TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning
Authors: Junwen Pan
Qizhe Zhang
Rui Zhang
Ming Lu
Xin Wan
Yuan Zhang
Chang Liu
Qi She
Venue: ICLR 2026 Poster (2026)
Keywords: temporal search
long video understanding
reinforcement learning
large video language model
Primary area: reinforcement learning
TL;DR: This paper proposes a model that learns to actively search for relevant temporal clips through end-to-end reinforcement learning.

Abstract: Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Many existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, we propose **TimeSearch-R**, which reformulates temporal search as interleaved text–video thinking, seamlessly integrating searching video clips into the reasoning process through reinforcement learning (RL). However, applying RL training methods, such as Group Relative Policy Optimization (GRPO), to video reasoning can result in unsupervised intermediate search decisions. This leads to insufficient exploration of the video content and inconsistent logical reasoning. To address these issues, we introduce GRPO with Completeness Self-Verification (GRPO-CSV), which gathers searched video frames from the interleaved reasoning process and utilizes the same policy model to verify the adequacy of searched frames, thereby improving the completeness of video reasoning.
Additionally, we construct datasets specifically designed for the SFT cold-start and RL training of GRPO-CSV, filtering out samples with weak temporal dependencies to enhance task difficulty and improve temporal search capabilities. Extensive experiments demonstrate that TimeSearch-R achieves substantial improvements on temporal search benchmarks such as Haystack-LVBench and Haystack-Ego4D, long-form video understanding benchmarks like VideoMME, MLVU, and LongVideoBench, as well as video reasoning benchmarks such as Video-Holmes, consistently and significantly outperforming other existing temporal search approaches and text-only reasoning models. Our code is available at *https://github.com/Time-Search/TimeSearch-R*.

Published as a conference paper at ICLR 2026
TIMESEARCH-R: ADAPTIVE TEMPORAL SEARCH FOR LONG-FORM VIDEO UNDERSTANDING VIA SELF- VERIFICATION REINFORCEMENT LEARNING
Junwen Pan
Qizhe Zhangl.2. Rui Zhang'
Ming Lu
Xin Wan'
Yuan Zhang!.2 Chang Liu' Qi She
1 ByteDance School of Computer Science, Peking University
(panjunwen, sheqi roger ) @byt com
ABSTRACT
Temporal search aims identify minimal set of relevant frames from of thousands based on given query, serving as foundation for accurate long-form video understanding. Many existing works attempt to progressively narrow the search space. However, these approaches typically rely on hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, propose TimeSearch-R. which reformulates temporal search interleaved text-video thinking, seamlessly integrating searching video clips into the reasoning process through reinforcement learning (RL). However, applying RL training methods, such as Group Relative Policy Optimization (GRPO), to video reasoning result in unsupervised intermediate search decisions. This leads insufficient exploration of the video content and inconsistent logical reasoning. To address these issues, we introduce GRPO with Completeness Self- Verificatin (GRPO-CSV), which gathers searched video frames from the interleaved reasoning process and utilizes the same policy model to verify the adequacy of searched frames, thereby improving the completeness of video reasoning. Additionally, we construct datasets specifically designed for the SFT cold-start and RL training of GRPO-CS CSV, filtering out samples with weak temporal dependencies to enhance task difficulty and improve temporal search capabilities. Extensive experiments demon- strate that TimeSearch-R achieves substantial improvements temporal search benchmarks such as Haystack- -Venckh---Bnccchhhh and (oyssack-Ego4]]] long-form video un- derstanding benchmarks like VideOMME, MLVU, and Long VideoBench, as well video reasoning benchmarks such as Video-Holmes, consistently and significantly outperforming other existing temporal search approaches and text-only reasoning models. Our code is available at
INTRODUCTION
Long-form video understanding requires models to navigate through tens of thousands of frames to identify the most relevant information for answering specific questions (Fu et al.) 2024: Zhou et al.) 2024, Wu et al., 2024). Temporal search lies at the heart of making long-video understanding both accurate and interpretable (Park al. 2025 et al. 2023 Ye et al. 2025). In contrast to the human visual system, which conducts adaptive temporal search Yarbus 1967 Hayhoe current large sideo-languae static frame sampling (Lin et al. 2023 Bai et al. 2025 Feng et al. 2025). Humans naturally alternate between broad scanning and targeted inspection, refining their focus iteratively based on intermediate findings (Castelhano & Henderson 2007 Henderson & Hayes] 2017). In contrast, existing methods are limited to a fixed set of frames established before the reasoning process begins. This design presents fundamental contradiction: video reasoning dynamic process where temporal search interleaves with video reasoning; however, the video frames accessible to the model remain fixed from the outset, ultimately hindering effective reasoning.
Hayhoe & Ballard (005)), models (LVLMs) primarily rely on hand-crafter search strategies with
Equal contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
Caption Generation
Query-based Retrieval (CLIP)
Question: close the drawer?
Action Prodiction
Frame Sampling
VideoAgent
To defermine whether the drawer was closed, I need to idendly frames where the drawer interacted with and check its state efterward. ("start time : O, "end time": 606, "query": "drawer Interaction") ")
Object Detection (YOLO)
Question Grounding
I can see the drawer being interacted with at timestamps 728, 80... 593s. However, need confirm its state at the end the video
Task Completion
Framee Sampling
("start time": 580, 606, "query": "drawer")
Q T+
g Video Interleaved Thinking
Frame Sampling
the drawer appears romain open last visible interactions (590s to 594a). There is no evidence of the drewer being oloaed... <answer>No<<answer>
End-to-Fnd RT. Training (Ours)
(a) Paradigms of Temporal Search
(b) Text- Interleaved Thinking
Figure I: (a) Different paradigms of temporal search. Previous works such as VideoAgent (Wang al., 2024) and T (Ye et al., (025) predominantly rely on handcrafted workflows, resulting in suboptimal strategies. Our approach adopts end-to-end reinforcement learning, enabling the model learn optimal search strategies directly from data. (b) Interleaved text-video thinking process. We reformulate the temporal search task as an interleaved text-video thinking process, where the temporal search is seamlessly interleaved into the reasoning process.
Inspired by the gap between human cognition and model reasoning, recent studies have explored interactive video agents that attempt to bridge this divide through multi-turn temporal search, as illustrated in Figure (a). VideoAgent (Wang al. 2024) first employs a large language model (LLM) as the central agent, which iteratively calls tools like vision- language models (VLMs) and CLIP (Radford al. (02T) for frame captioning and retrieval, and then aggregates information the textual modality to perform reasoning and predict answers. T* (Ye et al. 2025) extends this paradigm by introducing an object-oriented
search. first leverages a VLM extract target objects from the question, then employs object detection models (e.g.. YOLO (Cheng et al. (024)) to identify keyframes containing these objects, and finally uses the retrieved frame set to complete the task. Moreover, strategies that introduce tree-structured search to improve efficiency have also been explored (Wang et al. 2025) Li al., 2025 Pan et al. 2025). However, all of these approaches depend on manually designed workflows, which lead to suboptimal search strategies.
This motivates us to explore an end-to-end learning approach that discovers optimal temporal search strategies directly from data. In this work, we reformulate the temporal search task an interleaved text-video thinking process, and propose TIMESEARCH RR, a model that learns to actively search for relevant temporal clips through reinforcement learning (RL). As shown in Figure (b), our model alternates between textual reasoning and temporal exploration, iteratively refining its understanding of the video. We refer this dynamic process as Thinking with Videos-a paradigm where models gradually improves their comprehension by searching for relevant video content conditioned on intermediate reasoning states. This concept extends the recent advances in multimodal reasoning, Thinking with Images (Hu et al. 2024 Zheng et al. 2025). to the long-video domain.
Although recent works have successfully applied RI. algorithms like Group Relative Policy Optimiza- tion (GRPO) (DeepSeek-Al) 2025) to textual (Jin et al. 2025) and spatial search (Zheng et al. 2025), temporal search videos poses unique challenges. The original GRPO rewards only the final output while ignoring intermediate search decisions, leading several failure modes illustrated in Figure 2
The first mode, termed insufficient temporal exploration, arises because the final output reward provides incentive for comprehensive exploration of video frames. LVLMs may arrive correct answers through partial evidence or language bias without proper visual grounding (Niu et al. (021). missing critical frames required for reliable understanding. The second mode, termed inconsistent
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How many small tin prolagenist one by one while packing natabook with "ASTRO CARDS" writton on It?
Question:
Question: demonstrating Christmas inttially decorated with apples, candles and beriss, which kind has the largest number?
("start_ time". 672, "end_ time": 703,
("start time". 35, "end time": 45, "query": "Chnsrmaa
"query": "amali CARDS")
and
decorated appies, candles, and berress)
the prolaganist takes two small tin duiipé packing process involving the "ASTRG CARDS. the.gwarrre
the Christmae tree is decorated with apples, candlee, and bernes. The number of apples the number of canoles and the each number bemies Therefore, the </answer>
individually in frames 6978 a aaaaeend <answer-2<annwwer>
diecoration is the same canswer-Beriies
missing frame..
The seurched
insufficient answer the question correctly.
intemnediate reasoning steps output by the model disconnected from the final answer.
Figure 2: Two failure modes with the original GRPO reward. Left: Insufficient temporal exploration. The model misses critical frames required to correctly answer the question. Right: Inconsistent logical reasoning. The intermediate reasoning process contradicts the final answer.
logical reasoning, emerges when models produce plausible thinking processes disconnected from the final answers, phenomenon also observed in text-only reasoning (Lanham et al. 2023). These two failure modes hinder proper temporal search and diminish the benefits of video reasoning.
To address these challenges, we propose Completeness Self- Verification (CSV) as supplement to the original GRPO algorithm, providing supervision over the intermediate steps temporal search. GRPO-CSV tackles insufficient temporal exploration by ensuring the model to acquire sufficient visual evidence through self-verification, and promotes consistency between intermediate reasoning and the final answer by re-answering the question using the searched frames. Besides, we construct high-quality video reasoning dataset to support GRPO-CSV training. Existing datasets contain a large number of trivial samples solvable through pure linguistic bias, as well as noisy samples that remain unsolvable even with extensive search, severely hindering progress in long-video reasoning. We implement two-stage data filtering pipeline to curate high-quality samples tailored to the demands of video reasoning, ensuring that the model learns the correct process of temporal search.
We evaluate our T on both temporal search and long-form video understanding tasks, demonstrating its superiority in long video reasoning. On temporal search tasks, TimeSearch-R improving the temporal F1 score on Haystack-LVEE Lenchh by 5.9% and the accuracy on Haystack- Ego4D by 8.3%, compared to the previous state-of-the-art method. On long-form video understanding tasks, TimeSearch-R consistently surpasses existing temporal search methods and the advanced reasoning model Video-R1. Moreover, the learned search policy transfers readily other foundation models like GPT-4o, achieving significant performance improvements at lower cost. On video reasoning tasks, TimeSearch-R achieves an 11.8% improvement the base model Qwen2.5-Vi on Video-Holmes, and even outperforms advanced proprietary models such as Gemini-2.0.
In summary, main contributions are three-fold:
1. We propose the TimeSearch-R framework, which reformulates temporal search inter- leaved text-video thinking and learns optimal search strategies directly from data.
2. We introduce GRPO-CSV, a novel RL algorithm, which ensures sufficient and accurate video exploration by supervising the intermediate steps of temporal search. To support GRPO-CSV training, we also construct a high-que high-quality video reasoning dataset via a two- stage filtering pipeline, enabling the model to learn correct temporal search processes.
3. Extensive experiments demonstrate the superiority of approach both temporal search and long-form video understanding. TimeSearch-R consistently and significantly outper- forms other existing temporal search methods and advanced reasoning models.
METHODS
In this section, we first reformulate the temporal search task as an interleaved text-video thinking process, enabling the model to learn optimal search strategies directly from data. To address the challenges of insufficient temporal exploration and inconsistent logical reasoning, we introduce GRPO-CSV as a novel RL algorithm for long videos, which ensures both sufficient and accurate video exploration by supervising intermediate steps of temporal search. Finally, we describe the model training process, including the construction of a high-quality long-video reasoning dataset.
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2.1 TASK FORMULATION
Temporal Search within Thinking Process. To learn optimal search strategies directly from data, we reformulate temporal search as multi-turn thinking process interleaved with video clip retrieval. Given video V and corresponding question Q, an initial preview V is uniformly sampled from V for subsequent reasoning. At each thinking step k, the policy model Te generates a textual reasoning Tk. If T& contains a search instruction, the video environment executes it according frame timestamps, retrieving a clip V& S V that is appended to the ongoing chain of thought (CoT) as input for later steps. The interleaved tixt- CoT at reasoning step ls is formalized as:
Ck _ {TT, Vi), (T2, V2). .... (Tk, V.
This interaction process repeats until the model emits the final answer A or reaches the pre-defined reasoning budget. For further analysis, the entire reasoning chain can be decomposed into two components: temporal search and answer prediction, which can be formulated as:
Po(A,CIV,Q) = Po(C V.Q) Po(A C,,AI,,V,Q)
Temporal Search Answer Prediction
Dynamic Video Frames. During the interleaved thinking process, the model autonomously explores the video by searching for additional clips. At reasoning step k, if the model outputs a search instruction, it is also required to specify the temporal boundaries th and th to be explored, along with corresponding textual query g². The video environment then executes frame retrieval function to obtain additional F' frames V = search(V; t th: g", F) (fk. fe...., }. This function serves as an interface to the policy model wa, employing small VLM (e.g., SigLIP (Zhai et al. ))23)) to calculate the similarity among frames within the specified temporal clip 1/5 (5) as well as the relevance with the textual query q². The most informative F frames are then sampled using determinantal point process (DPP) (Kulesza & Taskar (2012). which has been widely used for information retrieval (Chen et al., 2018 Celis al.) 2018 [Sun al., 2025). This operation significantly improves the efficiency of temporal search, and more details can be found in Section
2.2 GRPO WITH COMPLETENESS VERIFFERIIICAIO
Evaluating temporal search typically requires frame-level annotations Ye et al. 2025), which are time-consuming and labor-intensive. Previous works YYu et al., 20256 Sun et al. 2025) evaluate the selected frame sets in downstream video understanding tasks as a surrogate metric. Inspired by this, we design Completeness sef--eeifiiation (CSV) mechanism for GRPO, which is annotation- free and can be seamlessly integrated into RL training, serving as a complementary to the original outcome reward. The overall pipeline of GRPO-CSV is illustrated in Figure 3
GRPO-CSV. We introduce CSV as a complement to GRPO with only outcome rewards. During the GRPO rollout phase, the policy model na generates a text-video interleaved CoT C' and a final answer A. Applying rewards only to the final answer may reduce the effectiveness of intermediate search processes. To address this, we extract the video clips from C' to form dynamic frame set V. as the input for the CSV phase. In the CSV rollout phase, the same policy model is required to re-answer the question Q using only Va. yielding CSV answer A,. Critically, the model is prohibited from any further temporal searching and must rely solely the currently searched frames to answer the question. The CSV answer A, is expected to remain consistent with the original answer A:
Po Ve. Q) = Po(A V.V.).
Completeness Reward.
We design a completeness reward for the CSV phase, which is computed using the original answer A, the CSV answer Ac, and the ground-truth answer A as follows:
R. = 1 [AAce(A, > 0.5] Acc(Aee A*).
where Acc(A, A+) and Acc(Ar, A*) denote the correctness scores of the original answer and the CSV answer, respectively, and 1[.] is an indicator function activated only when the original answer A is correct. This conditional design ensures that the CSV reward is applied only to promising reasoning trajectories, encouraging meaningful temporal search while verifying both the sufficiency of acquired visual evidence and the consistency between the reasoning process and the final answer.
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Published as a conference paper at ICLR 2026
GRPO
Policy Update
KL
Policy Model
Reference Model
Group Computation
a1
Rollout
22
Frame
Answer Verifier
Sampling
g
R2
4c
Input
VextoVideo Interleaved CoT
Answer Reward
Advantage
CSV
Rf
Policy Model
Answer Verifier
R
Q
Rollout
R2
Text Thinking
Video Clips
Dynamic Frame Set
CSV Answer
Completeness Reward
Final Answer
Figure 3: Overall pipeline of GRPO-CSV. Building upon the original GRPO, CSV extracts dynamic frame set from the multi-modal CoT and constructs a vision-only CoT for re-answering. This design verifies that the searched dynamic frames provide sufficient evidence for correct reasoning, ensuring completeness and consistency without requiring explicit frame-level supervision.
From information -ooommation--hcoretic perspective (Shannon, 1948). relying solely the final answer reward primarily maximizes the mutual information [(A; Q). making the final answers strongly dependent the questions, while neglecting T(A:V) the dependence of answers the video input. This permits the model to exploit language biases as shortcuts instead of performing sufficient video exploration. Our completeness reward enforces high [A; Vo), which reinforces the pathway from V to A, thereby increasing [(A; V) and ensuring that the model performs effective temporal search.
From optimization perspective, using only the final answer reward leads extremely sparse binary signal and a muuishing-advantee problem Yu et al. (025a).. Moreover, a correct final answer may arise from lucky guessing, yet receives the same incentive as one derived from proper temporal search, making it difficult to learn how to utilize video content. The completeness reward supplements correct answers with supervision over the intermediate reasoning process, transforming the originally sparse reward into denser signal, enabling better credit assignment and model optimization.
Format Reward. The format reward enforces adherence to a predefined schema through- out the multi-turn reasoning process, validating the structural integrity of the entire tra- jectory rather than individual steps. During reasoning, each step must follow either the <think>... ... w/thh/itttttooocaall... <etoo..//tooooalllloao <think>, wtt.s///hhhhhsssnse..e binary score the full trajectory: if all steps are structurally valid, and otherwise.
pattern for temporal search the ../ </aswwer> pattern for the final response. We assign
Accuracy Reward. We evaluate answer accuracy for two task types. For multiple-choice questions, we extract the option letter from the model's output and perform an exact match with the ground -ruth option. For open-ended questions, we adopt an LLM-as-a- a--dud approach (Zheng et al. 2023) to assess the semantic agreement between model's final answer and the reference answer. The scores for both cases are given in binary form, with indicating alignment with the standard answer.
Overall Reward. The total reward the sum completeness, format, and accuracy components:
R R. Rum
Race-
This composition encourages sufficient temporal exploration (R.), consistent reasoning structures (Rim), and correct final answers (Rac),, enhancing the model's ability to understand long-form videos.

