arXiv:2605.27186v1 Announce Type: new Abstract: Large language models often solve tasks from a fully specified prompt but degrade when the same requirements unfold over multiple turns, known as the lost-in-conversation (LiC) gap. We trace part of this degradation to self-contamination: intermediate assistant replies enter later context and carry early deviations forward. Motivated by this mechanism, we propose MAIGO, an on-policy self-distillation method that reduces this contamination using history-cleaned references from the model's own policy. For middle turns, MAIGO removes prior assistant
This research addresses a critical limitation of large language models, 'lost-in-conversation,' which is increasingly relevant as LLMs are integrated into multi-turn applications like AI agents.
Improved conversational capabilities of LLMs are essential for creating more robust and reliable AI systems, directly impacting the effectiveness of AI agents and complex automated workflows.
The ability of LLMs to maintain coherence and accuracy over extended conversations is enhanced, reducing errors caused by self-contamination and improving overall performance in dynamic contexts.
- · AI developers
- · SaaS companies leveraging LLMs
- · Users of conversational AI
- · AI research institutions
- · Inefficient LLM architectures
- · Companies relying on prompt-driven, single-turn AI
More reliable and less error-prone conversational AI systems become available.
The development and deployment of sophisticated AI agents functioning over long interactions accelerate.
Complex white-collar tasks become increasingly automatable as AI agents gain more accurate and context-aware conversational abilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL