The Observability Gap: Why Output-Level Human Feedback Fails for LLM Coding Agents
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910079794544640 |
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| author | Wang, Yinghao Wang, Cheng |
| author_facet | Wang, Yinghao Wang, Cheng |
| contents | Large language model (LLM) multi-agent coding systems typically fix agent capabilities at design time. We study an alternative setting, earned autonomy, in which a coding agent starts with zero pre-defined functions and incrementally builds a reusable function library through lightweight human feedback on visual output alone. We evaluate this setup in a Blender-based 3D scene generation task requiring both spatial reasoning and programmatic geometric control. Although the agent rediscovered core utility functions comparable to a human reference implementation, it achieved 0% full-scene success under output-only feedback across multiple instruction granularities, where success required satisfying object completeness, ground contact, collision avoidance, and scale plausibility simultaneously. Our analysis identifies a structural observability gap: bugs originate in code logic and execution state, while human evaluation occurs only at the output layer, and the many-to-one mapping from internal states to visible outcomes prevents symptom-level feedback from reliably identifying root causes. This mismatch leads to persistent failure mode oscillation rather than convergence. A diagnostic intervention that injected minimal code-level knowledge restored convergence, strongly supporting the interpretation that the main bottleneck lies in feedback observability rather than programming competence. We formalize this phenomenon as a feedback paradox in domains with deep causal chains between internal code logic and perceptual outcomes, and argue that effective human-agent collaboration in such settings requires intermediate observability beyond output-only evaluation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_26942 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Observability Gap: Why Output-Level Human Feedback Fails for LLM Coding Agents Wang, Yinghao Wang, Cheng Human-Computer Interaction Large language model (LLM) multi-agent coding systems typically fix agent capabilities at design time. We study an alternative setting, earned autonomy, in which a coding agent starts with zero pre-defined functions and incrementally builds a reusable function library through lightweight human feedback on visual output alone. We evaluate this setup in a Blender-based 3D scene generation task requiring both spatial reasoning and programmatic geometric control. Although the agent rediscovered core utility functions comparable to a human reference implementation, it achieved 0% full-scene success under output-only feedback across multiple instruction granularities, where success required satisfying object completeness, ground contact, collision avoidance, and scale plausibility simultaneously. Our analysis identifies a structural observability gap: bugs originate in code logic and execution state, while human evaluation occurs only at the output layer, and the many-to-one mapping from internal states to visible outcomes prevents symptom-level feedback from reliably identifying root causes. This mismatch leads to persistent failure mode oscillation rather than convergence. A diagnostic intervention that injected minimal code-level knowledge restored convergence, strongly supporting the interpretation that the main bottleneck lies in feedback observability rather than programming competence. We formalize this phenomenon as a feedback paradox in domains with deep causal chains between internal code logic and perceptual outcomes, and argue that effective human-agent collaboration in such settings requires intermediate observability beyond output-only evaluation. |
| title | The Observability Gap: Why Output-Level Human Feedback Fails for LLM Coding Agents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.26942 |