Multi-Turn Conversation
In multi-turn dialogue, the ability of a model to receive new user input while it is still generating a reply. This involves coordinating streaming generation with a message queue, and the degree of API support varies across model vendors. It determines whether a user can interrupt, redirect, or supplement an in-progress response, and how such interleaved input is buffered and ordered.
Core Facts
Multi-turn conversation configuration addresses the context memory problem, determining how many rounds of historical information an agent can retain during continuous dialogue.
Repeatedly stuffing redundant conversation history into the context window leads to increased token costs and higher inference latency.
Timeline (last 90 days)
The history concatenation approach leads to context bloat and dilution of key information as the number of dialogue turns increases.
In multi-turn conversations, the number of input tokens at the k-th turn grows linearly with k, and the cumulative input over the entire session grows quadratically (O(n²)) with the number of turns.
In multi-turn conversations, when the assistant and the user steer in opposite directions, it is often the side that matches the assistant's baseline bias that ultimately prevails.
In multi-turn conversations, baseline bias continuously reinforces itself through the model's ongoing reinterpretation of context, with wording biased toward one side being repeatedly cited and amplified, forming inertia.
Some steering that appears successful early in a conversation gradually decays or even reverses after a few turns.
So-called "multi-turn conversation" is entirely an effect faked by the client repeatedly sending a growing message array; the model itself does not store history.
Large models are stateless; in multi-turn conversations the client must resend the complete history of messages with each request, causing the input length of the Nth turn to be roughly the accumulation of all messages from the previous N-1 turns, so input cost grows quadratically with the number of turns.
Multi-turn conversation configuration addresses the context memory problem, determining how many rounds of historical information an agent can retain during continuous dialogue.
The difference between single-turn and multi-turn dialogue lies in context memory and state management; overcoming the limitations typically requires maintaining conversation history on the server side and introducing a tool-calling mechanism.
Evaluating the overall quality of a multi-turn conversational workflow of an Agent is more complex than evaluating a single response.
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All Facts (13)
Multi-turn conversation configuration addresses the context memory problem, determining how many rounds of historical information an agent can retain during continuous dialogue.
75%VerifiedRepeatedly stuffing redundant conversation history into the context window leads to increased token costs and higher inference latency.
65%UnverifiedMulti-turn dialogue is implemented by re-inputting all previous conversation history to the model with each request, rather than the model having genuine memory capability.
60%UnverifiedThe history concatenation approach leads to context bloat and dilution of key information as the number of dialogue turns increases.
50%UnverifiedIn multi-turn conversations, the number of input tokens at the k-th turn grows linearly with k, and the cumulative input over the entire session grows quadratically (O(n²)) with the number of turns.
50%UnverifiedIn multi-turn conversations, baseline bias continuously reinforces itself through the model's ongoing reinterpretation of context, with wording biased toward one side being repeatedly cited and amplified, forming inertia.
50%UnverifiedIn multi-turn conversations, when the assistant and the user steer in opposite directions, it is often the side that matches the assistant's baseline bias that ultimately prevails.
50%UnverifiedSome steering that appears successful early in a conversation gradually decays or even reverses after a few turns.
50%UnverifiedSo-called "multi-turn conversation" is entirely an effect faked by the client repeatedly sending a growing message array; the model itself does not store history.
50%UnverifiedLarge models are stateless; in multi-turn conversations the client must resend the complete history of messages with each request, causing the input length of the Nth turn to be roughly the accumulation of all messages from the previous N-1 turns, so input cost grows quadratically with the number of turns.
50%UnverifiedThe difference between single-turn and multi-turn dialogue lies in context memory and state management; overcoming the limitations typically requires maintaining conversation history on the server side and introducing a tool-calling mechanism.
50%UnverifiedEvaluating the overall quality of a multi-turn conversational workflow of an Agent is more complex than evaluating a single response.
50%Unverified现代对话系统通常引入槽填充（Slot Filling）机制或有状态的对话管理器来追踪多轮交互上下文
50%