Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Position: Towards Unified Alignment Between Agents, Humans, and Environment
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Position paper proposing Unified Alignment for Agents (UA²) across human intentions, environmental dynamics, and self-constraints, with a retrofitted WebShop proof-of-concept comparing ReAct, ReAct-SC, Reflexion, LATS, and a structured-memory agent.
Problem
Foundation-model agents are evaluated in simplified settings and do not jointly align with ambiguous human intentions, complex/temporal/stochastic environment dynamics, and their own time and monetary budget constraints.
Approach
Frames agent alignment as a unified objective over three roles (humans, environment, agent's own constraints), reviews benchmarks and methods through this lens, and retrofits WebShop with user profiles, personalized reranking, and runtime cost counters to test it.
Key results
- Proposed agent reaches 6.7% G_HI versus 11.7%-22.5% for baselines, with reward 51.9 and success rate 9.6%.
- Reflexion attains the top success rate (13.8%) but costs over 5x time and 3x money relative to other methods; LATS reaches the highest reward (52.4) at over 100x time and nearly 200x money.
- Human evaluation of ChatGPT-simulated user rankings gives average NDCG@10 of 0.871.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add structured memory with low-level action insights and high-level intra-task experience to ReAct | ReAct on retrofitted WebShop · Retrofitted WebShop | G_HI (%): 11.7 -> 6.7 | Helped |
| Add structured memory with low-level action insights and high-level intra-task experience to ReAct | ReAct on retrofitted WebShop · Retrofitted WebShop | Reward: 50.3 -> 51.9 | Helped |
| Add self-consistency sampling and marginalization | ReAct on retrofitted WebShop · Retrofitted WebShop | Reward: 50.3 -> 49.9 | No clear effect |
| Add self-consistency sampling and marginalization | ReAct on retrofitted WebShop · Retrofitted WebShop | Money ($): 0.013 -> 0.039 | Hurt |
| Use Reflexion's multiple-round self-correction | ReAct on retrofitted WebShop · Retrofitted WebShop | SR (%): 8.0 -> 13.8 | Helped |
| Use Reflexion's multiple-round self-correction | ReAct on retrofitted WebShop · Retrofitted WebShop | Time (s): 1.716 -> 5.539 | Hurt |
| Use LATS with self-reflection and Monte Carlo Tree Search | ReAct on retrofitted WebShop · Retrofitted WebShop (1/10 subset) | Reward: 50.3 -> 52.4 | Helped |
| Use LATS with self-reflection and Monte Carlo Tree Search | ReAct on retrofitted WebShop · Retrofitted WebShop (1/10 subset) | Time (s): 1.716 -> 125.935 | Hurt |
| Use LATS with self-reflection and Monte Carlo Tree Search | ReAct on retrofitted WebShop · Retrofitted WebShop (1/10 subset) | G_ED (%): 14.9 -> 14.3 | Helped |
Surprising findings
- ReAct-SC matches ReAct in reward and success rate despite roughly three times the money cost.
- Reflexion's self-reflection mechanism shows weaker human-intention and environment alignment gaps than simpler methods.
Limitations
- Proof-of-concept features are simplified and not a realistic dedicated benchmark, as acknowledged by the authors.
- Simulated users and preferences are generated by ChatGPT rather than real humans.
- LATS evaluated on only a 1/10 subset of instructions due to cost.
Experimental setup
Datasets: WebShop ChatGPT-simulated user preference data (30 simulated users, 50 products each)
Benchmarks: Retrofitted WebShop
Metrics: average reward success rate alignment gap with human intentions (G_HI) alignment gap with environmental dynamics (G_ED) time cost money cost
Baselines: ReAct ReAct-SC Reflexion LATS
Builds on: ReAct Reflexion LATS WebShop Constitutional AI
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