VQToken: Neural Discrete Token Representation Learning for Extreme Token Reduction in Video Large Language Models

Response to Reviewer RRqQ

We are deeply grateful to Reviewer RRqQ for their exceptional thoroughness, insightful feedback, and thoughtful suggestions. Your careful analysis has enabled us to sharpen our presentation, fortify our arguments, and address nuances we might have otherwise overlooked. We sincerely appreciate the time, expertise, and genuine engagement you’ve invested in our manuscript, your guidance has been instrumental in enhancing its clarity and impact.

Below, we present our revised responses, remaining faithful to our original method and refraining from introducing any new architectures. We trust these clarifications will fully address your concerns.

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Q1. Performance Trend w.r.t. Token Count

“A scaling experiment showing the performance trend with respect to the token count would be very helpful.”

A1:
Thank you for this suggestion. We performed a fixed-cluster scaling study with our pretrained parameters at three different token budgets and compared against three recent methods. All models were evaluated on NextQA under identical conditions:

Key insight:
Even at just 12 tokens, our fixed-cluster variant achieves 57.0 % accuracy—far above other methods—while maintaining the highest token efficiency.

Note on fewer clusters:
Our model was pretrained with a 32-cluster default; extending to higher K (e.g. 64) without additional architecture or fine-tuning yields smaller gains. From experiments, it seems the performance peak is at the token number used during finetuning. It might be shown if we use adaptive token counts during training rather than testing to have the performance trend grow with length. We view scaling to arbitrary token budgets as a valuable future direction with additional modules needed, as discussed below.

Future Direction (Long-Form Videos):
Split each video into multiple mid-length clips and apply our reduction to each segment—capping each at less than 32 tokens. Concatenating these segment-level tokens produces a compact representation of the full video: still far shorter than conventional methods, yet rich enough to capture long-form content. This segmentation strategy will enable our approach to scale to long-video understanding while preserving extreme token efficiency.

We will include this scaling table and discussion in the camera-ready version.

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Q2. LongVideoBench Subset Scores

“Would it be possible to provide the specific numerical scores for its various subsets?”

A2:
We evaluated our method on each LongVideoBench subset—defined by maximum frame counts of 16, 32, and 64—using the lmms-eval toolkit. Preliminary results:

Since the LongVideoBench repository does not provide separate evaluation scripts for each subset, we followed the official LongVideoBench and lmms-eval tutorials to generate these numbers. We will add this table as an appendix and reference it alongside Figure 3 in the main text.

We commit to extending more detailed subset analysis to more additional benchmarks and including all corresponding results in the camera-ready version.

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Q3. Latency & FLOPs Breakdown

“Add latency to the overall computation, but it is unclear what the specific FLOPs are or how much latency.”

A3:
We agree that a clear complexity breakdown is valuable. The overall latency and time complexity has two parts:

1. Token reduction module (e.g., K-Means clustering)
2. Subsequent LLM inference on the shorter token sequence

In addition to our measured results (see Table 1, p. 2), we will provide a theoretical analysis of time complexity and FLOPs for the K-Means step, which dominates our pipeline.

Theoretical Time Complexity of K-Means

• Notation:

  • $M$: number of data points (tokens)
  • $D$: embedding dimension
  • $K$: number of clusters
  • $N$: number of iterations

• Per-iteration cost:

  1. Assignment: $O(M\,K\,D)$ distance computations
  2. Update: $O\bigl((M+K)\,D\bigr)$ centroid re-estimation

• Total time complexity:

  $$\sum_{i=1}^{N} \bigl[O(M\,K\,D) + O((M+K)\,D)\bigr] = O(N\,M\,K\,D)$$

FLOPs Analysis for K-Means

• Notation:

  • $M$: number of tokens
  • $D$: embedding dimension
  • $K$: number of clusters
  • $N$: number of iterations

• Assignment step (per iteration):
  We can compute a $D$-dimensional distance for each of the $M$ tokens against each of the $K$ centroids. Each distance costs $2D$ subtractions, $D$ multiplications, and $D-1$ additions, for a total of $2D + (D-1) \approx 3D$ flops, so
  $\mathrm{FLOPs}_{\mathrm{assign}} = M \times K \times \bigl(2D + (D-1)\bigr) \approx 3\,M\,K\,D.$

• Centroid-update step (per iteration):
  Summing assigned token embeddings ($M \times D$ additions) and normalizing each of the $K$ centroids ($K \times D$ divisions) gives
  $\mathrm{FLOPs}_{\mathrm{update}} = M\,D + K\,D = (M + K)\,D.$

• Total per iteration:
  $\mathrm{FLOPs}_{\mathrm{iter}} = 3\,M\,K\,D + (M + K)\,D = D \times (3\,M\,K + M + K).$

• Total over $N$ iterations:
  $\mathrm{FLOPs}_{\mathrm{total}} = N \times \bigl[3\,M\,K\,D + (M + K)\,D\bigr] = N\,D \times (3\,M\,K + M + K).$

We will refer to Table 1 for measured module and end-to-end LLM latency, and include this theoretical breakdown in the revised manuscript.

In addition to the theoretical FLOPs breakdown, we will include actual latency measurements (in milliseconds) for each component in Table 1 of the camera-ready version, specifically:

1. K-Means clustering latency
2. LLM inference latency
3. End-to-end total pipeline latency This will enable readers to directly compare both the theoretical complexity and the practical runtime overhead of each step.