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Published as a conference paper at ICLR 2026
Table 1: Temporal search performance. We report temporal similarity, visual similarity, and question-answering (QA) accuracy on Haystack-LV Ego4D test-tiny subset. Baseline results are directly cited from Ye et al. (2025). f indicates the average number of keyframes determined by the model adaptively.
as well as QA accuracy on Haystack-
Temporal
Visual
QA
Method
Base Model
Keyframes
R
Ft
P
Fj
LVBench
Ego4D
Static Frume Sampling
Uniform Uniform
GPT-4o
1.4
6.3
56.0
62.7
41.5
GPT-4o
32
1.4
24.9
2.7
58.7
81.6
67.3
50.5
45,5
Adoptive Temporal Search
VideoAgent Wang et al.
2024
GPT-4
10.1
1.2
8.5
2.1
58.8
73.2
64.7
Retrival-baacu Te
2045
GPT-4o
8
1.5
6.3
2.3
63.1
65.5
64.1
T (Ye et al. 2025 Retrivval
GPT-4o
8
16
7.1
2.5
58.4
72.7
64.3
51.9
45.0
2025
GPT-4n
32
1.3
21.8
2.4
59.9
80.8
67.8
T Ye ct al.
GPT-4o
32
1.7
28.2
3.1
58.3
83.2
67.8
53.1
46.5
Text- Video Interleaved Reasoning
TimeSearch R (Ours)
Qwen2.5-VV D-.n2.5-VLL-7B VL.7B
7,81
5.8
21.5
8.4
63.2
75.8
69.0
52.4
53.3
TimeSearch R (Ours)
Qwen2.5. V1..72 VI.-7B
30.7!
4.2.
36.8
7.0
63.1
81.6
71.2
2.3
MODEL TRAINING
Dataset Construction. A fundamental challenge in RL for long-video reasoning lies in the fact that a large number of samples the existing datasets can be solved through pure linguistic bias, reducing the reliance on temporal search. Moreover, some noisy samples remain unsolvable even under ideal temporal search, preventing the model from effectively exploring the video. To address these challenges, we implement two-stage data filtering pipeline to construct a high-quality dataset tailored to video reasoning. In the first stage, we remove samples that the policy model can solve correctly using only 4 uniformly sampled frames, thereby discouraging reliance linguistic shorteuts. In the second stage, we further discard samples that remain unsolvable even with multiple temporal searches and numerous video frames, ensuring active video exploration. Additional details of this filtering pipeline are provided in Section B.I And we enhance dataset diversity through the incorporation of samples sourced from Haystack-Egode (Ye et al., 2025), VideoMarathon (Lin et al. 2025). and CinePile (Rawal et al., 2024). A detailed analysis of the dataset is presented in Section B.2
Model Training. We employ a two-stage training scheme for our TimeScarch-R. In the first stage, supervised fine-tuning (SFT) serves as cold start, guiding the model to follow the correct reasoning format and enabling effective policy learning in the subsequent RL stage. SFT training adopts the above dataset construction pipeline, using GPT-4o (OpenAl 2024) to generate the text-video interleaved reasoning processes and the corresponding final answers. Following practices in the text domain (Jin et al., 2025), we mask the temporal search results during training to force the model to learn meaningful temporal windows and textual queries. The objective in this stage is to minimize the standard cross-entropy loss over reasoning tokens, while excluding masked video tokens from gradient computation. Building on this cold-start, we further conduct RL post-training based on the proposed GRPO-CSV algorithm to stimulate the temporal reasoning capability of the model.
EXPERIMENTS
3.1 EXPERIMENTAL SETUP
Baselines. To comprehensively evaluate the effectiveness of TimeSearch-R, compare it against
three types of baselines: (1) Advanced foundation models with static frame sampling, including
both API models AOpenAl 2024 Team et al., 2024] and open-source models (Bai al., 2025)_ (2)
State-of-the-ae temporal search agents, such as VideoAgent (Wang et al. 2024). T (Ye et al. 2225
and VideoTree (Wang et al. 2025). (3) Video reasoning models like Video-RI (Feng al. 2025).
Datasets. We evaluate TimeSearch-R R on three tasks: (1) Temporal search Haystack-LVBenn and Boysstck-Egg44D (Ye et al. (202)). where the task is modeled as long video needle-in-a seedle-in-a-haysttcck measuring temporal and visual similarity as well QA accuracy. (2) Long-form video understanding
on VideoMME (Fu et al. 2024), MLVU (Zhou et al. (024), and Long VineoBeeeen (Wu et al., 2024).
(3) Complex video reasoning Video-Holmes (Cheng et al. 2025).

