The final third of the survey diagnoses where multi-turn LLM interaction still breaks — not as a grab-bag list, but as a structured map of where in the dialogue lifecycle things fail and which research threads are working on each.
Coherent state across many turns as instructions, attributes, and intent evolve.
Compounding errors, topic switching, and proactive information seeking.
Persistent personalization and on-the-fly knowledge update, without over-accommodation.
Data curation and metrics lag behind the complexity of multi-turn breakdown modes.
Prolonged dialogue amplifies bias, privacy leakage, overtrust, emotional dependence.
LLMs struggle to maintain a coherent, consistent state over many turns. The performance gap between early- and late-turn accuracy is the most robust finding in multi-turn evaluation (Kwan 2024, Bai 2024, sirdeshmukh 2025, laban 2025, liu 2026, yang 2026, chen 2026 ESMemEval, choi 2026 DyCP). Sub-challenges:
Errors in early turns compound. Topic switches break attention allocation. Proactive information seeking — arguably the single highest-value multi-turn behavior — remains largely absent. Sub-challenges:
LLMs lack persistent personalization and on-the-fly knowledge update. Worse, naïve adaptation becomes a liability: a model that eagerly updates its persona in response to user pressure also updates when that pressure is adversarial. Sub-challenges:
Data curation and metrics lag behind the complexity of multi-turn breakdown modes. Automated judges bring their own biases. Sub-challenges:
Prolonged dialogue magnifies demographic bias, privacy leakage, and attachment / companion risks. Safety needs to be modeled as a trajectory property, not a per-turn property. Sub-challenges:
If you are starting a multi-turn LLM research project, we recommend pairing this challenges roadmap with the benchmark explorer to find the existing evaluation that most closely probes your target failure mode.
Go to Benchmark Explorer → Cite the survey