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We hope these clarifications and the added tables will fully address the reviewer’s points. Thank you again for guiding us toward a stronger, more transparent presentation.

Dear Reviewer RRqQ,

We are extremely grateful for your continued engagement and your high standards in helping us improve our work. Thank you for your thorough review of Q1 and Q3, which provided valuable supplements, overviews, and insights for future research on the novel task of extreme short token reduction. Regarding Q2, we also appreciate your patience and detailed guidance, which led us to examine performance in greater depth and offered clear directions for improvement.

Using the VLMEvalKit script, we have extracted our 0.5 B VQToken model’s final accuracies on the LongVideoBench_8frame subsets in 32 tokens setting, summarized below:

From these results, we highlight some trends over the 0.5 B VQToken (32 token, 8 frames setting):

• Scene-referred Event (S2E)
  As defined, S2E asks “when or what happens in a given scene.” Our model’s accuracy rises from 0.429 (≤ 15 s) to 0.538 (60–600 s), demonstrating that VQToken effectively localizes events as temporal context grows—even in longer clips where traditional token methods struggle.

• Scene-referred Object Attribute (S2A)
  Tasked with identifying an object’s attribute (e.g., color, shape), VQToken maintains 0.500 on ≤ 15 s and 0.429 on 15–60 s, indicating reliable attribute encoding from minimal token sets.

• Event-referred Object (E2O)
  “[Which object participated in the event?]” sees a jump to 0.583 at 15–60 s, reflecting VQToken’s capability to tie events back to their associated objects when richer frame sequences are available.

• Object-referred Event (O2E)
  Defined as “what happens when an object appears,” O2E scores 0.579 on short clips and remains above 0.417 even for very long videos—evidence that VQToken can link object appearances to their ensuing actions under extreme token reduction.

• Text-referred Event (T2E)
  When matching subtitles to actions, VQToken reaches an impressive 0.750 at 15–60 s (the highest among all categories), showcasing exceptional alignment between textual cues and visual frames.

• Text-referred Object (T2O)
  For identifying objects mentioned in subtitles, our model hits 0.375–0.278 across medium and long clips—strong performance given only 32 tokens.

• L2 Relation Tasks
  Even in reasoning over sequences (e.g., E3E “what happened before/after”), VQToken maintains 0.333–0.556 across lengths, confirming that reduced tokens still support multi-timestamp reasoning.

These positive signals demonstrate VQToken’s versatile strengths despite extreme token sparsity. We hope this detailed subtask-level analysis further clarifies where VQToken excels and reassures you of its broad applicability.

Apart from the purely static‐scene tasks (S2O and S2A), which actually peak on the shortest clips (≤15 s)—with accuracies of 0.625 and 0.500 respectively, since they rely almost exclusively on single‐frame information and can even suffer from excess context—VQToken consistently excels on spatiotemporal queries that demand cross‐frame reasoning (e.g., spatial→event, event→object). These findings indicate that VQToken delivers its best performance on moderate‐length clips and benefits substantially from longer temporal context for spatial→event and object→event tasks.

Future work: Future researcher could split long videos into mid‐length segments, apply extreme token reduction to each segment, and then leverage those segment‐level token indices to model long‐range dependencies—thereby extending our approach toward deeper spatial, temporal, object, and event understanding.

We hope this detailed breakdown addresses your concerns. Please let us know if you have any further questions or suggestions, your insights are invaluable for improving VQToken. Thank you again for your time and expertise; we appreciate your consideration of these clarifications.

Sincerely and with gratitude,

The Authors of Submission 3333

Dear Reviewer RRqQ,

Thank you so much for your kind words and encouragement. We are delighted that our explanations fully addressed your questions. Your thoughtful engagement has been invaluable, and we sincerely appreciate the time and expertise you have shared. We were truly encouraged by your support and insightful contributions to improving our work.

We will incorporate your feedback into the camera-ready version. These enhancements will help other researchers identify subtask-level future directions for extreme token reduction, inspire further work in the academic community, and benefit a broad range of video-language model research topics.

Thank you again for confirming that your concerns have been resolved and for affirming your rating. If you have any additional suggestions for improving our paper, we would be most grateful to hear them.

We wish you all the best in your endeavors.

Sincerely and with gratitude,

The Authors of Submission 3333

Response to Reviewer LjpF

We are deeply grateful to Reviewer LjpF for the exceptionally thorough and professional review. Your insightful comments and clear understanding of the challenges in efficient video‐LLM research are invaluable. We appreciate your time and expertise in helping us strengthen our work, and we will gladly incorporate your suggestions and questions about details and will release our full code and implementation details.

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Q1: Adaptive‐length Reduction Mode

“Is the reduction ratio fixed for one given video clip? Is it possible to dynamically adjust the reduction ratio within a clip?”

A1:
Thank you for this excellent question. The reduction ratio in our adaptive‐length mode is not fixed per clip. We use adaptive clustering (e.g., adaptive K-Means) at test time, allowing different clips, and even different segments within a clip, to yield varying token counts. Because this clustering is decoupled from the core VQToken parameters, one can dynamically adjust the number of centroids (for example, by tuning the K in K-Means or substituting alternative segmentation models like SAM) without retraining the model. We appreciate your suggestion and will clarify this flexibility in the revised manuscript.

