n>However, their open-ended reasoning process is prone to decision errors and error accumulation, leading to instability in answer quality.<n>We propose an advantage-guided gating framework that dynamically intervenes in and corrects deviations during the reasoning process.
• Score: 70.8951332710039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks. However, their open-ended reasoning process is prone to decision errors and error accumulation, leading to instability in answer quality. To address this, we propose an advantage-guided gating framework that dynamically intervenes in and corrects deviations during the reasoning process. Specifically, we model step-by-step reasoning as a finite-horizon decision process and introduce Monte Carlo value evaluation on the reasoning tree to provide intermediate supervision signals. The framework includes Step-Advantage Gate and Trajectory-Advantage Gate, which dynamically select high-value reasoning steps and high-quality complete reasoning trajectories, respectively. During training, we perform supervised learning for the gates using reasoning trees generated via multi-branch sampling, and combine shared-parameter initialization with task-specific heads to achieve cross-task robustness and diversity. During inference, the model greedily selects high-value prefix reasoning steps while choosing the optimal reasoning head based on the problem type, thereby significantly improving the accuracy of the final answer. Furthermore, we constructed the Reasoning-Tree-160k dataset and performed two-stage learning on it. Extensive experiments demonstrate that this advantage-guided gating framework effectively enhances the performance of benchmark MLLMs in visual-based spatial understanding and reasoning tasks. The code is open to the public for research: https://github.com/LingLin-ll/Advantage-Guided-Gate.

Related papers

• LaRec: Unleashing LLM-based Latent Reasoning for Generative Recommendation [27.643675369277858]
  latent reasoning aims to balance efficiency by thinking within a continuous latent space.<n>$LaRec$ is an efficient generative recommendation framework designed to unleash the potential of latent reasoning.<n> Experiments on multiple datasets show that $LaRec$ significantly outperforms existing baselines with comparable efficiency.
  arXiv  Detail & Related papers  (2026-07-27T16:14:57Z)
• Heuristic Classification of Thoughts Prompting (HCoT): Integrating Expert System Heuristics for Structured Reasoning into Large Language Models [9.545957160908115]
  Heuristic- Classification-of-Thoughts prompting schema (HCoT)<n>Heuristic- Classification-of-Thoughts prompting schema (HCoT)<n>Heuristic- Classification-of-Thoughts prompting schema (HCoT)
  arXiv  Detail & Related papers  (2026-04-14T07:24:08Z)
• Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning [7.8668388431725695]
  Large language models (LLMs) achieve strong reasoning performance through chain-of-thought (CoT) reasoning.<n>Recent self-consistency-based approaches further improve accuracy but require sampling and aggregating multiple reasoning trajectories.<n>This paper introduces a confidence-aware decision framework that analyzes a single completed reasoning trajectory to adaptively select between single-path and multi-path reasoning.
  arXiv  Detail & Related papers  (2026-03-09T22:34:06Z)
• Reinforced Efficient Reasoning via Semantically Diverse Exploration [73.41112984160992]
  Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs)<n>We propose reinforced efficient reasoning via semantically diverse explorations, i.e., ROSE, for LLMs.<n>Our method incorporates a semantic-entropy-based branching strategy and an $varepsilon$-exploration mechanism.
  arXiv  Detail & Related papers  (2026-01-08T15:56:44Z)
• From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization [62.07990937720985]
  Dimension-level Reward Model (DRM) is a new supervision framework for Large Language Models.<n>DRM evaluates the quality of a reasoning process along three fundamental, complementary, and interpretable dimensions.<n> Experimental results show that DRM provides effective supervision signals, guides the optimization of LLMs and enhances their reasoning ability.
  arXiv  Detail & Related papers  (2025-10-13T14:29:15Z)
• Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning [22.177866778776814]
  We propose a two-stage framework designed to improve both high-level planning and fine-grained Chain-of-Thought (CoT) reasoning.<n>In the first stage, we leverage advanced LLMs to distill CoT into compact high-level guidance, which is then used for supervised fine-tuning.<n>In the second stage, we introduce a guidance-aware RL method that jointly optimize the final output and the quality of high-level guidance.
  arXiv  Detail & Related papers  (2025-10-02T09:28:13Z)
• PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
  PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
  arXiv  Detail & Related papers  (2025-05-29T17:55:49Z)
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models [49.61246073215651]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks.<n>Recent advancements in OpenAI o1 and DeepSeek-R1 have further improved performance in System-2 reasoning domains.<n>However, they also introduce significant computational overhead due to verbose and redundant outputs.
  arXiv  Detail & Related papers  (2025-03-20T17:59:38Z)
• Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing [61.98556945939045]
  We propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework.
  arXiv  Detail & Related papers  (2024-02-01T15:18:33Z)

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.