Adaptive Dense Evidence Refinement for Video Relational Reasoning for VRR-QA Challenge

• URL: http://arxiv.org/abs/2606.01104v1
• Date: Sun, 31 May 2026 08:45:06 GMT
• Title: Adaptive Dense Evidence Refinement for Video Relational Reasoning for VRR-QA Challenge
• Abstract summary: We present an inference-only system built around adaptive test-time computation.<n>The system first answers each question with a direct video-language model pass, then uses multiple lightweight views to find unstable questions.<n>On the test split, the final system obtains 90.07 average accuracy and 87.81 macro average accuracy.
• Score: 15.724849094439987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: VRR-QA evaluates whether video-language systems can infer spatial, temporal, viewpoint, depth, and visibility relations that are not always resolved by a single frame. We present an inference-only system built around adaptive test-time computation. The system first answers each question with a direct video-language model pass, then uses multiple lightweight views to find unstable questions. Only these difficult questions are routed to a high-budget dense evidence module that constructs timestamped frame observations, relation-specific probes, candidate verification, and conservative temporal aggregation. This design separates two problems that are often confused in video question answering: finding plausible alternative answers and deciding when a current answer should actually be changed. On the test split, the final system obtains 90.07 average accuracy and 87.81 macro average accuracy. The report focuses on the final test system and the implementation settings required to reproduce the adaptive dense verifier.

Related papers

• VisualFLIP: Do Predictions Depend on Task-Critical Visual Evidence in Multimodal Reasoning? [79.44137632338062]
  When a multimodal large language model answers a visual reasoning question correctly, is the prediction actually supported by the task-critical visual evidence?<n>We introduce VisualFLIP, a paired benchmark with 1,374 images arranged as same-question pairs across cardinality, attribute, spatial, and logic tasks.<n>We evaluate 24 MLLMs with pair accuracy, which requires solving both sides of a pair, and Collapse Rate (CR), which measures how often a model that solves at least one side repeats the same non-empty answer for both images.
  arXiv  Detail & Related papers  (2026-06-05T22:06:07Z)
• Answer Self-Consistency with Margin-Triggered Question Re-Arbitration for the CVPR 2026 VidLLMs Challenge [0.0]
  We present our solution for Track 2 of the CVPR 2026 VidLLMs Challenge.<n>This track evaluates visual reasoning in videos, where models must infer relations that are not always explicitly visible.<n>We propose ASC, a training-free test-time reasoning framework built on a multimodal reasoning model.
  arXiv  Detail & Related papers  (2026-06-03T00:51:55Z)
• Question-Aware Evidence Ledgers for Video Relational Reasoning [49.15616982043187]
  We present a test-time reasoning pipeline built around a strong GPT-5.5 video QA solver and a set of question-aware evidence ledgers.<n>The pipeline achieves 92.95% overall accuracy and 93.79% macro accuracy on the challenge test split.
  arXiv  Detail & Related papers  (2026-06-01T17:18:03Z)
• Temporal Evidence Routing with Structured Visual Evidence for TimeLogicQA [15.724849094439987]
  TimeLogicQA evaluates whether video question answering systems can reason over temporal relations.<n>We address this task with a visual evidence routing pipeline that separates perception from symbolic temporal reasoning.
  arXiv  Detail & Related papers  (2026-05-31T08:49:04Z)
• Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA [14.475086992410276]
  We study the problem of preference elicitation in multi-turn question answering by decomposing the problem into two components.<n>We show, using the PACIFIC benchmark, that supervised fine-tuning rapidly improves the clarification policy, however, final answer accuracy remains substantially lower even when the model takes the correct action.
  arXiv  Detail & Related papers  (2026-05-24T18:06:09Z)
• VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification [73.02304272829785]
  VideoBench is a hierarchical benchmark for long-video answering that rigorously verifies evidence.<n>It comprises 500 manually annotated questions across 13 domains, paired with temporal intervals and spatial bounding boxes as evidence.<n>Experiments show that even GeminiPro-3-Pro correctly answers fewer than 17% of questions under the standard end-to-end QA setting.<n>Results expose a significant gap between surface-level answer correctness and genuine evidence-based reasoning.
  arXiv  Detail & Related papers  (2026-04-02T03:29:43Z)
• On the Consistency of Video Large Language Models in Temporal Comprehension [57.985769348320616]
  Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments.<n>We conduct a study on prediction consistency -- a key indicator for robustness and trustworthiness of temporal grounding.
  arXiv  Detail & Related papers  (2024-11-20T00:47:17Z)
• Locate before Answering: Answer Guided Question Localization for Video Question Answering [70.38700123685143]
  LocAns integrates a question locator and an answer predictor into an end-to-end model. It achieves state-of-the-art performance on two modern long-term VideoQA datasets.
  arXiv  Detail & Related papers  (2022-10-05T08:19:16Z)
• Frame-Subtitle Self-Supervision for Multi-Modal Video Question Answering [73.11017833431313]
  Multi-modal video question answering aims to predict correct answer and localize the temporal boundary relevant to the question. We devise a weakly supervised question grounding (WSQG) setting, where only QA annotations are used. We transform the correspondence between frames and subtitles to Frame-Subtitle (FS) self-supervision, which helps to optimize the temporal attention scores.
  arXiv  Detail & Related papers  (2022-09-08T07:20:51Z)
• Fill-in-the-blank as a Challenging Video Understanding Evaluation Framework [19.031957183047048]
  We introduce a novel dataset consisting of 28,000 videos and fill-in-the-blank tests. We show that both a multimodal model and a strong language model have a large gap with human performance.
  arXiv  Detail & Related papers  (2021-04-09T04:00:10Z)
• Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction [46.38201136570501]
  We present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. Our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA.
  arXiv  Detail & Related papers  (2020-11-26T05:48:55Z)

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.