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Published as a conference paper at ICLR 2026
Table 2: Video understanding performance. E2E stands for end-to-end optimization. indicates the average number of frames determined by the model adaptively. Temporal Search Transfering refers to transferring the learned temporal search strategies (i.e. searched dynamic frames V. in Equation from TimeSearch-R other foundation models (e.g. GPT-4o).
VideoMME (w/o sub)
MLVU
LVB
Model
E2E # Frame
short
medium
long
overall
m-avg
val
Static Frame Sampling
Qwen2.5-VI.-78 Bai
2025
54.0
47.3
54.7
54.3
54.2
GPT-4o (OpenA] 2024
72.2
62.7
56.8
63.9
58.1
56.7
LLaVA-OV-72B ef 2024
68.0
58.2
55.2
60.5
62.8
56.8
Qwen2.5-VL VL-7B Bar et
2025
73.5
58.6
51.7
61.2
58.1
58.2
GPT-4o (OpenA1 2024
32
79.8
69.7
62.7
70.8
66.6
61.1
LLaVA-OV-72B
2024
32
77.2
63.6
58.4
66.4
69.2
62.4
Text-only Reasoning
Video-R R1-8 (Feng al.
2025
16
67.2
58.0
48.9
58.0
60.9
53.0
Video-R1-7B Feng al
2025
32
71.1
59.0
49.4
59.9
61.6
56.4
Adaptive Temporal Search
VideoAgent Wang et al. 2024
X
871
49.0
56.0
VideoTree Wang et al.
2025
X
128
67.8
59.9
54.2
T" + GPT-40
X
56.4
57.3
55.9
56.5
T + LLLVA--V-722
X
61.7
57.5
57.7
59.0
T + GPT-4o
X
32
69.5
63.5
59.3
64.1
T LLaVA-OV-72B
X
32
77.5
66.6
61.0
68.3
Text- Video Interleaved Reasoning
TimeSearch-RR R-7B (Ours)
7.8
71.0
56.8
49.6
59.1
68.6
57.1
TimeSearch-R-1 R-7B (Ours)
15.9
74.1
61.0
53.0
62.7
70.7
58.6
TimeSearch-R- R-7B (Ours)
31.9
76.0
63.0
53.3
64.1
71.3
61.6
Temporal Search Transfering
TimeSearch-BR R- + GPT-4o
X
7.81
76.7
68.6
60.1
68.5
68.6
60.8
TimeSearch R-7BB LLaVA-OV-72B
X
7.81
72.3
62.6
57.8
64.2
71.5
61.1
KimeSearch-R-777 GPT-4o
31.9
81.4
72.8
65.1
73.1
69.2
63.4
TimeSearch-R- LLaVA-OV-72B
31.9
66.6
63.9
69.5
72.7
Evaluation Metrics. Besides the original metries used in the benchmarks, additionally introduce two metrics to assess the quality of the text-video interleaved thinking process for ablation study, Among them, completeness measures whether the searched frame sufficient for the correct answer, while consistency measures the alignment between intermediate reasoning and the final answer. Further details on these two metrics are provided in Section
Implementation Details. We train TimeSearch-R based on Qwen2.5 2025). In the RL training, we use the Adam W (Loshchilov Hutter 2017) optimizer with learning rate of lc-6, KL penalty coefficient / 0.005, and batch size of with rollouts per prompt. We limit each search operation to retrieving at most frames from a specified temporal clip. with up to search steps in total. Training is conducted 32 A A100 GPUs. See more details in Section G During inference, we control the target number frames through the system prompt (Figure 8). and report the actual average frames used across test samples. See more details in Section
(Bai et al.
3.2 MAIN RESULTS
Temporal Search. On the temporal search task, TimeScarch-R establishes new state-of-the-art on L.--aastack, as shown in Table I Under a budget of 8 keyframes, our method achieves an F1 score of 8.4 in temporal similarity, more than three times the previous best result of 2.5 obtained by T*. In visual similarity, TimeSearch-R reaches an Fi score 69.0, surpassing the previous SOTA method VideoAgent by 5.3, and even outperforming the retrieval-base method and T with larger keyframe budgets. For the seedle-in-a-hastaee
QA, our TimeSearch-R consistently outperforms the advanced Haystack-LVBende and 53.3% on Soyystckk-Eoo4p
API model GPT-4o, achieving 52.4% accuracy while maintaining a lower inference cost. These results demonstrate the superiority of end-to-end learned temporal search strategies over handcrafted workflows based human heuristics.
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Table 3: Video reasoning performance on Video-Holmes. SR: Social Reasoning; IMC: Intention &
Motive Chaining; TCI: Temporal Causal Inference; TA: Timeline Analysis; MHR: Multimodal Hint Reasoning; PAR: Physical Anomaly Reasoning: CTI: Core Theme Inference.
Model
#Frame
SR
IMC
TCI
TA
MIH
PAR
CTI
Overall
GPT-40 OpenAAl 2024] Gemini-2.00--in Thinking
32
50.0
49.6
38.8
30.0
44.0
39.2
37.0
42.0
43.4
46.9
43.1
51.0
37.9
43.6
39.3
43.1
Qwen2.5-VL-22 (Bi et al.
2025
32
38.4
34.8
17.6
30.0
27.1
18.6
25.2
27.8
Qwen2.5-VL- [5a et a.
2025
768
34.1
42.6
29.9
18.5
30.7
32.5
33.0
32.1
Video-R1-7B (Feng al. 2025
32
48.6
41.7
28.9
34.5
31.0
33.5
35.9
36.5
TimeSearch-R-11 (Ours)
32
54.2
48.3
35.7
39.5
34.0
43.3
41.1
42.2
TimeSearch-R- (Ours)
768
53.9
52.7
33.2
40.0
40.1
44.9
42.2
43,9
Haystack-LVBennd
VideoMME
Number of Search Turns
Method
P
R
Comp.
Cons.
Acc.
G8-0 GRTO csV Reward
Qwen2.5. SL w/ search SFT
0.0
0.0
0.0
44.2
59.4
51.8
7.4
11.6
7,8
60.5
69.2
59.2
GRPO (Before Collapse)
5.2-2.9
18.8.r.
7.4-0.1
57.2-3.
69.340.1
65.
GRPO-CSV w/o Acc. Rwd
6.1
19.8...2
8.240.4
61.24..7
75.3
64.8...6
GRPO-CSV w/ Ace. Rwd
5.4_.0
22.3
8.140.3
60.2_..3
71.842.8
66.64774
152 350
350
Steps
(a) Ablation Results
(b) Training Dynamies
Figure 4: Ablation study of GRPO-CSV. (a) Comparison of different training schemes on temporal search and long-form video understanding. (b) When CSV is removed, training begins to collapse. The model gradually reduces the number of search calls and eventually stops searching altogether.
Long-Form Video Understanding. Our TimeSearch-R R also achieves strong performance on the long-form video understanding task, which is shown in Table Under budget of 8 input frames, our method outperforms the base model Qwen2.5-VL on VideoMME by 4.4%, and also surpasses the temporal search approach T with the proprietary API model GPT-4o. When the budget increases to 32 frames, TimeSearch-R achieves substantial gains, exceeding the base model by 13.2% on MLVU and 3.4% on Long VideoBench. It also significantly outperforms VideoAgeett and VideoTree, both of which use far more input frames, highlighting the advantages of end-to-end optimization. Compared with the latest video reasoning model Video-RI, our method consistently delivers superior performance across all three benchmarks under different frame budgets, validating that video interleaved reasoning is more effective than text-only reasoning for long-form video understanding. Notably, the search policy learned by TimeSearch-R RR transfers easily to other foundation models and yields substantial performance improvements, demonstrating strong generalization of the strategy.
Complex Video Reasoning. To evaluate the adaptability of TimeSearch-R, we conduct experiments on the video reasoning benchmark Video-Holmes, with results summarized Table 3 Our method consistently outperforms the base model Qwen2.5-VL. the video reasoning model Video-R11 RR,, and the proprietary API models GPT-4o and Gemini-2.0, demonstrating its strong generality across tasks.
3.3 ABLATION STUDIES
Training Scheme. We explore the impact of different training stages in Table 4a from zero-shot CoT to SFT and finally RL, yielding two key findings: (1) SFT enables search capability: The model cannot perform the search well only through zero-shot CoT prompts. SFT allows the model to rapidly acquire temporal search skills, dramatically improving temporal Fi from 0.0 to 7.8 and searched frame completeness from 44.2% to 60.5%. (2) RL. enhances video reasoning: While RL. provides modest improvements to temporal similarity and search completeness, its primary advantage lies in boosting overall understanding performance. The post- post-training stage improves reasoning consistency by 2.6%, which in turn raises QA accuracy from 59.2% to 66.6%.
GRPO-CSV Component. We further conduct an ablation study on the components of GRPO-CSV in Figure A and obtain three key findings: (1) GRPO reduces search completeness. Without CSV
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Table 4: Ablation study of data composition. Line 1 shows the accuracy of original Qwen2.5-VL.
General
Reasoning
Ego
Exo
Filter
short
medium
long
overall
temporal
spatial
action
object
76.3
66.0
54.6
65.1
51.4
76.8
56.8
59.5
74.2
62.7
51.3
62.8
40.1
67.9
60.0
56.2
76.4
64.7
54.9
65.3
54.8
73.2
58.2
59.0
76.8
67.1
56.0
66.6
58.8
75.0
62.5
61.9
Question: Why dogs lie by window depicted video? Begging for food. Preparing to sleep. Taking a photo with a person.
8
D. Ready for water.
<thinke To answer the question, need to identify the context in which dags are lying in row by the window I will search for frames where for their positioning. <think-
dags lined window and analyze the surrounding environment and actions determine
<tool_ call> ("start_ time": "end_ time": 2370, "query": "dogs lying row by a window", "num frames" " 8) <hooo callz
that dogs lying lined up near a window, and in the subsequent frames, they are seen in similar positians. The context suggests that they are likely pasing for photo with person, indicated by the presence
<think> Based provided fremes,
by window mulliple scenes,
freme, they are
taking photo second frome. Therefore, will analyze the fremes dags are up by window defermine they are laking photo. <Athikk> <tool call> ("start
749, "ond
2353, "query": "degs lying row by window and person taking photo", "num, frames". 8) <Htoo call>
<thinks From the selected frames. This strongly suggests that the dogs are pasing a photo. Therefore, the answer to the question is clear. <think> Sanswer-C<answeer
that the dogs are tying row by the window person taking photo them.
Figure 5: hypothesis-driven search. Given the context that dogs are lying in row across multiple scenes and remain still, the model hypothesizes that they are waiting to be photographed. It then searches for the person taking photo to gather supporting evidence and provides the final answer.
as a complement, GRPO drops completeness from 60.5% to 57.2% and temporal F1 from 7.8 to 7.4, demonstrating that outcome-only rewards lead to insufficient temporal exploration. (2) GRPO-CSV improves training stability. As illustrated in Figurd4b) removing CSV causes training collapse around step 300, after which the model ceases to make search calls and completeness drops zero. (3) GRPO-CSV with accuracy reward achieves the best QA performance. While completeness reward alone achieves the highest completeness and consistency, it slightly reduces QA accuracy by 0.3%. Combining GRPO-CSV with accuracy reward leads to the best overall QA performance.
Data Composition. We also analyze the data composition in RL training, as shown in Table revealing the contributions of data filtering and domain diversity. Without data filtering, RL training leads to substantial performance drop compared to the original Qwen2.5-VL. This degradation arises because linguistic biases induce advantage in GRPO group computation: when questions be trivially answered through linguistic shortcuts, all rollouts achieve perfect accuracy and completeness, yielding no learning signal and severely hindering RL efficiency and training stability. After applying data filtering, the model trained solely on egocentric data recovers baseline performance, but the lack of diversity weakens the benefits of RI. By incorporating exocentric data to enhance domain diversity, the model achieves its best general QA accuracy of 66.6%. Notably, although the training data only includes general long-video QA tasks, RL training significantly boosts the model's temporal and action reasoning capabilities, improving them by 7.4% and 5.7%, respectively. This remarkable performance demonstrates that TimeSearch-R learns fundamental cognitive patterns through end-to- end policy optimization, validating the strong generalization of our proposed GRPO-CSV algorithm.
3.4
CASE STUDIES
Emergent Search Patterns. We analyze the search patterns that emerge during end-to-end RL training, demonstrating how the model executes temporal search within its reasoning process in a manner analogous to human cognition. These search patterns exhibit adaptability and flexibility across different task types: (1) Hypothesis-driven search: The model formulates hypotheses based on limited context and executes targeted searches gather additional frames supporting evidence

