newsNature Machine IntelligenceTrust 88 · LabPublished 3d agoLive · yesterday
Towards principled knowledge editing methods for large language model reasoning
Nature Machine Intelligence, Published online: 14 August 2026; doi:10.1038/s42256-026-01276-y Chen et al. explore limitations of current knowledge editing techniques in large language models and propose three promising research directions that respect the complexity of knowledge representation in a real-world setting.
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- PossiblePossibly related (embedding) · 58%Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph →
- PossiblePossibly related (embedding) · 57%Travel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge Graphs →
- PossiblePossibly related (embedding) · 57%UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing →
- PossiblePossibly related (embedding) · 56%Grounding LLM Reasoning under Incomplete Graph Evidence →
- PossiblePossibly related (embedding) · 56%Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation →
Covers
paperDebate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge GraphpaperTravel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge GraphspaperUltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic EditingpaperGrounding LLM Reasoning under Incomplete Graph EvidencepaperLeveraging Instruction Tuning and Merging for Reasoning Model Adaptation
Related across the graph
paperDebate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge GraphpaperGrounding LLM Reasoning under Incomplete Graph EvidencepaperUltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic EditingpaperTravel-Oriented Reasoning Large Language Model via Domain-Specific Knowledge GraphspaperLeveraging Instruction Tuning and Merging for Reasoning Model Adaptation