Title: Finding the Thread: Context-Driven Incremental Compression for Multi-Turn Dialogue
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: multi-turn dialogue, context compression

Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant re-encoding and attention costs that grow with conversation length. To enhance the efficiency, naive truncation or summarization degrades fidelity, and existing context compressors lack mechanisms for cross-turn memory sharing or revision, causing information loss and compounding errors over long dialogues. We revisit the context compression under conversational dynamics and empirically present its fragility. To address both the efficiency and robustness problems, we introduce Context-Driven Incremental Compression (C-DIC), which treats a conversation as interleaved contextual threads and stores revisable per-thread compression states in a single, compact dialogue memory. At each turn, a lightweight retrieve → revise → write-back loop shares information across turns and corrects stale memories, stabilizing behavior over long term dialogue. A lightweight, \emph{gradient-free} policy is proposed to dynamically manage this memory, adapting on-the-fly as conversational contexts evolve without test-time optimization.
In addition, we adapt truncated backpropagation-through-time (TBPTT) to our multi-turn setting, learning cross-turn contextual dependencies without full-history backpropagation.
Extensive experiments on long-form dialogue benchmarks demonstrate superior performance and efficiency of C-DIC, supporting a scalable path to high-quality dialogue modeling.