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Published as a conference paper at ICLR 2026
(Figure 5 (2) Confirmation elimination: When the initial frame set provides insufficient support for an answer, the model employs multi- search strategies or elimination methods to collect additional evidence and reduce uncertainties (Figure 16 and [7]. (3) Sequential search: The model performs
analysis to accomplish temporal reasoning tasks that require understanding sequential relationship in the video (Figure T8).
Failure Cases. The analysis of failure cases reveals several limitations that suggest directions for fu- ture work. (1) Reasoning verbosity: A small fraction of cases exhibit verbose or repetitive reasoning patterns (Figure T9,, leading search budget exhaustion before answering. (2) Temporal reasoning errors: Despite the strong temporal understanding capabilities of the base model, we observe oc- casional mistakes time-related predictions, such as predicting timestamps that exceed the video duration (Figure 20 (3) Reasoning inconsistency: While our CSV module substantially improves reasoning completeness and consistency, we still observe instances of insufficient or inconsistent reasoning (Figure 21 22 and 23). Evaluating video-text interleaved trajectories remains challenging
(4) Frame number prediction: Determining the optimal number of frames appears overly difficult for current 7B-scale models, consequently retrieving irrelevant frames in some cases (Figure 24). These limitations
because obtaining human-annotated ground truth is prohibitively expensive.
highlight promising avenues for future research, including robust time prediction, efficient reasoning
mechanisms, automatic evaluation of reasoning quality, and adaptive frame number prediction.
4 RELATED WORK
Temporal Search for Long- Video Understanding. Traditional video understanding methods rely on static frame sampling, such as uniform sampling or heuristic-based strategies (Li et al. [024] Chen et al.) 2024 (Bai et al. 2025), which fail to adapt to varying information density and evolving reasoning contexts. Recent work has explored more sophisticated mechanisms. Similarity- methods like Key LideLLLM (Liang et al. 2024) achieve significant compression while maintaining performance, while learning-basse based approaches such as Frame- Voyager (Yu et al.) 2025b) rank frame combinations based on prediction losses, emphasizing task-specific selection. Advanced semantic frameworks have emerged to address temporal dependencies. Logic-in-Frames (Guo et al.) (025) defines logical relations including spatial co-occurrence and temporal proximity to guide dynamic frame sampling. T (Ye et al. (025)) reframes temporal search as spatial search with adaptive zooming mechanisms. Interactive agents like Video (Wang et al. 2024) and VideoTrree (Wang et al. 2025) enable multi-turn temporal exploration through prompt-driven orchestration. However, none of the aforementioned methods adopt end-to-end optimization, resulting suboptimal search strategies.
Reinforcement Learning for Multimodal Reasoning. Recent advances have explored RL to enhance reasoning capabilities in LLMs. GRPO (DeepSeek-AT 2023) demonstrates that outcome- based rewards can effectively elicit complex reasoning. Search-RI (Jin et al., (025) extends this paradigm to text-based search tasks, showing that RL can facilitate adaptive in information retrieval. Approaches like MM-Eureka (Meng et al., (025) and LMMMR1 (Peng al. 2025) have successfully applied RL to enhance multimodal reasoning, but focus primarily on static image understanding rather than dynamic video interaction. Video-RI (Feng et al. 2025) applies GRPO to video reasoning but limits the thinking process pure text without visual interaction, while DeepEyes (Zheng et al. (0225) uses RL for high-resolution image understanding through adaptive cropping operations but focuses on spatial rather than temporal exploration. Despite these advances, applying RL to interactive long video understanding remains largely unexplored and presents unique challenges.
5 CONCLUSION
In this work, we propose TimeSearch-R, a framework that reformulates temporal search as text-video interleaved thinking to learn optimal search strategies directly from data. To enhance temporal search through RL, we propose CSV as a complement to the outcome-only reward of GRPO, addressing the challenges of insufficient temporal exploration and inconsistent logical reasoning. TimeSearch- R achieves strong performance while exhibiting distinct search patterns across different task types. We hope this work contributes meaningful progress toward advancing long video understanding powered by reinforcement learning.
both temporal search and long-form video understanding tasks,
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TIMESEARCH-R: ADAPTIVE TEMPORAL SEARCH FOR LONG-FORM VIDEO UNDERSTANDING VIA SELF- VERIFICATION REINFORCEMENT LEARNING
APPENDIX
This appendix provides more details about our methods, dataset, training, more case studies, broader impacts, as well as the LLM usage, organized as follows:
Section
Search Function
Section
B
Dataset Details
Section
Prompt Design
Section
Evaluation Metrics
Section
Efficiency Analysis
Section
Search Analysis
Section
Training Details
Section
More Case Studies
Section
I
Boarder Impacts
Section
LLM Usage
A SEARCH FUNCTION
A.1 FRAME SELECTION
The video search function selects the most informative frames within predicted temporal clips. Specifically, we leverage determinantal point process (DPP) (Kulesza & Taskar (012) as the search optimization for its ability to naturally balance query relevance and diversity that penalizes redundancy, which has been widely applied in information retrieval (Celis et al.) 2018 Sun et al. (025).
Recall the definition of sea rch in Sec. 2.1 it aims select F optimal frames guided by temporal clip [t,, tz] and a query from the original video V. First, the function first subsamples N candidate frames Fi...el
{ within the temporal clip. Subsequently, we obtain visual embedding h E TRd for each candidate frame in Fit...., and a query embedding q E Rd for q. Then we define the pairwise cosine similarity for candidate frames as Sij = h hy and compute an unnormalized query relevance score for each frame as i; = q h, which is rescaled to [0, 1] by min-max normalization r; = max F-min F.... where e is a small constant to avoid division by zero. The kernel is constructed by diagonal conditioning with these relevance weights:
f-miin
L diag(r) S diag(r),
(6)
which is equivalent to Lig = r.ry h hy. The optimal subset V* C. Fir..t.) with |V* obtained through fast greedy MAP inference (Chen et al. 2018):
F is then
aig
max SCF
1S det(Ls).
This formulation ensures that selected frames are both diverse and relevant the query. When available frames are fewer than F, the search function degrades to uniform temporal sampling.
A.2 FRAME REPRESENTATION
The selected clip frames are sparse and non-uniform. To maintain the temporal pace, we attach an explicit absolute timestamp to each frame by inserting short text token with the time in seconds (e.g..
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Stage-1
Direct Answer
Correet?
X
N
4 frames
LLM Judge
For SFT
For RL
CoT & Answer
Correct?
Stage-2
frames
LLM a Judge
N
Human selection (Only synthetic noiry pairs)
Search
TimeScarch-R (Early version)
GPT-4o
Qwen2.5-VL
Figure 6: Illustration of the proposed two-stage data filtering pipeline.
"12.3s") immediately before the image. This simple interleaving of timestamp text and the correspond- ing image maintains absolute temporal grounding when inter-rrame intervals vary and complements the native temporal ids. Explicit absolute timestamp augmented frame representation has also been observed to improve temporal capability in prior work on long-video temporal grounding (Pan et al.) 2025). For uniformly sampled preview frames, employ the native dynamic-FPS and absolute time encoding following Qwen2.5-VL. (Bai et al.) 2025), which bind image token sequences to temporal ids aligned with real absolute timestamps.
B DATASET DETAILS
B.1 DATASET CONSTRUCTION
To ensure high-quality training data, we implement a two-stage filtering pipeline as shown Figure
Stage 1:
Visual Dependency Filtering. We uniformly sample 4 frames from each video and feed them along with the question to Qwen2.5-VV for inference. Questions that can be correctly answered with this limited visual information considered to have low visual dependency and are subsequently filtered out. Only questions requiring richer visual context proceed to the next stage.
Stage 2: Search Usefulness Filtering. We increase the frame input to up 64 frames and em- ploy different LVLMs to perform dynamic temporal search for question-releve video segments. Specifically,
GPT-4o to generate SFT (Supervised Fine- Tuning) data and early version of TimeSearch to obtain RL (Reinforcement Learning) training data. Although this stage produces COT, only the CoT generated by GPT-4o is used for SFT training, while RL training utilizes only the question-answe -nnweer pairs. To avoid search format errors, we implement format validation for LVLMs' responses, automatically retrying the model until obtaining properly formatted answers.
Human Selection for VircoMarathon (Panda-70M), Given that VideoMarathon's training set contains automatically generated question-anss answer pairs with potential unanswerable questions or incorrect ground-truth answers, we conduct manual annotation to ensure data quality. To minimize annotator bias model answer evaluation, we establish structured annotation protocol. First, annotators assess question reasonableness based on video content, filtering out unanswerable or ambiguous questions. Subsequently, annotators provide manual answers and compare them with synthetic ground-truth labels, removing data samples that are inconsistent with human responses.
B.2 DATASET ANALYSIS
The dataset exhibits a pronounced long-täl tail distribution in video duration with a mean length of 1,659 seconds. Most videos shorter than 2,000 seconds, while nontrivial tail extends beyond one hour,
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Video Duration Dustrnbution
800
1659s
700
600
Frequency
400
300
200
100
0
2000
4000
8000
10000
Duration (seconds)
Data Source Distribution
Question Type Distribution
Panda-70M
multiple_cheeeee choice
35.6%
39.7%
Ego4D
49.5%
60.3%
ended
9.5%
5.4%
CinePile
Others
Figure 7: Dataset analysis. (1) The training set is mainly composed of long videos. The average length 1659 seconds, and the maximum length exceeds 10,000 seconds. (2) Egocentric QA pairs come from Haystack-Ego4D. and Exocentric QA data mainly from VideoMarathon and Cinepile, where VideoMarathon employs Panda-70M as the video source. (3) Question types include multiple- choice and open-ended questions. To obtain open-ended QA pairs, we convert some multiple-choice tasks into open-ended questions.
posing significant challenges for static frame sampling. This distribution motivates adaptive temporal search and multi-turn interaction progressively retrieve evidence under tight keyframe budgets.
We curate data from four major sources to ensure coverage of diverse visual domains and camera styles. As shown in Figure 7 Ego4D from prsstack-Ego411 (Ye al. 2025) training set contributes 49.5% of samples, providing egocentric daily activities with frequent viewpoint changes. Panda-70M from VireoMarathon (Lin et al., 2025) accounts for 35.6%, expanding the variety of internet videos with heterogeneous motion patterns and scene dynamics. CinePile (Rawal et al. (2024) provides 9.5% of short videos with narrative structure and rapid scene transitions. The remaining 5.4% are from other sources and serve to reduce distributional bias.
Question types are intentionally imbalanced toward open- reasoning to better evaluate generative capabilities. Open-ended questions make up 60.3% of the data and emphasize step-by-step analysis, temporal grounding, and explanation quality. Multiple-choice questions comprise 39.7% and offer reliable automatic evaluation signals that complement outcome rewards in RL.
This composition yields wide coverage over motion intensity, scene diversity, and narrative structure while maintaining sufficient automatic evaluability. The mixture of long- tail durations and open- ended questions creates a setting where end-to-end RL and adaptive temporal search offer clear benefits over single-shot heuristies.
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C PROMPT DESIGN
We design prompts to standardize interaction formats, minimize ambiguity, and provide explicit priors for temporal reasoning. Figure 11 show the templates used during training and evaluation.
System Prompt
You are helpful video assistant.
f Tools
You call one more Cunctions to asslat with the user
You are provided with function signatures within <tools></tools: XML tags: <teols>
