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arxiv: 2605.16481 · v1 · pith:KFXPFXJAnew · submitted 2026-05-15 · 💻 cs.CV · cs.AI

Visual Agentic Memory: Enabling Online Long Video Understanding via Online Indexing, Hierarchical Memory, and Agentic Retrieval

Aiden Yiliu Li , Nels Numan , Anthony Steed This is my paper

Pith reviewed 2026-05-20 18:42 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords long video understandingvisual memoryagentic retrievalonline indexinghierarchical memorymultimodal large language modelsvideo question answeringstreaming video
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The pith

Visual Agentic Memory enables long video understanding by making evidence explicit, searchable, and verifiable through online indexing and agentic retrieval.

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

Long video understanding needs a way to decide what to retain, keep evidence searchable across extended time, and ground answers in recoverable observations rather than compressed states. The paper presents Visual Agentic Memory, a training-free system with online indexing for selective retention under streaming limits, hierarchical memory that aligns temporal context with spatial details in a parallel representation, and agentic retrieval that searches, inspects, and verifies candidates before answering. On OVO-Bench it reaches the highest RT+BT average of 68.41, above the 67.46 from direct use of the same underlying model, and on a 105.6-hour month-scale video split it scores 17.11 percent. A sympathetic reader would care because the approach treats memory as an inspectable substrate that can scale to real streaming video without retraining the base model.

Core claim

Visual Agentic Memory is a training-free framework with three components—Online Indexing for selective evidence retention, Hierarchical Memory organized in a Parallel Representation that aligns temporal context with spatial observations, and Agentic Retrieval that searches, inspects, and verifies candidate evidence—allowing multimodal models to produce grounded answers for online long video understanding, as evidenced by top benchmark scores on OVO-Bench and the month-scale split of MM-Lifelong.

What carries the argument

The Visual Agentic Memory framework, whose agentic retrieval searches, inspects, and verifies candidate evidence drawn from a hierarchical memory organized in a Parallel Representation that aligns temporal context with spatial observations.

If this is right

  • Streaming video systems can retain only selected evidence instead of attempting to hold entire long sequences in context.
  • Hierarchical organization supports reasoning that combines time order with spatial detail without forcing everything into a single latent state.
  • Grounded answers become possible because retrieval verifies observations before final output rather than trusting compressed representations.
  • The same framework can be applied to different underlying multimodal models to obtain similar relative gains on long-horizon tasks.
  • Month-scale video processing becomes feasible when memory is treated as an explicit, queryable resource.

Where Pith is reading between the lines

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

  • The same selective retention and verification pattern could be tested on continuous sensor streams or audio logs that also require long-horizon grounding.
  • Combining the hierarchical structure with existing video compression methods might reduce storage costs while preserving inspectability.
  • Whether the gains remain when the underlying model changes or when retrieval is limited to fewer verification steps is a direct next measurement.
  • The approach raises the question of how to set retention thresholds automatically so that important but rare events survive over weeks of video.

Load-bearing premise

Agentic retrieval can reliably locate, inspect, and verify the right evidence without introducing selection or verification errors that erase the reported gains.

What would settle it

An ablation that removes the agentic retrieval step or the hierarchical organization and measures performance on OVO-Bench and the month-scale MM-Lifelong split; if scores fall to or below the end-to-end baseline of 67.46, the central claim is falsified.

Figures

Figures reproduced from arXiv: 2605.16481 by Aiden Yiliu Li, Anthony Steed, Nels Numan.

Figure 1
Figure 1. Figure 1: VAM turns continuous video streams into searchable long-horizon memory. Unlike prior [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Three macro-paradigms in online long video understanding: direct generative inference, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: System architecture. Three coupled layers (Online Indexing, Hierarchical Memory, and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Frame filtering. Top: raw stream. Middle: frames rejected for blur ( [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Adaptive deduplication. When dc exceeds the adaptive τdedup, buffered moments are committed and the reference is reset. The same distance spikes drive event formation. At runtime each frame undergoes (1) encoding to et, (2) computing dc against the reference, (3) comparing with τdedup, and (4) buffering or committing. The process is strictly online, with no future-frame access Rege et al. [2026], Yuan et a… view at source ↗
read the original abstract

