CurveStream: Boosting Streaming Video Understanding in MLLMs via Curvature-Aware Hierarchical Visual Memory Management

Chao Wang; Xudong Tan; Jianjian Cao; Kangcong Li; Tao Chen

cs.CVMar 20, 2026

CurveStream: Boosting Streaming Video Understanding in MLLMs via Curvature-Aware Hierarchical Visual Memory Management

Authors: Chao Wang, Xudong Tan, Jianjian Cao, Kangcong Li, Tao Chen

Organizations: College of Future Information Technology, Fudan University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China

Abstract

Multimodal Large Language Models have achieved significant success in offline video understanding, yet their application to streaming videos is severely limited by the linear explosion of visual tokens, which often leads to Out-of-Memory (OOM) errors or catastrophic forgetting. Existing visual retention and memory management methods typically rely on uniform sampling, low-level physical metrics, or passive cache eviction. However, these strategies often lack intrinsic semantic awareness, potentially disrupting contextual coherence and blurring transient yet critical semantic transitions. To address these limitations, we propose CurveStream, a training-free, curvature-aware hierarchical visual memory management framework. Our approach is motivated by the key observation that high-curvature regions along continuous feature trajectories closely align with critical global semantic transitions. Based on this geometric insight, CurveStream evaluates real-time semantic intensity via a Curvature Score and integrates an online K-Sigma dynamic threshold to adaptively route frames into clear and fuzzy memory states under a strict token budget. Evaluations across diverse temporal scales confirm that this lightweight framework, CurveStream, consistently yields absolute performance gains of over 10% (e.g., 10.69% on StreamingBench and 13.58% on OVOBench) over respective baselines, establishing new state-of-the-art results for streaming video perception.The code will be released at https://github.com/streamingvideos/CurveStream.

Figures & tables

Fig. 1: Performance and mechanism of CurveStream. (a) CurveStream achieves state-of-the-art on OVOBench among training-free paradigms, boosting performance by 13.6% over the Qwen2.5-VL-7B baseline. (b) Curvature-aware memory management over infinite streams ( $t\rightarrow\infty$ ). By evaluating real-time semantic intensity (blue curve) against a K-Sigma dynamic threshold (pink dashed line), it adaptively filters redundant Low-Semantic Frames. Critical High-Semantic Frames (yellow dots) at curvature peaks are preserved, ensuring optimal visual context retention under strict token limits.

Fig. 2: Overview of the CurveStream framework. This training-free vision encoder enables infinite streaming video understanding by replacing traditional sampling with a dynamic-retention perception layer designed to prevent Out-of-Memory (OOM) errors in long-term sequences. The Curvature-Aware Scorer (CAS) evaluates semantic transition intensity by fusing first-order motion variation and second-order trajectory curvature within the latent feature manifold, while the Hierarchical Visual Memory Management (HVMM) module dynamically routes incoming tokens into a fixed-capacity ( $N_{\max}$ ) queue. By utilizing temporally adaptive $K$ -Sigma thresholds, the encoder adaptively categorizes visual information into Clear, Blurred, or Discard states based on the intensity of semantic shifts, thereby ensuring a constant memory footprint while preserving critical visual anchors for long-term multimodal reasoning.

Proprietary MLLMs
Method	Frame	StreamingBench [ 18 ]	OVOBench [ 19 ]
Human	-	91.46	93.20
Gemini 1.5 Pro [ 41 ]	1fps	75.69	69.32
GPT-4o [ 42 ]	64	73.28	64.46
Open-source Offline MLLMs
Qwen2-VL-7B [ 2 ]	64	69.04	60.65

TABLE I: Quantitative comparison of average accuracy across 10 Real-Time Visual Understanding sub-tasks in StreamingBench and 6 Real-Time Visual Perception sub-tasks in OVOBench. Best results are highlighted in bold, with absolute gains over respective baselines in red. Our frame count (10-20) reflects the dynamically changing size of the adaptive memory queue.

Proprietary MLLMs
Method	Frame	MVBench [ 20 ]	EgoSchema [ 21 ]	VideoMME [ 22 ]
GPT4-V [ 47 ]	1fps	43.7	55.6	60.7
GPT-4o [ 42 ]	64	64.6	72.2	77.2
Open-source Offline MLLMs
LLaVA-NeXT-Video [ 5 ]	32	33.7	43.9	46.5
Qwen2-VL-7B [ 2 ]	64	67.0	66.70	69.0

TABLE II: Quantitative comparison of the average accuracy on MVBench (20 sub-tasks), EgoSchema, and VideoMME benchmarks. The best results are highlighted in bold, with absolute performance gains over the respective baselines indicated in red.