I"type": "function", "function": (*nome"; "seek_vidao, wew__vviiooooroooeeeee fromes", "description"; *search and select video frames according to textual query and temporal window. lime is seconds. "parameters"; ("type": "abjeet", "properties": l"query": I"type": "string", "description"; "The query is used describe the object, scere, or event interest in the video thoroughly and clearly. "), "start_Lime"; ("Lype"; "aunbes", "description": "Start time of the segment of interest. "), "end_time"; (*sype": "sumber", "description", "End time of the segment of interest. "), "aum_frames"; ("type": "integer", "dascription": *Number of _UM___IMMAPSS FRANES PRR pRR_nURN)) Defsuls is $1
frames to sample (maximum S|MAX_ NUM
BALLNNL__ARAMSSSEEE TURA]. "",,, "required"; ["query"]i)] </tools>
For each iunction call, return a jaon object with function name and argumenta within pool_call></too_caal XXL tags:
<teoi_<oll>
I"name": <funct on-name>, Marguments:: <arge-Jjon--bjeett]
s/tool_call>
Figure 8: The system prompt with temporal search tools.
System Prompt. We follow the tool-use specification of the base Qwen2.5-VL. family (Bai et al. 2025) and adopt its tool_call schema for invoking temporal search. The hyperparameter MAX_NUM_ FRAMES__A SOEMMS_PER_TTESE FER_TORK in Figure controls the maximum number of frames per search turn, while NUM FRAMES PREVIEW specifies the number of preview frames provided initially. During training, MAX NUM FRAMES PLR__E_____R______ TURN is both parameters to control the target average frame count. Specifically, since the model typi- PREVIEN=4
by default. At test time, we adjust
cally performs ~1.2 2 search turns beyond the initial preview, setting NUM_ FMMMFRRAMSS... LEEEERRARREVIEESSSSSS and MAX_ L___N_M____ FRAMES PER_ 3 yields average of ~7.8 frames. Similarily, NOMM_RRAME___EEEEE_EE_E_SS PER_TURN=8 for the 768-frame set- ting. The frame numbers in the tables of main text represent actual statistics averages across all test samples after the inference is completed.
set
NUM FUAMES PREVIEW=758 and EEWEEEESEEEEEEEEEEEEERREEEEEEEEE
Question Answering Prompt. The QA template enforces thorough reasoning inside <think> before any tool call or final answer. restricts the output to exactly one of two formats and allows at most eight rounds of <tool_cc 1 >. It explicitly provides the line "The video duration: [duration] seconds." to help the model produce absolute timestamps better. See Figure 9
Question Answering
You must ATMAYS conduct tharough reasoning ins de <think> and </think> tags BEFORT cal 1in any or anawering question,
You zust lnvoke tcols to explore any video content you are inseres:ed in within <toel_call </tcol_call> tags.
You allowed te t=001_oeDD<<ttooLcaall
for maximum of rounds.
when you have enough information to answer the question, provide your answer within
should aupported evidence from the video. Your output muat fol the fornat: <think>Your reaaoning ttreesss/thinoxxtooo.oooo Parameterss/Looo_call >question: (question)
</answar> tags.
YthinkeYour reasoning proesss/thinoeeeeee>>oo ansver</answe:
video duration: [duration) seconds.
Figure 9: The template for question answering.
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Clip Frame Sampling and Search Response. After search, the template returns the selected frames and their corresponding timestamps. the frames are sufficient, the model must place the final answer in <answer>, Otherwise, the template asks the model to call the tool again with different parameters JSON, therehy encouraging reflection and re-query. See Figure T0
Temporal Search Response
llere selested fremes. They locsted stimestamps]
the frames provided above are sufficient to answer the user's question, please put your final within <anawer></answer> Otherwise invoke the tool again with cifferent paranetera in JSCN format.
Figure 10: The response template of the temporal search.
Completeness Self- Verification Prompt. The CSV template asks the model to answer as briefly as possible and say "I don't know" when the visual evidence is insufficient. No tools are available in this stage, which prevents new searches and ensures the answer is grounded only on the dynamic frame set gathered earlier. See Figure II
Completeness Self- Verification
You are helprul aasistant. Please answer visual questions briefly as poasible, When you don't have enough viaual information, please 'I don't know",
Figure 11: The template for CSV reasoning.
D EVALUATION METRICS
Completeness Rate. We measure the proportion of cases where the dynamic visual context alone suffices to produce the correct answer. Concretely, after the multi-turn search, re-answer the question using only the gathered dynamic frame set and disallow further search, following the CSV procedure in Sec. and the prompt illustrated in Figure 11 The resulting correctness is computed with the same tasc-fic accuracy used elsewhere, averaged over the whole dataset.
Consistency evaluates whether the intermediate reasoning coherently sup- ports the final answer under the given question. We prompt a LLM model (GPT-4o) with the question, the reasoning text extracted from <think> ... < t/think>, and the final answer from <answer>... </ </answer>, using the format in Figure 12 that requires a structured output: short analysis in <thi nk> followed by <answer> equal to "Yes" "No". In implementation, we parse the LLM'' output to obtain the binary decision; "Yes" counted as and "No" as O, and any parsing failure treated 0. The Consistency Rate is the dataset average of these binary outcomes.
Consistency Rate.
Consistency Score Evaluation
<oystem prompt>
You careful logical reviewer. process and the finel answer are consistent in eddressing the given question.
is verify whecher giver reasoning
Flease carefully read the following information:
Question: <Question>
Reasoning Precess: <Reasoning>
follow formas strictly: <think> Your analysis here </chtnk> <answer> Yes/No </anawer>
Figure 12: The template for calculating consistency.
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E EFFICIENCY ANALYSIS
E.1 TRAINING EFFICIENCY
profiingTTIme taken: takenn GwpoTraneetest Vidd, wttpo//neettveelgooooo generate
prohinn/Time taken GePOTrainercoooggog generatee
8202356
2V5
0.8
5000
10000
15002
20030
29300
33200
5000
10000
19000
20000
2500
35000
Step
V7 coldsta, Ok preview
colista.... .Ok provlow
prohiing /imme taken: GHPO Trainer.. token, Jogas
profiing/ Time taken: I-POrrainerse www.err.coopp..o
10000
10000
29000
20000
90260
5000
10000
10000
20000
30300
Step
V7-coldsta Dkepredew
V7-coktsta.. Deepreveew
Figure 13: Training efficiency of CSV. CSV phase requires 0.6 seconds on average, which is substantially lower than the complete rollout (7.5 seconds).
We conduct comprehensive profiling analysis to evaluate the training efficiency, specifically ex- amining the overhead introduced by the CSV phase. As illustrated Figure [13 the CSV phase requires 0.6 seconds average, which is substantially lower than the time required for complete text-video interleaved rollout (7.5 seconds). Our profiling also covers other components, including per-token log probability calculation and loss computation. Overall, the average training time is 56.09 seconds per iteration. The additional overhead from the CSV phase is minimal, peaking at 2.1 seconds with average of 0.6 seconds. This accounts for approximately 1. % of the total training duration, demonstrating that the impact on overall training efficiency is negligible.
E.2 INFERENCE LATENCY
Table 5: End-to-end inference efficiency. We report the search model, number of frames, search/over- all latency, number of LLM turns, VideoMME performance, and temporal/visual Fl scores.
Method
Frame
Search (see)
Overall
VideoMMME
Temporal
5-VL-BB
4.02
56.7
T'PGPT
56.5
TimeSearch
0.138
TimeSenchhR GPT-46
N/A
3.3
As shown in Table 5] we evaluate the inference efficiency of different methods on the VideoMME benchmark using NVIDIA A100-80GB GPUs. T'+GPT-40 requires 26.00 seconds per sample, with
the question grounding, iterative search, and question answering stages taking 11.7 s, 6.4 and 7.9 respectively. contrast, TimeSearch-1 R achieves a significantly faster end-to-end latency only 4.65 seconds per sample, using average of 7.8 frames and 2.3 LLM turns. Benefiting from the compact 7B backbone, TimeScarch-PP ensures superior inference efficiency while achieving better performance (59.1 vs. 56.5 VideoMME). The efficiency advantage of TimeSSarch-RR stems from three key
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factors. First, thanks to modern LLM infrastructure with KV-cache optimization, TimeSearch- R' multi-turn dialogue (2.3 turns) incurs only minimal additional overhead compared to single-turn inference of Qwen2.5-VL. Second, video frame features only need to be computed once and can be cached across multiple dialogue turns, with this computation parallelizable with the LLM's prefilling phase. We utilize industrial-level deployment mechanism seripce)- further accelerate SigLIP computation. Third, the temporal search operation itself is extremely lightweight, taking only 0.138 seconds per sample (less than 3% of the total latency), of queries and feature- based retrieval operations, Meanwhile, TimeSearch-R achieves substantially better temporal localization accuracy, with a Temporal Fi 8.1 compared 2.5 for +GPPTT- underscoring the effectiveness of our reinforcement-drivee search policies.
comprises only embedding
F
SEARCH ANALYSIS
F.1 DISTRIBUTION OF FRAME NUMBERS AND SEARCH TURNS
40
Number of frames
20
20
Median:
10
short
mudium
long
Perception
Reasoning
Synopsis
Task type
Task type
(a) Distribution by video duration
(b) Distribution by task type
Figure 14: Number of selected frames distribution. We visualize the distribution of sampled frames across different video durations (a) and task types (b) on VideoMME.
Distribution Search Turns
Distribution Predicted Keyframes
Distribution of Search Tums
Distribution Predicted Keyiirmmes
300
heosang
200
290
1000
Frequeccy
1200
200
20
Fromes
(a) Haystack-LVBence
(b) Video-Holmes
Figure 15: Number of search turns and frames distribution. We visualize the distribution for video understanding task Haystack-LVBendd (a) and video reasoning task Video-Holmes (b).
We visualize the distribution of selected frames across different video durations and task types on VideoMME Figure [4] We observe that (1) longer videos have more average frames, and (2) more complex reasoning tasks and comprehensive tasks obtain more frames. We further visualize the detailed distribution of search turns and frames on Haystack-LVBeenh
long-form video tasks and Video-Holmes for complex video reasoning tasks in Figure 15 Most questions in Haystack- Beresacck-LVBBndee and
require only one or two search turns to obtain the correct answer.
F.2 DIAGNOSTIC BENCHMARK ANALYSIS.
To better understand the specific capabilities improved by TimeSearch-R R, conduct diagnostic anal- ysis using VideoEvalPro (Ma et al. (025) and VideoMME (Ye et al.) (005)), which categorize video understanding tasks into different dimensions. VideoEvalPro distinguishes between local and holistic
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Table 6: Diagnostic performance on VideoEvalPro. TimeSearch-R significantly improves the
performance on local understanding tasks.
Models
#Frames
Local
Holistic
Overall
Qwen2.5-VL
512
51.1
37.2
46.9
TimeSearch-R
512
4
35.8
49.9
Table 7: Diagnostic performance on VideoMME.. We report the detailed perception and reasoning performance on VideoMME. TimeSearch-R significantly improves reasoning performance.
Model
Perception Details
Recesning Detaik
Synopsis
Perception
Reasening
Temporal
Spatial
Altebuto
Action
Object
OCR
Cuumingg
Temporal
Spatial
Action
Objoz
Dwe-2.5-VL-77
80.0
61.1
77.5
66.1
67.6
71.
44.8
51.4
76.8
56.8
99.5
80.9
65.1
S8.2
TineSeach-R. R-7B
72.7
66.7
77.9
66.8
73.2
74.1
43.7
58.8
73,9
02.5
61.9
79.6.13
66. 667.16
62.2
understanding, while VideoMME separates tasks into perception, reasoning, and synopsis categories. Table 6 shows that TimeSearch-R achieves substantial improvements on local understanding tasks (+4.7), leading to an overall gain +3.0 points. Table 7 reveals consistent enhancements in reasoning capabilities (+4.0), with the most significant improvement observed in temporal reasoning (+7.4). These results indicate that TimeSearch-R particularly excels at tasks requiring fine-grained temporal analysis and complex reasoning over video content. The improvements search strategies, which adaptively selects task-relevant frames through multiple search turns. This dynamic frame retrieval enables the model to capture precise spatiotemporal details critical for local understanding and temporal reasoning, rather than relying on uniform sampling of the entire video.
attributed to the learned
G TRAINING DETAILS
Table 8: Training hyperparameters of TimeSearch-R.
Category
Parameter
Value
Max FPS
Max Frames per Video
768
Video Processing
Total Video Tokens
10,240
Min Tokens per Frame
12
Max Tokens per Frame
256
Max Search Turns
8
Interaction Settings
Max Completion Length per Turn
256
Number of Generations
KL Penalty Coefficient (9)
0.005
Scale Rewards
false
GRPO Training
Batch Size per GPU
Gradient Accumulation Steps
2
DeepSpeed Configuration
ZeRO-3 Offload colocate
Infrastructure
VLLM Mode
Replay Buffer
truc
We summarize the key hyperparameters in Table for reproducibility.
