Reasoning Text-to-Video Retrieval

• Reasoning text-to-video retrieval is a paradigm that integrates semantic, temporal, spatial, and compositional reasoning to address implicit and multi-hop queries.
• It leverages digital twin representations that encode videos as structured scene graphs, enabling precise object-level grounding and transparent retrieval explanations.
• Emerging systems employ multi-step query decomposition, LLM-based chain-of-thought reasoning, and just-in-time model invocations to enhance accuracy and interpretability.

Reasoning text-to-video retrieval is the paradigm wherein a system processes text queries requiring semantic, temporal, spatial, or compositional reasoning over video content, going beyond explicit concept matching or direct embedding similarity. Unlike conventional retrieval, which assumes explicitly stated entities and actions in both query and visual data, reasoning-based approaches can handle implicit, multi-step, and compositional queries, provide object-level grounding, explain retrieval decisions, and leverage structured scene representations. This article surveys the formulation, algorithmic mechanisms, system architectures, evaluation benchmarks, and technical advances underpinning this emerging research direction.

1. Task Formulation and Distinction from Conventional Retrieval

Traditional text-to-video retrieval restricts itself to explicit queries; these queries directly name the objects and events to be found within videos, permitting global embedding-based retrieval optimized for cosine similarity between feature vectors. The core objective is to retrieve the relevant video $V^*$ from a large database $\mathcal{V} = \{V_1, ..., V_n\}$ with respect to a query $q$, maximizing some similarity score $s(q, V)$, usually computed as $$s(q, V) = \langle f_t(q), f_v(V) \rangle / (\|f_t(q)\| \|f_v(V)\|)$$, where $f_t$ and $f_v$ are the text and video encoders, respectively (Wu et al., 2023).

Reasoning text-to-video retrieval broadens this formulation to encompass:

• Implicit queries: Where satisfaction requires multi-step inference or world knowledge—e.g., "Find videos showing an animal acting out of curiosity"—and not all objects or actions are named explicitly (Shen et al., 15 Nov 2025).
• Multi-hop and compositional queries: Where the answer requires conjunction/disjunction of several sub-goals, logical constraints, or spatial/temporal relationships.
• Object-level grounding: Where, beyond retrieving a relevant video, the system specifies which object instances within frames satisfy the query (e.g., binary masks for grounded objects) (Shen et al., 15 Nov 2025).
• Rationalized retrieval: Where the system explains why a particular video matches the query, either via chain-of-thought reasoning or explicit annotation/rationale (Pulakurthi et al., 25 Sep 2025).

These aspects require systems to integrate high-level reasoning, sub-query decomposition, symbolic manipulation, and (optionally) structured representations, rather than relying solely on monolithic visual-language embeddings.

2. Structured Scene Representations and Digital Twins

To overcome the expressiveness and alignment limits of global embeddings, several recent systems represent video content as "digital twins": frame-wise structured graphs encoding object entities, attributes, spatial positions, temporal tracks, and relations (Shen et al., 15 Nov 2025). In this representation, each video $V$ (with $T$ frames) maps to

$$\mathcal{D}_V = \{ D^{(1)}, D^{(2)}, ..., D^{(T)} \}$$,

where each $D^{(t)}$ contains detected objects $i$, semantic classes $c_i^{(t)}$, attributes $a_i^{(t)}$, binary masks $m_i^{(t)}$, positions $s_i^{(t)}$, and is linked across time with identity tracking. Specialist vision models (e.g., foundational detectors, segmentation models like SAM-2, and depth estimators such as DepthAnything) populate these structures (Shen et al., 15 Nov 2025).

This explicit, uncompressed object-centric representation preserves local, relational, and temporal structure throughout the video, decoupled from the statistical bottleneck of visual-language embedding compression. Such a scaffold supports fine-grained correspondence with complex sub-queries, enables efficient candidate filtering via component-level matching, and facilitates LLM-based or symbolic reasoning at retrieval time.

