Title: Seeing the Arrow of Time in Large Multimodal Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: arrow of time

large multimodal models

video understanding

videoQA
Abstract: The Arrow of Time (AoT)—time's irreversible flow shaping physical events—is fundamental to video comprehension, yet remains a significant challenge for modern large multimodal models (LMMs). Current LMMs struggle to perceive and utilize temporal directionality in video when responding to language queries, obstructing deeper temporal understanding. We tackle this deficiency by first providing a critical analysis of existing benchmarks and models. We then introduce ArrowRL, a reinforcement learning (RL)-based training strategy with an innovative reverse reward that instills AoT awareness by encouraging divergent video interpretations between forward and reversed visual frames. For rigorous evaluation, we additionally develop AoTBench, a new multi-faceted benchmark probing temporally challenging questions. Experiments show ArrowRL greatly advances temporal perception: it not only achieves substantial improvements on our challenging AoTBench but also demonstrably boosts performance on standard video question answering (VQA) benchmarks (with peak accuracy gains reaching over 20% and 10% respectively). This validates ArrowRL's effectiveness and highlights the critical need for dedicated AoT understanding in LMMs.

Seeing the Arrow of Time in Large Multimodal Models

Abstract

The Arrow of Time (AoT)-time's irreversible flow shaping physical events-is fundamental to video comprehension, yet remains a significant challenge for modern large multimodal models (LMMs). Current LMMs struggle to perceive and utilize temporal directionality in video when responding to language queries, obstructing deeper temporal understanding. We tackle this deficiency by first providing a critical analysis of existing benchmarks and models. We then introduce ArrowRL, a reinforcement learning (RL)-based training strategy with an innovative reverse reward that instills AoT awareness by encouraging divergent video interpretations between forward and reversed visual frames. For rigorous evaluation, we additionally develop AoTBench, a new multi-faceted benchmark probing temporally challenging questions. Experiments show ArrowRL greatly advances temporal perception: it not only achieves substantial improvements on our challenging AoTBench but also boosts performance on standard video question answering (VQA) benchmarks (with peak accuracy gains reaching over 20% and 10% respectively). This validates ArrowRL's effectiveness and highlights the critical need for dedicated AoT understanding in LMMs. 1

1 Introduction

Our world unfolds with a distinct rhythm, governed by time's relentless forward march. We watch cream swirl into coffee, smoke disperse, or a glass shatter-common events whose reversal would feel instantly unnatural, or physically impossible (Fig. 1 (a)). This Arrow of Time (AoT) concept is rooted in physical laws and shapes the causal structure of reality [31]. We humans navigate the world with an innate grasp of the AoT, effortlessly perceiving the flow of events and decoding temporal directionality for visual narratives. Crucially, this sensitivity goes beyond identifying physical irreversibility; it fundamentally involves understanding the sequential progression of events and interpreting the semantic meaning embedded within that progression. Equipping intelligent systems with this broader AoT sensitivity-capturing how events unfold over time-is essential for achieving genuine video understanding.

Early research on video representation learning has leveraged the inherent temporal dimension of video as a form of self-supervision, designing a variety of interesting pretext tasks such as forward/reverse sequence classification (an early form of AoT) [52, 66, 23], video order prediction [48, 17, 30, 32] and enforcing temporal alignment [19, 75]. Throughout these developments, temporal understanding is treated as an intrinsic, vision-only problem based on physical cues.

Today the landscape of video understanding is rapidly shifting with the rise of large multimodal models (LMMs) [39, 8], which link video perception with the generative and interactive capabilities of large language models (LLMs) via textual interfaces. This evolution motivates us to rethink AoT from a language-aware perspective. A video's semantic meaning is often intertwined with the AoT: consider Fig. 1 (b), where the forward and reversed videos yield opposite interpretations conveyed through language. While many efforts aim to improve the general temporal understanding of these

(a) Which video segment is played in reverse?

Base LMM:

The gas stove burner is ignited and

producing a steady blue flame.

Base LMM + ArrowRL (ours): The video shows a gas stove burner being turned off, with the blue flame gradually extinguishing.

Base LMM:

A gas stove burner ignites and

produces a blue flame.

AoT-aware

Base LMM + ArrowRL (ours):

A gas stove

burner ignites and produces a blue flame.

temporally-

insensitive

(b) What is happening in the video?

Base LMM + ArrowRL (ours)

Base LMM

powerful LMMs [85, 20, 77, 89, 38], we posit that AoT sensitivity is a fundamental yet overlooked component required for deeper understanding.

Consequently, we aim to instill this core temporal sensitivity, empowering LMMs to leverage directional information when generating language responses as appropriate. This capability is essential for advancing LMMs past superficial pattern recognition, as it would ground their internal world model [28, 27, 47] in the fundamental reality of temporal progression and causality, towards human-level reasoning.

Naturally, this leads us to ask: how capable are current LMMs in perceiving the AoT? Perhaps surprisingly, despite rapid advancements elsewhere, our findings indicate a critical gap. First, a profound temporal insensitivity is evident even in state-of-the-art LMMs. They fail on basic AoTrelated tasks, such as distinguishing forward from backward video playback (Fig. 1 (a)), and ignore crucial semantic differences between forward and reverse sequences, erroneously providing identical descriptions (Fig. 1 (b)). 2 Second, broadening our investigation, we identify an alarming failure: on multiple standard video question-answering (VQA) benchmarks [70, 21, 51, 46, 9, 37, 36, 68], shuffling or reversing video frames often causes only slight or even no performance degradation for a leading representative LMM (see Fig. 2 and details in Supp. B.2).

The observed performance reflects a problematic interplay between intrinsic model capabilities and current benchmarks. First, on the modeling side, the observed failures clearly indicate a fundamental lack of temporal directionality understanding; this core perceptual capability for grasping causaltemporal dynamics remains underdeveloped in current LMMs. Second, on the benchmarking side, existing evaluations fail to adequately probe AoT sensitivity. While the single-frame bias issue [7, 33, 89, 15, 11, 69] (where one static frame suffices for answers) is widely studied, we identify a distinct critical limitation: many multi-frame questions still lack dependence on temporal order, allowing models to succeed even with shuffled or reversed video. We present a systematic study across eight existing VQA benchmarks to offer insights into this AoT insensitivity at both the benchmark and model levels, addressing a gap in prior evaluation studies.

Building on these insights, our work aims to empower LMMs to 'see' the AoT in video. We first propose ArrowRL, a novel reinforcement learning (RL) algorithm based on Group Relative Policy Optimization (GRPO) [57]. The core idea is a unique reverse reward that promotes divergence between the model's forward and backward video interpretations, fostering AoT sensitivity for temporally demanding questions. In addition, to address benchmark inadequacies, as a secondary

EgoSchema

LongVideoBench

MVBench

NExT-QA

PerceptionTest

TemporalBench (S)

TemporalBench (L)

VITATECS

VideoMME

0

25

50

75

100

MCQ Acc. (%)

36.8

49.7

57.9

78.3

57.3

60.8

58.6

85.3

56.7

36.4

47.9

55.9

77.8

56.5

58.4

57.5

85.0

57.0

35.9

47.4

53.8

77.3

55.8

56.1

55.8

83.0

55.6

Forward

Shuffle

Reverse

contribution, we develop AoTBench, a benchmark comprising three distinct tasks for rigorous evaluation of LMMs' AoT perception capabilities.

Our experiments demonstrate the efficacy of ArrowRL. Across three distinct base LMMs, it consistently boosts task performance on AoTBench, and, importantly, generalizes strongly to multiple standard VQA benchmarks [44, 80, 15] (e.g., +65.9% relative gain on [80]). These results underscore that improving temporal sensitivity in LMMs translates to broader performance gains, revealing the fundamental role of AoT awareness in achieving genuine temporal perception.

2 Related Work

'Time' in Video Time has long been recognized as a valuable self-supervised signal in video [52, 66, 23, 48, 17, 32, 30, 19, 75]. Early works capitalize on this property within the visual domain, leveraging the arrow of time (AoT) by using forward/reverse sequence classification as a pretraining task, which benefits downstream action recognition [52, 66, 23]. Relatedly, random shuffling has been explored for action analysis [48, 53, 56]. Current multimodal vision foundation models [22], such as CLIP [54] and VideoCLIP [72], can benefit action understanding, driven by text-video contrastive alignment objectives. A few works [3, 65, 18] employ temporal reversal as a source of negative examples for aligning video and text encoders, yet the discussion remains specific to dual-encoder architectures and only addresses basic temporal concepts with before/after relations [3] or action antonyms [65, 18]. Critically, while the field has advanced to LMMs capable of freeform language-based interaction with video content [39, 8], we find that these models exhibit a surprising insensitivity to fundamental temporal directionality. This suggests their internal world models [28, 27, 47] may fail to incorporate the basics of real-world event progression and causality. While recent work investigates AoT in the context of text-only LLMs [50], and concurrent efforts explore it for physical reasoning [2], how to instill AoT awareness within LMMs remains a critical but largely unexplored problem.

