DriveSpatial benchmark shows the strongest of 15 VLMs trails humans by 28.4 points on spatiotemporal tasks, with cognitive scene construction as the primary weakness.
Hssbench: Benchmarking humanities and social sciences ability for multimodal large language models
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
ConsumerSimBench evaluates 13 LLMs on reconstructing crowd reactions from 1,553 Chinese social-media topics using 23,122 auditable yes-no criteria, finding maximum coverage of 47.8% by Gemini-3.1-Pro.
Chain-of-thought underperforms direct answering in medical VQA due to a perception bottleneck, but ROI cues and textual grounding interventions can improve results and reverse the gap.
BenCSSmark is a proposed benchmark that adds social science datasets to LLM evaluation to improve model robustness and relevance across disciplines like sociology and economics.
A survey proposing a three-level capability taxonomy (L1 Predictor, L2 Simulator, L3 Evolver) for world models across physical, digital, social, and scientific domains.
citing papers explorer
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DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving
DriveSpatial benchmark shows the strongest of 15 VLMs trails humans by 28.4 points on spatiotemporal tasks, with cognitive scene construction as the primary weakness.
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Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench
ConsumerSimBench evaluates 13 LLMs on reconstructing crowd reactions from 1,553 Chinese social-media topics using 23,122 auditable yes-no criteria, finding maximum coverage of 47.8% by Gemini-3.1-Pro.
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Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine
Chain-of-thought underperforms direct answering in medical VQA due to a perception bottleneck, but ROI cues and textual grounding interventions can improve results and reverse the gap.
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BenCSSmark: Making the Social Sciences Count in LLM Research
BenCSSmark is a proposed benchmark that adds social science datasets to LLM evaluation to improve model robustness and relevance across disciplines like sociology and economics.
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Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
A survey proposing a three-level capability taxonomy (L1 Predictor, L2 Simulator, L3 Evolver) for world models across physical, digital, social, and scientific domains.
- NormAct: Benchmarking Embodied Agents' Proactive Compliance with Unspoken Social Norms