3. Reasoning-Driven Retrieval Pipelines

The canonical reasoning text-to-video retrieval pipeline instantiated in recent research (Shen et al., 15 Nov 2025, Pulakurthi et al., 25 Sep 2025) consists of multiple stages and may include:

3.1 Compositional Candidate Filtering

• Query decomposition: The text query $q$ is broken down into atomic sub-queries $\{q_1, ..., q_L\}$—for instance, via LLM-based parsing or custom heuristics.
• Sub-query encoding: Each $q_l$ is encoded (via $f_{query}$) into a normalized vector $e_{q_l}$.
• Video representation encoding: Each video's digital twin is decomposed into sets of object and relation embeddings, e.g., $E_V = \{ e_V^{obj_i}, e_V^{rel_j} \}$, through lightweight transformers.
• Compositional matching: The compositional alignment loss

$$\mathcal{L} = \sum_{l=1}^L \mathrm{NCE}(e_{q_l}, E_V^+, E_V^-)$$

is minimized during training, with $E_V^+$/$E_V^-$ denoting positive/negative entity sets. At inference, videos are scored by aggregating (e.g., min, mean) the maximal similarity between sub-query and entity embeddings,

$$s_i = \mathrm{Agg}\left(\left\{ \max_{e \in E_{V_i}} e_{q_l}^\top e \right\}_{l=1}^L \right)$$

and the top-$K$ candidates are filtered for downstream reasoning (Shen et al., 15 Nov 2025).

3.2 LLM-based Reasoning and Just-in-Time (JIT) Model Invocation

• Chain-of-thought reasoning: For each candidate video, an LLM is prompted with the decomposed query and the video's structured digital twin. The LLM reasons stepwise through query satisfaction, object identification, and relations, returning:
  • a scalar relevance score $r_i \in [0,1]$
  • explicit identification of object IDs $\mathcal{O}_i$ supporting the match
  • rationale or trace of the reasoning process
• Just-in-time refinement: If the LLM detects missing information (e.g., attributes not present in the digital twin or uncertain action labels), it emits structured model calls (e.g., CALL ActionRecognitionModel(frame_range)), which are executed, and the twin is augmented before reasoning resumes (Shen et al., 15 Nov 2025).

3.3 Output

The system returns the top-ranked videos, grounded masks for relevant objects, and optionally the parsed reasoning trace, bridging subsymbolic video analysis and symbolic explanation.

4. Model Architectures and Reasoning Modules

A variety of architectures advance reasoning capabilities, including the following principal methodologies:

Approach	Core Mechanism	Reasoning Capability
Digital Twin + LLM	Structured object/attribute graph + LLM CoT/JIT	Multi-hop, compositional, grounding
X-CoT (Pulakurthi et al., 25 Sep 2025)	Pairwise LLM CoT ranking, structured annotation	Human-like rationale, model/data diagnosis
UATVR (Fang et al., 2023)	Probabilistic distribution matching, DSA tokens	Multi-granular/high-level reasoning, uncertainty modeling
ViSERN (Feng et al., 2020)	Semantic region-level GCN with random-walk updates	Local object-interaction reasoning
X-Pool (Gorti et al., 2022)	Text-conditioned cross-modal attention pooling	Sub-region/frame relevance
Transcript-to-Video (Xiong et al., 2021)	Adaptive query masking, multi-shot selection & style coherence	Combinatorial sub-concept coverage, editing “style” reasoning

These models push reasoning along distinct axes: distributed probabilistic matching (Fang et al., 2023), region-relation propagation (Feng et al., 2020), attention-based subregion selection (Gorti et al., 2022), chain-of-thought rationalization via LLM (Pulakurthi et al., 25 Sep 2025, Shen et al., 15 Nov 2025), and structured scene grounding (Shen et al., 15 Nov 2025).