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Q2: NextQA‐MC vs. NExT‐QA and Clip Length

“How long does one video clip last typically in the NExT-QA evaluation benchmark, where you claim a 0.07% reduction ratio? What is NextQA-MC? Is it a subset of NExT-QA?”

A2:
We sincerely thank you for asking for these details. We evaluate on the mc test-set split from the HuggingFace repo (lmms-lab/NExTQA), commonly referred to as NextQA-MC. This split contains specially curated multiple‐choice QA pairs and is widely used by recent works (e.g., LLaVA-OneVision, DyCoke).

In our Ours-Dynamic setting (Table 1, last two rows):

• Average tokens per clip: 13.08
• Uncompressed baseline tokens: 23,328 (LLaVA-OV without reduction, row 243)
• Reduction ratio:
  $\displaystyle \frac{13.08}{23{,}328} \approx 0.07 \\%$

We will add this detailed calculation and a brief description of NextQA-MC in Section 4.2 to ensure full clarity.

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W1: LLM Scaling Up

“The base model is LLaVA-OneVision (0.5 B). It is doubtful whether token reduction becomes a bottleneck on larger models.”

A3:
We greatly appreciate this important perspective. While we selected the 0.5 B backbone to highlight practical efficiency gains, we agree that larger models may better recover compressed information. To validate, we scaled our experiments to the 7 B variant:

These results confirm that VQToken not only maintains but improves performance when paired with a larger backbone, supporting our theoretical expectation that increased model capacity aids recovery from extreme compression. We will include this table and discussion in the revised paper.

Dear Reviewer LjpF,

Thank you very much for your generous final rating and for your acknowledgment of our work. We are truly encouraged that you found our VQToken framework both practical and impactful, and we deeply appreciate the time and expertise you’ve invested in reviewing our submission.

If there is any additional information or clarification you would find helpful, please do not hesitate to let us know. We are eager to address any further questions and to ensure that our paper is as clear and comprehensive as possible.

Thank you again for your invaluable contributions to bringing extreme token reduction into the academic spotlight.

Sincerely,

The Authors of Submission 3333

Response to Reviewer BtRV

We thank Reviewer BtRV for raising these concerns. We have added more fair baselines than those used in current benchmarks, provided additional ablation studies across multiple datasets, and demonstrated why our approach qualifies as state-of-the-art in extreme token reduction efficiency. We also highlight the academic value of extreme token reduction task and how future work can leverage it to advance related but distinct tasks in broader research areas.

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Q1. Support on long Video Understanding Task

“According to Table 5, it seems longer videos lead to more severe performance drop. Can authors discuss and give deeper analysis about this?”

A1

We truly thank your valuable questions and suggestion for improving our work. We add more discussion and experiments hoping this can solve your questions. Please feel free to tell us if this solve your concerns during discussion period.

Meaning of these different tasks
We thank the reviewer for highlighting this important trend. The long-video understanding task is another important, yet completely different, task in video understanding. Our proposed extreme token reduction task is also crucial and supports unique application scenarios.
Indeed, compressing a 30 s clip into 32 tokens is very different from compressing a 3 hour clip into the same budget—longer clips necessarily bundle more diverse content per token, making fine-grained reasoning harder.

Different Task Objectives

• Extreme Token Reduction

  • Goal: “How can we represent a video clip with as few tokens as possible, while still supporting downstream tasks?”
  • Scope: Aggressively compress each clip (e.g., adaptively less than 32 tokens) for resource-constrained settings (edge devices, smart glasses, autonomous vehicles).

• Long-Video Understanding

  • Goal: “How can we model and reason over hours of continuous footage, preserving detailed temporal coherence and long-range dependencies?”
  • Scope: Retain an informative, ordered representation across many clips.

Our VQToken pipeline is specifically optimized for the extreme token reduction task and does not include mechanisms for stitching together multiple clips or explicitly modeling very long-range event structure for the long-video understanding task.

Empirical Length Analysis
In Table 5, despite achieving more than a 300× improvement over TokDense while using only 0.07 %–0.14 % of the tokens and and outperform on many effiency metrics , we observe NextQA (≈ 60 s clips): 0.45 % accuracy drop, ActivityNet-QA (≈ 2 minutes clips)1.93 % accuracy drop, VideoMME (≈ 17 minutes): 11.54 % accuracy drop, clearly illustrates the accuracy–efficiency trade-off.

This confirms that extreme compression incurs larger costs as clip duration increases.

Future Directions to Extend Extreme Token Reduction into Long-Video Understanding
We regard extending our extreme token reduction task to long-video understanding as a very promising future direction, which can benefit both tasks of research topics. Some ideas include:

1. Segmented Windowing
   • Split long videos into shorter (e.g., 1–2 minute) windows, apply VQToken per window, then fuse window embeddings with a lightweight transformer.
2. Hierarchical VQ
   • First quantize at the segment level, then perform a second-stage VQ on segment embeddings to capture inter-segment dependencies.
3. Adaptive Budgeting
   • Dynamically allocate more tokens to high-motion or semantically rich segments using a small importance predictor.

