Towards Unified Alignment Between Agents, Humans, and Environment

1. Introduction

• Realistic agent research remains limited by synthetic, simplified settings, while human expectations, complex environments, and agent budgets are not considered holistically.The paper treats human intentions, environmental dynamics, and self-constraints as parts of one working system.Source: §1 · pp. 1–2

2.1. Roles in a Working System of Agents

• The working system comprises humans who set goals, an environment that provides interaction feedback, and agents that act to assist users.The paper notes that realistic systems involve diverse or ambiguous intentions, changing dynamics, and self-constraints.Source: §1 · p. 2, §2.1 · p. 3

2.2. Unified Alignment with All the Roles

• UA2 calls for agents to recognize human intentions, adapt to environmental dynamics, and adhere to self-constraints such as time or monetary budgets.The paper connects this proposal to alignment research in language models and reinforcement learning.Source: §1 · p. 2, §2.2 · p. 3

2.3. Challenges from the Principles of UA2

• Challenges include inferring preferences and safety concerns across interactions, modeling partially observable, time-varying, stochastic environments, and adapting to changing resource limits.A precise world model requires techniques beyond ad-hoc exploration, coarse-grained memory, or ungrounded planning.Source: §2.3 · p. 3

3.1. Benchmarks

• The review assesses benchmarks by whether they require inference of human intentions, model complex environmental dynamics, and reflect resource constraints.The dynamics criteria include partial observability, temporality, and stochasticity; resource measures include time and action limits.Source: §3.1 · p. 4
• Most benchmarks state tasks explicitly rather than requiring agents to infer hidden intentions; digital benchmarks rarely include human interaction for elicitation.WebArena defines consistent intentions across episodes, but states them in the instructions.Source: §3.1 · p. 4
• Embodied benchmarks often model partial observability, while some include stochasticity or temporal change; digital benchmarks mostly overlook cost and time constraints.Some embodied benchmarks measure action counts or cost-reward trade-offs.Source: §3.1 · pp. 4–5
• Existing agent benchmarks remain inadequate under UA2, and fine-grained realistic environmental dynamics are underdeveloped in benchmark design.The review also notes that digital benchmarks generally lag embodied benchmarks.Source: §3.1 · pp. 4–5

3.2. Methods

• Methods solicit or analyze human goals through interaction, multi-agent discussion, or iterative experience analysis.The review cites HLA [Liu et al., 2023a], MemPrompt, and related approaches.Source: §3.2 · p. 5
• Methods align with environmental dynamics through external reward feedback, interaction trajectories, or model-based simulation.The review groups Reflexion, LATS, and other methods under these strategies.Source: §3.2 · p. 5
• Adaptive strategies and smaller-model fine-tuning can address self-constraints, but Reflexion and LATS illustrate a cost trade-off from repeated sampling.The review concludes that unified alignment requires balancing all three roles and coordinating agent research with foundation-model development.Source: §3.2 · pp. 5–6

4.1. Environment Construction

• The retrofitted WebShop gives 10 users hidden item preferences and 50 consecutive profile-based instructions each, adds personalized reranking that changes with actions, and tracks monetary and interaction-time costs.The reranking uses collaborative filtering and a Determinantal Point Process; monetary cost counts proprietary-model API calls.Source: §4.1 · p. 6

4.2. Agent Design with the Principles of UA2

• The UA2-guided agent adds structured memory to ReAct, storing high-reward trajectory actions with their instructions and retrieving similar-task actions to form plans.The authors describe the memory as supporting user profiling, environmental awareness, and cost minimization by avoiding planning from scratch.Source: §4.2 · pp. 6–7

4.4. Results and Discussions

• Our framework achieves the top unified performance, with the best balance between task completion and alignment measures.Table 2 reports average reward, success rate, human-intention and environment-dynamics alignment gaps, and time and money costs.Source: Table 2 · p. 7, §4.4 · p. 8

4.3. Experiments

• Experiments compare ReAct, ReAct-SC, Reflexion, LATS, and the proposed method using task reward and success rate, alignment gaps, and monetary and time costs.The study also leaves techniques for aligning with human intentions to future work because they require human involvement in the interaction loop and adaptation to the setting.Source: §4.3 · p. 7