Large Multimodal Models (LMM) LLM capabilities have been extended to the visual domain through the development of LMMs [39, 8], which integrate a vision encoder with a powerful LLM, enabling them to perceive and reason about visual content. While early LMMs focus on images [42, 41, 87, 61], research on video-LMMs [12, 85, 89, 38, 34, 63, 5, 62, 43, 73, 14, 74] is rapidly progressing, including the development of high-quality video datasets [12, 85] and exploration of the architecture design space [89, 38, 43]. Our work is orthogonal to these data and architecture innovations. We investigate how a core understanding of the AoT can be instilled directly through training, offering a complementary path towards enhanced temporal perception in LMMs.

Benchmarks with Temporal Sensitivity Evaluating the true temporal understanding of LMMs is challenging, even with many benchmarks available for video question answering [70, 21, 51, 46, 9, 44, 37, 36] and captioning [10, 62]. Recent studies [7, 33, 89, 11] reveal how questions on many popular video benchmarks can be answered correctly with minimal temporal context (single/no frame) due to static video content, non-temporal question design, or language queries that provide unintended hints. However, merely requiring multiple frames is insufficient for probing deep temporal understanding. Our work moves beyond the 'single-frame bias" to address the overlooked issue that many multi-frame questions still lack sensitivity to temporal directionality. Inspired by preliminary experiments that explore frame shuffling on limited datasets [15, 69], our work provides, to our

VideoChat2

ST-LLM

Tarsier-7B

Qwen2-VL-7B

LLaVA-OV-7B

LLaVA-Video-7B

Qwen2.5-VL-7B

0

20

40

60

80

MCQ Acc. (%)

35.0

35.7

34.9

46.9

48.6

48.9

53.2

54.7

32.1

34.7

34.1

28.4

34.4

33.9

34.7

33.7

Model Temporal Sensitivity

Random

Forward

Reverse

VITATECS

TemporalBench (S)

NExT-QA

PerceptionTest

VideoMME (S)

Vinoground (V)

Vinoground (T)

TempCompass

TVBench

0.0

0.2

0.4

TDS

.04

.07

.07

.07

0.11

0.15

0.52

0.42

0.53

.03

.06

.05

.06

.09

0.12

0.35

0.42

0.42

.05

.05

.10

0.12

0.13

0.37

0.36

0.54

0.56

Benchmark Temporal Sensitivity

LLaVA-OV-7B

LLaVA-Video-7B

Qwen2.5-VL-7B

0

20

40

Avg. Relative Acc. Drop (%)

knowledge, the first systematic and rigorous evaluation of AoT sensitivity across eight diverse VQA benchmarks. By comparing fine-grained metrics with forward, reversed, and shuffled video, we offer benchmark- and model-level insights currently lacking in the field.

Reinforcement Learning to Enhance LMMs Post-training using reinforcement learning (RL) is increasingly employed to refine LLMs, aligning model responses with human preferences and enhancing complex reasoning abilities [49, 88, 55, 26, 57]. A family of successful algorithms, including PPO [88], DPO [55], online DPO [26], and GRPO [57], is now being actively extended to LMMs, although applications have mainly focused on image-LMMs [59, 78, 81, 84], with video comprehension being a newer frontier. While these efforts have concentrated on mitigating visual hallucination [83, 64, 79] or (concurrently) enabling structured reasoning [20, 13, 45] like Chainof-Thought [67], our work investigates a distinct dimension: enhancing the model's fundamental temporal awareness, by leveraging AoT signals naturally embedded in videos.

3 Approach

Our preliminary experiments (Fig. 2) reveal a striking insensitivity to temporal directionality, suggesting deficiencies in both the benchmark design and/or LMMs' intrinsic ability to perceive the AoT. We systematically disentangle these intertwined factors, by first examining temporal sensitivity for existing benchmarks (Sec. 3.1), and then detailing our proposed approach to bestow AoT perception (Sec. 3.2) along with AoTBench, a new benchmark designed to evaluate this capability (Sec. 3.3).

3.1 Are Today's Benchmarks Time-Sensitive? Revisiting Current LMM Evaluation

To systematically study the inherent AoT sensitivity of existing benchmarks without costly human evaluation, we employ an automated strategy using LMMs to probe benchmark properties. To select suitable 'evaluator' LMMs, we assess a pool of candidate models under 10B parameters (for computational feasibility)-VideoChat2 [36], ST-LLM [43], PLLaVA-7B [73], Tarsier-7B [62], Qwen2VL-7B [63], LLaVA-OV-7B [34], LLaVA-Video-7B [85], Qwen2.5-VL-7B [5]-on TVBench [15]. TVBench is selected as its design explicitly requires a higher degree of temporal understanding [15]. Comparing accuracy for forward vs. reversed video on TVBench (Fig. 3 (left)) reveals leading models that exhibit both high overall accuracy and a significant difference (delta). Consequently, we select LLaVA-OV-7B [34], LLaVA-Video-7B [85], and Qwen2.5-VL-7B [5] for subsequent analysis.

We then evaluate the three selected LMMs across eight popular VQA benchmarks (VITATECS [37], TemporalBench [9], NExT-QA [70], PerceptionTest [51], VideoMME [21], Vinoground [80], TempCompass [44] and TVBench [15]). 3 To quantify benchmark and sample-level AoT sensitivity (facilitating later construction of AoTBench, c.f., Sec. 3.3), we propose the temporal divergence score

High-TDS Samples

Q.

What is happening to the pink

flowers?

Q.

In which hand does the person

conceal a small object or a drawn sign?

Q.

What does the white hair man do

after picking the girl up?

Low-TDS Sample

- Bring girl closer to the tree

- Follow her instructions to sit

-

Left

-

Right

- Turning from withered to full blossom

- Turning from full blossom to withered

(TDS). For an evaluator LMM f , consider an MCQ sample i , comprised of video v i , language query l i and ground truth answer y i from K i options. Denote the reversed video as ˜ v i . The MCQ setup instructs the model to respond only with the option's letter. The first-token probability distribution is therefore a direct measure of the model's confidence over the K i choices [6, 29, 40]. We compute the probability distribution for both forward and reversed video: p i = FirstTokenProb( f, v i , l i ) and ˜ p i = FirstTokenProb( f, ˜ v i , l i ) , where p i , ˜ p i ∈ R K i .To ensure our analysis focuses on instances genuinely requiring arrow of time (AoT) reasoning, we first filter out outlier samples where performance on the reversed video exceeds that of the forward one, as these cases typically indicate ambiguous or irrelevant temporal cues. The TDS for any remaining sample i is defined using KL divergence: TDS i = D KL [ p i ∥ ˜ p i ] . A high TDS signifies that the model's prediction is highly sensitive to the video's temporal direction-causing disagreement between forward and reverse responses-therefore suggesting the sample requires AoT understanding for a correct interpretation and answer.

Compared with prior efforts that examine accuracy delta (comparing full video vs. single-frame performance) [7, 33, 89, 15], which only reflects top-prediction flips, TDS is inherently more finegrained by comparing the output probability distributions from forward vs. reverse video inputs via KL divergence-capturing shifts in confidence or the ranking of alternatives. This enables robust quantification of AoT sensitivity and allows for effective sorting of samples by AoT sensitivity.

Fig. 3 (right) presents the benchmark-level AoT sensitivity analysis, comparing TDS values. See Fig. 4 for low and high-TDS examples. We also report the coarser relative accuracy drop (averaged across evaluator LMMs) for reference. Our analysis reveals varying levels of AoT sensitivity across benchmarks: TVBench [15], Vinoground [80], and TempCompass [44] emerge as sensitive whereas benchmarks like VITATECS [37], TemporalBench [9], and NExT-QA [70] show lower sensitivity to video playback direction. These benchmark-level insights, derived from TDS, can aid future benchmark interpretation, and they directly influence our AoTBench design (Sec. 3.3).

3.2 ArrowRL: Learning the Arrow of Time

We now discuss our approach to equipping a given LMM π θ with AoT understanding for enhanced temporal perception. Our core premise is that videos are inherently dynamic, and LMMs should capture this dynamic progression when responding. Specifically, given a question q = ( v, l ) (composed of video v and language query l ) and a target response o ⋆ , drawn from the underlying data distribution P ( Q,O ⋆ ) of a standard instruction tuning dataset, the goal is for π θ to produce responses that align well with o ⋆ . To foster broad AoT sensitivity, our training utilizes a diverse suite of tasks-MCQ, open-ended QA, and video captioning (see Sec. 4 for the specific datasets used).

Given that modern LMMs are well-pretrained from extensive data, we follow advances in LLMs [49] and employ RL as a targeted post-training strategy. GRPO [57] is a leading RL algorithm that refines model outputs by comparing multiple responses generated for one prompt, adjusting probabilities based on relative scores within that specific group. In this way it provides finer control compared to using absolute rewards or response pairs and is thus well-suited for our goal of teaching AoT sensitivity. We adapt GRPO with a novel reward structure focused on temporal directionality, and term our approach ArrowRL. Fig. 5 provides an overview of the ArrowRL training process.

Given question q , let { o i } G i denote the set of G candidate responses generated by π θ . We design a reward signal to rank these responses within the GRPO framework, integrating two complementary objectives: target fidelity and reverse reward.

𝑣

𝑙

Policy Model

𝜋

!