We will add this analysis and outline these future directions in the camera-ready version.

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Q2. Ablation Studies Across Multiple Benchmarks

“All ablation studies (Table 2, 3, 6) and comparisons report only NextQA. Please add ablations on other benchmarks.”

A2

We appreciate this suggestion. In the submitted draft, we focused our ablations on NextQA—both to control costs (avoiding commercial API calls) and to iterate rapidly over many settings. To demonstrate that each component’s contribution generalizes beyond NextQA, we have now run the three-stage ablation (None → +Codebook → +VQ-Attn) on additional benchmarks like ActivityNet-QA.

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Q3. Fair Comparison via Matched Fine-Tuning

“Please ensure fair comparison by aligning training regimes across baselines.”

A3

We appreciate the reviewer’s suggestion to ensure a fair comparison by aligning training regimes.

1. Many prior video‐LLM works compare models trained under different schedules or data regimes (because there are many closed-source methods trained on private or internet-scale dataset and resources) on almost every benchmark.

2. To address this common issue, we fine-tuned the LLaVA-OneVision baseline for one epoch on the same LLaVA-Video-178K[1] dataset, matching our VQToken training,and report:

• Accuracy retention: VQToken retains 99.5 % of NextQA’s accuracy, 98.1 % of ActivityNet-QA’s, and 88.7 % of VideoMME’s under matched fine-tuning.
• Token efficiency: Uses 800×–360× fewer tokens(0.14%), yielding a 300×+ improvement in TokDense across all benchmarks.

We will include this table, discussion, and citation in the camera-ready version.

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W1. Concerns on Performance Drops

“VQToken shows fairly large decreases on other three benchmarks. The only promising result on NextQA is not strong enough to prove effectiveness and needs more evidence.”

A4

We respectfully disagree that VQToken suffers “large” performance drops in the context of extreme token reduction. As shown in above table, under a 99.86 % reduction in token count, VQToken still retains (achieves an excellent efficiency–performance trade-off):

• NextQA: 99.5 % of baseline accuracy (57.4 vs. 57.7 pp)
• ActivityNet-QA: 98.1 % of baseline accuracy (46.3 vs. 47.2 pp)
• VideoMME: 88.7 % of baseline accuracy (39.3 vs. 44.4 pp)

This corresponds to a > 300× increase in token efficiency (TokDense) across all benchmarks. No prior method simultaneously:

1. Removes > 99 % of tokens,
2. Retains > 88 % accuracy on diverse video-QA tasks, and
3. Reduces inference throughput, FLOPs, memory footprint, and latency by orders of magnitude.

We will further clarify this argument in the camera-ready and highlight VQToken’s unprecedented balance of compression ratio, accuracy retention, and computational efficiency.

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Reference [1] Yuanhan Zhang, et al.Video Instruction Tuning With Synthetic Data. https://arxiv.org/pdf/2410.02713

Dear Reviewer BtRV,

Thank you very much for the time and care you invested in reviewing our submission and for the insightful suggestions you provided. We have incorporated a large number of new experiments and added detailed explanations throughout the paper to address your concerns.

Could you please let us know if there are any remaining issues or additional improvements you would recommend? Your feedback is invaluable to us as we work to strengthen our work.

Sincerely,

The Authors of 3333

Dear Reviewer BtRV,

Thank you again for your thoughtful feedback. In response, we have:

1. Added a detailed discussion of how clip length impacts extreme token reduction and highlighted the benefits and key differences between the task of extreme token reduction and the task of long video understanding.

2. Expanded our ablation studies across multiple benchmarks

3. Following your suggestion, we have incorporated dataset-matched fine-tuning baselines to ensure a fair comparison, even though most existing video-language models are trained on different datasets than the approaches they compare against.

4. Clarified why our approach achieves state-of-the-art performance with a 300× improvement in token efficiency (TokDense) across all benchmarks—reducing the number of tokens to just 0.07% of the original—and lowers throughput requirements, FLOPs, memory footprint, and latency by orders of magnitude.

We hope these updates fully address your concerns. If you have any further questions or suggestions, please let us know! We’d be happy to provide additional information to clarify any misunderstandings and discuss them before the end of the discussion period.

Thank you again for your time and invaluable guidance.

Sincerely,

The Authors of Submission 3333

Response to Some Concerns of Reviewer BtRV Arising During Author–Reviewer Discussion [PART-2/2]

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Q4: Citation and matched-baseline of LLaVA-Video

“…omitting any citation of LLaVA-Video when you use their data is not justified. A more fair baseline is encouraged.”

A4. More Fair Baseline and Correct Citation for LLaVA-Video-178K
We wholeheartedly agree that including a fully matched baseline is essential, even if prior work has not always followed this practice, and have now added the matched LLaVA-Video-178K baseline to our experiments above. This enhancement will raise the academic rigor of our study and ensure a fairer comparison.

We also appreciate your keen attention to citation details and your high academic standards. Thank you for identifying this problem. Because we retrieved the LLaVA-Video-178K dataset from the LLaVA-OneVision GitHub repository [2], we cited only [2] when referring to “178K videos” [3]. To ensure full transparency and reproducibility, we will:

• Cite LLaVA-Video-178K [3] explicitly in Section 4.1.1 (row 194):

  “…using the LLaVA-Video-178K dataset [3]—178 K videos paired with 1.3 M instruction-following samples…”

• Add a matched baseline in Section 4.1.5:

  “We fine-tuned the LLaVA-OneVision model [2] on LLaVA-Video-178K [3] for one epoch, using identical hyperparameters to VQToken and retaining 100 % of the original tokens, to provide a fully aligned comparison.”