Training Configuration. TimeSearch-R employs a distributed training setup using PyTorch's native distributed data parallel framework with ZeRO-3 memory optimization through DeepSpeed. The training process leverages gradient accumulation simulate larger batch sizes while maintaining memory efficiency GPU clusters. We utilize mixed precision training with bfloat16 accelerate computation while preserving numerical stability, coupled with Flash Attention 2.0 for efficient attention computation.
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GRPO Training Setup. The reinforcement learning phase uses Group Relative Policy Optimization with 8 generations per prompt to provide sufficient policy gradient estimates. The KL divergence penalty coefficient is set to 0.005 to balance between reward optimization and policy regularization. We employ VLLMM in colocate mode for efficient inference during rollout generation, enabling faster policy updates. This RL training stage is implemented top of the TRI. library (von Werra et al.) 2020), following standard practice for outcome-driven policy optimization in large language models.
Video Processing Configuration. The model processes videos with maximum of 768 frames and allocates up to 10,240 tokens for video content representation. Each interaction turn is limited to search operations, with a maximum of 8 interaction turns per question to ensure comprehensive temporal exploration while maintaining computational efficiency. Frame tokens dynamically allocated between 12 and 256 tokens per frame based on content complexity and relevance.
H MORE CASE STUDIES
This section provides case studies of TimeSSeach-R, including successful cases and failed cases.
Successful Cases. These representative success cases illustrate how TimeSearch-R conducts multi- turn exploration accumulate decisive visual evidence while maintaining alignment between the reasoning trace and the final answer. They encompass confirmation (Figure [16), elimination (Fig- ure 17). and sequential exploration patterns (Figure 18), collectively demonstrating that the policy preserves the high completeness and consistency reported in Sec. D
Failed Cases. Figure 19 1199/llussee lluuttaates a failure where the model generates repetitive reasoning that
exhausts the search budget. Figure 20llusstes failure where the model makes temporal prediction errors, such as outputting timestamps beyond the video duration. Figure |1 [22 and [23 illustrate
failures involving incomplete, inconsistent, or hallucinated reasoning. Figure 20 ilustraats failure where excessive frame budget leads the model to retrieve irrelevant frames.
I BROADER IMPACTS
TimeSearch-R contributes to several important areas beyond the immediate technical contributions:
Advancing Video Interpretability and Explainability. TimeSearch-R introduces interleaved text-video reasoning traces that provide transparent insights into the model's decision-making process. The completeness and consistency criteria we propose enable quantitative assessment of long-form vidco explanations, making temporal search decisions auditable and interpretable. This advancement represents a significant step toward more explainable AI systems the video domain, where understanding the reasoning process is crucial for building trust and ensuring reliability.
Transforming Video Reasoning from Static to Dynamic Paradigms. Our approach fundamen- tally shifts the paradigm from static frame sampling to dynamic, interactive reasoning in video understanding. By operationalizing hypothesis-driven exploration through iterative temporal search, we promote new methodology that emphasizes transparent, stepwise evidence gathering. This con- trasts sharply with traditional one-shot inference over fixed visual contexts, encouraging researchers develop more adaptive and interactive AI systems. The demonstrated effectiveness of our approach may inspire broader adoption of similar interactive paradigms across various multimodal tasks.
Exploring Scalable Soekly-Supervieed Process Rewards. We introduce outcome-based process supervision that eliminates the need for costly process annotations. Through the integration of weak supervision and reinforcement learning via completeness verificiifi,,,nn,, our method successfully aligns intermediate search decisions with correct outcomes. This approach offers scalable solution for training complex interactive systems without requiring fine- mne-grained procedural labels, potentially reducing annotation costs and enabling broader application across diverse domains.
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J THE USE OF LARGE LANGUAGE MODELS
The authors declare that Large Language Models were used this paper for polishing the writing. Specifically, the LLM assisted with tasks such grammar checking, sentence simplification, and improving the overall fluency of the text. It is important to note that the LLM was not used for any literature review research ideation. All research ideas and experimental analyses presented in the paper were solely conducted by the authors.
Question: What the pregnantt woman weering the video? A strapless, form-fitting
adomed sequins.
A mint green dreee with a straplees design.
C. An elogant black dress with long sieeves.
A.A striking dress with large, ruffled detail.
Shinko answer the question, need identify the prognant woman the video and determino what she wearing. will search for frames where pregnant woman is featured and analyze the aftire. <thinke
<tool calpx""sart time": time": 115, "query": "pregnant
carpet", "num frames" </tool calt>
<thinic Besed on the provided frames, the pragment woman la consisfently weering black dress with long aleeves. now enalyze the frames to confirm this. <ithinke
<tool Boroo_call"""ss"" time": "end_ time": 115, "query": "black dress with long sleeves", "num momn_frrmes' </tool_call
<think Based provided frames, anawer
is consistently wearing black dress with aleeves. provide the
<answer=Csanswer
Figure 16: Search pattern: search confirmation.
Question: Based on the visual cues provided by the video, where not the megician perform megic? A. On the street.
B. In church.
C. In hospital.
D. On a small boat on the lake.
performing <thinko delermine where the magician did perform magic. I need analyze the video frames for scenes where magic performed, identily
fremes options the one that is not present in the video. <think> <tool, paltt""ttrtt_iii time": "end, time". 83, "query": "magic parformance", "num frames". " 10) sitool, call>
magician
compare
given
<thinio Besed on the selected frames, magician performed magic in various locations such as a yage studio, restaurant park, and studio, However, there is no frame showing hospital setting. Therefore, will aearch for frame that does not include a hospital to confirm the <think>
cally""sta
"quary": "hospital sotting",
::
www.
<thinke Bused the selected magician perform hospital setting. frames cleanly performing locations yoge studio, restaurant, park, question clear. <thinko <answer=C</nnswee
hospital, Therefore,
Figure 17: Search pattern: elimination method.
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Question: Which the following options correctlyy orders sequence boy's daily itineray??
A. Go to class, go home, to study room and continue studying. Attend classes, private class, go home, to private tutoring facility D. Nalther
faciliiee for counsellng, study halls continue studying.
learning.
sthinio determine the correct sequence of the bay's daily tinerary, need to analyze the video frames to identify the key activitiss and their order. The video frames suggest daily routine that includes waking going to school, attending classes, going to private tutoring facility studlyig hall. search for frames that depict activitoss to establish <tool_call>""ee start_ time": "end time": : "query": "boy waking up", "num frames". 8J <itool call>
</tinks
www.aaa www.wotwwwwoooo.o...ooo www www.eeeww....................... www. www.wwwwwww..... www.wwtw www.wwwwwwww....wwwwww www.n
sthinio The frames cleany dispict the boy wakkng up, getting drassed, and lnaving the room. This suggests the beginning his dail routine. Ineed continue searching for frames that show him going to school, attendling classes, going private tutonig faciliy, and
studlyng <think>>
<tool callx "startt time": 30, "end, time". 100, "query": "boy going to school" "num frames": : & </tool call>
<thinic The frames clearly show the boy leaving home, warking to school and entering the achool This indicates the continuation of his daily mutine. need to search for frames that depict him attanding classes, going pávate futaring facilit, and studying in study hall. <ithink>
<tool, call-("start time": 100, "end time". 150, "query": "boy attending classes", "num_framme frames": : 8) <hool, call:
The framie olserly show the boy sifending cisases, which is part his daily routine. (nned search for framee that depict Nim going tutoring shudying study sithinks
sthinko
<tool <too|_call>""e time": :: 150, "end, time": 200, "query": "boy going to private tutoring facility", "num fran_ffrmms":: </tool call>
www.eeeneeeooo......... www.
w...aaaoooii... www.iooo wewro www. "query": "boy studyling study
<thinic The frames cleanl show the boy going to private tutoring faciliyy (need to search for frames that depict him studyingg in anudy hall sttinke calla""stert
5) <itoo__cal
<think> The frames dieerly show the studying study hall. Based the selected frames, the the boy's daily ilierray follos:: go private lutoring faciliy counseling, and study hall to continue studying. Thorefore, the correct option
<onswor-B</answee
Figure 18: Search pattern: sequential search.
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Question: What cantt leam from the video?
Winner of the 2016 Nobel Prize in Physics.
B. How an LED works.
invertion
LED. current research
answer this question, to identify content covered blue LDDs, and the work of Nakamura, also covers the science behind LEDs, including their structure and how they work. However the video does not provide information about Nakamun's current research interests. will search for frames related to Nakanura's current research contimm <Atiok?>
discusses history of
<tool, call>("start time": 00 "end time": 2024, "query": "Nakamura's 's current research interest", "num frames": 8) </tool call>
<think>> From provisted www.
Ware www.
KOA.RAA AAw. wAA.AAA M.A.AAEEKAA.A AAA.AAAX.O LEDs
work Nakamaraa However, does not provide information about Makamura's current research inferests. Therefore, will search for frames that might indicate Nakamura's current research inferests. <think>
<tool call>""saart time": "end_ fime": 2024, "query": "Nakamure's current research interest", "num Jrames": </ool, call>
<think>>
expicity
work and achievements, such developmont of blue LEDs and his patent disputes. Therefore, will search for framos that might indicate Nakamura's current research interests. <think>
call>"" "end "query": Nakamura's
frames": <tool _alol_call>
smanic From the provided frames, clear thet the video does not provide information about Nakemurs's a current research intereate. focuses on his pest work and schievements, such the development of blue LEDs and his patent dispules. Therefore, will se arch for frames that indicate Nakaaurr'ss misearch interests, <thinke 2024, "quary": Nakamura's
call>("stan (ssaart
<thinko Based on the provided frames, the video does expicily mention Nakamure's surret recearch interests. focuses on Nis past work and achievements, such development biue LEDs and Ns patent diaputsa. Thersfore, search for frames that might <think>
indicate Nakiamuns's time": 2024, "quary":
<tool_call> call>("star
current research interests "num <ttool calls
sthinko Based on provided frames, video does expicitly menton Nakamura's current research interests. locuses past wark achievements, such development
patent disputes. Therefore,
might
indicate Nakamura'' current research interests. <think>
"num_frames" <tool, calrt""star_ time": "and time": 2024, "query": Nakamura's curront research Interests "num_ frames": : BJ </tool_cat calls
<thinks Based on the provided frames, the vidto does not expicily mention Nakamura's current research intorests, locuses his past work achievements, such development
disputes.
might
indicate Nakemurs's current research interests. <think>>
<tool, stool_oall>""stee "atart"start_niee time": 0, "end
"num_frames" "end__ime: fimee: 2024, "query": Nakamurr's ' current research inferests "num_ frames" : <itoo_ call>
<thinko Based on the provided frames, the video does not expicily mention Nakamur'ss current redearch interests, locuses on his pas! work achievements, indieate Nakerunn's current research interssts. <thik>>
development blue LEDs and his
Therefore, frames might
<tool, calls("star time": 0, "end "ond_tima": time": 2024, "quary": Nakamura's curront research Interests "num_ frames" : Bj <itool call
Repeated searches have exhausted the number of rounds.
Figure 19: Failure case: repeated and exhausting search. The model repeatedly searches for the same information until the maximum number of search turns is reached. This case is rare in our experiments (1% of VideoMME, 0.4% LongVideoBenee and 0. 1% of MLVU).
25

