Communication-Efficient Desire Alignment for Proactive Embodied Human–Agent Interaction (2026.acl-long)
Copied to clipboard
| Challenge: | Effective real-world human–agent interactions are long-term and repeated. |
| Approach: | They propose a simulation that uses a proxy user with value-driven preferences and natural language behavior to evaluate how agents adapt to users across interactions and satisfy their desires. |
| Outcome: | HA-Desire, a home assistance simulation, shows that agents can adapt to user needs and provide proactive assistance within limited communication. |
Similar Papers
PrefIx: Understand and Adapt to User Preference in Human-Agent Interaction (2026.findings-acl)
Copied to clipboard
| Challenge: | Current benchmarks evaluate task accuracy but overlook how agents interact . Preference-aware agents show 7.6% average UX improvement and 18.5% gain in preference alignment. |
| Approach: | They propose a configurable environment that evaluates both what agents accomplish and how they interact. |
| Outcome: | The proposed model improves performance and improves user experience by 7.6% and 18.5% respectively. |
Aligning LLMs with Individual Preferences via Interaction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing studies on LLMs alignment focus on generalizing their behavior to generalized values such as helpfulness, harmlessness, and honesty. |
| Approach: | They train large language models to "interact to align" to implicitly infer user preferences . they use a multi-turn preference dataset to generate a personalized alignment . |
| Outcome: | The proposed method enables dynamic, personalized alignment via interaction with a multi-turn preference dataset. |
ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for embodied agents focus on directly executing instructions without considering whether objects can be manipulated. |
| Approach: | They propose a benchmark that evaluates embodied agents in dynamic environments . they use plug-and-play module that augments existing planners with explicit affordance reasoning . |
| Outcome: | The proposed benchmark evaluates embodied agents in dynamic environments with unpredictable affordances . ADAPT significantly improves robustness and task success across seen and unseen environments . |
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)
Copied to clipboard
Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li
| Challenge: | Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences. |
| Approach: | They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses. |
| Outcome: | The proposed framework outperforms baseline methods in real-time and in real applications. |
AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation practices for recommender systems rely on few-shot prompting and offline metrics are often misaligned with online behavior. |
| Approach: | They propose a framework that learns world-model-driven agents from human interactions. |
| Outcome: | The proposed framework enables agents to express rich preferences and feedback in natural language and interact with recommender systems in a simulation. |
PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records (2026.acl-long)
Copied to clipboard
| Challenge: | GUI agents have shown strong performance under explicit and completion instructions, but real-world deployment requires aligning with users’ more complex implicit intents. |
| Approach: | They propose a task that requires agents to leverage long-term user records as persistent context to resolve omitted preferences in vague instructions and anticipate latent routines by user state for proactive assistance. |
| Outcome: | The proposed task improves execution and proactive performance by 15.7% and 7.3% under explicit and completion instructions. |
tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing systems for natural language understanding (NLU) are limited due to the inherent ambiguity and incompleteness inherent in natural language. |
| Approach: | They propose a system to extract tasks from natural language instructions and map them to robots' established collection of skills. |
| Outcome: | The proposed system outperforms baseline models in the training and evaluation of a dataset featuring complex instructions. |
Aligning to Social Norms and Values in Interactive Narratives (2022.naacl-main)
Copied to clipboard
| Challenge: | Social value alignment is the ability to create agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games. |
| Approach: | They introduce a game-value ALignment agent that uses social commonsense to restrict its action space to actions that are aligned with socially beneficial values. |
| Outcome: | The proposed agent improves state-of-the-art task performance by 4% while reducing the frequency of socially harmful behaviors by 25% compared to strong contemporary value alignment approaches. |
LangSuit·E: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs. |
| Approach: | They propose a flexible and simulation-free testbed that simulates 6 representative embodied tasks in textual embodies. |
| Outcome: | The proposed testbed offers adaptability to diverse environments without multiple simulation engines and allows easy customization of communication and action strategies. |
Human Alignment: How Much Do We Adapt to LLMs? (2025.acl-short)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming a common part of our lives, yet few studies have examined how they influence our behavior. |
| Approach: | They propose a cooperative language game in which players aim to converge on a word and play a game in a group. |
| Outcome: | The proposed game shows that humans notice and adapt to differences regardless of whether they are aware they are interacting with an LLM. |