• Open-source all evaluation scripts:
  We will release both the matched LLaVA-Video-178K baseline and our VQToken training/evaluation code upon acceptance.

These revisions will guarantee a fair, transparent comparison and facilitate exact reproduction. We hope these updates fully address your concerns. Thank you again for your insightful feedback—please let us know if any issues remain.

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[1] Mangalam, Karttikeya et al. "Egoschema: A diagnostic benchmark for very long-form video language understanding." NeurIPS 2023.
[2] Li, Bo, et al. "LLaVA-OneVision: Easy Visual Task Transfer." Transactions on Machine Learning Research.
[3] Zhang, Yuanhan, et al. "Video instruction tuning with synthetic data." arXiv preprint arXiv:2410.02713 (2024).

Response to Some Concerns of Reviewer BtRV Arising During Author–Reviewer Discussion [PART-1/2]

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Q1: Short- vs. long-video benchmarks and FPS sampling

“…more short-video benchmarks such as EgoSchema should be introduced. In the future, authors can try fps sampling and keep the same compression ratio to handle different lengths of video into the same information density.”

A1. Future Directions Suggestion
Thank you for this insightful recommendation. We fully agree that applying a constant–compression–ratio scheme—e.g., via FPS sampling—across clips of varying lengths is a highly promising avenue for future work. We will incorporate this discussion into our “Future Work” section. Although the limited rebuttal period prevents us from introducing new architectural changes at this stage, we are enthusiastic about pursuing an FPS-based approach in our ongoing research to ensure uniform token reduction for both short and long videos.

More benchmark evaluation on EgoSchema
Thank you for encouraging us to evaluate on EgoSchema benchmark. As suggested, we tested on the EgoSchema dataset [1], which features an average length of 180 s, and is widely adopted in recent studies. Using the same LMMS-Eval setup and uploaded into Kaggle, we compared our VQToken approach against:

These results confirm that—even without specialized mechanisms for extended-duration videos—VQToken achieves remarkable token-efficiency improvements while maintaining (and in this case, improving) accuracy on videos of increased duration. We hope this additional evidence underscores the broad applicability and robustness of our approach.

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Q2: Fixed or adaptive token budgets ablation

“The fixed or adaptive-length token reduction ablation on other benchmarks is more important than Table 5. It is recommended to add it if time permits. ”

A2.
Thank you for this valuable suggestion. To address it, we conducted a two-stage ablation—first adding the discrete codebook (+Codebook), then the attention-based module (+VQ-Attn)—under fixed token budgets of 12, 32, and 64 on both NextQA and EgoSchema. The results are summarized below:

This ablation clearly demonstrates that our adaptive VQ-Attn consistently outperforms the codebook-only variant across token budgets and datasets. We will include these results in the revised manuscript. Thank you again for prompting this deeper analysis—your guidance has greatly strengthened our evaluation.

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Q3: VideoMME accuracy inconsistency due to hardware

“You report 39.3% in text but 38.2% in Table 4—please check.”

A3.
Thank you for catching this inconsistency. We re-ran the VideoMME evaluation on our latest VQToken checkpoint using an NVIDIA RTX 4090 and confirm the correct accuracy is 39.3%. We believe the small discrepancy (≤1 percentage point) arises from subtle hardware-driven variations between GPU architectures—such as differences in floating-point kernels and low-level libraries on the RTX 4090 versus the A100. To ensure full clarity and reproducibility, we will:

1. Update Table 4 and all manuscript references to report 39.3%.
2. Add a footnote noting run-to-run variability due to hardware and software differences.
3. Release our pretrained parameters, code, and evaluation scripts publicly so others can reproduce these results exactly.

We appreciate your careful attention to detail, and we hope these changes address your concern.

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Continued in [PART-2/2] due to text limit.

Dear Reviewer BtRV,

We have,

1. Add more benchmark evaluation on EgoSchema

2. Fixed or adaptive token budgets ablation

3. Addressed the single typo inside VideoMME accuracy

4. Citation and matched-baseline of LLaVA-Video

5. BTW, a new adaptive token reduction on multiple benchmarks attached,

Table 3: Adaptive-Length Token Reduction on Multiple Benchmarks

Have these changes addressed all your concerns?
Please let us know if you have any further suggestions.

