MINT combines symbolic trees with neural uncertainty estimation and LLM query curation to achieve near-expert planning performance by asking a small number of targeted questions that close knowledge gaps.
Inspo: Unlocking intrinsic self- reflection for llm preference optimization.arXiv preprint arXiv:2512.23126
3 Pith papers cite this work. Polarity classification is still indexing.
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Derives finite-round upper-tail guarantee on population-empirical gap for client-sampled orthogonalized matrix momentum under heterogeneous data, with Lipschitz condition on the orthogonalizer.
InSemRAG combines dynamic intent-aware hybrid retrieval and semantics-preserving chunk repair in an iterative loop, yielding 2.65 F1 gain on HotPotQA and 1.5 accuracy gain on FEVER with 4.32x lower latency than Multi-Hop RAG via SLMs.
citing papers explorer
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MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
MINT combines symbolic trees with neural uncertainty estimation and LLM query curation to achieve near-expert planning performance by asking a small number of targeted questions that close knowledge gaps.
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A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling
Derives finite-round upper-tail guarantee on population-empirical gap for client-sampled orthogonalized matrix momentum under heterogeneous data, with Lipschitz condition on the orthogonalizer.
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Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking
InSemRAG combines dynamic intent-aware hybrid retrieval and semantics-preserving chunk repair in an iterative loop, yielding 2.65 F1 gain on HotPotQA and 1.5 accuracy gain on FEVER with 4.32x lower latency than Multi-Hop RAG via SLMs.