4.4. Results and Discussions

• LATS has the highest average reward and Reflexion the top success rate, but Reflexion costs over 5× more time and 3× more money, while LATS costs over 100× more time and nearly 200× more money than other methods.The authors note LATS was tested on one-tenth of the task instructions because of its significant cost.Source: Table 2 · p. 7, §4.4 · p. 8
• Almost all baselines have human-intention or environmental-dynamics alignment gaps above 10%; our method has significantly lower GHI, while LATS has GED of 14.3%.The authors attribute LATS’s lower GED to accumulated exhaustive sampling, which also limits practical applicability.Source: §4.4 · p. 8

Synergizing agents with alignment research

• Constitutional AI ideas can help integrate unified-alignment principles into agent objectives.The paper presents this as an example of how alignment research could support agents in following UA2.Source: §5 · p. 8

Constructing realistic agent benchmarks

• More realistic simulations and sandboxes can reveal gaps between agents and realistic human demands and interactive environments.The paper advocates benchmarks that reflect intricate scenarios and nuanced logistics and details.Source: §5 · p. 8

Developing holistic evaluations for agents

• Evaluations should assess alignment with human intentions, environmental dynamics, and self-constraints, since role-specific gaps indicate directions for agent improvement.The paper contrasts this holistic evaluation with research focused mainly on final task success.Source: §5 · p. 8

Toward self-evolving agents through continual alignment

• The paper envisions agents self-evolving through lifelong interaction with humans and environments, improving their use and efficiency for complex real-world problem-solving.It notes that balancing the different alignment sources requires further elaboration in agent-method design.Source: §5 · p. 8

6. Conclusion

• UA2 proposes that agents align with human intentions, environmental dynamics, and self-constraints, with its significance examined through a literature review and proof-of-concept studies.The paper frames this as a basis for future agent research toward enhanced general problem-solving abilities.Source: §6 · p. 8

Impact Statement

• UA2 calls for agents to account simultaneously for human intentions, environmental dynamics, and self-constraints such as money, time, and battery use.The WebShop proof of concept adds user profiles, personalized reranking, and runtime cost counters; future work will examine safety concerns and random environmental events.Source: Impact Statement · p. 9
• The authors expect more realistic sandboxes or simulators to support study and improvement of agent capability and safety under realistic settings.They present unified alignment as a foundation for more intelligent and responsible autonomous agents.Source: Impact Statement · p. 9

1. Proof-of-Concept: Environment Construction

• Zonghan Yang led environment construction and designed profile-based tasks, while Fangzhou Xiong implemented the two reranking algorithms.Other contributions included simulated-preference data collection and processing, human evaluation, and implementation of runtime-cost and user-login mechanisms.Source: §1 · p. 9
• Kaiming Liu, Zonghan Yang, and Zeyuan Yang implemented the runtime budget counter, and Zhenhe Zhang estimated website response delays for its cost counter.Xinrui Chen implemented user login to isolate test agents.Source: §1 · p. 9
• Contributors checked task validity, and Chi Chen, Fuwen Luo, Ziyue Wang, Siyu Wang, and Xiaolong Wang helped evaluate ChatGPT-simulated preference data.Source: §1 · p. 9

2. Proof-of-Concept: Agent Design and Experiments

• An Liu and Zijun Liu co-led experimental analysis and contributed to the UA2-guided agent framework; Kaiming Liu conducted experiments and ablations and implemented ReAct and CoT variants.An Liu also implemented Reflexion and LATS, while Zhicheng Guo implemented LATS and Zonghan Yang initially implemented ReAct.Source: §2 · p. 9

3. Writing (Main Paper)

• The listed authors divided drafting of Sections 3 and 4 and Zonghan Yang drafted other parts and finalized the paper.Source: §3 · p. 9

4. Additional Contributions

• Peng Li and Yang Liu supervised the project, while other contributors gave draft feedback, joined early discussions, or helped with figures.Source: §4 · p. 9

A. Environment Construction in Section 4.1

• The environment features are intended to reflect the three alignment lines in proof-of-concept studies, with more realistic configurations and logistics left for a future benchmark.Source: A. Environment Construction in Section… · p. 14

