Papers with decision-making
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation (P18-4)
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| Challenge: | Money laundering (AML) is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets. |
| Approach: | They propose a framework that uses deep learning to augment AML monitoring and investigation . money laundering is the process of transferring criminal and illegal proceeds into ostensibly legitimate assets . |
| Outcome: | The proposed framework reduces time and cost by 30% compared to existing methods . money laundering is the world's third largest "industry" |
GeospaCy: A tool for extraction and geographical referencing of spatial expressions in textual data (2024.eacl-demo)
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| Challenge: | Spatial information in text enables to understand the geographical context and relationships within text for location-sensitive applications. |
| Approach: | They propose to use spatial information extracted from textual data to perform geoparsing and geocoding tasks. |
| Outcome: | The GeospaCy software tool is designed for the extraction and georeferencing of spatial information present in textual data. |
SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions (2024.emnlp-industry)
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Zhi-Qi Cheng, Yifei Dong, Aike Shi, Wei Liu, Yuzhi Hu, Jason O’Connor, Alexander Hauptmann, Kate Whitefoot
| Challenge: | EV battery supply chain is vulnerable to disruptions caused by natural disasters and geopolitical tensions. |
| Approach: | They propose a system integrating Large Language Models with domain expertise for EV supply chain risk assessment. |
| Outcome: | Evaluated on 12,070 paragraphs from 365 sources (2022-2023), SHIELD outperforms baseline GCNs and LLM+prompt methods in disruption prediction. |
ART: Adaptive Reasoning Trees for Explainable Claim Verification (2026.findings-eacl)
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Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, Abbaas Alif Mohamed Nishar, Pranab Mohanty, Swapnil Shinde, Yue Wu
| Challenge: | Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. |
| Approach: | They propose a hierarchical method for claim verification that uses a root claim and a pairwise tournament of its children to determine an argument's strength. |
| Outcome: | The proposed method outperforms baseline methods on multiple datasets and shows that it is more reliable and clearer than existing methods. |
PILOT: Legal Case Outcome Prediction with Case Law (2024.naacl-long)
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| Challenge: | predicting legal case outcomes requires identifying relevant precedent cases . predicting case outcomes in case law systems presents unique challenges . |
| Approach: | They propose a framework for making legal case outcome predictions with case law . they propose to use two modules for relevant case retrieval and temporal pattern handling . |
| Outcome: | The proposed framework shows significant improvement over previous models based on civil law cases . it is crucial to identify relevant precedent cases that serve as evidence for judges . |
Towards Fine-grained Classification of Climate Change related Social Media Text (2022.acl-srw)
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| Challenge: | a new study examines the fine-grained classification and classification of climate change-related social media text. |
| Approach: | They propose to use two datasets to analyze climate change-related social media text and propose a fine-grained classification based on the proposed dataset. |
| Outcome: | The proposed datasets are compared with existing datasets and benchmarked using the best-performing model. |
MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making (2024.emnlp-main)
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| Challenge: | Insight is a form of long-term memory for an agent but lack of general insight can undermine its effectiveness. |
| Approach: | They propose an embodied agent that summarises and utilizes insight effectively across different scales and generates task-specific and high-level insight, stores it in a database, and then uses relevant insight from it. |
| Outcome: | The proposed agent outperforms a similar agent when planning by GPT3.5 and is more robust when faced with domain-shifting scenarios. |
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain (2024.findings-acl)
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Liang Chen, Yichi Zhang, Shuhuai Ren, Haozhe Zhao, Zefan Cai, Yuchi Wang, Peiyi Wang, Xiangdi Meng, Tianyu Liu, Baobao Chang
| Challenge: | a new multimodal decision-making benchmark evaluates the integrated capabilities of multimodal large language models. |
| Approach: | They propose a multimodal decision-making benchmark for evaluating MLLMs . they propose an automatic evaluation protocol to assess 10 prevalent ML models . |
| Outcome: | The proposed benchmark improves performance of multimodal large language models in three scenarios . the model is required to integrate multiple capabilities to make accurate decisions . |
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts (2024.findings-acl)
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Shubhankar Singh, Purvi Chaurasia, Yerram Varun, Pranshu Pandya, Vatsal Gupta, Vivek Gupta, Dan Roth
| Challenge: | Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. |
| Approach: | They propose to use flowcharts as visual contexts to assess the capabilities of visual question-answering multimodal language models in reasoning. |
| Outcome: | The proposed benchmarks evaluate models' ability to follow visual information without pre-existing knowledge on a suite of open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. |
RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents (2025.coling-main)
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Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
| Challenge: | Existing approaches to assess the risk of bias in RCTs focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. |
| Approach: | They propose a framework for enhancing Large Language Models to assess the risk of bias in RCTs by reformulation, document parsing and multi-expert collaboration. |
