arxiv: 2605.07575 · v2 · submitted 2026-05-08 · 💻 cs.CV · cs.AI

Recognition: 1 theorem link

· Lean Theorem

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding

Ke Ma , Jiaqi Tang , Bin Guo , Xueting Han , Ruonan Xu , Qingfeng He , Ziheng Wang , Xu Wang

show 3 more authors

Qifeng Chen Zhiwen Yu Yunhao Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-12 02:49 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords scene graphstreaming videoproactive responseVideo-LLMresponse timingretrieval augmentedvideo understanding

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The pith

Response-G1 aligns video evidence with query conditions through explicit scene graphs to decide response timing in streaming video.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Response-G1 as a framework that creates explicit alignment between accumulated video evidence and a query's response conditions by representing both in scene graphs. It processes streaming video in three stages without any fine-tuning: generating query-guided scene graphs from incoming clips, retrieving the most relevant past graphs from memory, and prompting a decision to stay silent or respond on each frame. A reader would care because prior Video-LLM methods handle evidence implicitly and without reference to the query, which leads to unreliable timing in proactive settings, whereas the graph structure makes the decisions both more accurate and easier to interpret.

Core claim

Response-G1 establishes explicit, structured alignment between the accumulated video evidence and the query's expected response conditions via scene graphs. The framework operates in three fine-tuning-free stages: (1) online query-guided scene graph generation from streaming clips; (2) memory-based retrieval of the most semantically relevant historical scene graphs; and (3) retrieval-augmented trigger prompting for per-frame silence/response decisions. By grounding both evidence and conditions in a shared graph representation, Response-G1 achieves more interpretable and accurate response timing decisions.

What carries the argument

Explicit scene graph modeling that grounds both video evidence and query response conditions in a shared graph representation to enable memory retrieval and trigger prompting.

If this is right

The method shows higher accuracy than prior approaches on both proactive and reactive streaming video tasks.
Decisions become more interpretable because they rest on explicit graph alignments rather than hidden representations.
No fine-tuning is required because the three stages rely on generation, retrieval, and prompting.
Performance gains hold across established benchmarks for streaming video understanding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If reliable online scene graph generators become available for new domains, the same three-stage structure could apply to other timing-sensitive multimodal tasks such as live captioning or robotic monitoring.
The memory retrieval step suggests a path toward longer-term video memory in Video-LLMs beyond single-clip processing.
Replacing the scene graph generator with a stronger model would directly test whether the claimed gains scale with graph quality.

Load-bearing premise

Online query-guided scene graph generation from streaming clips works reliably and memory retrieval of historical graphs consistently surfaces relevant evidence for the timing decision.

What would settle it

An experiment in which query-guided scene graph generation produces noisy or incomplete graphs on a benchmark clip set, or memory retrieval returns irrelevant graphs, resulting in response timing accuracy no better than implicit baselines.

Figures

Figures reproduced from arXiv: 2605.07575 by Bin Guo, Jiaqi Tang, Ke Ma, Qifeng Chen, Qingfeng He, Ruonan Xu, Xueting Han, Xu Wang, Yunhao Liu, Zhiwen Yu, Ziheng Wang.

**Figure 2.** Figure 2: Overview of the Response-G1 framework. The system processes streaming video through three core components: (1) Online Query-Guided Scene Graph Generation, (2) Memory-Based Scene Graph Retrieval, and (3) Retrieval-Augmented Streaming Pipeline for proactive decision-making. the observed evidence in F1:t satisfies the response conditions implicit in Qtask , outputting a proactive action rt ∈ R = {silence, res… view at source ↗

**Figure 3.** Figure 3: Performance of different K Values for top-K [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Case study of Response-G1 on the CRR subtask in OVO-Bench. The user query describes a target object (“the boy wearing a red T-shirt”) and a relation (“talking with others”). The results show that at time “18:51”, Response-G1 accurately retrieves query-relevant scene graphs (i.e., evidence) and triggers a response, whereas the baselines fail to respond throughout the video stream. Strategies Proactive Subta… view at source ↗

**Figure 5.** Figure 5: Prompt template for query-guided online scene graph generation. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

**Figure 6.** Figure 6: Prompt template for query parsing [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Prompt template for the original trigger on the CRR subtask in OVO-Bench. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: Prompt template for Response-G1’s trigger on the CRR subtask in OVO-Bench. {query} Is it the right time to output \"{ground_truth_output}\"? You can only answer yes or no. Prompts [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt template for the original trigger on the PO subtask in StreamingBench. [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

**Figure 10.** Figure 10: Prompt template for Response-G1’s trigger on the PO subtask in StreamingBench [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗

read the original abstract

Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall short due to their implicit, query-agnostic modeling of visual evidence. We introduce Response-G1, a novel framework that establishes explicit, structured alignment between the accumulated video evidence and the query's expected response conditions via scene graphs. The framework operates in three fine-tuning-free stages: (1) online query-guided scene graph generation from streaming clips; (2) memory-based retrieval of the most semantically relevant historical scene graphs; and (3) retrieval-augmented trigger prompting for per-frame "silence/response" decisions. By grounding both evidence and conditions in a shared graph representation, Response-G1 achieves more interpretable and accurate response timing decisions. Experimental results on established benchmarks demonstrate the superiority of our method in both proactive and reactive tasks, validating the advantage of explicit scene graph modeling and retrieval in streaming video understanding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Response-G1 adds explicit scene graph stages to improve response timing in streaming video understanding, with the gains depending on solid graph generation and retrieval.

