Papers with iterative refinement
Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production (2026.acl-long)
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| Challenge: | Existing approaches to sign language production use autoregressive or diffusion models that generate one-by-one output tokens but suffer from exposure bias during inference. |
| Approach: | They propose a hybrid autoregressive-diffusion model that combines iterative refinement and sequential dependency modeling for Sign Language production. |
| Outcome: | The proposed model improves sign language production quality and real-time efficiency on PHOENIX14T and How2Sign. |
Inference-Time Feedback for Reasoning Controllability in Diffusion Language Models (2026.acl-srw)
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| Challenge: | In scientific NLP systems, model outputs often serve as interfaces to downstream systems that assume strict structural requirements. |
| Approach: | They evaluate machine-checkable controllability along three axes: structural control, iterative correction, and decoding dynamics. |
| Outcome: | The proposed model can be usefully decomposed into global structure versus local control . the proposed model improves global structure while improving iterative correction . |
sudo rm -rf agentic_security (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) are increasingly used as computer-use agents . authors present a novel attack framework that bypasses refusal-trained safeguards . |
| Approach: | They propose a new attack framework that bypasses refusal-trained safeguards in LLMs . SUDO iteratively refines its attacks based on a built-in refusal feedback . authors highlight need for robust, context-aware safeguards if LLM is to be used . |
| Outcome: | The proposed framework bypasses refusal-trained safeguards in commercial agents . it achieves a stark attack success rate of 24.41% (with no refinement) and up to 41.33% (by iterative refinement). |
Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)
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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities. |
| Approach: | They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation. |
| Outcome: | The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement. |
Semantic Role Labeling with Iterative Structure Refinement (D19-1)
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| Challenge: | Modern state-of-the-art methods for semantic role labeling model only local interactions between individual labels . |
| Approach: | They propose to model local interactions between argument labeling decisions using a refinement network instead of arbitrary interactions between roles and words. |
| Outcome: | The proposed model outperforms baseline models on all 7 languages and achieves state-of-the-art results on 5 languages, including English. |
GenDis: Generative-Discriminative Dual-View Co-Training for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods rely on one-hot discriminative supervision, leading to overfitting on seen classes and poor generalization to unseen ones. |
| Approach: | They propose a Generative–Discriminative Dual-View Co-Training framework that unifies discriminative classification and semantic label generation within an LLM. |
| Outcome: | The proposed framework outperforms existing methods on five benchmarks on the generalized category discovery (GCD) task. |
Do GUI Grounders Truly Understand UI Elements? (2026.findings-eacl)
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| Challenge: | Existing grounding models and benchmarks are skewed toward web and mobile environments, neglecting desktop interfaces (especially windows). |
| Approach: | They propose a GUI Grounding Sensitivity Benchmark to assess UI grounding sensitivity to multiple descriptions of the same UI element. |
| Outcome: | The proposed model generates multiple valid instructions per UI element and develops nuanced validation methods to validate them. |
Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing prompt engineering methods rely on randomly selected evaluation subsets, leading to suboptimal prompts. |
| Approach: | They propose an iterative evaluation data selection approach for effective prompt optimization using real time model performance. |
| Outcome: | The proposed approach improves effectiveness by 1.6% to 3.1% and stability by 50% to 55.5% on two datasets BIG-bench and LIAR and two models GPT-3.5 and GPT-4o-mini. |
AttributeForge: An Agentic LLM Framework for Automated Product Schema Modeling (2025.emnlp-industry)
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Yunhan Huang, Klevis Ramo, Andrea Iovine, Melvin Monteiro, Sedat Gokalp, Arjun Bakshi, Hasan Turalic, Arsh Kumar, Jona Neumeier, Ripley Yates, Rejaul Monir, Simon Hartmann, Tushar Manglik, Mohamed Yakout
| Challenge: | e-commerce platforms are producing only tens of attributes per month for schema modeling . authors present a framework to automate end-to-end product schema modeling using Large Language Models . |
| Approach: | They introduce a framework to automate end-to-end product schema modeling using Large Language Models. |
| Outcome: | The proposed framework achieves an 88 increase in modeling throughput while delivering superior quality. |
PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation (2026.findings-acl)
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| Challenge: | Existing multi agent frameworks for large language models are brittle on code generation tasks. |
| Approach: | They propose a framework that brings pair programming to autonomous LLM collaboration. |
| Outcome: | Using PairCoder, large language models achieve better results on code generation tasks and reduce token usage by 40% to 70% on eight representative backbones. |
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (D18-1)
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| Challenge: | Despite its success, neural autoregressive modeling has its weakness in decoding, i.e., finding the most likely sequence. |
| Approach: | They propose a conditional non-autoregressive neural sequence model based on iterative refinement based upon latent variable models and conditional denoising autoencoders. |
| Outcome: | The proposed model significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart. |
