Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models
Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models explores A framework to identify and control conflicting knowledge in language models using mechanistic interpretability methods.. Commercial viability score: 5/10 in LLM Interpretability.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisUnderstanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025Viability: 2.0
- Builds On ThisSeamless Deception: Larger Language Models Are Better Knowledge ConcealersViability: 2.0
- Prior WorkBeyond the Covariance Trap: Unlocking Generalization in Same-Subject Knowledge Editing for Large Language ModelsViability: 5.0
- Prior WorkLarge Reasoning Models Struggle to Transfer Parametric Knowledge Across ScriptsViability: 5.0
- Prior WorkTemporal Fact Conflicts in LLMs: Reproducibility Insights from Unifying DYNAMICQA and MULAN