Cross-Video Reasoning: Methods & Challenges

Updated 18 December 2025

Cross-video reasoning is a process that synthesizes, aligns, and infers relationships among entities, events, and scenes across multiple video streams.
It underpins applications such as multi-camera surveillance, cross-view activity recognition, and procedural retrieval by leveraging methodologies like explicit intermediate reasoning and graph-structured representations.
Recent advancements focus on persistent memory, object disambiguation, and hierarchical graph fusion to address challenges in inter-video context retention and causal inference.

Cross-video reasoning is the computational process by which systems synthesize, align, and infer relationships among entities, events, and scenes distributed across multiple independent video streams. Unlike single-video analysis, cross-video reasoning requires establishing semantic or spatiotemporal correspondences at multiple levels of granularity—object identity, action/event continuity, and complex causal or relational inference—across disparate or overlapping visual domains. This ability underpins applications in multi-camera surveillance, cross-view activity recognition, procedural retrieval, and multi-source video question answering, and it constitutes a central challenge for contemporary multimodal LLMs (MLLMs) and vision-language systems.

1. Problem Formulation and Task Taxonomy

Cross-video reasoning encompasses a hierarchy of tasks requiring joint analysis over a set of videos $V = \{v_1, ..., v_K\}$ given a natural-language query $q$ , with the aim of producing an answer $a$ among $M$ possible choices. The CVBench framework (Zhu et al., 27 Aug 2025) formalizes three principal tiers:

Cross-video object association: Identifying shared entities or attributes across videos.
Cross-video event association: Linking temporally or causally connected events distributed across separate clips.
Cross-video complex reasoning: Synthesizing multi-hop facts, external commonsense, or domain knowledge spanning multiple sequences.

Formally, each task $t \in \{\text{O}, \text{E}, \text{C}\}$ adopts the schema: $f_t(V, q_t) = \arg\max_{a \in A_t} P(a | V, q_t)$ where $A_t$ is the candidate answer set for task $t$ . The evaluation metric is scalar accuracy at each tier and overall, with

$\mathrm{Acc}_t = \frac{1}{N_t} \sum_{i=1}^{N_t} \mathbf{1}\bigl(f_t(V^{(i)}, q_t^{(i)}) = a_t^{(i)}\bigr),$

where $N_t$ is the number of examples for each task (Zhu et al., 27 Aug 2025).

This taxonomy is mirrored by diverse benchmarks such as CrossVideoQA (Meng et al., 5 Aug 2025), which provide datasets annotated for object/person association, behavioral event reasoning, and narrative summarization, and by earlier scene-centric parsing research in cross-view camera networks (Qi et al., 2017).

2. Methodological Approaches

2.1 Explicit Intermediate Reasoning

Visual Chain-of-Thought (vCoT) (Yang et al., 17 Nov 2025) introduces an explicit intermediate reasoning scaffold for long-form video QA: for a sequence of $T$ frames $F = \{F_1, ..., F_T\}$ , inferred “bridging events” $e_i$ summarize transitions between $F_i$ and $F_{i+1}$ . These e_i are produced via:

Contextual grounding: prompting for shared visual attributes between frame pairs.
Transitional inference: eliciting plausible intermediate events.

The resulting interleaved sequence $S_\text{vCoT} = [F_1, e_1, F_2, ..., e_{T-1}, F_T]$ is tokenized and passed to an LLM as input. vCoT significantly boosts reasoning performance for image-only LLM-vision models on multi-frame and relational reasoning benchmarks, though video-finetuned models derive only marginal or no benefit, implying these transitions are already internalized through finetuned temporal inductive biases.

2.2 Graph-Structured and Hierarchical Representations

Structured approaches construct explicit knowledge graphs or spanning trees from video content:

Scene-centric joint parsing (Qi et al., 2017) merges independent view-centric proposals from each camera into a unified parse-graph anchored in a grounded ontology of objects, actions, and attributes, using energy-based compatibility terms for spatial, appearance, action, and attribute consistency. Optimization employs Metropolis-Hastings MCMC for attachment structure and sum-product belief propagation for node values.
VideoForest (Meng et al., 5 Aug 2025) encodes each video as a hierarchical multi-granularity spanning tree with person-anchored nodes derived from ReID-tracked trajectories, enabling cross-video alignment of person-level features for reasoning.
Multi-video graph fusion (He et al., 16 Sep 2025) generates spatio-temporal graphs per video (nodes: tracked objects, edges: frame-level and temporal links), fuses knowledge via a Graph Fusion Module (within-video GAT, cross-graph attention), and serializes structured multimodal tokens for downstream LLM consumption.

