Papers with intelligent agents
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track (2025.emnlp-industry)
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| Challenge: | EMNLP 2025 Industry Track highlights key insights, novel research trends and challenges encountered in practical language technology applications. |
| Approach: | Kai Chen will present the technical advances behind the open-source Intern-series large models . he will highlight how models acquire expert-level skills in specialized domains . |
| Outcome: | This talk will highlight the technical advances behind the open-source Intern-series models . it will highlight how models acquire expert-level skills in specialized domains while retaining broad generalization ability. |
Connecting Language and Vision to Actions (P18-5)
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| Challenge: | Recent advances in language and vision have made incredible progress in describing images and interacting with visual content in a physical or embodied environment. |
| Approach: | This tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding. |
| Outcome: | This tutorial will review the state-of-the-art approaches to selected tasks such as image captioning, visual question answering and visual dialog. |
SMILEE: Symmetric Multi-modal Interactions with Language-gesture Enabled (AI) Embodiment (N18-5)
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| Challenge: | SMILEE is a conversational agent system that interprets a user’s communicative intent from verbal utterances and non-verbal behaviors, such as gestures. |
| Approach: | They propose to use a computer-generated avatar to embody a human-machine conversational agent system that interprets verbal utterances and non-verbal behaviors to facilitate natural symmetric multi-modal interactions. |
| Outcome: | The proposed system interprets a user’s communicative intent from verbal utterances and non-verbal behaviors, such as gestures, and communicates with natural language and gestures through its embodiment as an avatar. |
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs (2023.emnlp-demo)
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Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
| Challenge: | Existing frameworks for Augmented Language Models lack flexibility, democratization, and holistic evaluation. |
| Approach: | They propose a lightweight and extensible framework for Augmented Language Models called Gentopia. |
| Outcome: | The proposed framework integrates language models, task formats, prompting modules, and plugins into a unified paradigm. |
MiniChain: A Small Library for Coding with Large Language Models (2023.emnlp-demo)
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| Challenge: | Programming augmented by large language models (LLMs) opens up many new application areas, but also requires care. |
| Approach: | They introduce a tool for augmented programming that provides basic primitives for coding LLM calls. |
| Outcome: | The proposed tool provides core primitives for coding LLM calls and separating out prompt templates. |
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models (2025.emnlp-demos)
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Zhiwei Liu, Jielin Qiu, Shiyu Wang, Jianguo Zhang, Zuxin Liu, Roshan Ram, Haolin Chen, Weiran Yao, Shelby Heinecke, Silvio Savarese, Huan Wang, Caiming Xiong
| Challenge: | Existing evaluation frameworks suffer from limitations such as static task benchmarks, limited scope, and inadequate integration with practical applications. |
| Approach: | They propose an open-source, Model Context Protocol-based evaluation framework specifically tailored for comprehensive and systematic assessment of LLM-powered agents. |
| Outcome: | The proposed framework uncovers nuanced performance patterns and identify domain-specific strengths and weaknesses, providing valuable insights beyond traditional binary success metrics. |
Measuring the Effect of Influential Messages on Varying Personas (2023.acl-short)
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| Challenge: | a new task estimates the response a persona might have upon seeing a news message . a first benchmark dataset is used to evaluate the performance of the proposed task . |
| Approach: | They propose a task to estimate the response a persona might have upon seeing a news message. |
| Outcome: | The proposed task estimates the response a persona might have upon seeing a news message. |
DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory (2020.aacl-main)
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| Challenge: | Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection. |
| Approach: | They propose a model that embeds personas from natural language into text . they evaluate the transferability of the model by simulating low-resource scenarios . |
| Outcome: | The proposed model can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. |
Let’s Negotiate! A Survey of Negotiation Dialogue Systems (2024.findings-eacl)
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Haolan Zhan, Yufei Wang, Zhuang Li, Tao Feng, Yuncheng Hua, Suraj Sharma, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Reza Haf
| Challenge: | Recent research has focused on negotiation dialogue systems, but no systematic review of this task has been conducted. |
| Approach: | They propose to provide a systematic review of negotiation dialogue systems and to provide an overview of current research. |
| Outcome: | The proposed systems are based on the literature and are compared against existing systems. |
Enhancing the General Agent Capabilities of Low-Paramter LLMs through Tuning and Multi-Branch Reasoning (2024.findings-naacl)