Long video understanding requires more than large context windows. It also needs a memory mechanism that decides what visual evidence to retain, keeps it searchable over long horizons, and grounds later reasoning in recoverable observations rather than compressed latent state alone. We propose Visual Agentic Memory (VAM), a training-free framework with three components. Online Indexing supports selective evidence retention under streaming constraints. Hierarchical Memory organises retained evidence in a Parallel Representation that aligns temporal context with spatial observations. Agentic Retrieval searches, inspects, and verifies candidate evidence before producing a grounded answer. On OVO-Bench, VAM achieves the highest RT+BT average (68.41) across all reported baselines, improving over end-to-end use of the same underlying MLLM (Gemini 3 Flash, 67.46). On the month-scale split of MM-Lifelong train@month (105.6 hours over 51 days), VAM reaches 17.11%, second only to ReMA with GPT-5 (17.62%). These results suggest that long-horizon video understanding benefits from treating visual memory as an explicit, inspectable, and queryable substrate. Code is available at https://github.com/yiliu-li/Visual-Agentic-Memory.

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.

Referee Report

3 major / 1 minor

Summary. The paper proposes Visual Agentic Memory (VAM), a training-free framework for online long video understanding consisting of three components: Online Indexing for selective evidence retention under streaming constraints, Hierarchical Memory organized via Parallel Representation aligning temporal and spatial observations, and Agentic Retrieval that searches, inspects, and verifies candidate evidence before generating answers. It reports empirical results on OVO-Bench achieving the highest RT+BT average of 68.41 (improving over direct Gemini 3 Flash at 67.46) and 17.11% on the month-scale MM-Lifelong split (105.6 hours over 51 days), second to ReMA with GPT-5.

Significance. If the gains are attributable to the proposed mechanisms, the work shows that explicit, inspectable memory structures can modestly enhance long-horizon video reasoning in MLLMs without additional training. The training-free design and public code release at https://github.com/yiliu-li/Visual-Agentic-Memory are strengths that support reproducibility and practical adoption for extended video streams.

major comments (3)
  1. [Abstract] Abstract: the 0.95-point lift on OVO-Bench RT+BT (68.41 vs. 67.46) is presented as evidence of the framework's value, yet no error bars, multiple-run statistics, or ablation isolating Agentic Retrieval's contribution are reported; this is load-bearing for the central claim that the components add recoverable evidence without offsetting errors.
  2. [Abstract] Abstract: Agentic Retrieval is described as performing search-inspect-verify to produce grounded answers, but the manuscript supplies no quantitative retrieval accuracy, verification error rates, or comparison of answer quality with vs. without the loop; this directly affects whether the modest benchmark gains can be confidently attributed to the mechanism rather than variance or prompt effects.
  3. [Benchmark results] Benchmark results: no ablation tables or experiments are referenced that separately disable Online Indexing, Hierarchical Memory, or Agentic Retrieval to measure individual impact on the reported OVO-Bench and MM-Lifelong scores; without this, the attribution of performance to the three-component design remains unverified.
minor comments (1)
  1. [Abstract] The abstract would benefit from briefly naming the exact metrics (RT+BT) and dataset splits for immediate clarity to readers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and agree that additional analyses are needed to strengthen attribution of the reported gains.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the 0.95-point lift on OVO-Bench RT+BT (68.41 vs. 67.46) is presented as evidence of the framework's value, yet no error bars, multiple-run statistics, or ablation isolating Agentic Retrieval's contribution are reported; this is load-bearing for the central claim that the components add recoverable evidence without offsetting errors.

    Authors: We acknowledge that error bars, multiple-run statistics, and a targeted ablation for Agentic Retrieval would strengthen the claim. The reported numbers reflect single-run evaluations, which is common for large MLLM benchmarks due to cost. We will add error bars from repeated runs and an ablation isolating Agentic Retrieval in the revised manuscript. revision: yes

  2. Referee: [Abstract] Abstract: Agentic Retrieval is described as performing search-inspect-verify to produce grounded answers, but the manuscript supplies no quantitative retrieval accuracy, verification error rates, or comparison of answer quality with vs. without the loop; this directly affects whether the modest benchmark gains can be confidently attributed to the mechanism rather than variance or prompt effects.

    Authors: We agree that quantitative retrieval accuracy, verification error rates, and a with/without comparison would better support attribution to the agentic loop rather than variance. The current manuscript emphasizes end-to-end results; we will include these metrics and comparisons in the revision. revision: yes

  3. Referee: [Benchmark results] Benchmark results: no ablation tables or experiments are referenced that separately disable Online Indexing, Hierarchical Memory, or Agentic Retrieval to measure individual impact on the reported OVO-Bench and MM-Lifelong scores; without this, the attribution of performance to the three-component design remains unverified.