𝑣 $

𝑙

𝑞

𝑜

"

𝑜

#

𝑜

$

…

Reward Computation

𝑜

%

𝑜

⋆

𝑜 $

1 - Sim

𝑟

%

'%(

𝑟

%

)*+

𝑟

%

𝑟

"

𝑟

#

𝑟

$

…

𝐴

"

𝐴

#

𝐴

$

…

Ref Model

𝜋

)*'

𝑜 $

𝑜

⋆

Video Reversal

∈ [0, 1]

update

Target Fidelity Reward First, we define a fidelity reward encouraging alignment between each candidate response o i and the target response o ⋆ , r fid i = Similarity( o i , o ⋆ ) . The function Similarity( · , · ) returns a scalar between 0 and 1 and is calculated via accuracy matching (1.0 if o i = o ⋆ , else 0.0) for MCQ tasks, or via an LLM-based similarity score for open-ended QA and captioning (see Supp. B.3 for the specific prompts and the LLM judge employed).

Reverse Reward Second, central to ArrowRL is the reverse reward. Rewarding only fidelity to the target response o ⋆ (via r fid i ) can be insufficient for capturing directional nuance-particularly if o ⋆ lacks temporal progression details. Therefore, we utilize the reversed video (denoted by ˜ v ) as a natural directional contrast signal, which often distinguishes itself from the forward version, to enforce AoT sensitivity. Intuitively, an AoT-sensitive model should yield different responses when observing the same video in different sequential order (though not without exceptions, which we handle below). Specifically, we maximize the dissimilarity between forward candidate responses o i and the reverse response ˜ o , i.e., the response produced by π θ when given ˜ v . The reverse reward is thus defined as: r rev i = 1 -Similarity( o i , ˜ o ) . This reward design discourages generic or static responses and compels attention to the dynamic, directional details conveyed by AoT in forward v . Note that in this process, the reversed video ( ˜ v ) is utilized solely as a signal for computing the reverse reward ( r rev i ); the training objective always pertains to generating correct and temporally-aware responses for the forward video ( v ), not to improving responses conditioned on the reversed video itself.

Fig. 6 illustrates this mechanism. A candidate response o i that incorrectly mirrors the reverseconditioned response ˜ o will receive a low r rev i . For instance (left example), both o 2 and ˜ o describe 'cooking beef' (where ˜ o 's description is contextually wrong for the forward video), thus o 2 is penalized via a lower reward. In the right example, given that the reversed video yields an ˜ o of 'picking up', candidate o 2 stating 'placing...on' shows larger dissimilarity from ˜ o than the more generic candidate o 1 stating 'arranging'. Thus, o 2 receives a higher reward as it better reflects the forward video's distinct temporal dynamics.

The two reward components work in tandem: the reverse reward compels the model to perceive AoT by enforcing divergence between forward and reverse responses, while the fidelity reward maintains relevance to the target response, preventing trivial outputs that merely differ from the reverse case. This complementary design encourages meaningful, temporally-aware responses. The final reward for ArrowRL on response i is defined as: r i = r fid i + α i r rev i , where α i balances the two rewards.

A natural concern arises regarding the applicability of the reverse reward when its underlying premise-semantic distinctiveness between forward and reverse video-is not met (e.g. for static queries, non-dynamic or cyclic videos). We address this through training data selection (c.f., Sec. 4), adopting samples exhibiting high temporality, and also through a dynamic weighting scheme for the reverse reward's contribution α i . During training, we measure the similarity between the reverse response ˜ o and the target one o ⋆ . If Similarity(˜ o, o ⋆ ) > γ (where γ is a threshold hyperparameter),

[Candidate response

𝒐

𝟏

]

No, the chef doesn't immediately

start chopping vegetables. After seasoning the beef, we see

the chef preparing the pan with oil and duck fat.

[Reverse response

𝒐 "

]

No, the chef does not immediately

start chopping vegetables. Instead, he continues to cook the

beef in the pan.

Q.

After seasoning the beef, does the chef immediately start

chopping vegetables?

[Candidate response

𝒐

𝟐

]

No, the chef starts cooking the

beef in a pan instead of chopping vegetables.

𝑟

#

$%&

= 0.45

𝑟

'

$%&

= 0.15

Q.

Describe the video.

[Reverse response

𝒐 "

]

The video shows a person picking up

a piece of cheese from a wooden board.

[Candidate response

𝒐

𝟏

]

The video displays a woman's

hands arranging various types of cheese on a serving board.

[Candidate response

𝒐

𝟐

]

The video depicts a female hand

reaching out and placing a piece of yellow cheese on a

wooden table, which already has several other cheeses, as

well as bread and berries.

𝑟

#

$%&

= 0.6

𝑟

'

$%&

= 0.8

suggesting the sample is not highly AoT-sensitive, we disable the reverse reward for that sample by setting α i = 0. Otherwise, α i takes a default hyperparameter value α .

With this reward in hand, we proceed to the optimization step. For each question q and the corresponding group of generated responses { o i } G i , we compute the group reward r = { r i } G i , and then obtain advantage ˆ A i,t as the normalized reward, i.e. ˆ A i,t = ˜ r i = r i -mean ( r ) std ( r ) . The policy model is optimized by maximizing the following GRPO objective:

where ϵ and β are hyperparameters controlling clipping and KL regularization respectively, π θ is the current policy being optimized, π θ old is the policy from the previous iteration used for importance sampling and π ref is the reference model (set as the initial model checkpoint) used for regularization.

3.3 AoTBench: Addressing Temporal Evaluation Gaps in Video Understanding

Our preceding analysis (Sec. 3.1) and ArrowRL (Sec. 3.2) highlight the need for focused evaluation benchmarks. To this end, we propose AoTBench, the first dedicated benchmark to assess temporal direction sensitivity-a core component of robust video perception-through three distinct elements:

Sequence Direction Classification . The task is to classify video playback as forward or reverse based on visual cues (Fig. 1 (a)), a capability with applications like video forensics, yet remains unexplored for LMMs. Following early vision work [66, 23], we adopt videos from ReverseFilm [66] and UCF101 [58], from which we manually select and verify a total of 613 videos demonstrating strong temporal properties suitable for this evaluation.

Directional Caption Matching . This task probes the model's ability to connect video dynamics with corresponding textual descriptions. We target semantically directional video sequences where forward and reverse playback correspond to distinct textual descriptions (Fig. 1 (b)). Leveraging the RTime benchmark [18], which conveniently provides 2,000 such high-temporality videos alongside human captions, we formulate two complementary MCQ tasks: (1) video-to-text (V2T): choosing the correct caption (forward vs. reverse) for a given video, and (2) text-to-video (T2V): choosing the correct video sequence (forward vs. reverse) for a given caption.

AoT-sensitive VQA . We curate temporally sensitive VQA samples from existing benchmarks. Using the per-sample TDS computed earlier, we select the top 200 high-TDS questions from each source benchmark, after removing easy samples unanimously answered correctly by all evaluator LMMs. This yields a subset of 1,800 VQA samples that necessitate robust AoT understanding and challenge current models' temporal perception capabilities. We illustrate the effectiveness of using TDS to isolate temporally challenging VQA samples in Fig. 4.

4 Experiments

Post-training Data Our ArrowRL training data comprises a comprehensive suite of tasks, with data selected or adapted for each to emphasize scenarios requiring AoT awareness: (1) MCQ tasks: we format sequence direction classification (Fig. 1 (a)) as MCQ, using 1.1K training videos selected from UCF101 [58] (following prior use of this dataset [23]); (2) Open-ended QA: we curate a hightemporality subset of LLaVA-Video-178K [85], filtering based on the perplexity difference between forward and reverse video to retain samples with great temporal sensitivity, totaling 11.8K samples; (3) Video Captioning: we employ the training set [18] of RTime, which provides high-temporality videos alongside distinct human captions for their forward and reverse versions, comprising 11.7K samples. See Supp. B.3 for data examples and prompts used.

ArrowRL Implementation Our framework presents a straightforward and highly efficient approach by enabling direct application to pretrained LMMs without an intermediate supervised fine-tuning (SFT) step; we find ArrowRL alone is sufficient to instill AoT awareness. We demonstrate its broad applicability across three leading base LMMs : LLaVA-OV-7B [34], Qwen2-VL-7B [63], and Qwen2.5-VL-7B [5]. For efficiency and memory, we limit the maximum number of video frames to 16 during training. Hyperparameter α is set as 0.25 and γ is set as 0.75. The response group size G is set as 8. Training consists of 2000 RL steps on 6 NVIDIA GH200 GPUs.

Evaluation Setup Our evaluation is designed to assess LMM temporal perception capabilities, using MCQ accuracy as the metric across multiple benchmarks. This includes our proposed AoTBench (Sec. 3.3), as well as three existing video benchmarks emphasizing temporal properties, as identified in Sec. 3.1: TempCompass [44], TVBench [15], and Vinoground [80]. We evaluate ArrowRL by comparing enhanced models to their respective base LMMs. Additionally, to situate these results within the broader landscape, we also report a diverse suite of leading LMMs as baselines. These include general-purpose models Aria [35] and MiniCPM-V-2.6 [76], video-focused models

0.0

0.5

1.0

value

55

60

65

70

AoTBench Acc. (%)

ArrowRL

Base LMM

0.25

0.50

0.75

1.00

value

55

60

65

70

ArrowRL

Base LMM

LLaVA-Video [85], InternLM-XC-2.5 [82] (strong in fine-grained video understanding), and VideoR1 [20] (emphasizing video reasoning), along with two major proprietary (and much larger and more extensively trained) models GPT-4o [1] and Gemini-1.5-Pro [60].

