IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
arXiv preprint arXiv:2508.10848 , year=
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A cue-coverage reward plus a modality-token KL penalty makes multimodal emotion-reasoning models cite more real visual/audio evidence and hallucinate less, with reported SoTA on emotion benchmarks.
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
CogEvolution combines ICAP cognitive taxonomy, IRT memory retrieval, and evolutionary algorithms into a generative agent that simulates dynamic student cognitive evolution and outperforms baselines in fidelity and learning curves.
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IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics
IntervenSim is an intervention-aware social network simulation that couples source interventions with crowd interactions in a feedback loop, improving MAPE by 41.6% and DTW by 66.9% over prior static frameworks on real-world events.
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Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
A cue-coverage reward plus a modality-token KL penalty makes multimodal emotion-reasoning models cite more real visual/audio evidence and hallucinate less, with reported SoTA on emotion benchmarks.
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Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
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CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution
CogEvolution combines ICAP cognitive taxonomy, IRT memory retrieval, and evolutionary algorithms into a generative agent that simulates dynamic student cognitive evolution and outperforms baselines in fidelity and learning curves.