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#llm-extraction

Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors

arXiv cs.LG ↗ · 2026-09-16 Cached

SCTab-Diff is a semantics-consistent tabular diffusion framework that uses weak semantic priors to generate high-fidelity synthetic tabular data, improving distributional fidelity and semantic consistency over existing methods.

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#llm-extraction

I wrote up my own findings on model routing based on difficulty

Reddit r/AI_Agents ↗ · 2026-08-25

The author conducted a small experiment on LlamaIndex's extractbench benchmark, comparing Claude Opus 5 and Qwen models, finding similar performance with cost advantages and a correlation between extraction difficulty and document length.

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#llm-extraction

Agent memory layers don't need an LLM deciding what to remember

Reddit r/LocalLLaMA ↗ · 2026-08-05

The author argues that agent memory layers should skip LLM-based extraction for deciding what to remember, instead using simple storage, embeddings, and retrieval, exemplified by their open-source memU tool.

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