2.3 Multi-Agent and Modular Reasoning

Collaborative reasoning frameworks (e.g., VideoForest (Meng et al., 5 Aug 2025)) coordinate specialized agents for pre-filtering videos, retrieval, hierarchical tree navigation (via relevance scoring between query and semantic node embeddings), and final evidence integration, enabling modular traversal and answer synthesis across large multi-video corpora.

3. Evaluation Protocols and Benchmarking

Robust cross-video reasoning assessment requires domain-diverse benchmarks with tiered tasks.

CVBench (Zhu et al., 27 Aug 2025): 1,000 QA pairs spanning five video clusters (sports, life, artistic, knowledge, film), partitioned into three tiers and evaluated via accuracy. State-of-the-art MLLMs (e.g., GPT-4o, Gemini-2.0-flash, Qwen2.5-VL) achieve 51–71% on typical cross-video tasks, but only 60% on complex causal reasoning, vs. 91% human performance.
CrossVideoQA (Meng et al., 5 Aug 2025): Targets person recognition, behavioral analysis, and complex reasoning for cross-view and cross-temporal tasks, with VideoForest attaining 71.93% (person), 83.75% (behavior), and 51.67% (reasoning) compared to lower single-video baselines.
InternVid-QA, MSRVTT-QA, ActivityNet-QA (He et al., 16 Sep 2025): Used for evaluating incrementally structured and multi-video graph-fusion models, with graph fusion consistently yielding +2–4% accuracy over naive concatenation.

Experimental results consistently highlight significant challenges for retention of inter-video context, disambiguation of entities (especially persons or objects with appearance variation), and robust event ordering or causal inference.

4. Limitations and Current Bottlenecks

Extensive evaluation has surfaced persistent bottlenecks:

Deficient inter-video context retention: Existing architectures lack persistent memory mechanisms to retain object or event state across disjoint streams (Zhu et al., 27 Aug 2025).
Entity disambiguation failures: Overlapping or visually similar entities are routinely mis-associated between videos, impeding robust object/event linking.
Inefficient naively concatenated representations: Simple serial aggregation of frame or video tokens causes information overload, redundancy, and degraded performance (He et al., 16 Sep 2025), necessitating structured compression and fusion.
Inadequate temporal-causal reasoning: Models rarely model long-range dependencies or multi-hop event chains without dedicated structure (Zhu et al., 27 Aug 2025).

LoRA and structured prompting ablations confirm that mere data scale or exposure is insufficient; architectures must encode explicit cross-video linking, temporal, and relational inductive biases (Yang et al., 17 Nov 2025).

5. Architectural Insights and Future Directions

Emerging cross-video reasoning systems increasingly integrate:

Persistent memory banks to cache entity/event representations between videos, enabling longer-term state and identity retention (Zhu et al., 27 Aug 2025).
Disambiguation layers for dynamic object embedding and robust cross-view identity alignment.
Hierarchical graph and spanning-tree representations to encapsulate multi-level visual, spatial, and temporal structure, both within and across videos (Qi et al., 2017, Meng et al., 5 Aug 2025, He et al., 16 Sep 2025).
Graph neural reasoning modules for explicit modeling of causal/event relations spanning multiple video sources.
Explicit structured prompts and video index tags to aid temporal and source awareness.

Further research targets richer intermediate representations (e.g., event chains or scene graphs), scaling to multi-agent interaction and dense 3D scene understanding, and synthesizing methods from cross-view geometry (Qi et al., 2017) with LLM-driven multimodal fusion. Cross-video benchmarks such as CVBench and CrossVideoQA will continue to serve as diagnostic instruments for measuring architectural improvements and guiding the evolution of cross-video reasoning toward human-level performance.

6. Comparative Summary of Representative Systems

System / Approach	Key Mechanism(s)	Cross-Video Task Focus
vCoT (Video Finetuning) (Yang et al., 17 Nov 2025)	Bridging-event infill, LoRA finetuning, interleaved frame–event tokens	Frame-to-frame event inference, transfer to relational tasks
Scene-centric parsing (Qi et al., 2017)	Ontology graph, MCMC + BP inference	Multi-camera object, action, attribute, and scene understanding
VideoForest (Meng et al., 5 Aug 2025)	Person-anchored spanning trees, ReID, multi-agent reasoning	Person-centric cross-video QA, behavior, summarization
Multi-video graph fusion (He et al., 16 Sep 2025)	Spatio-temporal video graphs, cross-graph attention, structured prompts	Complimentary fact integration, zero-shot QA

These approaches collectively advance the field by encoding explicit structure, identity, and event persistence, yet significant gaps to human-level multi-hop, cross-source inference remain, as quantified by current benchmarks.