— Authors

Dear Reviewer BtRV,

Thank you for your careful examination and sharp insight. You are correct that an accuracy inconsistency of this magnitude should not occur due to hardware nondeterminism alone. After re-examining our historical logs and evaluation scripts, we found that the values had been inadvertently copied from a different experiment (a single typo carried over from the neighboring column for LongVideoBench)—the correct accuracy is 38.22 %. We sincerely apologize for this isolated clerical error, which we have confirmed is unique after carefully re-checking all other reported results. Thank you for pointing out this oversight in Table 4. We have corrected the transcription error, re-run the evaluation under identical conditions, and updated the “VideoMME Acc (%)” column to the correct value of 38.22 %. We have updated our script, re-run the VideoMME evaluation under identical conditions, and revised Table 4 accordingly. We have:

1. Corrected the evaluation typo and re-run VideoMME under identical conditions.

2. Updated Table 4 with the accurate metrics:

   Model	Fine-Tuning	NextQA Acc (%) ↑	NextQA TokDense ↑	ActivityNet-QA Acc (%) ↑	ActivityNet-QA TokDense ↑	VideoMME Acc (%) ↑	VideoMME TokDense ↑	Token % ↓
   LLaVA-OneVision (baseline)	1 epoch on LLaVA-Video-178K [1]	57.71	0.0049	47.16	0.0040	44.37	0.0038	100 %
   VQToken	1 epoch on LLaVA-Video-178K [1]	57.44	1.7950	46.25	1.4453	38.22	1.1944	0.14 %
   Trade-off	–	99.53 %	× 366	98.07 %	× 361	86.14 %	× 314	99.86 %

3. We have audited all tables and scripts in the same way and confirmed no other transcription errors—all other numbers match our logs exactly.

4. For full transparency, we will make our evaluation code, pretrain models, and full training code publicly available.

This correction does not affect our core conclusions regarding VQToken’s efficiency–accuracy trade-offs. We appreciate your attention to detail and the opportunity to improve our work.

We truly thank you again for your patience and your professional and sharp insight. Please let us know if you have any further questions or concerns.

Dear Reviewer BtRV,

Do you still have any concerns? We are very eager to address them before the extended dealine on Aug 8th AoE.

Best,

Authors

Response to Reviewer Gyx4

We thank Reviewer Gyx4 for recognizing our contributions, especially the novel application of vector quantization for token reduction in video large language models.

Unlike the other three reviewers, Reviewer Gyx4’s comments center on paper organization, writing style, and overall clarity, while slightly having some confusion on baseline selection.

We appreciate the detailed feedback and will carefully revise the camera-ready version to improve clarity and organization.

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1. Motivation of the VQ-Attention Module

Q1: “Why does such an attention module ‘enrich each centroid with motion context without increasing token count’? Why is hashing the patches to an integer an effective way to retain position and temporal information?”
A: Sorry for the misunderstanding. We will expand the current motivation in the Methods (§ 3, rows 137–141;143-144; 148-158; 169-171) to explain that our VQ-Attention module serves two complementary purposes:

• Preserve Motion Context:
  After Adaptive Discretization, we reduce a full video to just 0.14 % of its original tokens. A naïve clustering of these centroids discards all temporal cues—leading to a 22.3 % drop in accuracy (Table 6). VQ-Attention re-introduces motion context by using each centroid’s original patch index (hashed from its 2D + time coordinates) as additional key/query input, allowing the transformer to recover temporal patterns without increasing the centroid count.

• Hashing for Positional Fidelity:
  Unlike prior merging methods that average away positional encodings or pruning approaches that drop tokens entirely, we map each patch’s spatio-temporal coordinates into a simple integer hash. This hash serves as a “positional supplement” in VQ-Attention, ensuring each centroid remains linked to its exact location in time and space. In our ablation (Table 6), restoring hashed indices nearly fully recovers full-token accuracy (57.2 → 57.5) at only 0.14 % of tokens.

Update in Camera-Ready:

• Add a dedicated paragraph in § 3.2.
• Include a schematic illustrating hashed-index injection.
• Clarify the module’s dual objectives.

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2. Comparison to Recent Token-Pruning Methods

Q2: “The paper should compare with more recent token pruning methods. To establish the value of this work, the comparison should focus on recent competitive methods.”
A: Sorry for the misunderstanding and thank you for highlighting the importance of foregrounding our empirical comparisons. In the current draft, we evaluate and outperform more than three very recent state-of-the-art methods, all published within the last six months, under comparable (their original) or more generous (10 %) token budgets, for example:

• VidToMe (NeurIPS 2024, Dec 24) – a very recent video token-merging approach (Table 1–2)[1].
• DyCoke (CVPR 2025, June 25) – the current SOTA in dynamic token-pruning for video LLMs (Table 3)[2].
• Interpolative Merging from LLaVA-OneVision (TMLR, Feb 7, 2025) – an alternate token-merging strategy[3].

In contrast, VQToken achieves higher top-1 accuracy and denser representations (TokDense) while using only 0.07 %–0.14 % of the original tokens—an order of magnitude fewer than these baselines.

Revision & Highlight Plan:

1. Add a “Baselines” subsection in Section 4 detailing selection criteria (recency, token-budget, task alignment).
2. Enhance table captions with each method’s mechanism, venue, and token budget.
3. Provide an explicit performance breakdown in the main text, highlighting VQToken’s accuracy gains at matched token counts.
4. Include any (if have, currently we don't find any) newly published extreme token reduction methods (≥ 99 % compression) prior to camera-ready submission.

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3. Novelty of Extreme Token Reduction

Q3: “Being able to reduce the number of tokens drastically is impressive, but such observation has been made before.”
A: To our knowledge, no prior work has:

1. Formally defined extreme token reduction with minimal token counts as a standalone task, and compressing token inputs by up to 99.84%.
2. Introduced evaluation metrics that quantify information preservation at such scales.
3. Demonstrated robust performance on standard video understanding benchmarks under these constraints.

