Interaction / Background Model Split

Sources#

Summary#

Interaction Models are architected as two cooperating models:

a time-aware interaction model that maintains real-time presence — perceiving and responding in a continuous loop (see Time-Aligned Micro-Turns);
an asynchronous background model that handles sustained reasoning, tool use, and longer-horizon work.

The payoff: the user gets both responsiveness and depth — "the planning, tool-use, and agentic workflows of reasoning models at the response latency of non-thinking ones."

How delegation works#

When a task needs deeper reasoning than can be produced instantly, the interaction model delegates to the background model, which runs asynchronously.
The handoff is a rich context package — not a standalone query, but the full conversation.
The interaction model stays present throughout — answering follow-ups, taking new input, holding the thread.
Results stream back as the background model produces them; the interaction model interleaves updates into the conversation at a moment appropriate to what the user is currently doing — not as an abrupt context switch.

Both halves are intelligent#

This isn't a "dumb frontend, smart backend" design. The interaction model on its own is "competitive on both interactive and intelligence benchmarks" — see Interactivity Benchmarks (e.g. TML-Interaction-Small beats every non-thinking baseline on Audio MultiChallenge APR even without the background agent; benchmarks marked * use the background agent for reasoning/tool tasks).

Relationship to other multi-model patterns#

This is the latency-vs-depth axis of multi-model orchestration, distinct from:

the role-based model selection in Client-Side Agent Optimization (assign cheap/expensive models per role in an agent graph) — there the split is cost-driven and static; here it's latency-driven and dynamic-per-turn;
the three-agent / reviewer-in-fresh-context pattern (Deep Modules for Agents, Agent Harness Engineering) — there the split is for context isolation; here it's for temporal concerns (stay responsive vs. think hard).

The background half gets a name: Inkling (July 2026)#

At the split's introduction the background model was an unnamed capability. Inkling fills the slot: TML states that "a major goal of Inkling's design is to serve as the background reasoning model in the interaction models system" — which is why a 975B open-weights foundation model was trained natively multimodal (encoder-free dMel audio and hMLP vision, the same input stack as the interaction model) rather than as a text reasoner with adapters. The symmetry runs deeper: Inkling-Small is a 276B/12B MoE, TML-Interaction-Small's exact shape, suggesting the two halves of the split share a lineage. Both halves of the architecture are now public artifacts rather than one model and one promise.

The split ships in production: GPT-Live (July 2026)#

OpenAI's GPT-Live is the same two-model architecture arrived at independently and deployed at ChatGPT scale — a full-duplex voice model holds the conversation while deeper reasoning and tool use delegate to frontier models such as GPT-5.5 on an asynchronous path. OpenAI's phrase for the payoff mirrors TML's: "effectively decoupling 'talking' from deeper 'thinking'." Two months after TML's research preview, both halves of the split now exist as production systems at a second lab.

The production account adds the engineering the research framing left open — the delegation loop as a latency budget (case-study, first-party):

The rich context package becomes a standing prefilled session. At voice-session start, the application server pre-creates the frontier model's inference session and prefills it with the initial conversation context, so the prompt is fully processed before the first delegation is requested — TML's delegate-the-full-conversation handoff with its prefill cost paid in advance.
Session affinity + prompt caching keep successive delegations cheap for the conversation's duration, with worker failure still cheaply recoverable.
Every lever on time-to-useful-result is tuned: reasoning effort, output limits, tool schemas, and model↔tool round trips.
The interaction half can stall, not hide. The voice model "can briefly keep the exchange moving while a frontier model reasons or uses tools, but it cannot hide an arbitrarily slow response" — the empirical bound on how much latency the split's front half can absorb.

Serving-side detail in Live-Path Minimalism.

Open / acknowledged#

TML calls background agents "an essential capability" they've "just scratched the surface" on — both pushing background agentic intelligence to the frontier and exploring how background agents work together with the interaction model.

Connections#

Interaction Models — parent concept
Time-Aligned Micro-Turns — what keeps the interaction model present while the background model thinks
Interactivity Benchmarks — *-marked results use the background agent; shows the split's contribution
Client-Side Agent Optimization — a different axis of multi-model design (cost/role, not latency/depth)
Deep Modules for Agents / Agent Harness Engineering — multi-agent splits for context isolation rather than latency
Harness Shrinkage as Models Improve — open question whether the split is permanent or a transitional artifact until one model is both fast and deep enough
Encoder-Free Early Fusion — the other half of the architecture (the perception/generation side)
Full-Duplex Interaction — where the concurrent deep work goes while the interaction model stays present
TML-Interaction-Small — the model that implements the split; competitive on intelligence benchmarks even without the background agent
GPT-Live — OpenAI's production instance of the split: full-duplex voice model delegating to GPT-5.5
Live-Path Minimalism — the serving architecture that keeps delegation off the live media path

Sources#

Interaction Models: A Scalable Approach to Human-AI Collaboration
Inkling: Our Open-Weights Model — Inkling as the designed background reasoning model (vendor-claim)
How we built a realtime system for responsive voice AI in six months — OpenAI, 2026-07-29 (case-study, first-party): GPT-Live's delegation path as a latency budget (pre-created prefilled sessions, affinity + caching, tuned effort/schemas/round-trips)