| Outcome: | The proposed framework outperforms existing methods on the RoB-Item and RoB domains. |
FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making (2025.findings-emnlp)
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| Challenge: | Large language models often overlook key behavioral patterns underlying human financial behavior. |
| Approach: | FinHEAR is a multi-agent framework for human expertise and Adaptive Risk-aware reasoning. |
| Outcome: | FinHEAR outperforms baseline models in trend forecasting and decision-making. |
Improving Fairness of Large Language Models in Multi-document Summarization (2025.acl-short)
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| Challenge: | Recent studies focus on summary-level fairness, while corpus-level focuses on corpus of summaries. |
| Approach: | They propose a preference tuning method that focuses on both summary-level and corpus-level fairness in MDS. |
| Outcome: | The proposed method outperforms baselines while maintaining critical qualities of summaries. |
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)
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| Challenge: | Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability. |
| Approach: | They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination . |
| Outcome: | The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark. |
White-Box Multi-Objective Adversarial Attack on Dialogue Generation (2023.acl-long)
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| Challenge: | Pre-trained transformers are popular in state-of-the-art dialogue generation systems . however, they are vulnerable to adversarial samples crafted by small and imperceptible perturbations. |
| Approach: | They propose a multi-objective attack method that balances two objectives: generation accuracy and length. |
| Outcome: | The proposed method significantly degrades state-of-the-art DG models with a higher success rate than traditional accuracy-based methods. |
Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration (2024.findings-emnlp)
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| Challenge: | Existing methods for eliciting and calibrating large language models have focused on general reasoning datasets, yielding only modest improvements. |
| Approach: | They propose a method which leverages atypical presentations to adjust model confidence estimates. |
| Outcome: | The proposed method reduces calibration errors by approximately 60% on three medical question answering datasets and outperforms existing methods such as vanilla verbalized confidence, CoT verbalised confidence and others. |
Towards Rationality in Language and Multimodal Agents: A Survey (2025.naacl-long)
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Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Yuan Yuan, Zhuoqun Hao, Xinyi Bai, Weijie J Su, Camillo Jose Taylor, Tanwi Mallick
| Challenge: | despite advances in language and multimodal agents, large language models lack rationality . despite their progress, large-scale models lack real-world grounding and feedback mechanisms . |
| Approach: | They propose to build more rational language and multimodal agents . they also examine what criteria define rationality in intelligent systems . |
| Outcome: | This paper assesses the state-of-the-art in language and multimodal agents . it also outlines open challenges and future research directions . |
Language-based General Action Template for Reinforcement Learning Agents (2021.findings-acl)
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| Challenge: | Prior knowledge is important in decision-making, and humans preserve it in the form of natural language (NL). |
| Approach: | They propose an environmentagnostic action framework that incorporates prior knowledge into decision-making . they propose to use general semantic schemes to facilitate agent in finding plausible actions . |
| Outcome: | The proposed agent performs better than agents that rely on gamespecific actions. |
Travel on the ICD Tree: Benchmarking Agentic Reasoning for ICD Coding from Chinese Electronic Medical Records (2026.findings-acl)
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| Challenge: | Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance. |
| Approach: | They propose a framework to evaluate ICD coding based on complete EMRs . they use a dataset of 560 real clinical records covering 434 common diseases . |
| Outcome: | The proposed framework explores the capability boundaries of large language models under different paradigms. |
AVA: Attentive VLM Agent for Mastering StarCraft II (2026.findings-acl)
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| Challenge: | Existing StarCraft II benchmarks rely on abstract state representations that deviate from human perception . Existing systems rely only on abstract representations, creating an artificial gap between how humans process battlefield information and limiting ecological validity of learned behaviors. |
| Approach: | They introduce AVACraft, the first multimodal benchmark environment for complex decision-making in StarCraft II. |
| Outcome: | The AVACraft benchmark supports both traditional and modern multi-agent reinforcement learning paradigms. |
DeFine: Decision-Making with Analogical Reasoning over Factor Profiles (2025.findings-acl)
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| Challenge: | Large language models are ideal for decision-making, but they can be difficult to process when they are verbose and include repetition, hedging, and vagueness. |
| Approach: | They propose a framework that constructs probabilistic factor profiles from complex scenarios and integrates them with analogical reasoning to guide LLMs in making decisions in new situations. |
| Outcome: | The proposed framework separates the tasks of quantifying uncertainty and incorporating it into LLM decision-making. |
Exploring the Sensitivity of LLMs’ Decision-Making Capabilities: Insights from Prompt Variations and Hyperparameters (2023.findings-emnlp)
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| Challenge: | Prior studies have compared the decision-making abilities of large language models with those of humans from a psychological perspective. |
