aichannel

Identity AI is about giving LLM-based agents social/professional identities so they behave like real people—allying when it helps, deceiving if needed, forging trust or rivalry. In multi-agent environments, think stochastic games where each ‘state’ is who has resources & alliances, and the game transitions randomly. Real workplaces & social platforms are ‘general-sum’: not purely zero-sum but a mix of competition + cooperation.

Now, a big new approach is multi-agent inverse reinforcement learning (MIRL). Researchers (Lin, Adams & Beling) have ways to guess 'hidden motives'—the agent’s payoff structure—just by watching how it acts. That’s huge if you want to see whether an agent’s “cooperative persona” is real or just a veneer.

stochastic games explainer:https://www.youtube.com/watch?v=_Fq8_Jg25pY

prev founded rocketr.net, swish.id now exploring aiXcrypto identity ai. expired sprint car driver & energy data engineer.

Practical scenarios: (1) A job-candidate bot and a hiring-manager bot. We watch their Q&A, partial policies, see if the candidate’s real motivation is honesty or just slick bluffing. MIRL recovers that hidden payoff, bridging the gap between claimed identity and actual behavior. (2) A big company with multiple LLM-based ‘departments’—engineering, marketing, finance. All negotiating budgets or time-schedules. Observing their repeated interactions, a general-sum MIRL framework can identify each department’s deeper preference: are they truly synergy-seekers or quiet saboteurs?

If we add Theory of Mind (ToM) to the mix, an LLM-based agent can keep track of each other’s mental states, detect deception, and see if outward statements match the MIRL-based payoff structure. Real synergy arises when the agent’s chain-of-thought says: 'They might cheat next round, so I’d better hedge!'

But let's talk costs & complexity:

Short-term fixes? 
Use partial solutions (like uniform or random placeholders for missing data) and do basic offline MARL + a small MIRL step. Quick to implement, building on your existing LLM.

Medium-term? 
Fine-tune the LLM on a small dataset so it can do 'MIRL-based reward reasoning'—where it systematically identifies what solution concept (co-op, adversarial, correlation) is in play.

Long-term? 
Potentially train a new foundation model from scratch that bakes in multi-agent concepts deeply, enabling truly dynamic identity transformations and real-time re-estimation of others’ payoffs.

Ultimately, identity AI means each agent’s ‘personality’ might shift as it plays repeated multi-agent games. The new MIRL approach shows how to re-infer payoffs if we see them flip from cooperative to adversarial over time—a real identity shift. More robust online MIRL can re-estimate reward functions on the fly. That’s how we track transformations in real-time.

In short: Stochastic games let us handle synergy or conflict among identity-driven LLM agents. MIRL reveals the hidden 'why' behind an agent’s strategy. Combine it with a strong ToM approach, and you’ve got a powerful engine for emergent, dynamic identity in social or professional contexts. Looking forward to your builds on top of this!

#AI #LLM #MultiAgent #GameTheory #MIRL #IdentityAI"

Why does MIRL matter for identity-based AI? Each identity is basically a reward function behind the scenes—cooperative folks get a dopamine-like reward from group success, while more aggressive ones chase personal advantage. The new MIRL paper covers multiple solution concepts: from cooperative or correlated equilibria (the 'we’re in this together' style) to adversarial or coordination equilibria (the 'winner-takes-all' or 'we do it in sync' style).

This is crucial because an agent may claim 'I’m a team player!' but its actual policy might be adversarial. With MIRL, we can invert the policy to see if the real reward function is all about self-gain. Or if it’s consistent with that persona.

so you’re saying the ai will know who i should be friends with?