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REVIEW 2 major objections 8 references

An agentic framework lets open-source Omni-LLMs actively perceive and reason over dispersed audio-visual evidence using hierarchical memory and an observe-reflect-replan loop.

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T0 review · grok-4.3

2026-06-29 12:35 UTC pith:B5WYZOL4

load-bearing objection MOV-Bench and AOP-Agent give a new test set and agent loop for multi-hop audio-visual reasoning, but the performance claims rest on end-to-end results without isolating the proposed components. the 2 major comments →

arxiv 2605.28192 v1 pith:B5WYZOL4 submitted 2026-05-27 cs.AI

Agentic Active Omni-Modal Perception for Multi-Hop Audio-Visual Reasoning

Ke Xu , Yuhao Wang , Ziyang Cheng , Hongcheng Liu , Yanfeng Wang , Yu Wang This is my paper
classification cs.AI
keywords multi-hop reasoningaudio-visual reasoningomni-modal perceptionagentic frameworkMOV-Benchactive perceptionOmni-LLMshierarchical memory
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper creates MOV-Bench, a collection of 519 questions that require multi-hop reasoning across temporally dispersed audio and visual evidence, and shows that current Omni-LLMs perform poorly on it. It then introduces AOP-Agent, an agent framework built on open-source Omni-LLMs that adds a hierarchical omni-modal memory together with an observe-reflect-replan loop. This combination enables active perception without any additional training or proprietary models. Experiments on MOV-Bench and OmniVideoBench report consistent accuracy gains, with the largest improvements appearing on long videos and reasoning-intensive questions.

Core claim

AOP-Agent is an efficient agentic framework built on open-source Omni-LLMs for active omni-modal perception. By combining a hierarchical omni-modal memory with a collaborative observe-reflect-replan loop, AOP-Agent enables open-source Omni-LLMs to perform active perception without additional training or proprietary models. Experiments on MOV-Bench and OmniVideoBench demonstrate that AOP-Agent consistently improves reasoning performance, with particularly notable gains on long videos and reasoning-intensive questions.

What carries the argument

Hierarchical omni-modal memory combined with the observe-reflect-replan loop, which supports active gathering and integration of sparse cross-modal evidence over time.

Load-bearing premise

The performance gains arise specifically from the hierarchical omni-modal memory and observe-reflect-replan loop rather than other unstated factors in the evaluation setup.

What would settle it

Removing the observe-reflect-replan loop from AOP-Agent and measuring no drop in accuracy on MOV-Bench would indicate that the loop is not the cause of the reported gains.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Open-source Omni-LLMs gain the ability to handle multi-hop cross-modal reasoning without retraining.
  • Accuracy improvements are larger on long videos and on questions that require intensive reasoning.
  • The method directly targets the sparsity and temporal dispersion of evidence across audio and visual streams.
  • Active perception becomes possible on open-source models without relying on proprietary systems.

Where Pith is reading between the lines

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

  • The same agentic structure could be tested on tasks that combine still more modalities or longer temporal spans.
  • Agentic loops of this kind might reduce dependence on ever-larger base models for perception-heavy work.
  • Real-world deployment would require checking how the loop behaves under streaming rather than offline video input.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 0 minor

Summary. The paper introduces MOV-Bench, a new benchmark of 519 multi-hop audio-visual reasoning questions requiring temporally dispersed cross-modal evidence, demonstrates that existing Omni-LLMs struggle on it, and proposes AOP-Agent: an agentic framework that augments open-source Omni-LLMs with a hierarchical omni-modal memory and an observe-reflect-replan loop to perform active perception without additional training or proprietary models. Experiments on MOV-Bench and OmniVideoBench report consistent performance gains, especially on long videos and reasoning-intensive questions.

