Tokengeist: Multi-Turn Attribution Tracing in Agentic Conversations
When a language model produces a response in a multi-turn conversation, which tokens from prior turns shaped that answer, and how did those dependencies propagate across prior turns? Existing context attribution methods process the full context in a single pass, recovering surface-level dependencies but missing the layered, non-linear structure of real-world dialogues and multi-step reasoning tasks. We introduce multi-turn context attribution (MTCA): given a target span in a model response, the task of tracing attribution backward across turns to identify not only which prior turns were directly relevant, but also how those turns themselves depended on earlier context. We propose Tokengei...
| Authors | Jessica Tang, Shraddha Barke, Sharad Agarwal |
|---|---|
| Primary category | cs.AI |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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