Most existing methods cap out at 30 %–80 % reduction with some accuracy loss. We welcome references to any methods exceeding 99 % reduction and will include direct comparisons in our revision.

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4. Clarity and Method Structure

Q4: “Overall clarity needs improvement. The Method section does not explain how each design addresses the stated weaknesses.”
A: Thank you for pointing out the need for a clearer organization. In the current draft, we present high-level ideas immediately after each of the three challenges (rows 49–59, 60–71, and 72–81), then dive into mathematical details. To improve readability and explicitly link each design choice to its motivating challenge, we will:

• Insert section markers in the Introduction (e.g. § 3.1 “Adaptive Discretization,” § 3.2 “VQ-Attention,” etc.) so readers can instantly locate each component.
• Precede every mathematical derivation with a brief “Motivation” paragraph stating which challenge it solves and why it matters under extreme token reduction.

These changes will make the logical flow crystal clear. If there are still parts that feel disconnected, we would greatly appreciate pointers to specific sentences or transitions so we can further tighten the narrative.

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Conclusion

We appreciate these observations and will integrate the above revisions to clarify our motivations, strengthen empirical comparisons, and improve readability. We are confident these changes will fully address Reviewer Gyx4’s concerns and underscore VQToken’s novelty and impact.

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References

1. Lee, Seon-Ho, et al. “Video token merging for long-form video understanding.” NeurIPS (Dec 24, 2024).
2. Tao, Keda, et al. “DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models.” CVPR (June 25, 2025).
3. Li, Bo, et al. “LLaVA-OneVision: Easy Visual Task Transfer.” Transactions on Machine Learning Research (Feb 7, 2025).

Dear Reviewer Gyx4,

Thank you very much for taking the time to review our submission and for the constructive suggestions you provided. We have worked diligently to address each of your concerns, adding extensive new experiments and detailed explanations to strengthen our work.

Could you please let us know whether these rebuttal have resolved your remaining concerns? Your feedback is invaluable as we finalize our manuscript.

Thank you again for your effort and insight.

Sincerely, The Authors of 3333

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Q3. “Standard” of DyCoke comparisons

“The comparisons are not really sufficient and to the standard of other papers (e.g., table 1 and 2 in DyCoke).”

A3. We reviewed DyCoke [2] and found that:

1. They compare multiple benchmarks in only 2 of their 6 tables (Tables 1 and 5).
2. The remaining tables (2, 3, 4, 6) each report on a single dataset.

By contrast, our submission includes:

1. 6 quantitative experiments (NextQA, EgoSchema, ActivityNet-QA, VideoMME, etc.),
2. 2 qualitative visualizations,
3. 7 additional experiments in this rebuttal, many of which are multi-benchmark.

If there is a specific “DyCoke standard” we have overlooked—such as evaluating on a particular combination of tasks—please let us know which benchmarks or metrics you would like to see, and we will prioritize adding them before the camera-ready deadline.

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Q4. Clarity and motivation in the Method section

“The concerns about clarity/motivation are hard to assess how much they will be improved in the rebuttal.”

A4. We appreciate this feedback. To make the connection between each stated challenge and our design choices crystal clear, we will:

• Insert section markers in the Introduction for each component (e.g., § 3.1 “Adaptive Discretization,” § 3.2 “VQ-Attention,” § 3.3 “Material Physics MoE”).
• Precede every mathematical derivation with a brief “Motivation” paragraph, explicitly stating which of the three challenges it addresses.
• Add a schematic for the VQ-Attention module showing how hashed indices re-introduce motion context without increasing token count.
• If there are particular sentences or transitions that still feel disconnected, we would be grateful for pointers so we can tighten the narrative further.

We hope these clarifications and our expanded experiments fully resolve your remaining concerns. Thank you again for your time and constructive feedback.

Sincerely,
The Authors

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References
[1] Lee et al., “Video token merging for long-form video understanding,” arXiv:2410.23782, Oct 2024; NeurIPS 2024, Dec 2024.
[2] Tao et al., “DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models,” CVPR 2025, Jun 2025.
[3] Li et al., “LLaVA-OneVision: Easy Visual Task Transfer,” TMLR, Feb 2025.

Dear Reviewer Gyx4,

Thank you for your careful follow-up and for engaging with our rebuttal. Below, we address your new questions in turn.

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Q1. More Dataset in Tables 1 and 3

“Table 2 and 3 didn’t specify the dataset for the accuracy number, and it seems to be for a single dataset. Usually we would expect comparisons on multiple benchmarks, like those in Table 4.”

A1. We apologize for the omission. In the main paper we report results on 5 benchmarks (NextQA, ActivityNet-QA, VideoMME, etc.), and in this rebuttal we have added EgoSchema to demonstrate that our method generalizes across both short- and long-video tasks. No prior work (e.g., DyCoke [2]) evaluates as many benchmarks in every table: they only compare across multiple datasets in two of six tables.

Below is the consolidated comparison on NextQA and EgoSchema (extended from Table 2/3):

Table 1: Updated Table 1 Comparison

Table 3: Adaptive-Length Token Reduction

We hope this clarifies that our comparisons indeed span multiple benchmarks and that our approach yields consistent gains across them, even under extreme token compression.

