This section turns each inspiration paper into a short working note for the repo.
Each note uses the same structure:
Core idea: the shortest accurate summary of the paperWhy it matters here: the transfer toagent_debuggerKey takeaways for the repo: the most relevant lessonsConcrete opportunities: changes the repo could actually makeCaution: what not to over-applyBest next experiment: the highest-value thing to try first
- Towards a Neural Debugger for Python
- MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning
- CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays
- NeuroSkill(tm): Proactive Real-Time Agentic System Capable of Modeling Human State of Mind
- Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
- Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts
- AgentTrace: Causal Graph Tracing for Root Cause Analysis
- XAI for Coding Agent Failures: Transforming Raw Execution Traces into Actionable Insights
- REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation
- FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair
These are not literature reviews. They are design notes.
The goal is to answer one question per paper:
- what should this repo do differently because this paper exists