Significance. If the reported gains prove robust and causally attributable to the proposed memory hierarchy and loop, the work would offer a practical, training-free route for open-source Omni-LLMs to handle sparse, multi-hop audio-visual evidence, addressing a clear limitation in current models and benchmarks.

major comments (2)
  1. [Experiments] Experiments section: the central claim that the hierarchical omni-modal memory combined with the observe-reflect-replan loop drives the measured improvements is not supported by any ablation that removes or isolates these components while holding the base Omni-LLM, observation budget, and context length fixed; end-to-end results alone leave open the possibility that gains arise from longer effective context, sampling differences, or benchmark artifacts.
  2. [MOV-Bench] MOV-Bench description and evaluation: the paper states that current Omni-LLMs struggle but provides no quantitative baseline numbers, error analysis, or breakdown by number of reasoning hops or modality distribution, making it impossible to assess whether the benchmark genuinely stresses multi-hop cross-modal reasoning beyond what simpler prompting already achieves.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point by point below and commit to revisions that strengthen the manuscript.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: the central claim that the hierarchical omni-modal memory combined with the observe-reflect-replan loop drives the measured improvements is not supported by any ablation that removes or isolates these components while holding the base Omni-LLM, observation budget, and context length fixed; end-to-end results alone leave open the possibility that gains arise from longer effective context, sampling differences, or benchmark artifacts.

    Authors: We agree that the manuscript would be strengthened by explicit ablations isolating the hierarchical omni-modal memory and observe-reflect-replan loop. The current version reports only end-to-end gains on MOV-Bench and OmniVideoBench. In the revision we will add controlled ablations that remove or disable each component individually while fixing the base Omni-LLM, observation budget, and context length, thereby clarifying the source of the observed improvements. revision: yes

  2. Referee: [MOV-Bench] MOV-Bench description and evaluation: the paper states that current Omni-LLMs struggle but provides no quantitative baseline numbers, error analysis, or breakdown by number of reasoning hops or modality distribution, making it impossible to assess whether the benchmark genuinely stresses multi-hop cross-modal reasoning beyond what simpler prompting already achieves.

    Authors: We acknowledge that the current MOV-Bench section would benefit from additional quantitative detail. While the manuscript already reports that Omni-LLMs struggle on the 519 questions, it does not include the requested baselines, error analysis, or stratified breakdowns. In the revision we will add tables with baseline performances under standard prompting, an error categorization, and results broken down by number of reasoning hops and modality distribution to better demonstrate the benchmark's demands. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework with independent benchmark results

full rationale

The paper introduces MOV-Bench and AOP-Agent as an agentic framework, claiming performance gains via experiments on MOV-Bench and OmniVideoBench. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the abstract or described structure. The central claim rests on end-to-end empirical outcomes rather than any derivation that reduces to its own inputs by construction. This is a standard empirical contribution with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the contribution rests on the empirical claim of improvement from the proposed agent loop.

pith-pipeline@v0.9.1-grok · 5722 in / 1114 out tokens · 26832 ms · 2026-06-29T12:35:28.775544+00:00 · methodology

0 comments
read the original abstract

Multi-hop audio-visual reasoning remains challenging for Omni-LLMs, as relevant evidence is often sparse, temporally dispersed, and distributed across both audio and visual streams. Existing benchmarks provide limited investigation of this setting, typically involving only a limited number of modalities, relevant temporal segments, or reasoning steps. In this work, we introduce MOV-Bench, a benchmark containing 519 carefully curated questions that require multi-hop reasoning over temporally dispersed audio-visual evidence. Evaluations on MOV-Bench reveal that current Omni-LLMs still struggle with multi-hop cross-modal reasoning. To address this challenge, we further propose AOP-Agent, an efficient agentic framework built on open-source Omni-LLMs for active omni-modal perception. By combining a hierarchical omni-modal memory with a collaborative observe-reflect-replan loop, AOP-Agent enables open-source Omni-LLMs to perform active perception without additional training or proprietary models. Experiments on MOV-Bench and OmniVideoBench demonstrate that AOP-Agent consistently improves reasoning performance, with particularly notable gains on long videos and reasoning-intensive questions.

Figures

Figures reproduced from arXiv: 2605.28192 by Hongcheng Liu, Ke Xu, Yanfeng Wang, Yuhao Wang, Yu Wang, Ziyang Cheng.

