NeuroSync: Intent-Aware Code-Based Problem Solving via Direct LLM Understanding Modification
summary
NeuroSync extracts an LLM’s intermediate LM understanding as a task graph and lets users inspect and edit it before code generation, implementing intent–task direct matching. A distillation pipeline trains a small LM from synthetic multi-agent data to run this pipeline about 40× faster while retaining ~90% of accuracy. A three-panel UI shows conversation, full graph, and a simplified intent-aware graph; a lab study (N=12) finds fewer rounds, lower cognitive load, and faster code completion.
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chapters (8)
- 0:00Introduction and paper overview
- 1:36Misalignment in coding scenarios
- 3:19Formative study findings
- 5:48NeuroSync concept and panels
- 7:55Speeding up with knowledge distillation
- 9:00Graph simplification and evaluation
- 10:04Takeaways and conclusion
- 10:33Q&A on scalability
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