---

Published as a conference paper at ICLR 2026
Question: There is men, a woman, and child on the screen. the top-left corner, there yellow US! symbol. Aed pointing the little giri. What is litll doing?
Closing eyes
B. Danzing
C. Running forward
Crying
Opening her mouth to breathe
The input video Lasts seconds.
provided frames, Thorafore, will analyze the frames to contrm this action. </thinke <tool_
<thinic
girl elosing her ayas. arrow points har ayas visibly closed
Bacll___lll"" (wa.callaatarrrrtas ("start_fime) [ime": 10.0, "end_fime [im_m 15.0, "query": "little girl closing eyes", Trum_frames <itool_cal
The previous reaponse is invalld. want tools, ahould output jeon object with function name end arguments within <tool call=s/tool_all coll> lags: If want give the final anawer, s'houudd put the answer between <answer> and 4/answer> Let me hy again.
<think> From the frames provided, is clear that the little girl is closing her The red arrow pointing her and the frames conclude
showing eyes closed confirm <answer>A</nnwwer
girl closing eyes.<ithnnk>>
Oracle
("start time": 0, "ond, time": 10, "query": "a yallow "US" symbol, red arrow is pointing to the little girl")
US
Kanswer> Es/answer>
Figure 20: Failure case: error time parameters. The predicted start time exceeds the video duration, leading to unexpected search results and incorrect answer.
26

---

Published as a conference paper at ICLR 2026
Question: Which of the following contents was NOT mentioned in the video? NBA Raptors player gambling.
The price Bitcoin olummeling.
C. Breakdown of tsrsel-Hamss ceesefra talks,
Joumslist describes the cheos Helli,
<thinko determine which content NOT mentioned video, will review main topics discuszed the video clipe The video Canada, whale deaths Manineland, Supreme Court arguments, NBA
multiple including bridge colapse, bridge safety investigations, news from analyze each segment idintity fopics covered and then compare </think>>
given options.
<teol calax""strrt time": "ond_ time": : 2656, "query": "NBA investigates Raptors for gambling" frames", </tool calls
NEA Invesligation consiatently
options ensure zone were mantionad
<tool cally""start_ time": 0, "end_ time". : 2656, "query": "The price of Bitcoin plummeting
im mn__framee": 8J <tool_call celf
<tiink From the frames provided, the content related to NSA investigation consislendly mentioned. will now rowlow the other options to ensure none of them were mentioned in the video, </tinis> <answer> Sanswer=C<answeer
Cinly 2/4 options ware examined.
B. The price Bitcoin plummating.
Oracle
("start time": :: 0, "end, time": 2656, "query": "NBA investigates Raptors player for gambling 7
("start_
2656, "query": price plummeting."
No Related Frames Found
("start time": 0, "ond, time": 2656, "quory": "Breakdown of Israol-Hamas ceasofire talks, ")
("start time": " O, "end time" 2656, "query": "Journalist describes the chaos Haiti.
2278s
<answer Beeesweer--//nnnee Bs/answer>
Figure 21: Failure case: insufficient search. There were options in total, but only were reviewed before search was terminated.
27

---

Published as a conference paper at ICLR 2026
Question:
the video after feeding the ducks, what did the make protagonial do after riding his bike? Check the cattie
ezgs.
growth apple
Nore of the above.
<think To answer this question, need infenilly the sequence of events related to feading the duccks and then determin what the main protagonist did alter riding his bike. will search for the relevant segiienns the video where the ducks are being fed and then look for the where protagonial rites his bike aflawwerd. </iiidks <tool. call ('start time". 120, "and, time "query": waking "num. frames". 8j </tool call>
www.eei www.wao feeding
clearly sequence
male protagonist
duccks, riting
where
<tool calat"" time": 200, "and time 300, "query": "ridlnng bike". "num frames" </tool calls
www. www.
<thinkk Based the provided frames. ma malo profaganist seen ristng his bike and mon walking in field. will now search for the scono where he checks the caille herd, <fiinnk fime "quary" cattle hard",
call>
<thinkk the provided fomes, the make profegonial asen checking caitle herd after riding his fromes cleanly him waileing in field with cattle Therefore, the answer the question <think>>
<ariswe>>A</aa A/answerr>
Oracle
time "end time": "quary": "feading ducks")
400s
(sstrt time": 400, "end, time": 1989, "query"; "man riding bike")
("start 465, 585, "quory": dolnng?
Sanswor>A sanswer>>
Figure 22: Failure case: visual hallucination. No information related to riding was found in the scarch results.
28

---

Published as a conference paper at ICLR 2026
Question: parrots
appear?
A. Blue, rlackrad.
6. Yellow, red. greegeeen.
C. Ye Yellowbbr blu.u.uuu.
green.
question, identily parrots
segment parrot
sppear together and then determine their colors. </think>
<tool, calls("sstrr ("start_ time". " 0, "end, time 637, "query": "three parrots together", "num frames 8j <tooll call=
Related Fremes
<think The frames cleerly show three perrofs: one red, one blue, and one green, Besed on theee frames, I can confidentll provide the enswer to the question, s/thinke <answer-D<aanswer>
Incorreci recogrition
Oracle
("start time "end_ time": 637, "query": : "three parrots together")
Pirpe Imay
Blue, Green, Yellow
Canswer-Clanswer>>>
Figure 23: Failure case: correct f'rame set but incorrect answer. The model correctly sampled the frame but perception reasoning capabilities insufficient, leading to incorrect answer.
29