Figure 1
Figure 1. Figure 1: A representative example from MOV-Bench il [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Motivation of AOP-Agent. Existing meth￾ods either passively process the full video or depend on high-resource settings such as extensive training or proprietary models. AOP-Agent enables Omni-LLMs to actively decide which video evidence to observe un￾der low-resource settings. Short-term Memory denotes working memory and evidence memory in AOP-Agent. source Omni-LLMs. The main contributions of this work ar… view at source ↗
Figure 3
Figure 3. Figure 3: Overall pipeline for data collection, question synthesis, and quality filtering in MOV-Bench. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Framework of AOP-Agent. AOP-Agent reformulates omni-modal reasoning as active perception through hierarchical omni-modal memory and an observe-reflect-replan loop. servations, enabling AOP-Agent to adaptively se￾lect observation granularity, and efficiently inspect relevant video content during iterative perception. More implementation details of the video segmen￾tation strategy are provided in Appendix D.… view at source ↗
Figure 5
Figure 5. Figure 5: Performance of AOP-Agent based on Qwen3- [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Statistics of MOV-Bench. (a) MOV-Bench covers five reasoning-intensive question types with reasoning [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative example of active observation in AOP-Agent on MOV-Bench. For the query about breeding a [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Failure case of active observation in AOP-Agent. For a query asking why the host suddenly skips the [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Segment description prompt used to generate [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Overall description prompt used to generate [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Tool description prompts used by AOP￾Agent to support planning and reflection. You are the Planner in a long-video QA agent system. INPUTS: - User query: {query} - Options (if any): {options_text} - Whole-video overall summary: {overall_summary} - Existing working-memory thought process and planning history: {previous_working_memory} - Reflector feedback from previous round (empty on first planning): {obs… view at source ↗
Figure 13
Figure 13. Figure 13: The reflector prompt used to evaluate obser [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Reasoner prompt used to generate the final [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

8 extracted references

  1. [1]

    [Subject] [Specific Action/Verb] [Object] [Direction/Source/Destination]

    VIDEO_KEYPOINTS (Visual Retrieval Facts): Extract retrieval-critical facts from video stream only. - FORMATTING RULE: Every event must strictly follow this structure: "[Subject] [Specific Action/Verb] [Object] [Direction/Source/Destination]" (e.g., "The girl extracts a baby FROM the glowing lantern" NOT "The girl interacts with the lantern"). - DISAMBIGUA...

  2. [2]

    - Include speech intention, emotion, sound events, environment sounds, and audio-only evidence that may not be visually obvious

    AUDIO_KEYPOINTS (Audio Retrieval Facts): Extract retrieval-critical facts from audio stream only. - Include speech intention, emotion, sound events, environment sounds, and audio-only evidence that may not be visually obvious. - Keep items atomic and chronological

  3. [3]

    video_keypoints

    KEYWORDS (Sparse Search Anchors): - Output short noun phrases and entity terms for lexical/BM25 retrieval. - Prefer concrete objects, persons, places, attributes, and decisive action nouns. - Keep each keyword short (1-4 words), no full sentences. STRICT CONSTRAINTS: - Do NOT merge multiple actions into one string. - Do NOT hallucinate events outside this...

  4. [4]

    query":

    search_mid_level_by_description(query, top_k) - Vector retrieval over mid-level descriptions. Best for broad semantic recall. params: query (str, required): natural language retrieval query. params: top_k (int, optional, default=5): number of segments to return. Must be >= 1. note: Use this first for broad semantic coverage. note: Avoid empty query string...

  5. [5]

    keyword_queries

    search_mid_level_by_keywords(keyword_queries, top_k) - BM25 retrieval over sparse keywords. Best for exact lexical anchors. params: keyword_queries (list[str], required): each item must be one standalone keyword or short phrase. params: top_k (int, optional, default=5): number of segments to return. Must be >= 1. note: Do not pass one long sentence as a s...

  6. [6]

    query":

    search_mid_level_by_keypoints(query, top_k, use_vector, use_keyword, vector_weight)- Hybrid retrieval over video/audio keypoints with configurable vector- keyword weighting. params: query (str, required): event-centric retrieval query. params: top_k (int, optional, default=5): number of segments to return. Must be >= 1. params: use_vector (bool, optional,...

  7. [7]

    mid_level_id

    get_neighbor_mid_level_by_id(mid_level_id, k)- Fetch nearby mid-level segments around a seed segment for temporal expansion. params: mid_level_id (int, required): seed segment id from previously retrieved results. params: k (int, optional, default=2): neighbor window size on each side. Must be >= 1. note: mid_level_id must come from existing retrieval evi...

  8. [8]

    mid_level_id

    get_fine_level_by_mid_level_id(mid_level_id)- Drill down from a mid-level segment to all its fine-level child segments. params: mid_level_id (int, required): parent id from previously retrieved mid-level results. note: Use for evidence verification after mid-level localization. note: Only valid when mid_level_id is from observed results. example_arguments...