5. Benchmarks and Evaluation

Benchmarking reasoning-based retrieval systems requires explicit evaluation of both retrieval performance and supporting reasoning quality. Key datasets and metrics include:

• ReasonT2VBench-135/1000 (Shen et al., 15 Nov 2025): 447 implicit queries (each demanding ≥2 reasoning hops), 135–1000 videos, with ground-truth video and object masks. Metrics include Recall@K, mean average precision (mAP), Jaccard score $\mathcal{I}$, and contour alignment $\mathcal{J}$ for object grounding.
• MSR-VTT, MSVD, VATEX: Standard explicit-query retrieval tasks; reasoning methods are evaluated for backward compatibility.
• TextVR (Wu et al., 2023): Emphasizes cross-modal reading comprehension, requiring joint reasoning over in-frame text (scene OCR tokens) and visual context.
• Qualitative explanation metrics: Some works (X-CoT (Pulakurthi et al., 25 Sep 2025)) additionally evaluate interpretability by examining textual rationales and alignment with human expectations as well as error diagnosis capability.

Empirical results indicate reasoning-based pipelines achieve substantial gains: e.g., the digital twin pipeline achieves R@1=81.2% on ReasonT2VBench-135 versus CLIP4Clip’s 29.3% (Shen et al., 15 Nov 2025), and X-CoT improves R@1 by 1–5 points over baseline retrievers on standard datasets while providing detailed justifications (Pulakurthi et al., 25 Sep 2025).

6. Interpretability and Explanation

Interpretability is a core motivation for reasoning-based retrieval approaches. X-CoT (Pulakurthi et al., 25 Sep 2025), for example, employs an LLM-based sliding-window pairwise comparison and aggregates via the Bradley–Terry model to both re-rank videos and provide stepwise chain-of-thought rationales. Qualitative examples show the system’s ability to identify model misbehavior, highlight data annotation errors, and assist human users in diagnosis. The inclusion of explicit grounding (object masks and entity references) in digital twin approaches (Shen et al., 15 Nov 2025) further enhances system transparency and supports downstream auditability.

7. Open Challenges and Future Directions

Key technical challenges and research opportunities include:

• Efficient and scalable scene representation: Constructing, maintaining, and querying digital twins for large-scale corpora remain computationally intensive (Shen et al., 15 Nov 2025).
• Temporal and causal reasoning: Extending region-level reasoning and object graphs to span temporal, causal, and action structures across long horizons (Feng et al., 2020, Shen et al., 15 Nov 2025).
• Hybrid retrieval-reranking frameworks: Combining fast embedding-based candidate retrieval with high-fidelity LLM reasoning for top-ranked candidates to optimize latency and precision (Shen et al., 15 Nov 2025, Pulakurthi et al., 25 Sep 2025).
• Robustness to annotation noise and open-vocabulary phenomena: Handling out-of-vocabulary terms, ambiguous queries, OCR errors, and missing modalities (Wu et al., 2023, Wu et al., 17 Jul 2024).
• Self-supervised refinement and feedback: Using retrieval outcomes and user feedback for continual improvement of digital twin construction, query decomposition, and specialist model integration (Shen et al., 15 Nov 2025).
• Interpretability at scale: Scalable generation and summarization of chain-of-thought rationales, integration with domain-specific LLMs, and fine-tuning explanatory granularity.

A plausible implication is that as vision-LLMs are integrated with explicit symbolic representations and LLM-based reasoning, future systems will combine the retrieval scalability of embeddings with the interpretability, robustness, and compositional reasoning exhibited in human-level understanding.

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References:

(Feng et al., 2020) Exploiting Visual Semantic Reasoning for Video-Text Retrieval (Gorti et al., 2022) X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval (Fang et al., 2023) UATVR: Uncertainty-Adaptive Text-Video Retrieval (Wu et al., 2023) A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension (Wu et al., 17 Jul 2024) LLM-based query paraphrasing for video search (Pulakurthi et al., 25 Sep 2025) X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning (Shen et al., 15 Nov 2025) Reasoning Text-to-Video Retrieval via Digital Twin Video Representations and LLMs