| Approach: | They examine LLMs' performance on the Horizon decision-making task studied by Binz and Schulz (2023) they observe that the decision- making abilities fluctuate based on input prompts and temperature settings. |
| Outcome: | The results show that LLMs display a human-like exploration–exploitation tradeoff after simple adjustments to the prompt. |
Beyond Inherent Cognition Biases in LLM-Based Event Forecasting: A Multi-Cognition Agentic Framework (2025.findings-emnlp)
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| Challenge: | Large Language Models exhibit human-like cognitive biases in event forecasting . a human-curated dataset reveals significant cognitive bias in LLMs . |
| Approach: | They propose a human-curated dataset to explore LLMs' cognitive biases . they leverage LLM participants to act as multi-cognition event participants . |
| Outcome: | The proposed framework alleviates cognitive biases in LLMs and offers diverse perspectives. |
Conformal Event Prediction with Temporal Knowledge Graph (2026.findings-acl)
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| Challenge: | Current event prediction methods lack rigorous uncertainty quantification, which limits their reliability for decision-making. |
| Approach: | They propose a conformal prediction framework that applies conformal predictions to event prediction to address this challenge. |
| Outcome: | The proposed framework guarantees coverage while improving efficiency on three public datasets. |
Predicting Stances from Social Media Posts using Factorization Machines (C18-1)
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| Challenge: | Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. |
| Approach: | They propose to use factorization machines to model user preferences toward topics from social media data to predict whether a given text/user is in favor (agree), against (disagreer), or neutral toward a target topic. |
| Outcome: | The proposed method can predict stances of silent users based on their stance toward other topics and the social media posts of the user. |
RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis (2026.findings-eacl)
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| Challenge: | Multimodal Large Language Models (MLLMs) provide no visibility into which parts of visual data informed their conclusions. |
| Approach: | They propose a semi-automatic approach to attribute reasoning process by highlighting regions in charts and graphs that justify model answers. |
| Outcome: | The proposed method improves attribution accuracy by up to 15 percentage points compared to baseline methods and achieves high semantic similarity with ground truth responses. |
A Corpus of German Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification (2022.lrec-1)
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| Challenge: | Political authorities in democratic countries consult the public in order to allow citizens to voice their ideas and concerns on specific issues. |
| Approach: | They propose a publicly-available corpus that includes citizen contributions from six mobility-related planning processes in five german municipalities. |
| Outcome: | The proposed corpus includes several thousand citizen contributions from six mobility-related planning processes in five German municipalities. |
ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models (MLLMs) have shown impressive capabilities in vision-language understanding but their visual input remains fixed throughout the reasoning process. |
| Approach: | They propose a model-agnostic tree search algorithm tailored for vision-level reasoning that allows MLLMs to explore textual tokens while visual input remains fixed throughout reasoning process. |
| Outcome: | The proposed algorithm outperforms strong large models such as GPT-4o on high-resolution benchmarks and improves performance on a series of elaborate high-level benchmarks. |
NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering (2026.eacl-long)
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Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing methods for nutrition question answering face limited reasoning capacity and contextual overload . poor dietary patterns are associated with more than 11 million deaths in 2017 . |
| Approach: | They propose a framework that enables supervised multi-agent collaboration for nutritional QA. |
| Outcome: | The proposed framework outperforms single-agent and ensemble baselines in multi-agency reasoning tasks. |
DA-Pred: Performance Prediction for Text Summarization under Domain-Shift and Instruct-Tuning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) often don’t perform as expected under Domain Shift or after Instruct-tuning. |
| Approach: | They propose a method that uses the known performance in high-resource domains and fine-tuning settings to predict performance in low-resourced domains or base models. |
| Outcome: | The proposed method can help researchers decide if resources should be allocated for data labeling and LLM Instruct-tuning. |
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)
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| Challenge: | Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift. |
| Approach: | They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks. |
| Outcome: | The proposed framework outperforms baselines on Chinese and English CCR datasets. |
Towards Benchmarking Situational Awareness of Large Language Models:Comprehensive Benchmark, Evaluation and Analysis (2024.findings-emnlp)
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| Challenge: | Situational awareness is crucial for decision-making, anticipating potential issues, and adapting to dynamic circumstances. |
| Approach: | They propose a benchmark that covers three tiers of situational awareness capabilities . they conduct extensive experiments on advanced LLMs including GPT-4, LLaMA3, Qwen1.5 . |
| Outcome: | The proposed benchmark covers environment perception, situation comprehension and future projection. |
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)
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| Challenge: | Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects . |
