Think Visually Across Space and Time: Grounded Reasoning for Video Segmentation

By Wen, Miao, Lu +3Oct 18, 2026

Score████░░░░░░4.4

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

Video Reasoning Segmentation (VRS) requires a model to identify an implicitly described target and predict its pixel-level mask trajectory, while resolving the spatial relations and temporal events that determine the answer. Text-only Chain-of-Thought (CoT) can explain a target choice without making the underlying visual references explicit, leaving a video reference gap between linguistic inference and the evidence distributed across frames. We introduce Temporal Chain of Video Primitives (TCoVP), a spatiotemporally grounded form of Video CoT that organizes query-relevant observations into a chronological reasoning chain. Each primitive associates an entity reference with temporal support and timestamped spatial evidence, and the chain interleaves these references with language to guide reasoning about which entity is relevant, when the queried event holds, and where its evidence appears. The selected target primitives then provide box prompts and temporal support for video mask prediction. We construct TCoVP-SFT-15k and TCoVP-RL-13k to learn this representation through supervised fine-tuning and chain-aligned reinforcement learning. Extensive experiments across five VRS benchmarks and three model backbones demonstrate the strong performance of TCoVP, highlighting the effectiveness of organizing spatial and temporal evidence within the reasoning process. All models and code will be publicly released. Project page: \url{https://victor-wjw.github.io/TCoVP/}

Junwei Wen, Deshui Miao, Guangming Lu, Ming-Hsuan Yang, Xin Li, Wenjie Pei

The editor's rubric

Heuristic review

Leverage4 / 5: A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).; 24% of this desk’s rubric
Magnitude2 / 5: Solid incremental gain on a meaningful problem.; 18% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 14% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 16% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 18% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	████░ 4	24%	A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
Magnitude	██░░░ 2	18%	Solid incremental gain on a meaningful problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); breadth (many tasks); verification (multiple benchmarks, code released); stakes (general AI).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.4

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.2 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.5 / 10; Shrunk toward the desk prior by editor confidence (38%).
Attention: 36%; Citations, upvotes, points, mentions.
Freshness: 54%; Half-life decay since publication.

Hugging Face upvotes13 (reference 25, via hf-daily, Oct 20, 2026, 13:49 UTC)

The record

Reviewed by heuristic-v7 on Oct 20, 2026, 13:49 UTC. Paper type: method.
Categories: cs.CV
TOP, No.5 in the Artificial Intelligence edition of October 20, 2026.