{
  "id": "multi-turn-degradation",
  "code": "PTL-0174",
  "term": "Multi-Turn Degradation",
  "aliases": [
    "lost in conversation",
    "multi-turn performance drop"
  ],
  "category": "failure-modes",
  "definition": "Multi-turn degradation is the drop in model performance when a task's requirements are revealed across several conversation turns instead of all at once, because models commit to early assumptions and fail to recover.",
  "description": "A large 2025 study found that models that performed well on fully specified single-turn instructions did markedly worse in multi-turn settings, mainly through increased unreliability rather than a loss of capability. The practical advice is to consolidate requirements into a fresh prompt when a conversation goes off track.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "instruction-following",
    "context-compaction",
    "crescendo-attack",
    "context-rot"
  ],
  "introduced": 2025,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "LLMs Get Lost In Multi-Turn Conversation",
      "authors": "Laban et al.",
      "year": 2025,
      "url": "https://arxiv.org/abs/2505.06120"
    }
  ],
  "url": "https://protologue.com/t/multi-turn-degradation/",
  "citation": "Protologue. (2026). Multi-Turn Degradation. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0174). https://protologue.com/t/multi-turn-degradation/"
}