Main Results As shown in Table 1, ArrowRL effectively enhances models' temporal perception capabilities across diverse settings. Results on AoTBench (left table) reveal critical deficiencies in current LMM AoT perception. Visually-focused tasks like sequence direction classification prove especially challenging: nearly all open-source models exhibit a universal failure mode: their responses are identical regardless of video input, resulting in consistent chance-level performance (50.0% on ReverseFilm and 51.6% on UCF101). Leading proprietary models (GPT-4o, Gemini-1.5-Pro), while slightly better, still perform far below satisfactory levels. This is in stark contrast to the proprietary models' absolute accuracy on other mainstream VQA tasks, where accuracy hovers around 70-85%. Our finding and the proposed AoTBench illuminate visual temporal bottlenecks. Importantly, this improved AoT awareness also translates to gains on existing, unfiltered temporal VQA benchmarks (right table), such as +10.8 group score (+65.9% relative gain) for Qwen2.5-VL-7B on Vinoground. These results underscore the promise of enhancing AoT perception for overall temporal perception. See Supp. C.1 for a follow-up discussion of the relative strengths of different base models and how they translate to ArrowRL's largest margins.

The most important outcome is ArrowRL's improvements over each base model, and, following that, its advantage over the open-source baselines-all of which are similar in scale at 10B parameters or less. The significantly larger proprietary models trained on much larger datasets (gray rows) are a useful high-level reference, but do not constitute a direct apples-to-apples comparison.

Results on General Video Benchmarks To ensure that specializing in AoT does not degrade general video understanding, we evaluate ArrowRL-enhanced LLaVA-OV-7B on five benchmarks previously identified as less temporally sensitive (see Fig. 3 right). As shown in Table 2, ArrowRL preserves or even slightly improves performance on these general benchmarks where AoT is not required, indicating it enhances temporal perception without sacrificing general video understanding.

Ablations Fig. 7 presents our hyperparameter analysis, confirming two key design choices. First, the reverse reward is essential: the left plot shows that removing it ( α = 0 ) degrades performance to below that of the base LMM. Second, the dynamic weighting mechanism is beneficial: the right plot shows that disabling it ( γ = 1 ) yields less improvement than our dynamic approach, which can

Q.

What is the order of the letters on the table at the end?

Qwen2-VL-7B:

a toddler plays around the grass

field before he picks up a water bottle and drinks.

Qwen2.5-VL-7B:

storm

Q.

Which caption best describes this video?

Qwen2.5-VL-7B + ArrowRL:

tmrso

Qwen2-VL-7B + ArrowRL:

a toddler picks up a water

bottle and drinks before he plays around the grass field.

TMRS0

TMRSO

STOEC

BREO

Withaquickoowot hydration.

LLAVA-OV-7B:

a hand unwrapping …

LLAVA-OV-7B + ArrowRL:

a hand removing aluminum foil to reveal …

LLAVA-OV-7B:

The video showcases a close-up view of a baked pastry, its flaky crust and rich filling, and the

process of unwrapping it to reveal its contents.

LLAVA-OV-7B + ArrowRL:

The video features a close- up view of a pastry, showcasing its layers and filling,

followed by a hand interacting with the foil wrapping.

zero out the reward for non-AoT-sensitive samples. We further investigate different inference frame settings and find ArrowRL generalizes robustly despite its fixed-frame training. See Supp. C.1 for details.

Qualitative Results Fig. 8 provides qualitative examples, comparing base LMMs against our ArrowRL enhancement. The VQA examples (left) demonstrate ArrowRL enabling base models to correctly answer temporally challenging questions. Similarly, when prompted to describe forward and reverse sequences (right), the base model provides the 'unwrapping' description for both, failing to connect frames dynamically to understand the temporal progression and possibly influenced by language bias (favoring the more common 'unwrapping'). Conversely, ArrowRL demonstrates clear temporal awareness by producing distinct and correct captions reflecting the temporal direction. See Supp. C.2 for more qualitatives, including failure cases.

Limitations and Future Work While AoTBench grounds temporal directionality in language, we acknowledge that describing irreversible events remains inherently difficult in natural language. Future work could explore how learned representations encode time beyond language [4], and probe whether models develop specialized activations for time-reversed, physically implausible inputs [16]. For ArrowRL, important future directions include extending our methods to long videos (e.g., via keyframe selection) and exploring integration with temporal reasoning tasks that necessitate Chain-of-Thought reasoning. See Supp. D for a comprehensive discussion of limitations.

5 Conclusion

Our work underscores the crucial yet unexplored role of AoT understanding within LMMs. Towards this end, we introduce ArrowRL, an GRPO-based reinforcement learning algorithm with a novel reverse reward design to enhance temporal perception, and develop AoTBench, a comprehensive benchmark targeting temporal sensitivity. Great performance gains on both AoTBench and existing VQA benchmarks validate our approach and highlight the crucial role of AoT in advancing temporal understanding. We hope ArrowRL offers a promising path towards LMMs with true temporal understanding capabilities, and that AoTBench serves as a valuable tool for future research.

Acknowledgements This research was supported in part by the UT Austin IFML NSF AI Institute, with compute on the Vista GPU Cluster through the Center for Generative AI (CGAI) and the Texas Advanced Computing Center (TACC). We would like to thank the anonymous reviewers for their thoughtful and constructive feedback, which provided valuable guidance in refining this work.

References

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 , 2023.

Alisson Azzolini, Junjie Bai, Hannah Brandon, Jiaxin Cao, Prithvijit Chattopadhyay, Huayu Chen, Jinju Chu, Yin Cui, Jenna Diamond, Yifan Ding, et al. Cosmos-reason1: From physical common sense to embodied reasoning. arXiv preprint arXiv:2503.15558 , 2025.

Piyush Bagad, Makarand Tapaswi, and Cees GM Snoek. Test of time: Instilling video-language models with a sense of time. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 2503-2516, 2023.

Piyush Bagad and Andrew Zisserman. Chirality in action: Time-aware video representation learning by latent straightening. arXiv preprint arXiv:2509.08502 , 2025.

Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923 , 2025.

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems , 33:1877-1901, 2020.

Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, and Juan Carlos Niebles. Revisiting the" video" in video-language understanding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2917-2927, 2022.

Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Marcella Cornia, and Rita Cucchiara. The revolution of multimodal large language models: a survey. arXiv preprint arXiv:2402.12451 , 2024.

Mu Cai, Reuben Tan, Jianrui Zhang, Bocheng Zou, Kai Zhang, Feng Yao, Fangrui Zhu, Jing Gu, Yiwu Zhong, Yuzhang Shang, et al. Temporalbench: Benchmarking fine-grained temporal understanding for multimodal video models. arXiv preprint arXiv:2410.10818 , 2024.

David Chen and William B Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies , pages 190-200, 2011.

Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330 , 2024.

Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Zhenyu Tang, Li Yuan, et al. Sharegpt4video: Improving video understanding and generation with better captions. Advances in Neural Information Processing Systems , 37:1947219495, 2024.

Yi Chen, Yuying Ge, Rui Wang, Yixiao Ge, Lu Qiu, Ying Shan, and Xihui Liu. Exploring the effect of reinforcement learning on video understanding: Insights from seed-bench-r1. arXiv preprint arXiv:2503.24376 , 2025.

Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi, Triantafyllos Afouras, Tushar Nagarajan, Muhammad Maaz, Yale Song, Tengyu Ma, Shuming Hu, Suyog Jain, et al. Perceptionlm: Openaccess data and models for detailed visual understanding. arXiv preprint arXiv:2504.13180 , 2025.

Daniel Cores, Michael Dorkenwald, Manuel Mucientes, Cees GM Snoek, and Yuki M Asano. Tvbench: Redesigning video-language evaluation. arXiv preprint arXiv:2410.07752 , 2024.

Gustavo Deco, Yonatan Sanz Perl, Laura de la Fuente, Jacobo D Sitt, BT Thomas Yeo, Enzo Tagliazucchi, and Morten L Kringelbach. The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network. Network Neuroscience , 7(3):966998, 2023.

Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, and Davide Modolo. Scvrl: Shuffled contrastive video representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 4132-4141, 2022.

Yang Du, Yuqi Liu, and Qin Jin. Reversed in time: A novel temporal-emphasized benchmark for cross-modal video-text retrieval. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 5260-5269, 2024.

Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. Temporal cycle-consistency learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1801-1810, 2019.

Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Benyou Wang, and Xiangyu Yue. Video-r1: Reinforcing video reasoning in mllms. arXiv preprint arXiv:2503.21776 , 2025.

Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv preprint arXiv:2405.21075 , 2024.

Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, Jianfeng Gao, et al. Visionlanguage pre-training: Basics, recent advances, and future trends. Foundations and Trends® in Computer Graphics and Vision , 14(3-4):163-352, 2022.

Amir Ghodrati, Efstratios Gavves, and Cees GM Snoek. Video time: Properties, encoders and evaluation. arXiv preprint arXiv:1807.06980 , 2018.

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 , 2024.

Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, et al. A survey on llm-as-a-judge. arXiv preprint arXiv:2411.15594 , 2024.

Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, et al. Direct language model alignment from online ai feedback. arXiv preprint arXiv:2402.04792 , 2024.

David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122 , 2018.

Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305.14992 , 2023.

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 , 2020.

