insight-v-plus-plus-visual-reasoning

name	insight-v-plus-plus-visual-reasoning
title	Insight-V++: Towards Generalized Spatial-Temporal Visual Reasoning
version	0.0.3
engine	skillxiv-v0.0.3-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2603.18118
keywords	["Visual Reasoning","Spatial-Temporal","ST-GRPO","J-GRPO","Reinforcement Learning"]
description	Extend visual reasoning to spatial-temporal sequences via two-agent reasoning+summary pipeline with ST-GRPO (temporal alignment) and J-GRPO (evaluative robustness) algorithms. Achieves +8.1% on image and +6.9% on video benchmarks through autonomous data generation and self-evolving rewards; enables continuous improvement loops for visual reasoning without human annotation.

Component ID

Long-chain visual reasoning framework with spatial-temporal generalization.

Motivation

Image-centric visual reasoning models like Insight-V lack structured support for sequences, and scaling visual reasoning requires expensive human annotation. A unified framework supporting both images and temporal sequences with self-directed improvement would unlock scalable long-chain reasoning.

The Modification

Unified Multi-Agent Architecture

Replace single-model reasoning with a two-role decomposition: reasoning agent (generates long chains) and summary agent (integrates findings).

# Spatial-temporal reasoning architecture: reason over frames, then summarize
class InsightVPlusPlusPipeline:
    """
    Extends image-centric Insight-V into generalized spatial-temporal architecture.
    Multi-granularity assessment synthesizes structured reasoning trajectories autonomously.
    """
    def __init__(self, image_encoder, reasoning_agent, summary_agent):
        self.image_encoder = image_encoder
        self.reasoning_agent = reasoning_agent  # Handles temporal alignment
        self.summary_agent = summary_agent      # Integrative evaluation

    def forward_long_chain(self, frames_or_image, reasoning_depth=10):
        """
        Systematic evolution: image-centric → spatial-temporal sequence reasoning.
        Progressive data generation pipeline with multi-granularity assessment.
        """
        embeddings = [self.image_encoder(f) for f in frames_or_image]

        # Reasoning chain with temporal alignment feedback
        reasoning_trajectory = []
        for step in range(reasoning_depth):
            state = self.reasoning_agent(embeddings, reasoning_trajectory)
            reasoning_trajectory.append(state)

        
        summary = .summary_agent(reasoning_trajectory)
         summary, reasoning_trajectory