| Approach: | They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs . |
| Outcome: | Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings. |
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)
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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou
| Challenge: | Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support. |
| Approach: | They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims . |
| Outcome: | The proposed benchmark evaluates behavioral biases of large language models across economic scenarios. |
Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers (2023.findings-acl)
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| Challenge: | ReasonFormer is a unified reasoning framework for complex decision-making . it is based on the dual-process theory of cognitive science, where two cognitive systems interact to form a whole reasoning process. |
| Approach: | They propose a unified reasoning framework that mirrors the modular reasoning process of humans . they decouple the representation module and the reasoning modules to capture different levels of cognition . |
| Outcome: | The proposed framework shows that humans can perform better in complex decision-making tasks. |
Towards Quantifying Commonsense Reasoning with Mechanistic Insights (2025.naacl-long)
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| Challenge: | Recent studies have evaluated commonsense reasoning abilities using text-based tasks. |
| Approach: | They propose to capture commonsense knowledge in a graphical representation of 37 daily human activities in graphical form and frame them to frame commonsensical queries. |
| Outcome: | The proposed model can frame an enormous number of commonsense queries ( 10 17) and perform rigorous evaluations of common sense reasoning in LLMs. |
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026.findings-acl)
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Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
| Challenge: | Time series anomaly detection (TSAD) has traditionally focused on binary classification and lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. |
| Approach: | They propose a time-series reasoning task that reformulates TSAD from discriminative to reasoning-intensive paradigm. |
| Outcome: | The proposed task reformulates TSAD from discriminative to reasoning-intensive paradigm. |
What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection (2023.findings-acl)
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| Challenge: | Mental health disorders (MHD) are one of the greatest challenges facing our healthcare systems and modern societies in general. |
| Approach: | They integrate and extend the research by conducting extensive experiments with three types of deep learning-based fusion strategies: feature-level fusion, model fusion and task fusion. |
| Outcome: | The proposed techniques show that they can be used to improve mental health detection from textual data. |
AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have improved text generation and reasoning. |
| Approach: | They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility. |
| Outcome: | The proposed framework embeds multi-bit provenance into planning decisions while preserving utility. |
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation (2025.emnlp-main)
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| Challenge: | Existing methods for forward counterfactual generation face limitations . large language models (LLMs) offer promise but remain unexplored for this application . |
| Approach: | They propose a benchmark to support forward counterfactual generation in finance . they use financial news headlines to curate financial news and provide structured evaluation . |
| Outcome: | The proposed benchmark aims to provide scalable, automated insights into potential market opportunities and risks for stakeholders. |
Voting or Consensus? Decision-Making in Multi-Agent Debate (2025.findings-acl)
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| Challenge: | Increasing the number of agents improves performance, while more discussion rounds before voting reduces it. |
| Approach: | They propose two new methods to improve multi-agent debates by increasing agent diversity and reducing discussion rounds before voting. |
| Outcome: | The proposed methods improve task performance by up to 3.3% with AAD and up to 7.4% with CI. |
HARP: Hesitation-Aware Reframing in Transformer Inference Pass (2025.naacl-long)
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| Challenge: | a recent study has shown that inference steps are not equally challenging, with some being "harder" and others "easier." |
| Approach: | They propose a modified Transformer forward pass that selectively applies additional computation when the model encounters uncertainty during token generation. |
| Outcome: | The proposed method achieves performance gains while maintaining inference times twice faster than beam search. |
CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
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Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
| Approach: | They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods . |
| Outcome: | The proposed methods perform better than existing methods on a range of tasks. |
FORECAST2023: A Forecast and Reasoning Corpus of Argumentation Structures (2024.lrec-main)
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| Challenge: | Existing work on the role of reasoning in forecasting has focused on surface-level features such as linguistic markers, the use of comparison classes, and overall dialectical complexity. |
| Approach: | They propose to use a dataset of such prediction rationales to create a fully automated annotation system that can be used to enhance the argumentation. |
| Outcome: | The proposed dataset provides a uniquely fine-grained and close characterisation of the structure of argumentation with potential impact on forecasting domains from intelligence analysis to investment decision-making. |
FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)
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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets. |