Panel of Figure 5 — Fig. 3: Scalability and memory allocation analysis. (a) CurveStream consistently delivers significant performance gains across varying model capacities (4B, 8B, 32B) of the Qwen3-VL series. (b) Impact of the clear memory (High-Res) retention ratio on overall accuracy and token cost. An adaptive $\sim$ 50% ratio achieves the optimal trade-off between semantic integrity and computational overhead.

Fig. 4: Ablation on K-Sigma dual thresholds. CurveStream exhibits strong hyperparameter robustness across various

k_{1}

and

k_{2}

configurations on OVOBench. The dynamic mechanism effectively balances memory allocation between High-Res and Low-Res frames, ensuring an optimal accuracy-efficiency trade-off without tedious tuning.

Table 7

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 8 — Fig. 5: Action Recognition in dynamic virtual environments. Fast-paced viewpoint shifts often cause baseline models to lose track of transient actions, resulting in severe hallucinations (e.g., misinterpreting the action as setting up a camera). CurveStream captures the sharp curvature peak during the “drinking” animation, preserving it as a key semantic node to deliver an accurate response.

Panel of Figure 9 — Fig. 7: Attribute Recognition requiring fine-grained spatial details. Standard memory limits often force base models to downsample past frames uniformly, blurring complex textures. CurveStream dynamically assigns high-resolution Clear Memory to informative frames where the pot’s pattern is unobscured, allowing it to correctly identify the nested diamond shapes.

Proprietary MLLMs
Model	Frame	OP	CR	CS	ATP	EU	TR	PR	SU	ACP	CT	Avg.
Human	-	89.47	92.00	93.60	91.47	95.65	92.52	88.00	88.75	89.74	91.30	91.46
Gemini 1.5 Pro [ 41 ]	1 fps	79.02	80.47	83.54	79.67	80.00	84.74	77.78	64.23	71.95	48.70	75.69
GPT-4o [ 42 ]	64	77.11	80.47	83.91	76.47	70.19	83.80	66.67	62.19	69.12	49.22	73.28
Claude 3.5 Sonnet [ 49 ]	20	73.33	80.47	84.09	82.02	75.39	79.53	61.11	61.79	69.32	43.09	72.44
Open-source Offline MLLMs

Appendix

TABLE V: Comprehensive evaluation results on the StreamingBench benchmark for real-time visual understanding. Our method consistently improves the performance of base MLLMs across all contextual reasoning and event understanding metrics, as indicated by the “

\uparrow

” symbol. The highest scores in each column are marked in bold .

Proprietary MLLMs
Model	Frame	OCR	ACR	ATR	STU	FPD	OJR	Avg.
Human	-	93.96	92.57	94.83	92.70	91.09	94.02	93.20
Gemini 1.5 Pro [ 41 ]	1 fps	85.91	66.97	79.31	58.43	63.37	61.96	69.32
GPT-4o [ 42 ]	64	69.80	64.22	71.55	51.12	70.30	59.78	64.46
Open-source Offline MLLMs
LLaVA-Video-7B [ 23 ]	64	69.80	59.63	66.38	50.56	72.28	61.41	63.34

Appendix

TABLE VI: Detailed performance comparison on the OVOBench dataset across various real-time visual perception sub-tasks. We report the evaluation metric ( e.g. , Accuracy %) for both the standard base models and our proposed CurveStream. The “

\uparrow

” denotes the absolute performance gain achieved by integrating our curvature-aware memory management into the respective base models. Best results are highlighted in bold .

Proprietary MLLMs
Model	Frame	AS	HAC	SAD	MAD	CM	NSM	Avg.
Gemini-1.5-Pro [ 41 ]	1 fps*	49.22	53.73	48.80	54.85	41.58	56.25	49.87
GPT-4o [ 42 ]	1 fps*	40.65	45.10	42.84	45.48	36.00	48.44	42.09
Claude-3.7-Sonnet [ 50 ]	1 fps*	45.20	43.02	41.82	48.05	39.07	46.88	43.73
Open-source MLLMs
Video-LLaVA-7B [ 51 ]	8 frms	24.91	21.54	25.45	30.54	26.23	21.88	25.37

Appendix

TABLE VII: Detailed performance comparison on the FavorBench dataset. ”

\uparrow

” indicates the performance improvement of our method compared to the base model.