Longlong Jing, Xiaodong Yang, Jingen Liu, and Yingli Tian. Self-supervised spatiotemporal feature learning via video rotation prediction. arXiv preprint arXiv:1811.11387 , 2018.

David Layzer. The arrow of time. Scientific American , 233(6):56-69, 1975.

Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang. Unsupervised representation learning by sorting sequences. In Proceedings of the IEEE international conference on computer vision , pages 667-676, 2017.

Jie Lei, Tamara L Berg, and Mohit Bansal. Revealing single frame bias for video-and-language learning. arXiv preprint arXiv:2206.03428 , 2022.

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326 , 2024.

Dongxu Li, Yudong Liu, Haoning Wu, Yue Wang, Zhiqi Shen, Bowen Qu, Xinyao Niu, Fan Zhou, Chengen Huang, Yanpeng Li, et al. Aria: An open multimodal native mixture-of-experts model. arXiv preprint arXiv:2410.05993 , 2024.

Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, et al. Mvbench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 22195-22206, 2024.

Shicheng Li, Lei Li, Yi Liu, Shuhuai Ren, Yuanxin Liu, Rundong Gao, Xu Sun, and Lu Hou. Vitatecs: A diagnostic dataset for temporal concept understanding of video-language models. In European Conference on Computer Vision , pages 331-348. Springer, 2024.

Yun Li, Zhe Liu, Yajing Kong, Guangrui Li, Jiyuan Zhang, Chao Bian, Feng Liu, Lina Yao, and Zhenbang Sun. Exploring the role of explicit temporal modeling in multimodal large language models for video understanding. arXiv preprint arXiv:2501.16786 , 2025.

Zijing Liang, Yanjie Xu, Yifan Hong, Penghui Shang, Qi Wang, Qiang Fu, and Ke Liu. A survey of multimodel large language models. In Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering , pages 405-409, 2024.

Stephanie Lin, Jacob Hilton, and Owain Evans. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958 , 2021.

Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 26296-26306, 2024.

Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. Advances in neural information processing systems , 36:34892-34916, 2023.

Ruyang Liu, Chen Li, Haoran Tang, Yixiao Ge, Ying Shan, and Ge Li. St-llm: Large language models are effective temporal learners. In European Conference on Computer Vision , pages 1-18. Springer, 2024.

Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, and Lu Hou. Tempcompass: Do video llms really understand videos? arXiv preprint arXiv:2403.00476 , 2024.

Mi Luo, Zihui Xue, Alex Dimakis, and Kristen Grauman. When thinking drifts: Evidential grounding for robust video reasoning. arXiv preprint arXiv:2510.06077 , 2025.

Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding. Advances in Neural Information Processing Systems , 36:46212-46244, 2023.

Russell Mendonca, Shikhar Bahl, and Deepak Pathak. Structured world models from human videos. arXiv preprint arXiv:2308.10901 , 2023.

Ishan Misra, C Lawrence Zitnick, and Martial Hebert. Shuffle and learn: unsupervised learning using temporal order verification. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I 14 , pages 527-544. Springer, 2016.

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems , 35:27730-27744, 2022.

Vassilis Papadopoulos, Jérémie Wenger, and Clément Hongler. Arrows of time for large language models. arXiv preprint arXiv:2401.17505 , 2024.

Viorica Patraucean, Lucas Smaira, Ankush Gupta, Adria Recasens, Larisa Markeeva, Dylan Banarse, Skanda Koppula, Mateusz Malinowski, Yi Yang, Carl Doersch, et al. Perception test: A diagnostic benchmark for multimodal video models. Advances in Neural Information Processing Systems , 36:42748-42761, 2023.

Lyndsey C Pickup, Zheng Pan, Donglai Wei, YiChang Shih, Changshui Zhang, Andrew Zisserman, Bernhard Scholkopf, and William T Freeman. Seeing the arrow of time. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 2035-2042, 2014.

Will Price and Dima Damen. Retro-actions: Learning'close'by time-reversing'open'videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , pages 0-0, 2019.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , pages 8748-8763. PmLR, 2021.

Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36:53728-53741, 2023.

Laura Sevilla-Lara, Shengxin Zha, Zhicheng Yan, Vedanuj Goswami, Matt Feiszli, and Lorenzo Torresani. Only time can tell: Discovering temporal data for temporal modeling. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 535-544, 2021.

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 , 2024.

Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 , 2012.

Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, et al. Aligning large multimodal models with factually augmented rlhf. arXiv preprint arXiv:2309.14525 , 2023.

Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 , 2024.

Peter Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Adithya Jairam Vedagiri IYER, Sai Charitha Akula, Shusheng Yang, Jihan Yang, Manoj Middepogu, Ziteng Wang, et al. Cambrian-1: A fully open, vision-centric exploration of multimodal llms. Advances in Neural Information Processing Systems , 37:87310-87356, 2024.

Jiawei Wang, Liping Yuan, Yuchen Zhang, and Haomiao Sun. Tarsier: Recipes for training and evaluating large video description models. arXiv preprint arXiv:2407.00634 , 2024.

Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution. arXiv preprint arXiv:2409.12191 , 2024.

Yuxuan Wang, Yueqian Wang, Dongyan Zhao, Cihang Xie, and Zilong Zheng. Videohallucer: Evaluating intrinsic and extrinsic hallucinations in large video-language models. arXiv preprint arXiv:2406.16338 , 2024.

Zhenhailong Wang, Ansel Blume, Sha Li, Genglin Liu, Jaemin Cho, Zineng Tang, Mohit Bansal, and Heng Ji. Paxion: Patching action knowledge in video-language foundation models. Advances in Neural Information Processing Systems , 36:20729-20749, 2023.

Donglai Wei, Joseph J Lim, Andrew Zisserman, and William T Freeman. Learning and using the arrow of time. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 8052-8060, 2018.

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems , 35:24824-24837, 2022.

Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for longcontext interleaved video-language understanding. Advances in Neural Information Processing Systems , 37:28828-28857, 2024.

Junbin Xiao, Nanxin Huang, Hangyu Qin, Dongyang Li, Yicong Li, Fengbin Zhu, Zhulin Tao, Jianxing Yu, Liang Lin, Tat-Seng Chua, et al. Videoqa in the era of llms: An empirical study. International Journal of Computer Vision , pages 1-24, 2025.

Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of questionanswering to explaining temporal actions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 9777-9786, 2021.

Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, and Tao Yu. Text2reward: Reward shaping with language models for reinforcement learning. arXiv preprint arXiv:2309.11489 , 2023.

Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zeroshot video-text understanding. arXiv preprint arXiv:2109.14084 , 2021.

Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava: Parameter-free llava extension from images to videos for video dense captioning. arXiv preprint arXiv:2404.16994 , 2024.

Zihui Xue, Joungbin An, Xitong Yang, and Kristen Grauman. Progress-aware video frame captioning. In Proceedings of the Computer Vision and Pattern Recognition Conference , pages 13639-13650, 2025.

Zihui Sherry Xue and Kristen Grauman. Learning fine-grained view-invariant representations from unpaired ego-exo videos via temporal alignment. Advances in Neural Information Processing Systems , 36:53688-53710, 2023.

Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: A gpt-4v level mllm on your phone. arXiv preprint arXiv:2408.01800 , 2024.

En Yu, Kangheng Lin, Liang Zhao, Yana Wei, Zining Zhu, Haoran Wei, Jianjian Sun, Zheng Ge, Xiangyu Zhang, Jingyu Wang, et al. Unhackable temporal rewarding for scalable video mllms. arXiv preprint arXiv:2502.12081 , 2025.

Tianyu Yu, Yuan Yao, Haoye Zhang, Taiwen He, Yifeng Han, Ganqu Cui, Jinyi Hu, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun, et al. Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 13807-13816, 2024.

Jiacheng Zhang, Yang Jiao, Shaoxiang Chen, Na Zhao, Zhiyu Tan, Hao Li, and Jingjing Chen. Eventhallusion: Diagnosing event hallucinations in video llms. arXiv preprint arXiv:2409.16597 , 2024.

Jianrui Zhang, Mu Cai, and Yong Jae Lee. Vinoground: Scrutinizing lmms over dense temporal reasoning with short videos. arXiv preprint arXiv:2410.02763 , 2024.

Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-vl: Learning to reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint arXiv:2503.12937 , 2025.

Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, et al. Internlm-xcomposer-2.5: A versatile large vision language model supporting long-contextual input and output. arXiv preprint arXiv:2407.03320 , 2024.

Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander Hauptmann, Yonatan Bisk, et al. Direct preference optimization of video large multimodal models from language model reward. arXiv preprint arXiv:2404.01258 , 2024.

Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, and Yiming Yang. Improve vision language model chain-of-thought reasoning. arXiv preprint arXiv:2410.16198 , 2024.

Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713 , 2024.

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems , 36:46595-46623, 2023.

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 , 2023.

Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 , 2019.