Zero-Shot Cross-Domain Aspect-Based Sentiment Analysis via Domain-Contextualized Chain-of-Thought Reasoning (2025.findings-emnlp)
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| Challenge: | Cross-domain aspect-based sentiment analysis (ABSA) aims to learn specific knowledge from a source domain to perform various tasks on a target domain. |
| Approach: | a new framework is proposed to learn specific knowledge from a source domain . the framework uses domain adaptation techniques to transfer domain-agnostic features . |
| Outcome: | a new learning framework for cross-domain aspect-based sentiment analysis is proposed . it effectively eliminates dependency on target-domain annotations, authors say . |
Learning to Model Editing Processes (2022.findings-emnlp)
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| Challenge: | Existing sequence generation models produce outputs in one pass, usually left-to-right . current models model only a single edit step, and do not fully model editing . |
| Approach: | They propose to model editing processes, modeling the whole process of iteratively generating sequences. |
| Outcome: | The proposed model improves performance on a variety of axes compared to previous models . iterative refinement and editing are central parts of human creative workflow . |
FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)
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| Challenge: | Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing. |
| Approach: | They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report . |
| Outcome: | Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models. |
Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models (2026.acl-long)
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Linhao Zhong, Linyu Wu, Bozhen Fang, Tianjian Feng, Chenchen Jing, Wen Wang, Jiaheng Zhang, Hao Chen, Chunhua Shen
| Challenge: | Existing Diffusion Language Models rely on hard binary masking and discrete token assignments, which hinder the revision of early decisions. |
| Approach: | They propose a diffusion-based language modeling approach that replaces hard binary masks with evolving soft token distributions. |
| Outcome: | The proposed approach outperforms existing DLMs on multiple benchmarks. |
HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025.findings-emnlp)
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| Challenge: | Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise. |
| Approach: | They propose a conditional denoising diffusion framework that learns to reverse structured noise corruption. |
| Outcome: | The proposed framework outperforms state-of-the-art HKGC methods in a variety of noisy conditions. |
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights (2025.findings-acl)
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| Challenge: | Existing approaches to automate scientific research are limited by human cognitive constraints and timeintensive workflows. |
| Approach: | They propose a framework that enhances medical paper generation through iterative refinement and structured feedback. |
| Outcome: | The proposed framework achieves significant improvements over conventional methods across multiple models and evaluation dimensions. |
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data (2026.acl-long)
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| Challenge: | Existing methods for automated feature generation rely on predefined operator libraries and do not incorporate feature semantics, limiting their ability to produce high-quality features. |
| Approach: | They propose a Memory-Augmented LLM-based Multi-Agent System (MALMAS) that decomposes the generation process into agents with distinct responsibilities. |
| Outcome: | The proposed method extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. |
LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval (2026.findings-acl)
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| Challenge: | Document editing requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. |
| Approach: | a framework that constructs lightweight dependency graphs captures semantic relationships and structural hierarchies across document elements is proposed for agentic document editing . a scaLing agentic agentic framework is based on a dependency graph framework that captures dependencies and refactors function dependencies. |
| Outcome: | a new framework achieves 76 consistency versus 56 baseline while reducing token usage by 85 . the framework is based on a framework that captures semantic relationships and structural hierarchies across document elements . it can be used to improve document consistency, but it also reduces token costs and latency . |
One Unified Model for Diverse Tasks: Emotion Cause Analysis via Self-Promote Cognitive Structure Modeling (2025.naacl-long)
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| Challenge: | Existing models for emotion cause analysis overlook common ground rooted in cognitive emotion theories, in particular, the cognitive structure of emotions. |
| Approach: | They propose a unified model capable of tackling diverse emotion cause analysis tasks . they propose 'self-promote mechanism' that constructs the emotion cognitive structure through LLM . |
| Outcome: | The proposed model outperforms existing models and baselines on multiple emotion cause analysis tasks. |
Demystify the Role of Memory in Machine Learning Engineering Agents (2026.findings-acl)
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Xinyu Zhao, Junpeng Wang, Yuzhong Chen, Menghai Pan, Chin-Chia Michael Yeh, Jiarui Sun, Yan Zheng, Mahashweta Das, Tianlong Chen
| Challenge: | Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors can inform future success. |
| Approach: | They propose a dynamic coding memory that captures and reuses debugging experiences and integrates it into two representative agent paradigms. |
| Outcome: | The proposed agent model captures and reuses debugging experiences and integrates it into two agent paradigms. |
A Strategic Coordination Framework of Small LMs Matches Large LMs in Data Synthesis (2025.acl-long)