| Approach: | They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization. |
| Outcome: | The proposed framework improves performance in trading and other financial domain tasks. |
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis (2025.findings-acl)
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| Challenge: | Existing large language models (LLMs) are proving to be effective in medical automatic diagnosis, but their interpretability remains unaddressed. |
| Approach: | They propose to use a "Chain-of-Diagnosis" approach to enhance the interpretability of medical automatic diagnosis by outputting the disease confidence distribution. |
| Outcome: | The proposed model outperforms other LLMs on automatic diagnostic tasks across three real-world benchmarks and provides interpretability while ensuring controllability in diagnostic rigor. |
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation (2024.findings-acl)
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| Challenge: | Existing vision-and-language navigation methods do not incorporate environmental feedback into their decision-making processes. |
| Approach: | They propose a framework that incorporates environmental feedback into decision-making and a 3D simulator that renders realistic scenarios using Unreal Engine 5. |
| Outcome: | The proposed framework outperforms existing vision-and-language navigation methods in a zero-shot multi-task setting by 28.1% on average. |
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems (2025.emnlp-main)
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| Challenge: | Conventional LLM-based MAS rely on explicit coordination, e.g., prompts or voting, risking premature homogenization. |
| Approach: | They propose to preserve partial diversity by combining in-context learning with explicit coordination to form consensus in dynamic environments. |
| Outcome: | The proposed model outperforms explicit consensus models on three scenarios showing that partial deviation from group norms boosts exploration, robustness, and performance. |
How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation (2025.findings-acl)
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| Challenge: | Recent studies have focused on dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins. |
| Approach: | They propose to integrate persona metadata into LLMs and use it to iteratively infer contextually appropriate behaviors within dynamic scenarios. |
| Outcome: | The proposed model is based on 15,846 distinct behaviors across 1,001 unique personas and incorporates persona metadata to iteratively infer appropriate behaviors within dynamic scenarios. |
Can Post-Training Transform LLMs into Causal Reasoners? (2026.findings-acl)
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| Challenge: | Causal inference is a core component of human cognition and requires decision-makers to distinguish between causation and association. |
| Approach: | They propose a dataset comprising seven core causal tasks for training and five diverse test sets and evaluate five different post-training approaches. |
| Outcome: | The proposed model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. |
Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs’ Reasoning (2025.emnlp-main)
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Zezhong Wang, Xingshan Zeng, Weiwen Liu, Yufei Wang, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Existing methods that use Chain-of-Thought suffer from path homogenization and inefficient use of intermediate results. |
| Approach: | They propose a framework that introduces checkpoints between reasoning steps to reduce path homogenization and create fault-tolerant mechanisms. |
| Outcome: | The proposed framework reduces path homogenization and creates fault-tolerant mechanism by utilizing high-quality intermediate results. |
Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach (2025.findings-acl)
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| Challenge: | a new study addresses bias and stereotypes in language models by exploring how learning them together improves performance. |
| Approach: | They propose a dataset for bias and stereotype detection that integrates religion, gender, socio-economic status, race, profession, and others. |
| Outcome: | The proposed dataset compares encoder-only models and fine-tuned decoder- only models . the results show that learning stereotypes together improves bias detection . |
Program Chairs’ Report on Peer Review at ACL 2023 (2023.acl-long)
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| Challenge: | ACL'23 makes its peer review report public and an official part of the conference proceedings. |
| Approach: | They present an analysis of the factors affecting peer review and identify the most problematic issues that the authors complained about. |
| Outcome: | The authors identified the most problematic issues and provided suggestions for the future chairs. |
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation (2025.emnlp-main)
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Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang
| Challenge: | Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. |
| Approach: | They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs. |
| Outcome: | Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats. |
VIVA+: Human-Centered Situational Decision-Making (2025.findings-emnlp)
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| Challenge: | Multimodal Large Language Models (MLLMs) show promising results in complex, human-centered environments, yet evaluating their capacity for nuanced, humanlike reasoning and decision-making remains challenging. |
| Approach: | They introduce VIVA+, a cognitively grounded benchmark for evaluating the reasoning and decision-making of MLLMs in human-centered situations. |
| Outcome: | The VIVA+ model is based on 1,317 real-world situations paired with 6,373 multiple-choice questions . it consists of three core abilities for decision-making: (1) Foundational Situation Comprehension, (2) Context-Driven Action Justification, and (3) Reflective Reasoning. |
Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration (2025.findings-emnlp)
