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Chain-of-Thought

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RESEARCH · 1 SOURCE · arXiv cs.AI

EDMA: entropy-driven framework for multimodal question answering

EDMA (Entropy-Driven Multimodal Answering) is an arXiv v1 paper that formalises multimodal question answering as an iterative process of logical-entropy reduction. It proposes modality-conditioned partitioning, entropy-driven multimodal fusion, and entropy-based trajectory selection to quantify each modality's contribution and selects low-entropy reasoning paths; evaluated on standard MMQA benchmarks, EDMA reports average gains of 12.8 F1 and 4.6 LLM-J over Planning Prompting and 18.6 F1 and 8.3 LLM-J over Chain-of-Thought, and shows higher entropy correlates with more false positives.

6.0

MODELS · 1 SOURCE · arXiv cs.AI

Hunyuan-A13B: open-source 80B Mixture-of-Experts LLM activating 13B

An arXiv paper (arXiv:2609.27284v1) introduces Hunyuan-A13B, an open-source Mixture-of-Experts large language model with 80 billion total parameters that activates 13 billion during inference. It was pretrained on a filtered 20T-token corpus with enhanced STEM curation, uses supervised fine-tuning and large-scale reinforcement learning, adds a dual-mode Chain-of-Thought mechanism for fast vs. slow reasoning, and reports competitive results across math, science, programming and agent tasks with high inference throughput.

8.0

RESEARCH · 1 SOURCE · Google Research

Chain-of-Table: Iteratively evolving tables as a reasoning chain for improved table understanding

Researchers (Zilong Wang and Chen-Yu Lee of the Cloud AI Team) propose Chain-of-Table, a framework that trains LLMs via in-context learning to iteratively generate table operations and update intermediate tables as an explicit reasoning chain; this transforms complex tables into simpler, question-aligned views and reportedly achieves new state-of-the-art results on WikiTQ, TabFact, and FeTaQA benchmarks.

7.0