    Authors: The referee correctly identifies the absence of component-wise ablations. We will add experiments that disable Online Indexing, Hierarchical Memory, and Agentic Retrieval individually and report their effects on both OVO-Bench and MM-Lifelong scores in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark results on external datasets

full rationale

The paper presents a training-free framework whose central claims are measured performance numbers on public benchmarks (OVO-Bench RT+BT average of 68.41 and 17.11% on MM-Lifelong month-scale split). These are direct empirical outcomes rather than quantities derived from internal equations, fitted parameters, or self-referential definitions. No mathematical derivation chain, uniqueness theorem, or ansatz is invoked that reduces to the paper's own inputs by construction. The three components (Online Indexing, Hierarchical Memory, Agentic Retrieval) are described procedurally; their value is asserted via external evaluation, not by renaming or self-citing prior results as load-bearing premises. This matches the default case of a self-contained empirical system against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard assumptions about multimodal LLMs and retrieval rather than new axioms or invented entities; no free parameters are described in the abstract.

axioms (1)
  • domain assumption An off-the-shelf MLLM can be effectively augmented with external memory mechanisms for long-horizon tasks without retraining.
    Invoked by the training-free design and the comparison to end-to-end MLLM use.

pith-pipeline@v0.9.0 · 5764 in / 1254 out tokens · 39401 ms · 2026-05-20T18:42:01.969732+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · 1 internal anchor

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    ONLY one valid JSON object

    URLhttps://arxiv.org/abs/2501.19098. Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, Zhuang Liu, Hu Xu, Hyun- woo J. Kim, Bilge Soran, Raghuraman Krishnamoorthi, Mohamed Elhoseiny, and Vikas Chan- dra. Longvu: Spatiotemporal adaptive compression for long vi...

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  4. [4]

    type ":

    {" type ":" final " ," text ": ". .." }

    work page

  5. [5]

    type ":

    {" type ":" tool " ," name ":" r et rie ve " ," args ":{" qu es ti on " : " . . . " } }

    work page

  6. [6]

    type ":

    {" type ":" tool " ," name ":" s u m m a r i z e " ," args ":{" m in _ti me ":0.0 ," ma x_ ti me ":1800.0 , " t i m e _ m o d e ":" rel at iv e " ," g r a n u l a r i t y _ s e c o n d s ":60.0 ," prompt " : " . . . " } } Tool s e l e c t i o n rules : - Use ’ retrieve ’ for direct q u e s t i o n s about content , objects , actions , locations , or event...

    work page

  7. [7]

    ’ search ’: Perform Agentic R e t r i e v a l over frame ev id enc e and T em por al R e p r e s e n t a t i o n d o c u m e n t s

    work page

  8. [8]

    ’ inspect ’: Perform direct Visual I n s p e c t i o n on frames from a known time or from a p r e v i o u s l y found result

    work page

  9. [9]

    Tool bo un dar y rules : 19 - ’ search ’ is the default tool for a n s w e r i n g q u e s t i o n s

    ’ summarize ’: Create re usa bl e summary d o c u m e n t s in H i e r a r c h i c a l Memory for a sp ec if ic time range at a r e q u e s t e d g r a n u l a r i t y . Tool bo un dar y rules : 19 - ’ search ’ is the default tool for a n s w e r i n g q u e s t i o n s . - Use ’ inspect ’ when you already know the re le va nt time or result r e f e r e n...

    work page

  10. [10]

    Analyze the user ’ s request and what you have found so far

    work page

  11. [11]

    Decide whether you have enough i n f o r m a t i o n to answer

    work page

  12. [12]

    action

    If YES , output {" action ":" answer " ," res po ns e ":"..." ," be st _re f ":{" tu rn _id x ": int , " r e s u l t _ i d x ": int } ," thought ": ". .." }

    work page

  13. [13]

    action

    If NO , choose a tool : - ’ search ’: {" action ":" search " ," queries ":[{" q ":"..." ," top_k ": int , " i n s p e c t _ k ": int ," t h r e s h o l d ": float }] ," t i m e _ r a n g e ":{...} ," sources ": [" frame "|" event "|" summary "] ," s u m m a r y _ f i l t e r ":{" s u m m a r y _ s t r u c t u r e ":"..." , " g r a n u l a r i t y _ s e c ...

    work page