read the letter

The main point to know about this paper is that it proposes Response-G1, a framework that uses explicit scene graphs to decide when a Video-LLM should respond during streaming video, rather than relying on implicit modeling. The new part is the specific three-stage pipeline. It starts with generating scene graphs from incoming video clips, guided by the user's query. Then it retrieves the most relevant historical scene graphs from a memory store. Finally, it uses those retrieved graphs in a prompt to decide for each frame whether to stay silent or generate a response. This creates an explicit link between the video evidence and the conditions for responding, which the authors argue leads to better interpretability and accuracy. They avoid any fine-tuning, keeping the method lightweight and applicable to existing models. The abstract indicates that experiments on standard benchmarks show improvements in both proactive streaming tasks and reactive ones. One area that could be softer is the dependence on the scene graph generation step. Streaming video means clips arrive sequentially, and producing reliable graphs online requires the underlying vision models to handle partial information well. Similarly, the memory retrieval has to consistently pull useful past graphs; otherwise the augmentation doesn't help. The paper claims the overall system outperforms baselines, and the stress test confirms the stages are clearly defined without internal contradictions. Still, it would be good to see how much the performance drops if the graph quality varies. This paper targets researchers and engineers working on multimodal AI systems that need to handle live video feeds intelligently. Anyone looking for ways to add structure to timing decisions in large vision-language models could find it relevant. I think it deserves peer review. The motivation is clear, the method is described in enough detail to be reproducible in principle, and the claims are testable against benchmarks. Referees can dig into the experimental setup and any ablations provided.

Referee Report

1 major / 2 minor

Summary. The paper introduces Response-G1, a three-stage framework for proactive streaming video understanding in Video-LLMs. It performs online query-guided scene graph generation from streaming clips, memory-based retrieval of semantically relevant historical scene graphs, and retrieval-augmented prompting to decide per-frame whether to output a response or remain silent. By grounding both visual evidence and query response conditions in an explicit shared scene-graph representation, the method claims to deliver more interpretable and accurate trigger decisions than implicit, query-agnostic baselines, with reported superiority on established benchmarks for both proactive and reactive tasks.

Significance. If the empirical claims hold, the work offers a structured alternative to black-box modeling in streaming video understanding, leveraging scene graphs for explicit alignment between evidence and query conditions. The fine-tuning-free pipeline and emphasis on retrieval-augmented interpretability represent a concrete advance that could improve reliability in applications such as real-time monitoring or interactive video systems.

major comments (1)

Experimental Results section: The manuscript asserts experimental superiority on benchmarks yet provides no details on the specific datasets, baseline methods, evaluation metrics, error bars, statistical tests, or ablation studies. This absence leaves the central claim of improved response timing without visible empirical support and prevents assessment of whether the gains are attributable to the scene-graph grounding or to other factors.

minor comments (2)

The description of the memory-based retrieval step would benefit from an explicit algorithmic outline or pseudocode to clarify how semantic relevance is computed and how historical graphs are stored and queried.
A pipeline diagram illustrating the three stages, the flow of streaming clips, and the per-frame decision process would improve readability and help readers follow the retrieval-augmented prompting mechanism.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and the opportunity to improve the manuscript. We address the major comment below.

read point-by-point responses

Referee: Experimental Results section: The manuscript asserts experimental superiority on benchmarks yet provides no details on the specific datasets, baseline methods, evaluation metrics, error bars, statistical tests, or ablation studies. This absence leaves the central claim of improved response timing without visible empirical support and prevents assessment of whether the gains are attributable to the scene-graph grounding or to other factors.

Authors: We agree that the Experimental Results section requires substantially more detail to support the claims of superiority. In the revised manuscript we will expand this section to specify the exact datasets and benchmarks used for both proactive and reactive tasks, describe all baseline methods in full, list the evaluation metrics, report error bars from multiple runs, include statistical significance tests, and present comprehensive ablation studies that isolate the contributions of query-guided scene graph generation, memory-based retrieval, and retrieval-augmented prompting. These additions will make the empirical support explicit and allow readers to assess the role of explicit scene-graph grounding. revision: yes

Circularity Check

0 steps flagged

No significant circularity; procedural framework with no derivations

full rationale

The paper presents Response-G1 as a three-stage procedural pipeline (online query-guided scene graph generation from streaming clips; memory-based retrieval of historical scene graphs; retrieval-augmented trigger prompting) without any equations, parameter fittings, or mathematical derivations. Claims of improved interpretability and accuracy rest on explicit shared graph representations and external benchmark comparisons rather than self-referential definitions or predictions that reduce to inputs by construction. No self-citation chains or ansatzes are invoked as load-bearing elements in the provided description, rendering the method self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that scene graphs form a sufficient shared representation for both accumulated video evidence and query response conditions; no free parameters or invented entities are mentioned in the abstract.

axioms (1)

domain assumption Scene graphs generated from streaming clips can be aligned with query-expected response conditions in a way that supports accurate silence/response decisions
Invoked by the design of the three stages and the claim of improved interpretability and accuracy.

pith-pipeline@v0.9.0 · 5492 in / 1229 out tokens · 59554 ms · 2026-05-12T02:49:59.533787+00:00 · methodology

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discussion (0)

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Relation between the paper passage and the cited Recognition theorem.

three fine-tuning-free stages: (1) online query-guided scene graph generation from streaming clips; (2) memory-based retrieval...; (3) retrieval-augmented trigger prompting

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Reference graph

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