Video-QTR: Query-Driven Temporal Reasoning

Updated 13 December 2025

The paper introduces an adaptive system that selectively processes video segments based on natural language queries, achieving up to a 73% reduction in redundant frame processing.
It details a modular architecture, including RTP for semantic planning, TCR for temporal consistency, and TM for cross-scene evidence accumulation, enhancing interpretability and accuracy.
Empirical results demonstrate that Video-QTR outperforms dense encoding approaches on key benchmarks, resulting in superior efficiency and improved VideoQA performance.

Video-QTR (Query-Driven Temporal Reasoning) refers to a set of modeling strategies, system architectures, and learning objectives in video understanding that focus on answering temporally complex queries by dynamically selecting, aligning, and reasoning over temporally relevant segments of long-form video content. Unlike traditional dense video encoding, which exhaustively processes every frame before reasoning, Video-QTR systems employ query-adaptive mechanisms that allocate computational resources based on the semantic intent of the user's question, aiming to maximize interpretability, accuracy, and efficiency in tasks such as Video Question Answering (VideoQA), temporal video grounding, and related multimodal reasoning.

1. Formal Problem Definition and Motivation

Video-QTR redefines video comprehension as a query-guided, adaptive temporal reasoning process. A Video-QTR system receives an input video $V = \{v_i\}_{i=1}^N$ , spanning $N$ frames or clips, and a natural-language query $q$ . The central objective is to produce an answer $A$ or a temporal prediction $[s, e]$ by focusing on the subset of frames most relevant to $q$ , as opposed to processing all of $V$ . This motivates architectures and learning objectives that dynamically plan "where and when" to analyze the visual stream, yielding significant reductions in redundant computation and memory usage (Zhao et al., 10 Dec 2025).

The formal task can be specialized to temporal grounding (localizing a segment matching $q$ ) or general VideoQA (producing a contextually correct answer with respect to temporal relations among events).

2. Architectural Principles and Components

State-of-the-art Video-QTR frameworks decompose the end-to-end pipeline into several interacting modules:

Reason-Temporal Proxy (RTP): Generates a semantic plan, producing intent tokens $\{p_t\}$ and associated intervals $\{\tau_t\}$ , indicating which regions of $V$ to attend for answering $q$ .
Perception Module: Selectively samples frames within $\tau_t$ , encodes them (typically via CLIP-ViT), and projects features into the LLM input space.
Temporal Consistency Refiner (TCR): Measures and corrects misalignments between the reasoning plan and actual video evidence to enforce chronological consistency.
Temporal Memory (TM): A graph-based accumulation of event nodes and their temporal relations across reasoning iterations.

The modules interact via an adaptive feedback loop: each reasoning step can alter future sampling weights and semantic plans based on previous alignment errors, with TM retaining cross-scene evidence across iterations (Zhao et al., 10 Dec 2025).

for t in range(T):
    p_t, tau_t = RTP(q, TM, ...)
    V_hat = select_frames(V, tau_t)
    h_t = encode_CLIP_and_project(V_hat)
    r_t, conf = LLM_answer(q, h_t)
    if conf > threshold:
        return answer
    C_t = compute_alignment(r_t)
    update_RTP_and_TM(C_t, r_t)

3. Query-Adaptive Temporal Perception and Reasoning

Video-QTR centralizes the query as a driver of perception. Instead of uniform sampling, systems now employ temporal planning, intent refinement, and selective sampling (Zhao et al., 10 Dec 2025): $\{(\langle tp_t\rangle, \tau_t)\}_{t=1}^T, \quad \tau_t \subseteq [1, N]$ This episode decomposition tightly couples query semantics with perceptual selection. The alignment module (TCR) computes similarity scores between reasoning cues and candidate frames, gating perceptual attention in future steps: $C_t(i) = \mathrm{sim}(E_q(r_t), \phi_i), \quad P_t(i) = \mathrm{Softmax}(C_t(i))$ This mechanism implements query-driven attention over video time, optimizing the allocation of resources as video length increases (frame consumption ratio $\rho \simeq 0.73$ , i.e., up to 73% fewer frames processed) (Zhao et al., 10 Dec 2025).

4. Temporal Consistency, Learning Objectives, and Feedback

Video-QTR introduces loss functions that enforce consistency between the LLM's plan and actual video features. The temporal consistency loss (TCR) is defined as: $\mathcal{L}_{\mathrm{tcr}} = \frac{1}{T} \sum_{t=1}^T \left\| P_t - \mathrm{one\_hot}(\tau_t) \right\|_2^2$ where $P_t$ is the alignment distribution over frames, and $\mathrm{one\_hot}(\tau_t)$ is a supervision signal for the target interval.

Final objective combines answer prediction (cross-entropy) and temporal consistency: $\mathcal{L} = \mathcal{L}_{\mathrm{qa}} + \lambda_{\mathrm{tcr}} \mathcal{L}_{\mathrm{tcr}}$ End-to-end training updates all components: semantic planner, perception projector, temporal alignment (Zhao et al., 10 Dec 2025).

5. Efficiency, Scalability, and Empirical Performance

Empirical results demonstrate that Video-QTR achieves superior performance across a range of short and long-form QA benchmarks while drastically reducing computational overhead. Table below summarizes the efficiency and accuracy gains:

Method	MovieChat Global Acc.	MovieChat BP Acc.	Frames Processed
qwen2.5-vl-max	86.70%	56.59%	512
gemini2.5-pro	83.30%	54.76%	1024
Video-QTR	88.72%	74.72%	202.4 (adaptive avg)

On ActivityNet-QA and MSVD-QA, Video-QTR matches or exceeds current VL baselines with 2.5–10× fewer frames (Zhao et al., 10 Dec 2025). Ablation reveals the critical impact of query-adaptive planning (RTP) and temporal feedback (TCR): removing RTP drops accuracy by –19.28 points (global) and –33.74 points (BP). Removing TM yields –9.02, –7.55 point drops respectively.

As video length increases, the proportion of encoded frames decreases—Video-QTR processes just 15% of frames for 3000 s videos, demonstrating adaptive scalability.

6. Context in the Field and Comparative Benchmarks

Video-QTR advances upon traditional process-then-reason paradigms, which analyze all frames prior to semantic reasoning, by introducing adaptivity and temporal reasoning as first-class architectural motivators. Systems such as those employing object-centric representations (Dang et al., 2021), hierarchical spatio-temporal graphs (Dang et al., 2021), question-guided temporal querying (Amoroso et al., 26 Dec 2024), and chain-of-evidence distillation (Lu et al., 17 Mar 2025) share the general ethos of query-driven selection, but Video-QTR provides explicit feedback and semantic-temporal planning in an end-to-end manner.

Recent benchmarks confirm that such query-driven approaches outperform both static, heuristic, or uniform sampling strategies, particularly on temporally and causally complex queries.

7. Limitations, Deployment Insights, and Future Directions

The Video-QTR approach is highly amenable to real-world constraints in resource-constrained scenarios, where tight computation or memory budgets necessitate frame reduction. The adaptive feedback loop allows the planner to trade minor accuracy losses for additional frame minimization. Event graphs (TM) can be pruned for memory efficiency, and maximum iterations or interval constraints can be controlled (Zhao et al., 10 Dec 2025).

Future research will likely focus on refining alignment distributions, enriching the semantic planner, and extending temporal graphs for richer cross-scene reasoning. The overall shift toward query-driven perception and temporal feedback loops defines the current frontier in scalable, interpretable video understanding.