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Q2. Baseline clarification

“VidToMe is on arXiv on 2023/12, and interpolative merging from [3] is not really a reasonable baseline (the paper was not about token compression).”

A2. We regret the confusion:

• VidToMe reference. We actually refer to
  • Lee et al. “Video token merging for long-form video understanding.”
    arXiv:2410.23782 (Oct 2024); NeurIPS 2024 proceedings (Dec 2024).
    We will correct the dates in the camera-ready manuscript.
• Interpolative Merging (AnyRes). The LLaVA-OneVision paper [Li et al., TMLR Feb 2025] introduces AnyRes, a pooling-based interpolation layer for dynamic resolution. Although that work focuses on zero-shot transfer, the same pooling operation reduces token count by interpolating frames—and thus is a practical, fast token-reduction baseline that has been largely overlooked. We include it to ensure a fair comparison and to acknowledge its engineering contribution.

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Continue On [PART 2/2] due to text limit.

Dear Reviewer Gyx4,

We've provided additional information based on your comments. Do you still have any concerns? We are very eager to address them before the extended dealine on Aug 8th AoE.

Best,

Authors

My point was that the submission does not have any table that compares with multiple token compression baselines AND on multiple benchmarks. Counting how many tables include multiple benchmarks OR multiple baselines, or how many other experiments were done, does not demonstrate the original submission already did sufficient comparisons. Many of the experiments with multiple benchmarks, are comparing the proposed method to 100% token baselines, not previous token compression work.

Now with the new added experiments in the response, in my understanding, there is now one table (Table 3) that has two token compression baselines (Interpolating, and dycoke) on two benchmarks (NextQA and Egoschema). I get it that interpolating is a baseline, but it is not a competitively or carefully designed one. Simply put, it is not its own method paper, but this submission is. Compare this (1x2=2 numbers for comparison) to Dycoke's Table 1, which has two baselines (fastV and pruneMerge) on five benchmarks (in total 2x5=10 numbers for comparisons), this paper's comparison is still lacking.

Dear Reviewer Gyx4,

Thank you for your continued engagement and feedback. We would like to respectfully address your concerns point by point:

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Q1. “The original submission does not have any table that compares with multiple baselines AND on multiple benchmarks.”

In the original paper, we reported comparisons across multiple benchmarks—specifically, five datasets including NextQA, EgoSchema, ActivityNet-QA, VideoMME, etc.—in several experiments such as Table 4 and Figure 3. These results evaluate our method against multiple existing approaches, analyzing both performance and compression effectiveness.

In response to other reviewers’ requests during the discussion phase, we further extended our evaluation by adding additional benchmarks including EgoSchema (adaptive setting) and LongVideoBench_subtask, bringing broader validation to our claims.

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Q2. “Counting how many tables include multiple benchmarks OR multiple baselines … does not demonstrate sufficient comparisons.”
We agree that the rebuttal phase is an opportunity to further strengthen the evaluation. That said, our original submission already includes 7 experiments across various tasks and metrics. In this rebuttal, we added 11 more experiments, including those on adaptive token reduction and comparisons to DyCoke, per reviewer requests.

Importantly, this paper is the first to introduce the task of extreme token reduction (up to 99.93% token reduction), for which no prior work is carefully designed to operate under such a regime. Thus, while we strive to compare fairly, exactly matched baselines simply do not exist.

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Q3. “Now with the new added experiments... there is one table (Table 3) that has two token compression baselines (Interpolating and DyCoke) on two benchmarks...”
Thank you for your recent feedback. As the adaptive token reduction experiment was requested during the final two days of the discussion period, we focused on implementing it promptly and cleanly within the limited timeframe.

Moreover, the adaptive setting in Table 3 was very recently introduced (e.g., DyCoke, CVPR 2025, June 2025), and there are currently few public adaptive baselines available. We selected DyCoke and Interpolating as strong representatives to reflect recent progress, and to demonstrate our method's competitiveness in this new task formulation.

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Q4. “Interpolating is not a competitively or carefully designed baseline...”
While Interpolating may not originate from a standalone paper, it is a key design component of LLaVA-OneVision, introduced prominently on the first page as "AnyRes". It is practically impactful and widely used in vision-language pretraining pipelines. We believe this makes it a meaningful baseline for understanding how aggressive token reduction compares to standard resolution interpolation.

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Q5. “This paper’s comparison is still quite lacking...”
We respectfully disagree. Our original submission already presented 7 experiments across multiple datasets and tasks. In the rebuttal, we added 11 new experiments, totaling 18, many of which would not even fit within the main paper’s 9-page limit.

This effort reflects our commitment to fair and thorough comparison, even in the absence of directly matched prior baselines for this new task. We hope this addresses your concern.

🟥Moreover, we would like to respectfully point out that you previously acknowledged the following in your Strengths section:🟥

🟥 "Experiments and ablation are relatively extensive."🟥

🟥This directly supports the view that our evaluation is comprehensive. We hope this reaffirms that our paper is, in fact, experimentally well-supported, and that this addresses your concern.🟥

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Thank you again for your feedback. If you have any remaining concerns, we would be very eager to address them before the discussion deadline of today August 8th 23:59 AoE.

Sincerely,
Authors of Paper #3333