Random-reward discrete phase-type distributions are defined and used to construct the two-parameter Inertia-Escalation model for latent severity, with parameter inference and validation on warfare and churn data.
In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguis- tics (Volume 1: Long Papers), pp
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A self-paced curriculum learning module with dual-level difficulty scoring improves weighted F1 scores by 1.2-10.4% when added to existing multimodal emotion recognition models on IEMOCAP and MELD.
Dynamic scaled gradient descent prevents fine-tuning collapse by dynamically down-weighting gradients of correct examples, yielding lower performance variance and higher accuracy than standard methods on classification benchmarks.
CW-GRPO weights GRPO advantages with per-round contribution scores from an LLM judge, improving search agent performance by 5.0% on Qwen3-8B and 6.3% on Qwen3-1.7B over standard GRPO.
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
citing papers explorer
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Random Reward Phase-Type Distributions with Applications in Latent Severity Modeling
Random-reward discrete phase-type distributions are defined and used to construct the two-parameter Inertia-Escalation model for latent severity, with parameter inference and validation on warfare and churn data.
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Leveraging Self-Paced Curriculum Learning for Enhanced Modality Balance in Multimodal Conversational Emotion Recognition
A self-paced curriculum learning module with dual-level difficulty scoring improves weighted F1 scores by 1.2-10.4% when added to existing multimodal emotion recognition models on IEMOCAP and MELD.
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Dynamic Scaled Gradient Descent for Stable Fine-Tuning for Classifications
Dynamic scaled gradient descent prevents fine-tuning collapse by dynamically down-weighting gradients of correct examples, yielding lower performance variance and higher accuracy than standard methods on classification benchmarks.
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Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization
CW-GRPO weights GRPO advantages with per-round contribution scores from an LLM judge, improving search agent performance by 5.0% on Qwen3-8B and 6.3% on Qwen3-1.7B over standard GRPO.
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MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.