Orr Zohar, Xiaohan Wang, Yann Dubois, Nikhil Mehta, Tong Xiao, Philippe Hansen-Estruch, Licheng Yu, Xiaofang Wang, Felix Juefei-Xu, Ning Zhang, et al. Apollo: An exploration of video understanding in large multimodal models. arXiv preprint arXiv:2412.10360 , 2024.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: We clearly describe our main claims such as the findings, contributions, and results in the abstract and introduction. They match our idea, methodology, and experimental results presented in the paper.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: We provide a detailed discussion of limitations in the Supplementary.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: The paper does not include theoretical results.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: Please see Sec. 4 and Supplementary for full implementation details to reproduce the experimental results.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: We are committed to reproducibility and will open-source the code, data and model upon acceptance.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: Please see Sec. 4 and Supplementary for full experimental setup.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: Please see Fig. 3 for error bars.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: Please see Sec. 4 and Supplementary.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We strictly conform the NeurIPS Code of Ethics.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: Please see Supp. for societal impact discussion.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: We propose a training algorithm (ArrowRL) and an evaluation benchmark (AoTBench) derived from standard academic datasets. The paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: Please see Sec. 4 and Supplementary.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [NA]

Justification: The paper does not release new assets.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects. Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Contents

A Supplementary Video

We invite readers to view the supplementary video available at https://vision.cs.utexas.edu/ projects/SeeAoT for a visual demonstration of our work's overview and additional qualitative examples. Understanding how video content differs between forward and reverse playback-a key aspect of our study-is most effectively conveyed through video. Therefore, the supplementary video will offer readers a more intuitive grasp of how ArrowRL successfully enhances temporal sensitivity in LMM responses and how AoTBench effectively probes this crucial capability.

B Method Details

B.1 Control Task to Disentangle OOD Effects

To verify that LMMs' poor performance on AoT tasks (observed in Fig. 1 of the main paper) is not simply due to reversed videos being treated as out-of-distribution (OOD) inputs, we design a control experiment inspired by [3]. Using videos and action labels from UCF101 [58], we formulate a binary-choice question with the prompt, 'What action is being performed in this video?' The correct action is provided as one choice, and the incorrect option is randomly drawn from the set of all other action labels. We then evaluate several LMMs on both the original (forward) and time-reversed versions of these videos.

The results in Table 4 show that all models perform at near-perfect accuracy on this control task, irrespective of video direction. This contrasts sharply with their near-random performance on our AoT-sensitive sequence direction classification task (Table 1, UCF column). This analysis provides strong evidence that the failure of existing LMMs on AoT tasks is due to their inability to perceive temporal directionality, not a general failure to process reversed videos as OOD inputs.

B.2 Temporal Sensitivity Analysis

The temporal sensitivity analysis, visualized in Fig. 2 of the main paper, is conducted using LLaVA-OV-7B [34] across several evaluation benchmarks: EgoSchema [46], LongVideoBench [68], MVBench [36], NExT-QA [70], PerceptionTest [14], TemporalBench [9], VITATECS [37], and VideoMME [21]. To ensure consistent and fair evaluation, we utilize lmms-eval 4 and a standardized setting of 16 input frames across three conditions: forward, reversed and shuffled video. We specifically select deterministic MCQ tasks to obviate the need for potentially unreliable third-party LLM evaluators and mitigate evaluation ambiguity.

Our subsequent analyses, including the development of AoTBench, focus on short videos. This approach allows for a targeted investigation of AoT awareness, separating it from complexities specific to long video processing, especially since current LMMs already struggle with shorter temporal sequences. Table 5 presents the forward, reversed, and shuffled frame performance (i.e., MCQaccuracy) for three selected LMMs (LLaVA-OV-7B [34], LLaVA-Video-7B [85], and Qwen2.5VL-7B [5]) on several VQA benchmarks: VITATECS [37], TemporalBench [9], NExT-QA [70], PerceptionTest [51], VideoMME [21], Vinoground [80], TempCompass [44] and TVBench [15]. These results, which supplement the TDS-based benchmark sensitivity analysis in Fig. 3 of the main paper, further illustrates the great variance in temporal order sensitivity across benchmarks: some, such as Vinoground, TempCompass, and TVBench, demonstrate a stronger ability to probe this, whereas others, like VITATECS, show extreme insensitivity.

B.3 ArrowRL

Post-training Data To enhance AoT understanding, our training data incorporates three core tasks. For MCQ-based sequence direction classification, we use selected videos from UCF101 [58]. For video captioning, we leverage the RTime dataset [18] with a varied set of 16 prompts. For open-ended QA, we employ original questions from LLaVA-NeXT-178K [85]. The prompts used for MCQ and captioning tasks are detailed below. For the MCQ-based sequence direction classification task, we follow [80] to concatenate the forward video and its reversed version-separated by a 2-second black frame-into a single video input, as LMMs typically process one video stream at a time.

Training Prompt I (MCQ-based Sequence Direction Classification)

This video contains two segments showing the same action, separated by a 2-second black frame. One segment is played forwards, and the other is played in reverse. Which video segment is played in reverse?

The first segment (before the black frame)

The second segment (after the black frame)

Answer with the option's letter from the given choices directly.

Training Prompt II (Video Captioning)

Describe the following video in detail.

Provide a detailed description of the given video.

Give an elaborate explanation of the video you see.

Share a comprehensive rundown of the presented video.

Offer a thorough analysis of the video.

Explain the various aspects of the video before you.

Clarify the contents of the displayed video with great detail.

Characterize the video using a well-detailed description.

Break down the elements of the video in a detailed manner.

Walk through the important details of the video.

Portray the video with a rich, descriptive narrative.

Narrate the contents of the video with precision.

Analyze the video in a comprehensive and detailed manner.

Illustrate the video through a descriptive explanation.

Examine the video closely and share its details.

Write an exhaustive depiction of the given video.

[Target response

𝒐

⋆

]

The person

steps into the closet appearing to

search for something, specifically to

retrieve a brown cardboard box.

[Candidate response

𝒐

𝟏

]

The person steps into the closet likely

to fetch an item or retrieve a package. This is supported by the

fact that the person seems to be looking around inside the

closet, and then retrieves a cardboard box, which suggests it was

inside the closet. The box's presence in their hands at the end of

the sequence indicates what they were searching for.

𝑟

#

$%&

= 0.7

𝑟

#

'()

= 1.0

𝑟

*

$%&

= 0.85

𝑟

*

'()

= 0.7

𝑟

+

$%&

= 0.7

𝑟

+

'()

= 0.2

Q.

Why does the person step

into the closet?

[Reverse response

𝒐 #

]

The person

steps into the closet to place the box

inside. This suggests they are

organizing or storing the box,

possibly to keep it out of the way or

to make space in the room.

[Candidate response

𝒐

𝟐

]

The person steps into the closet to

retrieve a box from the shelf.

[Candidate response

𝒐

𝟑

]

The person steps into the closet likely

to retrieve the item they are carrying, which appears to be a

cardboard box. It seems that they are either placing the box in the

closet, retrieving something from it, or organizing items. The

action suggests a purposeful movement to fetch or store the box.

sim

Reward Calculation Fig. 9 demonstrates ArrowRL's reward calculation using a VQA example that necessitates causal-temporal reasoning. Here, the fidelity reward ( r fid i ) alone might be insufficient;

for instance, candidates o 1 and o 3 both exhibit high similarity to the target o ⋆ . The reverse reward distinguishes them by leveraging the semantic difference between forward and reverse video plays. A response ˜ o to the reversed video (e.g., describing 'organizing or storing the box') is used as a negative reference. The reverse reward, r rev i = 1 -Similarity ( o i , ˜ o ) , then penalizes forward-video responses like o 3 if they incorrectly align with the reverse response ˜ o (as indicated by red highlighting), thereby favoring temporally aware responses like o 1 that accurately reflect the forward video's AoT.

Implementation As discussed in Sec. 3.2 of the main paper, Similarity( · , · ) return a similarity score between 0 and 1 and is implemented as follows. For MCQ tasks, it uses deterministic value checking (1.0 for a correct match, 0.0 otherwise); For open-ended QA and captioning, we employ Llama-3.1-70B-Instruct [24] as an LLM judge, which is prompted to output a semantic similarity score within the [0, 1] range, using the prompts below. The previously defined language query l , target response o ⋆ , candidate response o i and reverse response ˜ o are referenced here.

LLM-based Similarity Calculation

[Open-ended QA prompt]

Please compare the following two answers for the question below and rate their similarity on a scale of 0 to 1.

Question: l

Answer 1: o i

Answer 2: ˜ o

Output only a single numeric value between 0 and 1 (no additional text or explanation).

[Captioning Prompt]

Compare the following video caption with the ground truth caption and rate their similarity on a scale of 0 to 1.

Generated caption: o i

Ground truth caption: o

⋆

Output only a single numeric value between 0 and 1 (no additional text or explanation).

During training, candidate responses o i are generated with a temperature of 1.0 to encourage exploration. The reverse-conditioned response ˜ o is generated deterministically (temperature set to 0). ArrowRL training for each model involves 2000 RL steps over approximately 3 days on 6 NVIDIA GH200 GPUs.

To ensure a fair comparison, inference settings for base LMMs and their ArrowRL-enhanced are identical, differing only by model checkpoint. Our default input configuration is 16 frames for LLAVA-OV-7B, and 1 FPS (with a maximum of 16 frames) for Qwen2-VL-7B and Qwen2.5-VL-7B. Benchmark-specific adjustments include processing up to 32 frames (sampled at 1 FPS for Qwen models) for TVBench (due to video length) and reporting Vinoground at 4FPS for Qwen models to align with [80] (further frame rate analysis in Fig. 11). For TempCompass, adhering to [82], we report only on its deterministic subtasks (multi-choice QA, yes/no QA, caption matching; 5536 samples).