A.1. Task Design in the retrofitted WebShop

• The tasks model user profiles as persistent preferences that agents may need to infer because users do not always state every intention precisely.Source: §A.1 · p. 14
• The environment uses 10 users, each with a textual basic preference appearing in 50 consecutive instructions; rewards follow original WebShop rules, making hidden-attribute matches essential, and category counts are 298/97/105 (first/second/third).Source: §A.1 · pp. 14–15

A.2.1. OVERVIEW

• Personalized reranking combines DPP weights based on the agent’s own history and collaborative-filtering weights based on similarity to other users, at coefficients 0.2 and 0.8, respectively.User click histories change the search ordering; both algorithms are disabled in the ablated environment used to calculate RED and GED.Source: §A.2.1 · p. 15
• Collaborative filtering reranks search results using the agent’s click-through rates and their Pearson correlation with simulated users’ preferences.Source: §A.2.1 · p. 15

A.2.2. USER BEHAVIOR SIMULATION

• ChatGPT generated 30 shopping roles and rankings of 50 products per role to simulate user behavior for a dynamic recommendation environment.The authors note that the roles, including demographic details, were entirely ChatGPT-generated and plan broader demographic coverage.Source: §A.2.2 · p. 15
• 0.871 NDCG: ChatGPT’s ranking results scored this value in human evaluation, which the authors say indicates rankings close to human preferences.Table 7 reports NDCG@10 scores for ChatGPT and eight annotators.Source: §A.2.2 · p. 16, Table 7 · p. 19

A.3. Runtime Environment

• The runtime wrapper tracks API-call monetary costs and agent-environment interaction time so users can monitor compliance with self-constraints.Monetary costs are computed from official model pricing; API-call duration is excluded from tracked time.Source: §A.3 · p. 19
• Action delays are estimated by sampling actions, fitting a uniform distribution to observed delays, and using its expected value as a static experimental time cost.The estimated times are listed in Table 8.Source: §A.3 · p. 19, Table 8 · p. 19

B.1. Descriptions on Agent Design with the Principles of UA2

• High-reward trajectories are analyzed in one batched API call to identify key actions, integrating environmental experience at lower cost than Reflexion and LATS.Key actions are defined as actions that positively affect task-completion efficiency or efficacy.Source: §B.1 · p. 20
• Structured experience pairs prior instructions with their key actions, and BM25 retrieves the experience associated with the instruction most similar to the current one.The memory is constructed under each user to support inference of human intentions from prior instructions.Source: §B.1 · pp. 20–21
• After each task, new trajectory insights and experience learned from the retrieved reference are recorded together for possible use on incoming tasks.The retrieved high-level experience serves as a cross-task plan intended to guide task completion while reducing time and monetary costs.Source: §B.1 · p. 21

• Evaluation covers 10 users with 50 tasks each, except LATS, which is tested on one user because of its high cost.Source: §B.2 · p. 21
• All agents use gpt-3.5-turbo-instruct-0914 except LATS, which uses gpt-3.5-turbo-1106 to match the original paper’s setting.Source: §B.2 · p. 21
• Each task allows at most 15 web-interaction steps, including invalid actions.Source: §B.2 · p. 21

B.3. Details of Experimental Setups

• ReAct, Reflexion, and the proposed method use temperature 0.0; ReAct-SC uses three samples at temperature 0.05, while LATS uses temperature 1.0, five samples, and 30 iterations.Source: §B.3 · p. 21
• The fully retrofitted environment is compared with versions excluding hidden user-intention attributes or disabling personalized reranking to identify GHI and GED, respectively.Source: §B.3 · p. 21

B.4. Results

• CoT-related methods’ GHI and GED differences are not considered useful for comparison because their substantially lower reward and success rate make those gaps vulnerable to stochastic effects.Source: §B.4 · p. 21
• The proposed agent has success rate comparable to Reflexion, emphasizes minimizing costs rather than performance improvement, and balances reward and cost, while remaining substantially short of the oracle goal.Table 9 reports reward, success rate, alignment gaps, time, and monetary cost; Figures 6 and 7 plot performance against self-constraint alignment gaps.Source: §B.4 · p. 21, Table 9 · p. 22, Figure 6 · p. 22