AI social systems can understand more than interests. A user may explicitly say, “I want to meet a cofounder,” “I need someone to practice Japanese with,” or “I’m looking for creators in Ho Chi Minh City this month.” These intents are powerful matching signals, but they are often temporary. Treating them as permanent profile traits can produce stale and intrusive recommendations long after the goal has disappeared.

Intent should have a time dimension

A social graph typically stores relatively stable edges such as follows or contacts. An intent graph needs timestamps, expected duration and current activity state. A request for a travel companion next weekend should decay much faster than a long-term professional interest.

Separate durable goals from episodic needs. “I’m learning Japanese this year” is different from “I need a Japanese translator tomorrow.” Both can improve discovery, but they deserve different expiry rules. Intent classification should include expected lifetime.

Let users set duration explicitly

Where practical, a user can say “for this week,” “until I find someone,” or “ongoing.” Explicit duration is stronger evidence than inference. The system can still suggest a default expiry when no duration is given.

Use reinforcement to extend intent. If the user keeps discussing the same goal or repeatedly acts on matching results, the intent can remain active longer. Reinforcement should extend relevance without converting every repeated behavior into a permanent identity trait.

Decay inactive intents gradually

Instead of switching from active to deleted overnight, the system can lower ranking weight as an intent ages without reinforcement. This creates smoother transitions and reduces abrupt recommendation shifts.

Completion can close the intent. If the user accepts a cofounder introduction or finishes a planned event, the system may ask whether the goal is complete. Closing an intent prevents continued matching after the user already achieved the purpose.

Conflicting intents can coexist

A founder may seek investors while also looking for casual local friends. Different relationship goals should remain separate rather than being compressed into one profile vector. Context determines which intent should dominate a given discovery session.

Privacy improves when stale intent disappears. A temporary request can reveal sensitive context. If the system no longer needs it for matching, lowering retention and visibility reduces unnecessary profiling. Expiry is therefore both a relevance mechanism and a privacy mechanism.

Digital twins can express current intent

A user’s digital twin can answer public questions about what the person is currently open to: collaboration, language practice or creator partnerships. The twin should use only intents the user has allowed to be shared.

Explain recommendations with current intent. “You said you’re looking for an AI-video collaborator this month” is more understandable than a generic compatibility score. Temporal context helps the explanation feel purposeful rather than invasive.

Evaluate stale-match rate

Benchmarks can measure how often the system recommends candidates for goals that should have expired. This is a distinct quality problem from ordinary ranking relevance. User rejection reason can help identify stale intent.

Support manual pause and reset. Users should be able to pause a goal without deleting their account or social history. A temporary “not looking right now” state can preserve context while stopping discovery. This makes the graph more responsive to real life.

Use separate visibility states

An intent can be active for the user’s own recommendations but hidden from other people. This lets the system help internally without broadcasting the goal publicly. Visibility and ranking relevance should be separate fields.

Track which event refreshed the intent. If a goal remains active because of a new message, profile edit or accepted introduction, record that provenance. This makes it easier to understand why a supposedly temporary intent did not expire. Temporal graphs need explainable refresh logic.

Avoid turning every conversation into intent inference

Users discuss ideas they are not actually pursuing. High-impact social intent should require stronger evidence than a passing mention. Explicit requests, repeated behavior and user confirmation can raise confidence before the graph changes.

Intent graphs should complement relationship graphs. Our earlier work on AI-native social intent graphs focuses on matching structure. Temporal intent adds the missing question of when that structure remains valid.

Social goals should be allowed to end

AI can make matching more precise only if it also knows when yesterday’s goal is no longer today’s goal. Time-aware intent prevents personalization from becoming a permanent record of temporary needs.