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| Challenge: | Open-source pre-trained Large Language Models exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks. |
| Approach: | They propose a method to construct agent-specific data using GPT-4 and supervised fine-tuning . they find that supervised tunning can significantly reduce hallucination outputs and formatting errors in agent tasks . |
| Outcome: | The proposed method improves on five agent tasks of AgentBench. |
Reinforced Dynamic Reasoning for Conversational Question Generation (P19-1)
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| Challenge: | Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method . large-scale highquality conversational question answering datasets such as CoQA and QuAC can help train models to answer sequential questions. |
| Approach: | They propose a task called Conversational Question Generation which generates a question based on a passage and a conversation history to generate the next question. |
| Outcome: | The proposed method is based on a question-answering style conversation dataset . it can be used to generate meaningful questions on QA and SQuAD datasets . |
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game (P18-1)
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| Challenge: | supervised language learning is limited by the ability of capturing mainly the statistics of training data. |
| Approach: | They propose to use conversational games to train agents to use new knowledge . they propose to mimic and reinforce conversational game and use it in one-shot fashion . |
| Outcome: | The proposed approach is able to acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. |
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning (2025.naacl-long)
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| Challenge: | Strategic reasoning requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. |
| Approach: | They propose a framework that enables Large Language Models to achieve varying levels of strategic depth by recursive mechanisms that allow agents to form higher order beliefs about others' beliefs. |
| Outcome: | The proposed framework enables LLMs to achieve varying levels of strategic depth, allowing agents to form higher order beliefs—beliefs about others’ beliefs. |
Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models (2024.emnlp-main)
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| Challenge: | Recent studies have shown that large language models may possess preliminary planning capabilities. |
| Approach: | They examine the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations. |
| Outcome: | The proposed model can decode the decision from the output of MHSA in the middle layers at the last token. |
GA-S3: Comprehensive Social Network Simulation with Group Agents (2025.findings-acl)
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| Challenge: | Existing social network simulations focus on discrete events or system dynamics instead of elucidating underlying mechanisms or causal relationships. |
| Approach: | They propose a Social network simulation system that leverages newly designed Group Agents to make intelligent decisions regarding various online events. |
| Outcome: | The proposed system can make intelligent decisions regarding online events at a manageable cost. |
Making Large Language Models into World Models with Precondition and Effect Knowledge (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) are not inherently designed to model real-world dynamics, but can be induced to perform two critical world model functions: determining the applicability of an action based on a given world state and predicting the resulting world state upon action execution. |
| Approach: | They propose to use Large Language Models to model world states and preconditions . they validate that precondition and effect knowledge generated by LLMs aligns with human understanding of world dynamics . |
| Outcome: | The proposed model can predict valid actions and state transitions, thereby replicating existing models. |
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions (2022.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a research topic that is gaining attention in the field of artificial intelligence. |
| Approach: | They propose to build an embodied agent that can communicate with humans in natural language and navigate in real 3D environments. |
| Outcome: | This paper reviews current studies in the emerging field of vision-and-language navigation . it highlights limitations and opportunities for future work . |
tagE: Enabling an Embodied Agent to Understand Human Instructions (2023.findings-emnlp)
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| 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. |
SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning (2026.acl-long)
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| Challenge: | Existing methods for training large language models waste computation budget on trivial steps while failing to guarantee sample quality. |
| Approach: | They propose a framework that selectively branches at critical decision states for resource-efficient exploration. |
| Outcome: | The proposed framework activates adaptive branching exploration at critical decision states to probe promising trajectories, thereby achieving precise resource allocation that prioritizes sampling quality over blind coverage. |
Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for storing and retrieving memory are limited by shallow semantic retrieval. |
| Approach: | They propose a memory mechanism that organizes and retrieves past experiences to support decision-making. |
| Outcome: | Experiments on LoCoMo and NarrativeQA show that CompassMem improves retrieval and reasoning performance across multiple backbone models. |