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ARXIV:2604.12320 · VIDEO LLMS · SUBMITTED 15 APR · 16:59 UTC · FRESHNESS STALE
ARXIV:2604.12320VIDEO LLMSSUBMITTED 15 APR · 16:59 UTCFRESHNESS STALEJianzhe Ma · Zhonghao Cao · Shangkui Chen · Yichen Xu · Wenxuan Wang · Qin Jin · arXiv
A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development.
Opportunity summary
Pain A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast,…
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual environments remain under-explored. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Comprehensive evaluations of state-of-the-art Video-LLMs reveal that current models still fail to achieve satisfactory performance, with the best model only 71.58%. Code availability is…
Video LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development.
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10.48550/arXiv.2604.12320A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development.
Abstract
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual environments remain under-explored. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasoning in virtual scenarios. To fill this gap, we introduce EgoEsportsQA, a pioneering video question-answering (QA) benchmark for grounding perception and reasoning in expert esports knowledge. We curate 1,745 high-quality QA pairs from professional matches across 3 first-person shooter games via a scalable six-stage pipeline. These questions are structured into a two-dimensional decoupled taxonomy: 11 sub-tasks in the cognitive capability dimension (covering perception and reasoning levels) and 6 sub-tasks in the esports knowledge dimension. Comprehensive evaluations of state-of-the-art Video-LLMs reveal that current models still fail to achieve satisfactory performance, with the best model only 71.58%. The results expose notable gaps across both axes: models exhibit stronger capabilities in basic visual perception than in deep tactical reasoning, and they grasp overall macro-progression better than fine-grained micro-operations. Extensive ablation experiments demonstrate the intrinsic weaknesses of current Video-LLM architectures. Further analysis suggests that our dataset not only reveals the connections between real-world and virtual egocentric domains, but also offers guidance for optimizing downstream esports applications, thereby fostering the future advancement of Video-LLMs in various egocentric environments.
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Dimensions overall score 7.0
PROBLEM
A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasonin...
METHOD
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual environments remain under-explored. Existing benchmarks focus on daily activities, yet lack a rigorous t...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Comprehensive evaluations of state-of-the-art Video-LLMs reveal that current models still fail to achieve satisfactory performance, with the best model only 71.58%. Code availability is flagged in the pro...
WHY NOW
Video LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasoning in virtual scenarios.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
While video large language models (Video-LLMs) excel in understanding slow-paced, real-world egocentric videos, their capabilities in high-velocity, information-dense virtual environments remain under-explored. Existing benchmarks focus on daily activities, yet lack a rigorous testbed for evaluating fast, rule-bound reasoning in virtual scenarios.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Comprehensive evaluations of state-of-the-art Video-LLMs reveal that current models still fail to achieve satisfactory performance, with the best model only 71.58%. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new benchmark and dataset for evaluating Video-LLMs in high-velocity esports environments, revealing significant performance gaps and guiding future development.
Segment
Video LLMs
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7.0/10 public viability
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missing
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