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| Challenge: | Large Language Models suffer from high computational costs and environmental inefficiency . smaller LMs are more accessible and sustainable, but their individual capabilities often fall short . a collaborative framework for small LM combines specialized roles to iterative refinement and quality control . |
| Approach: | They propose a framework that aggregates specialized roles across small LMs to iterative refinement and quality control typically achieved by a single large LM. |
| Outcome: | The proposed framework aggregates specialized roles across small LMs to iterative refinement and quality control typically achieved by large LM. |
MedDCR: Learning to Design Agentic Workflows for Medical Coding (2026.findings-acl)
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| Challenge: | Medical coding is the process of translating unstructured clinical notes into standardized diagnostic and procedural codes. |
| Approach: | They propose a closed-loop framework that treats workflow design as a learning problem. |
| Outcome: | The proposed framework outperforms state-of-the-art workflows on benchmark datasets and produces interpretable, adaptable workflows that better reflect real coding practice. |
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog (P19-1)
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| Challenge: | Existing models for visual dialog infer the answer through multiple reasoning steps. |
| Approach: | They propose a model for visual dialog that uses multi-step reasoning to answer questions about an image. |
| Outcome: | The proposed model achieves a new state-of-the-art of 64.47% on the VisDial v1.0 dataset . |
PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)
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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu
| Challenge: | Large language models (LLMs) have advanced automatic code generation, but their ability to produce high-performance code remains limited. |
| Approach: | They propose a family of large language models that generate performance-enhanced code through interpretable and customized optimization strategies. |
| Outcome: | The proposed model outperforms existing models on the PIE code performance benchmark and produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow. |
MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis (2025.acl-long)
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Daniel Philip Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem, Kiril Gashteovski, Carolin Lawrence
| Challenge: | Existing approaches to DDx are limited by single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. |
| Approach: | They propose a Modular Explainable DDx Agent framework that allows physicians to iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. |
| Outcome: | The proposed framework achieves over 10% accuracy improvements in interactive DDx across large and small LLMs while offering critical explainability into its diagnostic reasoning process. |
Robertha: Eigenspectrum Regularized Attention for Robust Natural Language Understanding (2026.acl-long)
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| Challenge: | Existing robustness methods sacrifice clean performance or fail to generalize to higher corruption levels. |
| Approach: | They propose a mechanism that uses semantic patterns to pull corrupted embeddings toward correct representations by Eigenspectrum Regularization. |
| Outcome: | The proposed mechanism outperforms robustness methods on 13 GLUE and SuperGLUE tasks while maintaining competitive clean performance. |
Efficient Transformer Parameter Reuse via Zero-Token Mechanism (2026.findings-acl)
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| Challenge: | Existing approaches to scaling up parameter counts are impractical for users with limited computational resources. |
| Approach: | They propose a decoupled parameter cycling strategy that employs a head-tail decoupling strategy to decouple the first (head) and last (tail) layers from the parameter cycling process. |
| Outcome: | The proposed approach achieves superior performance under strict parameter constraints and significantly reduces computational overhead via early exits. |
mPresenter: An Agentic Framework for Generating Multilingual Presentation Videos from Scientific Papers (2026.findings-acl)
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| Challenge: | Existing Paper2Video systems are monolingual and often rely on single-pass pipelines. |
| Approach: | They propose a multilingual agentic Paper2Video system that decomposes the task into planning, audience-oriented critique, layout-aware slide generation, and multilingual figure interpretation. |
| Outcome: | The proposed system improves question-answering accuracy relative to previous systems while maintaining affordable cost and latency. |
ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness? (2024.emnlp-main)
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| Challenge: | Current methods for optimizing program efficiency improve performance measured by execution time, but they often come at the cost of severely decreasing the functional correctness. |
| Approach: | They propose a reproducible benchmark for evaluating program efficiency via two paradigms: natural language (NL) based code generation and history-based code editing. |
| Outcome: | The proposed approach improves performance while maintaining correctness while adding execution information. |
Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations (2025.acl-long)
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| Challenge: | Recent work has shown that the interaction of large language models (LLMs) with theorem provers (TPs) can help verify and improve the validity of NLI explanations. |
| Approach: | They propose to use logical expressions to guide LLMs in generating structured proof sketches and to use them to improve their accuracy. |
| Outcome: | The proposed strategies improve autoformalisation, syntactic errors and explanation refinement over the state-of-the-art model. |
SceneGenAgent: Precise Industrial Scene Generation with Coding Agent (2025.acl-long)
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| Challenge: | Recent work on scene generation focuses on generating 3D scenes from textual descriptions . however, the task of generating industrial scenes with LLMs is complex and requires precise measurements and positioning . |
