Feedback Loops
13 篇文章
AI-Assisted Code Review / Verification
As AI models generate increasing amounts of code, the bottleneck in software development shifts from code generation to code verification and review. Ensuring that AI-generated code is not only syntac…
emergingBackground Agent with CI Feedback
Long-running tasks tie up the editor and require developers to babysit the agent.
validated-in-productionCoding Agent CI Feedback Loop
When a coding agent tackles multi-file refactors or feature additions, running tests and waiting for test feedback **synchronously** ties up compute and prevents the agent from working on parallel tas…
best-practiceDogfooding with Rapid Iteration for Agent Improvement
Developing effective AI agents requires understanding real-world usage and quickly identifying areas for improvement. External feedback loops can be slow, and simulated environments may not capture al…
best-practiceGraph of Thoughts (GoT)
Linear reasoning approaches like Chain-of-Thought (CoT) and even tree-based methods like Tree-of-Thoughts (ToT) have limitations when dealing with problems that require complex interdependencies betwe…
emergingInference-Healed Code Review Reward
Simple reward functions that only check for "all tests passed" fail to capture nuanced code quality issues (e.g., performance regressions, style violations, missing edge-case handling). A single binar…
proposedIterative Prompt & Skill Refinement
Agent usage reveals gaps in prompts, skills, and tools—but how do you systematically improve them? When a workflow fails or behaves sub-optimally, you need multiple mechanisms to capture feedback and …
proposedReflection Loop
Generative models may produce subpar output if they never review or critique their own work.
establishedRich Feedback Loops > Perfect Prompts
Polishing a single prompt can't cover every edge-case; agents need ground truth to self-correct. Additionally, agents need to integrate **human feedback** (positive and corrective) to improve session…
validated-in-productionSelf-Critique Evaluator Loop
Human preference labels are costly and quickly become outdated as base models improve.
emergingSelf-Discover: LLM Self-Composed Reasoning Structures
Different reasoning tasks require different thinking strategies. While techniques like Chain-of-Thought (CoT) work well for some problems, they may be suboptimal for others. Current approaches typical…
emergingSpec-As-Test Feedback Loop
Even in spec-first projects, implementations can drift as code evolves and the spec changes (or vice-versa). Silent divergence erodes trust.
proposedTool Use Incentivization via Reward Shaping
Coding agents often underutilize specialized tools (e.g., compilers, linters, test runners) when left to optimize only for final task success. They default to "thinking" tokens—generating internal cha…
emerging