B.4 AoTBench

To evaluate AoT perception in LMMs, we construct AoTBench. The dataset composition is detailed in Table 6, with illustrative examples in Fig. 10. For the first and second T2V task, we concatenate the forward, a two-second black video and reversed video segments into a single input, and use the same prompt as Training Prompt I above, but with shuffled MCQ options to test model generalization.

Furthermore, Table 7 quantifies the increased AoT sensitivity of our selected VQA subset, showing significantly higher TDS values compared to the original benchmark.

C Additional Results

C.1 Quantitative Results

Further Results Analysis From Table 1 in the main paper, we see that ArrowRL appears to leverage inherent base model strengths for improvements. For instance, the visually adept Qwen2-VL-7B

AoTBench L

[Sequence Directionality Classification] Two video segments are presented: one plays forward, the other in reverse.

Which video segment is played in reverse?

[Directional Caption Matching - T2V] Two video segments are presented: one plays forward, the other in reverse. Which video segment matches the caption 'someone taking personal care items out of a clear plastic bag'?

[Directional Caption Matching - V2T] Which caption best describes the video?

A bulb is installed to the white round lamp shade by a person and is lighted up.

A person removes the luminous bulb from the white round lamp shade with his hands.

[AoT-sensitive VQA] What change is occurring to the 3D house model?

being constructed

being dismantled

being renovated

SCC

terstcCk

10

1

terstc

(evidenced by its base performance on T2V caption matching) sees a +21.0% gain with ArrowRL on the visually-focused sequence direction classification task. Meanwhile, Qwen2.5-VL-7B, which exhibits greater language proficiency (reflected in its base V2T caption matching scores), achieves its largest gains (+9.2%) on the more language-centric AoT-VQA task after ArrowRL enhancement.

Frame Rate Analysis Fig. 11 presents our inference frame analysis on Vinoground, comparing ArrowRL-enhanced Qwen2.5-VL-7B against its base model across different frame settings (1-4 FPS). Crucially, although ArrowRL training utilizes a fixed 16 frames per input (for efficiency), the performance gains provided by ArrowRL remain consistent across varying temporal granularities at inference, showcasing its generalization beyond the specific training setup, and giving evidence that the base models overlook temporal detail even with higher framerates.

VITATECS

TemporalBench (S)

NExT-QA

PerceptionTest

VideoMME (S)

Vinoground (V)

Vinoground (T)

TempCompass

TVBench

All

0.039

0.062

0.073

0.083

0.113

0.212

0.408

0.461

0.549

Selected

0.738

1.258

1.757

3.185

0.690

0.512

1.287

4.617

3.504

1FPS

2FPS

3FPS

4FPS

0

20

40

60

80

Score

31.8

40.6

42.6

46.2

38.4

43.0

47.0

48.8

26.6

30.8

32.6

31.4

36.4

39.8

42.0

42.4

8.4

15.0

16.6

16.4

17.0

23.2

25.6

27.2

Video

Text

Group

Video

Text

Group

Video

Text

Group

Video

Text

Group

Qwen2.5-VL-7B

+ArrowRL

C.2 Qualitative Results

More Qualitative Results Supplementing Fig. 8 in the main paper, Fig. 12 presents additional qualitative examples that reinforce the effectiveness of ArrowRL. These comparisons highlight how base LMMs often overlook temporal progression or direction, often relying on static cues or language biases. Conversely, our ArrowRL-enhanced models exhibit improved AoT sensitivity, leading to more accurate and temporally coherent responses across these challenging scenarios.

Failure Cases Fig. 13 presents one failure case on AoTBench, where neither the base LMM nor the ArrowRL-enhanced version answers correctly. The failure stems from the uniform sampling of 16 frames (6 visualized here) not capturing the key visual moments where a man in a dark suit holds a ring-a crucial cue for inferring he is about to get married. Such failures suggest potential avenues for future improvement, like advanced keyframe selection methods over uniform sampling, or the incorporation of auxiliary modalities such as audio, which in this case contains helpful cues.

D Limitations

Our construction of AoTBench relies on using selected LMMs as evaluators, meaning its sensitivity is inherently dependent on the initial AoT perception capabilities of these models. Potentially challenging samples might be missed if current evaluator models universally fail to exhibit sensitivity (i.e., yield low TDS) despite underlying temporal relevance. Nevertheless, to provide a quantitative check of AoTBench, we manually verify a small subset and find our TDS-based selection of temporally challenging examples aligns with human judgment 44 out of 50 times, suggesting reasonable concordance where models do possess baseline sensitivity.

Additionally, the reward calculation for ArrowRL utilizes an auxiliary LLM to generate similarity scores; this dependence on an LLM judge, while common [86, 25, 71], can introduce a degree of

Q. Why does the dog walk toward the lady after the lady reaches her hand toward it?

Qwen2-VL-7B: to catch the food

Qwen2-VL-7B + ArrowRL:

let the lady hug it

CRUCIAL

R

CRUCIAL

CRUCIAL

Q. Which caption best describes this video?

Qwen2.5-VL-7B: A woman is speaking while she is handling an air filter with both hands. Using her right hand to hold in place the filter device, the left hand pulls the filter out of its place in the device. Both hands then pick up the outer shield of the filter from the table.

Qwen2.5-VL-7B + ArrowRL: A woman is speaking while she is handling an an air filter with both hands. Using her right hand to hold in place the filter device, the left hand pushes the filter into its place in the device. Both hands then pick up the outer shield of the filter from the table.

shut

shutt

Q.

Provide a one-sentence description of the video, focusing solely on the primary actions taking place.

LLaVA-OV-7B: The video captures the serene and dynamic movement of clouds against a tranquil blue sky, creating a peaceful and picturesque scene.

LLaVA-OV-7B + ArrowRL: The video features a series of images capturing the movement of clouds in the sky, with the clouds transitioning from lighter to darker shades as the light diminishes.

Figure 12: Additional qualitative examples comparing base LMMs with their ArrowRL-enhanced counterparts. ArrowRL enables models to succeed on AoT-sensitive VQA and produce temporally coherent captions, while base LMMs often struggle with understanding temporal progression.

According to the video, who is about to get married?

A. The man in the dark suit.

Qwen2.5-VL-7B: It is unclear

Qwen2.5-VL-7B + ArrowRL (ours): It is unclear.

uncertainty or potential bias into the reward signal. Nevertheless, we found this approach viable for our purposes, as the LLM performs a relatively easy, straightforward text-to-text similarity assessment. We empirically find that (also illustrated in Fig. 6 of the main paper and Fig. 9) the resulting similarity scores, which underpin our reward calculation, are consistent and appear reasonable.

Furthermore, our current investigation focuses on short videos, a scope chosen due to the significant temporal challenges already evident in these scenarios for leading LMMs. We view this work as a initial step towards improving AoT understanding. Future directions include extending these methods to long videos (potentially incorporating keyframe selection) and exploring integration with temporal reasoning tasks that necessitate explicit reasoning traces (e.g. Chain-of-Thought).