---

Published as a conference paper at ICLR 2026
Unform denso
core. Question: What the second stop the production process?
Cut off the head.
C. Peel off the skin. Clean the skin.
Utinke determine the second
production orocess of preparing pirespole, need identily of actions ahown video. The videa with bost intradlucing fallowed by the proparation of pinsapple. analyze fromes identify staps in the process. <think>>
<tool <tool_call>""a (astar" time": 10, "and time": 59, "quory": "Cutting pineapple", "num, _rrr_frrm""" B) <hool calls
<think> The frames clearly show sequence actions preparing pineapple. The step cutting head, followed by cutting the pineapple half, removing the core, and then cutting it into smaller pleces. Based on the provided frames, the second step after cutting off the head cutting the pineapple half. <Ahink>
<answer-B<lanswer>
C, Peel off the skin.
Oracle
("start time": "end__iin fime": "query": "remove the core")
46s
("start time": 4, "ond, timeo: 59, "quary": "cut off the head")
10s
("start_ lime 4, "end, time": 59, "query": "Peel off the skin
21s
("start, time": 4, "end time": 59, "query": "Clean the outer skin
No Related Frames Found
Wynswer-C<aasswwr
Figure 24: Failure case: misleading search result. Due to the limited number of frames in single search, the sparse frames obtained by the model may contain misleading information, leading to an incorrect answer.
30


Reviews:
[{"review_id": "Z53tcOtVHo", "reviewer": "Reviewer_Ds7H", "summary": "The goal of this paper is to propose an RL framework to solve the temporal search problem in video-language data in a general way without human annotations.\n* Solid contribution with practical value: The paper presents a well-executed approach that achieves consistent improvements across downstream Video QA tasks on appropriate benchmarks (VideoMME, MLVU, LVB), demonstrating that the method works broadly. The framework successfully addresses the temporal search problem without requiring human annotations, and the planned release of code, models, and data will facilitate future research in this actively growing area.\n* Well-grounded work with clear limitations: While the performance gains are moderate rather than substantial, and the novelty is somewhat incremental (applying GRPO to frame search), the paper is well-motivated, clearly written, technically sound, and appropriately positioned in the literature. The work answers a relevant question (\"Does RL-based frame search work without human annotations?\") affirmatively with proper empirical support.", "questions": "1. More comprehensive analysis:\nCases where the base model performs better than the proposed method\nClear failure mode analysis\nThis would demonstrate deeper understanding and make the work more valuable for practitioners deciding when to use this approach\n2. Stronger empirical results in at least one of these ways:\nMore substantial performance gains (not just moderate/consistent)\nDemonstrating the method works well on additional benchmarks\nShowing the approach scales better or generalizes to new domains\n3. Addressing the finetuning limitation:\nEither: showing the finetuning requirement is justified by significantly better performance\nOr: demonstrating other practical advantages that offset not being able to use API endpoints (e.g., efficiency, cost, control)", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The paper is well written and is easy to understand.\n* The paper proposes a novel algorithm for frame search that does not rely on human annotations and leverages “thinking” with video frames and text.\n* The authors have a fairly good set of eval benchmarks: VideoMME MLVU and LVB.  To better understand when the authors’ work should be used, it would be interesting to see the cases where the base model performs better, or any potential failure modes of the proposed method.\n* The code, models, and data being released will help facilitate research in this area, which is actively growing area of research in long video understanding.", "weaknesses": "* Performance is not substantially better than existing methods, often only moderate performance gains at best.  However, there are consistent gains on the downstream Video QA task, which indicates the approach can work broadly \n* This approach requires finetuning the base VLM via RL, whereas other approaches (e.g., T*) do not require any finetuning and can thus leverage API endpoints.  \n* The novelty of the method may be somewhat limited in the sense that it applies a known technique, GRPO, to the setting of frame search in videos.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lzF2kYoVMJ", "reviewer": "Reviewer_aDf9", "summary": "The goal is to train models to be able to reason about long-form video, which means an iterative process of search/retrieval paired with state-tracking for eventual answer generation. The authors provide a formulation for training first via SFT and also an updated RL formulation.", "questions": "- Can you enumerate some degenerate solutions the training is likely to find? And what mitigation was required (or if none, why didn't we end up with reward hacking)? My first thought was something about exhaustive search, wide temporal windows, etc\n- I don't understand the role of 0.5 in (4), \n- Fig 3 makes it seem like there are a set of answers one per frame in the retrieval? But many intermediate clips would not align with the actual final answer, correct? \n- Can a distribution of the range of frames (8.8 being average) be provided? More generally, insight into which frames are selected, why, and/or what biases the count?\n- L323 says there's a budget of 8, but table says 8.8 average? Also, how was this value determined? \n- Can Table 2 be easily run with smaller budgets for a more direct comparison to prior work?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Long-form video understanding and reasoning is an important task on which models perform poorly.  This approach aims to move the needle and leverages a nice balance of search and \"reasoning\" (as CoT is usually done) to integrate the video content where appropriate to validate a hypothesis (the question). The approach appears to work well and is intuitive.", "weaknesses": "1. See questions below\n2. I fear that I'm missing some intuition about where to go next after this paper. I'm trying to use the examples in the appendix (Fig 16/17) to reason about where/why things fail and if there's anything that can be done about them.  The author's guidance on the importance of specific parameters (e.g. 8) would be appreciated but also where the approach is not appropriate.  For example, in the insufficient search, is the data well characterized such that we know how well an oracle would do and how often the number of data points is out of scope for the budget, the model, etc? Do some tasks reduce to exhaustive search? Are there other aspects of visual reasoning/continuity/etc that are missing from models such that even with the perfect set of frames the model cannot succeed?  I would like to be able to factor the various components/concerns and understand their interplay.\n\nminor\n- L128, prue", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AeHQ6tIHvZ", "reviewer": "Reviewer_mLMd", "summary": "This paper presents TimeSearch-R, a novel framework that reformulates temporal search in long-form video understanding as an interleaved text-video thinking process, optimized end-to-end with reinforcement learning. The key innovation is the Completeness Self-Verification (CSV) mechanism, which addresses critical failure modes (insufficient exploration, inconsistent reasoning) in standard outcome-based RL by forcing the model to re-answer questions using only its dynamically retrieved frames. The authors also construct a high-quality dataset via a two-stage filtering pipeline to eliminate samples solvable by linguistic bias or unsolvable even with ideal search. Extensive experiments show that TimeSearch-R achieves new state-of-the-art results on temporal search benchmarks (e.g., Haystack-LVBench) and long-video QA benchmarks (e.g., LongVideoBench), while also exhibiting emergent, human-like search strategies.", "questions": "1. Can you verify whether TimeSearch-R trained with Qwen2.5-VL-7B can work directly with other model variants (e.g., Qwen2.5-VL-3B, 34B, or 72B) without retraining?\nIf not, how much fine-tuning or adaptation is required?\n\n2. Have you tested whether the learned search policy can generalize to other Video-LLMs beyond Qwen2.5-VL, such as LLaVA-Video or Gemini-style multimodal backbones?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear motivation and problem formulation. The paper correctly identifies the bottleneck of current Video-LLMs and reformulates temporal search as an interactive reasoning process.\n\n2. The GRPO-CSV algorithm introduces self-verification to provide intermediate supervision within reinforcement learning.\n\n3. The proposed model shows consistent gains across several long-video understanding benchmarks and produces explicit reasoning–search traces, enhancing interpretability over prior approaches.", "weaknesses": "The main concern lies in model coupling and generalization.\nIt remains unclear whether the learned TimeSearch-R policy is specific to the base VLM (Qwen2.5-VL-7B) or transferable to other model sizes and architectures.\nSince the reinforcement training is done with a fixed backbone, it is possible that the learned policy overfits to the internal embedding or feature distribution of that model.\nThis would limit the practical usefulness of TimeSearch-R as a general temporal search module for other VLMs (e.g., 3B, or 72B versions).\nThe paper would be much stronger if it could demonstrate robustness across different backbones or scales.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zymPNm9i2p", "reviewer": "Reviewer_VVki", "summary": "This paper tackles the challenge of temporal search in long-form video understanding—how to efficiently identify a small subset of relevant frames among tens of thousands to answer video-based questions. Existing works depend on hand-crafted multi-step pipelines, whereas **TimeSearch-R** proposes an end-to-end reinforcement learning (RL) formulation that interleaves text-video reasoning and frame retrieval. The key technical contribution is GRPO-CSV (Group-Relative Policy Optimization with Completeness Self-Verification), which supplements the standard RL outcome reward with an additional self-verification phase: the model must re-answer the question using only the frames it has searched, thereby improving temporal completeness and reasoning consistency. The paper further introduces a two-stage data filtering pipeline to remove linguistically solvable or unsolvable samples. Empirically, TimeSearch-R achieves new state-of-the-art (SOTA) results on Haystack-LVBench, Ego4D, and long-video reasoning benchmarks, including VideoMME, MLVU, and LongVideoBench, outperforming both open-source and closed-source models. Ablations show CSV prevents RL collapse and improves search completeness.", "questions": "- Could the authors clarify how Completeness Self-Verification (CSV) is triggered during training — is it applied to every rollout or only when the original answer passes a correctness threshold?\n- How are the reward weights (completeness / format / accuracy) combined or tuned? Would alternative weighting (e.g., annealing completeness) affect training stability?\n- Could the authors quantify the number of samples filtered at each stage and analyze its impact on performance and generalization?\n- Could the authors report variance or confidence intervals over multiple runs to demonstrate result robustness?\n- Could the authors provide statistics on average search turns and retrieved frames compared to baseline methods? \n- How does the inference time (or throughput in videos/sec) compare to baselines? \n- What are the training GPU hours or per-iteration cost introduced by the CSV phase?\n- Could the authors provide examples or analysis of typical CSV failure cases (e.g., incomplete retrieval or reasoning drift)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- **Clear Motivation.** Addresses the key limitation of static frame sampling with a well-motivated reformulation of temporal search as text-video interleaved reasoning. Well-written and logically structured, making ideas easy to follow.\n- **Algorithmic Novelty.** Proposes GRPO-CSV, a simple yet effective reinforcement-learning mechanism that improves search completeness and reasoning consistency.\n- **Remarkable Empirical Results.** Achieves new SOTA on multiple long-video benchmarks with thorough ablations and comparisons.\n- **Insightful Analysis.** Provides clear case studies and ablations demonstrating stability gains and human-like search behavior.", "weaknesses": "- **Incremental novelty.** While the integration of self-verification into GRPO is interesting, the idea remains an incremental extension of existing RL-based reasoning frameworks, rather than introducing a fundamentally new optimization principle.\n- **Limited theoretical justification.** The completeness reward (Eq. 4) is heuristic and may become unstable if the base answer is wrong; no theoretical analysis of convergence or variance reduction is provided.\n- **Heuristic parameterization.** Several hyperparameters (e.g., search budget, reward weights, KL penalty) are empirically tuned without sensitivity analysis, leaving uncertainty about robustness across tasks and video lengths.\n- **Marginal quantitative gains.** Although the model achieves clear improvements, the gains are moderate on some benchmarks and may not fully justify the added complexity.\n- **Scalability and Cost Concerns** Training requires 32 × A100 GPUs with multiple rollouts per prompt. The scalability to larger backbones and the claimed efficiency remain unverified due to missing runtime or complexity analysis.\n- **Missing qualitative evaluation.** While one qualitative case is shown, the paper lacks broader visualizations of temporal search evolution or failure cases to illustrate where TimeSearch-R surpasses prior methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)