Model	Avg.	Action Antonym	Action Count	Episodic Reasoning	Action Localization	Action Prediction	Action Sequence	Character Order	Counterfactual Inference	Egocentric Navigation
Qwen3-VL-8B	60.17	84.00	37.50	51.50	34.50	57.49	65.95	61.50	65.50	38.00
+ Ours (CurveStream)	63.60 ( $\uparrow$ 3.43 )	68.50	50.50	54.00	39.50	79.00	70.50	77.50	60.50	37.50

Appendix

TABLE VIII: Detailed performance comparison on the MVBench dataset across 19 fine-grained sub-tasks. Due to space constraints, the results are split into two blocks. ”

\uparrow

” indicates the performance improvement of our method.

Model Configuration	CAS	HVMM	OP	CR	CS	ATP	EU	TR	PR	SU	ACP	CT	Avg.
Qwen2-VL-7B [ 2 ]			77.38	76.56	73.19	75.08	75.00	67.91	73.15	65.04	66.57	35.75	69.04
w/ CAS	✓		85.29	80.47	89.59	87.58	74.53	82.24	76.85	70.73	73.94	43.52	78.16 ( $\uparrow$ 9.12)
w/ HVMM		✓	87.19	76.56	89.59	86.93	75.16	86.92	76.85	70.33	74.50	43.01	78.80 ( $\uparrow$ 9.76)
CurveStream	✓	✓	88.56	77.34	88.61	89.84	76.25	92.52	76.85	76.83	76.70	45.31	81.04 ( $\uparrow$ 12.00)

Appendix

TABLE IX: Ablation study on StreamingBench . We evaluate the individual and combined effects of CAS and HVMM. Red arrows specifically denote the absolute average performance improvements achieved over the respective base models.

Model Configuration	CAS	HVMM	OCR	ACR	ATR	STU	FPD	OJR	Avg.
Qwen-3VL-8B [ 1 ]			71.14	65.14	75.86	64.61	75.25	70.65	70.10
w/ CAS	✓		87.92	83.49	81.90	64.04	76.24	80.98	78.49 ( $\uparrow$ 8.39)
w/ HVMM		✓	87.25	71.56	78.45	66.29	74.26	72.83	74.79 ( $\uparrow$ 4.69)
CurveStream	✓	✓	93.96	82.57	83.62	68.54	78.22	80.43	80.76 ( $\uparrow$ 10.66)

Appendix

TABLE X: Ablation study on OVO-Bench . We report the performance across various real-time visual perception sub-tasks to validate the synergistic effect between the proposed memory modules.

Hyperparameter	Value
Queue Size ( $N_{max}$ )	20
Curvature score weight ( $\lambda$ )	0.2
TRANSITION_SIZE	224
K_SIGMA_TRANS ( $k_{1}$ )	0.0
K_SIGMA_KEY ( $k_{2}$ )	1.0

Appendix

TABLE XI: Detailed core experimental hyperparameters utilized by the CurveStream framework throughout the entire inference phase.

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Streaming video understanding requires multimodal large language models (MLLMs) to preserve relevant evidence from continuously evolving streams under strict causality and bounded memory. Yet existing paradigms remain limited: model-based methods require intrusive backbone updates, while memory-based methods expend substantial visual-encoding computation on temporally redundant content and rely on rigid access to visual history. To address these limitations, we introduce StreamFlow, an efficient visual memory framework that enables dynamic, on-demand access to historical visual information. StreamFlow combines a lightweight, dynamics-aware mid-term memory that filters temporal redundancy before visual encoding with a latent long-term memory that consolidates historical video content into visual latents accessible to subsequent reasoning. During generation, an attention-guided retrieval mechanism injects relevant visual latents when the model's reliance on visual evidence weakens. StreamFlow achieves state-of-the-art streaming video understanding performance, reaching 67.73% overall accuracy on StreamingBench, while also delivering strong performance on offline long-video benchmarks. Relative to the vanilla setting, it improves the visual attention score (VAS) by 59.1% while reducing end-to-end latency and peak memory by 50.4% and 21.1%, respectively, enabling more visually grounded and efficient reasoning.

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