CRUCIAL

CRUCIAL

CRUCIAL

R

E Societal Impacts

This work focuses on improving a fundamental aspect of temporal perception (AoT sensitivity) in LMMs. Positive impacts stem from creating more reliable and rational AI systems. Better AoT perception can lead to LMMs with more accurate internal world models, improving their utility in tasks requiring understanding of processes, procedures, or event timelines. This could benefit applications like education, assistive technology and robotics. The primary risks are associated with the general capabilities of advanced LMMs rather than AoT sensitivity specifically. Any improvement could potentially be misused if integrated into systems for generating disinformation or invasive surveillance, though our method doesn't directly enable these.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\n**Problem**: In this work, the authors highlight the problem of modern large multimodal models (LMMs) not using the \"arrow of time\" while perceiving videos. \n\n**Key contributions**: First, it is shown that existing LMM benchmarks (e.g., TemporalBench) and LMM models (e.g., Qwen2-VL) are not sufficiently sensitive to the arrow of time, i.e., shuffling or reversing the frame order does not substantially affect the accuracy. Second, as a redressal, an RL based post-training strategy is proposed to instil this time-sensitivity into an existing LMM (e.g., Qwen2-VL). Two reward signals are used: target fidelity (to encourage the correct answer) and reverse reward (to force different answers if the video is presented in a reverse order). Third, a new benchmark, AoTBench is proposed with three sub-tasks: (i) direction classification: whether a video if played forward/reverse, (ii) caption matching: matching forward/reversed videos to the right language description, (iii) VQA samples from existing benchmarks that are more likely to be sensitive to the arrow of time.\n\n**Main outcome:** It is experimentally shown that the proposed strategy not only improves performance on the proposed AoTBench, but also improves performance on existing video benchmarks like TV-Bench and Vinoground.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. Elegant adaptation of GRPO for time-sensitive video understanding. While the problem of arrow of time is not new, I think this paper presents a thorough method to tackle it in context of modern video-LLMs. The \"reverse reward\" for distinctive answers for forward/reverse videos is a sensible choice.\n2. The weaknesses shown in existing video models/benchmarks should guide the video community towards better models/benchmarks. For example, the insight that LLAVA-OV-7B, Qwen2.5-VL-7B are more time-sensitive models (on TVBench) and Vinoground/TVBench/TempCompass are more time-sensitive benchmarks is very useful.\n3. The design of new AoTBench and the subsequent experiments are very thorough and well thought-out. The proposed ArrowRL shows clear benefits on arrow of time tasks while also improving performance on existing benchmarks. \n4. The paper is written very well; the flow is very smooth and logical.\n\n\n**Weaknesses**\n1. Grounding arrow of time for irreversible videos in language is probably ill-posed. In the first AoTBench task, the authors propose a VQA-like task where the model is tasked with choosing which of the two videos is played in forward/reverse directions.\n\t1. For irreversible videos (e.g., where reverse video violates a physical law), the task is purely visual. It is awkward to describe a waterfall going upwards in language.\n\t2. Why should an existing video LLM model understand what one means by \"forward or reverse direction of the video\"? In this work, the train set contains such questions. So the resulting model knows what \"forward\" means. But what about existing models which may not have \"forward\"/\"reverse\" in their train set?\n\t3. Reversed videos may be out of distribution for existing models. Could the low performance be attributed to that instead of just not understanding arrow of time?\n2. The metric designed to measure if the LMM understands arrow of time is slightly unconvincing. As described in L140-L145, the probability of the first token over the MCQ options is considered. A justification for this is lacking. Also, \"We first filter out instances where the average reverse performance exceeds its forward performance.\" -- this is confusing.\n\nQUESTIONS:\n**Questions**\n\nSome of these are already mentioned in the weaknesses section. I have elaborated a bit on them here.\n\n1. Why should an existing video LLM model understand what one means by \"forward or reverse direction of the video\"? I do agree that checking understanding of arrow of time for modern language-based video models is essential. \n2. Describing direction of time in language is tricky for irreversible videos. How would one describe a reversed video of \"smoke diffusing\"? Although I know this is not the main task, but it is worth asking how the model's perception changes when encountering a reversed video (e.g., are certain activations activated more when this happens?)\n3. Does the added post-training affect \"spatial\" performance at all? For example, on non temporal benchmarks, does the model suffer at all or retain its performance?\n\n**Suggestions**\n\n1. Reversed videos may be out of distribution for existing models. Perhaps, is there a control task (like in [1]) where the question is insensitive to the arrow of time (e.g., describe all objects appearing in the video) and the model does well with both forward and reverse videos? This can establish that low performance of existing models need not be because the video is OOD.\n2. A justification for the choice of measure (KLD between output distributions of **first token**) of arrow of time consistency would make the paper stronger. Likewise, it would be helpful to clarify L145 on filtering out samples.\n\n[1] Test of Time: Instilling Video-Language Models with a Sense of Time. Bagad et al. CVPR 2023.\n\nLIMITATIONS:\nWhile the Supp includes failure cases, it may be worth adding a couple of lines in the main text on what kinds of samples the model fails on and if there is a pattern to it. That could also guide future work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper identifies the problem that existing Video LLMs are insufficiently sensitive to the temporal directionality of events in videos. It designs ArrowRL to enhance the perception of temporal directionality in Video LLMs through a reverse reward mechanism, and develops AoTBench to evaluate this capability. Experiments show that the method significantly improves model performance on time-related tasks.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n* The problem identified by the authors is mentioned in previous Video Benchmarks (e.g., Tempcompass), but no related work has attempted to solve it. This paper fills this gap, demonstrating significant research significance.\n* The authors' method is rationally designed: leveraging the intuition that \"forward-time sampled outputs should differ substantially from reverse-time ground truth outputs\", they use an LLM to judge the similarity between the two as a reward to supervise Video LLMs in enhancing their temporal direction perception.\n* The authors provide AoTBench to systematically quantify Video LLMs' understanding of temporal order, effectively drawing the community's attention to this issue.\n* The experimental results are solid, showing notable improvements for SOTA models on temporal benchmarks (e.g., Qwen2.5VL achieves a 1.7% improvement on Tempcompass).\n\n### Weaknesses\n\n* Whether the difference between Reverse Response and Forward Response can reflect temporal directionality is crucial to the method's effectiveness, yet the authors lack description on how to construct reverse-forward response pairs that meet this requirement.\n* The method proposed in the paper mainly improves benchmark performance for tasks explicitly related to temporal directionality (e.g., temporal direction classification, forward-reverse temporal caption matching), but it remains unclear whether it can enhance the general ability to understand temporal information in videos.\n\nQUESTIONS:\n* How did the authors construct response pairs where the difference between reverse responses and forward responses can reflect temporal directionality?\n* Does this method still show improvements on general temporal benchmarks like VideoMME?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the temporal directionality in LMMs for video understanding. The authors show that existing LMMs often fail to distinguish between forward and reversed videos, leading to semantic errors in tasks such as question answering or captioning. To tackle this, they propose ArrowRL, a reinforcement learning approach using a novel reverse reward that encourages models to produce different responses for forward versus reversed inputs. They also introduce AoTBench, a benchmark specifically designed to evaluate AoT sensitivity with tasks like sequence direction classification, directional caption matching, and AoT-sensitive VQA. Extensive experiments show that ArrowRL significantly improves AoT perception across multiple open-source LMMs and outperforms baselines on both AoTBench and existing VQA datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n(1) The paper demonstrates that AoT sensitivity is a fundamental yet neglected requirement for video LMMs.\n\n(2) ArrowRL introduces a novel reverse reward in a GRPO framework to explicitly teach temporal directionality, which is an original and well-motivated idea.\n\n(3) The authors show consistent improvements across three base LMMs, multiple AoTBench tasks, and existing video QA benchmarks, demonstrating strong generalization.\n\n(4) The paper contributes a new, carefully curated benchmark with multiple sub-tasks targeting AoT sensitivity, filling a clear evaluation gap in the field.\n\nWeaknesses:\n\n(1) Most of the multiple-choice questions do not require temporal reasoning and therefore do not clearly demonstrate the need for time awareness. As a result, it is not convincing to use MCP as examples to illustrate the motivation for AoT.\n\n(2) The formulation is confusing, e.g., what does P denote in line 167?\n\n(3) Applying GRPO (or other RL methods) may not be necessary. Augmenting the pre-training (or SFT) datasets with reversed videos could potentially address this problem.\n\n(4) Why is maximizing the dissimilarity between forward and backward responses essential? Enforcing them to be dissimilar does not guarantee that the reversed response will be correct.\n\n(5) The evaluation is not comprehensive. It lacks comparison with other popular video benchmarks (e.g., MVBench, VideoMME).\n\nQUESTIONS:\nWhy, in Fig. 3 (left), are the forward and reverse results similar? How should this be explained?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper focuses on enhancing the temporal directionality (Arrow of Time) perception of MLMMs. By analyzing the limitations of existing benchmarks and models, the authors find that even SOTA MLMMs exhibit a clear insensitivity to temporal order, they often fail to distinguish between forward and reversed video playback, and may even generate identical descriptions for semantically opposite videos. To address this issue, the authors propose a GRPO-based reinforcement fine-tuning method that encourages the model to produce different interpretations for forward and reversed video frames, thereby cultivating AoT sensitivity. Additionally, they design a comprehensive benchmark to evaluate models’ AoT perception capabilities and introduce the Temporal Divergence Score (TDS) to quantify temporal sensitivity at both the benchmark and sample levels.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\t\t\n1.\tThe reverse reward mechanism in ArrowRL is an innovative design that effectively leverages the natural contrastive signal of video temporal directionality. By employing reinforcement learning, it enhances the model’s sensitivity to the Arrow of Time.\n2.\tIn addition to proposing a novel method, the paper also develops a dedicated benchmark (AoTBench) and conducts a systematic analysis of several existing benchmarks, providing extensive experimental data.\n\nWeaknesses:\n1.\tThe Reverse Reward mechanism may be overly simplistic and brute-force in nature. By forcing the model to distinguish reversed videos, it risks encouraging learning of superficial temporal cues rather than a true understanding of event dynamics. For instance, it may fail to differentiate between physically irreversible actions (e.g., glass shattering) and reversible ones (e.g., opening/closing a door). Specifically, while the method is practical and effective for reversible actions, it might lead the model to generate physically implausible descriptions for irreversible actions, as the reward function does not explicitly model physical constraints. Moreover, for cyclic or repetitive events, the Reverse Reward could have adverse effects.\n2.\tWere the questions in AoTBench directly extracted from existing benchmarks, or were the forward and reversed versions manually re-annotated based on videos from those datasets? If it’s the former, how is temporal sensitivity ensured? Wouldn’t that essentially mean the new benchmark is just a collection of previously difficult samples, rather than a dedicated test of temporal directionality?\n3.\tThe paper only discusses the impact of extreme values of 𝛼 and 𝛾 (i.e., 0 and 1) on training performance, but lacks analysis of intermediate values (e.g., 0.5), which could provide more insight into the trade-offs and sensitivity of the proposed method.\n\nQUESTIONS:\n1.\tForcing the model to learn from both forward and reversed videos may lead to the generation of physically implausible descriptions. It is recommended to include an additional set of experiments to analyze and illustrate this potential issue.\n2.\tIt would be helpful to clarify in the paper how the questions in AoTBench were constructed, whether they were directly extracted from existing datasets or re-annotated for forward and reversed videos.\n3.\tIf possible, providing a more detailed analysis of how different values of 𝛼 and  𝛾 affect training performance would make the paper more comprehensive and informative.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-05"}