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ARXIV:2603.16859 · BENCHMARKING AND EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16859BENCHMARKING AND EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALETianyu Xie · Jinfa Huang · Yuexiao Ma · Rongfang Luo · Yan Yang · Wang Chen · +6 at arXiv
SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models.
Opportunity summary
Pain SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental…
Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. More encouragingly, these diagnostics from SocialOmni yield actionable signals for bridging the perception-interaction divide in future OLMs.
Benchmarking and Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models.
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10.48550/arXiv.2603.16859SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models.
Abstract
Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues. To this end, we propose SocialOmni, a comprehensive benchmark that operationalizes the evaluation of this conversational interactivity across three core dimensions: (i) speaker separation and identification (who is speaking), (ii) interruption timing control (when to interject), and (iii) natural interruption generation (how to phrase the interruption). SocialOmni features 2,000 perception samples and a quality-controlled diagnostic set of 209 interaction-generation instances with strict temporal and contextual constraints, complemented by controlled audio-visual inconsistency scenarios to test model robustness. We benchmarked 12 leading OLMs, which uncovers significant variance in their social-interaction capabilities across models. Furthermore, our analysis reveals a pronounced decoupling between a model's perceptual accuracy and its ability to generate contextually appropriate interruptions, indicating that understanding-centric metrics alone are insufficient to characterize conversational social competence. More encouragingly, these diagnostics from SocialOmni yield actionable signals for bridging the perception-interaction divide in future OLMs.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity...
METHOD
Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundament...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. More encouragingly, these diagnostics from SocialOmni yield actionable signals for bridging the perception-interaction divide in future OLMs.
WHY NOW
Benchmarking and Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Omni-modal large language models (OLMs) redefine human-machine interaction by natively integrating audio, vision, and text. However, existing OLM benchmarks remain anchored to static, accuracy-centric tasks, leaving a critical gap in assessing social interactivity, the fundamental capacity to navigate dynamic cues in natural dialogues.
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. More encouragingly, these diagnostics from SocialOmni yield actionable signals for bridging the perception-interaction divide in future OLMs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Benchmarking and Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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SocialOmni is a benchmark for evaluating audio-visual social interactivity in omni-modal large language models.
Segment
Benchmarking and Evaluation
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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next verification path
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Technical feasibility
partial
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