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| Challenge: | Pre-trained large language models (LLMs) with world knowledge and semantic understanding are promising for task-oriented dialogue systems. |
| Approach: | a framework that synergizes pre-trained large language models with DRL is proposed . a lightweight action pruning mechanism is employed to eliminate implausible actions . |
| Outcome: | a new framework synergizes pre-trained large language models with DRL to guide decision-making . the proposed framework eliminates semantically implausible or low-potential actions from multi-turn dialogue context . |
Game on Tree: Visual Hallucination Mitigation via Coarse-to-Fine View Tree and Game Theory (2024.emnlp-main)
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| Challenge: | Large vision-language models produce unfaithful visual hallucinations, also known as visual halluinations, which hinders their application in multimodal understanding and decision-making. |
| Approach: | They propose a plug-and-play train-free decoding algorithm for mitigating visual hallucinations . they leverage visual information to construct a coarse-to-fine visual view tree . |
| Outcome: | The proposed algorithm reduces visual hallucinations (VH) by leveraging visual information to construct a coarse-to-fine visual view tree (CFTree) |
Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making (2025.findings-emnlp)
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| Challenge: | Modern embodied AI uses multimodal large language models as policy models, predicting actions from final-layer hidden states. |
| Approach: | They propose a hierarchical action probing method that aggregates representations from all layers, mirroring the brain's multi-level organization. |
| Outcome: | Experiments show that hierarchical probing improves on last-layer embodied models and achieves a 46.6% success rate and a 62.5% gain in spatial reasoning tasks. |
From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis (2025.findings-acl)
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| Challenge: | Problem-Solving Therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues. |
| Approach: | They developed a framework for PST annotation using established PST Core Strategies and a set of novel Facilitative Strategies to analyze a corpus of real-world therapy transcripts to determine which strategies are most prevalent. |
| Outcome: | The proposed framework outperforms existing models and LLMs to identify the most prevalent strategies in a corpus of real-world therapy transcripts. |
Investigating Human and LLMs’ Decisions in Unverifiable Environments: A Case Study with GitHub Activity Overview (2026.findings-acl)
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| Challenge: | examining the behaviors of Large Language Models as artificial social actors is underexplored, especially in unverifiable scenarios where conventional benchmarking has little to help improve their abilities. |
| Approach: | They propose a method to collect, compare, and reason about human and LLMs' decisions in an unverifiable scenario and use it to examine their behaviors. |
| Outcome: | The proposed method compared human and LLM decisions in an unverifiable scenario on GitHub and found that proprietary LLMs behave more like humans than open-source LLM systems. |
Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)
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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo
| Challenge: | Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity. |
| Approach: | They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability. |
| Outcome: | The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability. |
MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion (2026.findings-acl)
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| Challenge: | Existing approaches to persuasion generate generic or weakly grounded responses even when such cues are identified. |
| Approach: | They propose a meta-cognitive autonomous intelligent agent framework for complex persuasion that coordinates perception management, mental-state inference, strategy execution, memory maintenance, and performance evaluation. |
| Outcome: | The proposed framework achieves a higher persuasion success rate than baselines. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
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YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs’ Contextual Sensitivity (2026.findings-acl)
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| Challenge: | a new benchmark measures the contextual sensitivity of large language models in role conflict scenarios . role conflicts are social dilemmas where multiple roles cannot be fulfilled simultaneously . authors: models are forced to arbitrate between dynamic contextual cues and learned preferences . |
| Approach: | They propose a benchmark to measure the contextual sensitivity of large language models in role conflict scenarios. |
| Outcome: | The proposed benchmark measures the contextual sensitivity of large language models in role conflict scenarios. |
Data Descriptions from Large Language Models with Influence Estimation (2025.emnlp-main)
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| Challenge: | Existing explainable AI approaches focus on interpreting how models make predictions. |
| Approach: | They propose a pipeline that generates textual descriptions using large language models . they propose 'cross-modal transfer classification' task to examine effectiveness of textual description . |
| Outcome: | The proposed method improves classification accuracy compared to baselines and sheds light on how the model prioritizes and utilizes information for decision-making. |
IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration (2026.findings-acl)
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| Challenge: | Existing approaches fail to integrate domain expert insights beyond simple prompting. |
| Approach: | They propose a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors. |
| Outcome: | Experiments show that IDEA outperforms DeepSeek R1 and GPT-5.2 in accuracy and accuracy. |