| Approach: | They propose an LLM-based agent for generating industrial scenes through C# code. |
| Outcome: | Experiments show that LLMs powered by SceneGenAgent exceed their original performance . the agent achieves 81.0% success rate in real-world industrial scene generation tasks . |
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on simple attribution that retrieves textual evidence as references. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to generate reliable attributions. |
| Outcome: | The proposed benchmark evaluates the ability of LLMs to generate long-form answers with reliable and nuanced attributions. |
LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning (2025.emnlp-main)
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Tianshi Zheng, Cheng Jiayang, Chunyang Li, Haochen Shi, Zihao Wang, Jiaxin Bai, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Modern large language models (LLMs) employ diverse logical inference mechanisms for reasoning. |
| Approach: | They analyze the comparative dynamics of inductive (System 1) versus abductive/deductive (system 2) inference in large language models by using a controlled analogical reasoning environment and a MCQ/free-text task format. |
| Outcome: | The proposed methods can significantly scale LLM reasoning. |
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent (2026.findings-acl)
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| Challenge: | Existing approaches to individualized glucose regulation are generic and do not account for individual-specific glucose dynamics. |
| Approach: | They propose a physio-feedback agentic loop that integrates individualized absorption modeling with dietary intervention to regulate glucose response. |
| Outcome: | The proposed system improves prediction accuracy and reduces glucose excursions. |
RACC: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding (2026.findings-acl)
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| Challenge: | Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement, but their current decoding paradigms are static and myopic. |
| Approach: | They propose a Regret-Aware Confidence Calibration framework that aligns decoding decisions with the model’s latent self-correction capabilities. |
| Outcome: | The proposed framework aligns decoding decisions with model’s latent self-correction capabilities. |
LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning (2026.findings-acl)
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| Challenge: | Recent large language models struggle with high computational costs and logical inconsistencies . a framework that translates natural language into Answer Set Programming (ASP) is developed . |
| Approach: | They propose a framework that translates natural language into Answer Set Programming (ASP) stable model semantics allow LLMs to express default rules and exceptions, they show . |
| Outcome: | The proposed framework outperforms existing methods on nonmonotonic reasoning tasks without any per-task engineering and applies uniformly across reasoning tasks. |
A Decoupled Multi-Agent Framework for Complex Text Style Transfer (2025.findings-emnlp)
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| Challenge: | Existing models for text style transfer struggle with complex styles . existing models perform well on simple styles like sentiment and formality . |
| Approach: | They propose a multi-agent self-check framework that includes a large language model as a planner for disentangling subtasks and expert agents for executing the subtask. |
| Outcome: | The proposed framework significantly improves style strength and content preservation on simple and complex style datasets. |
SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) struggle with complex semantic and structural correctness required for automated code repair. |
| Approach: | They propose a hybrid neural-symbolic framework that unifies code synthesis with compiler-informed symbolic feedback to improve LLM-based vulnerability repair. |
| Outcome: | The proposed framework improves code repair accuracy and efficiency over strong SFT and RFT training strategies on the FixJS and CodeFlaws benchmarks. |
Creativity in LLM-based Multi-Agent Systems: A Survey (2025.emnlp-main)
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Yi-Cheng Lin, Kang-Chieh Chen, Zhe-Yan Li, Tzu-Heng Wu, Tzu-Hsuan Wu, Kuan-Yu Chen, Hung-yi Lee, Yun-Nung Chen
| Challenge: | Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. |
| Approach: | They present a taxonomy of agent proactivity and persona design and an overview of generation techniques. |
| Outcome: | The proposed framework and roadmap offers a roadmap for advancing the development, evaluation, and standardization of creative MAS. |
Rethinking Text-to-SQL: Dynamic Multi-turn SQL Interaction for Real-world Database Exploration (2026.findings-acl)
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Linzhuang Sun, Tianyu Guo, Hao Liang, Ruitong Liu, Yuying Li, Qifeng Cai, Jingxuan Wei, Yuchen Wu, Bihui Yu, Xiangxiang Zhang, Wentao Zhang, Bin Cui
| Challenge: | Structured Query Language (SQL) is the cornerstone for data-driven decision-making. |
| Approach: | They propose a benchmark to rigorously evaluate Large Language Models within a dynamic interaction framework. |
| Outcome: | The proposed benchmark aims to rigorously evaluate LLMs within a dynamic interaction framework. |
Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as finance where unsafe behavior can lead to serious regulatory risks. |
| Approach: | They propose a black-box multi-turn risk-concealed redteaming framework that progressively conceals surface-level risk while exploiting regulatory-violating behaviors. |
| Outcome: | Experiments on nine widely used LLMs show that the proposed framework achieves 93.19% average attack success rate (ASR) and improves the average ASR to 95.00%. |
Many-Shot Scaling of In-Context Learning with Self-Generated Demonstrations (2026.findings-acl)
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| Challenge: | In-context learning methods that use self-generated annotations do not scale to many-shot scenarios. |
| Approach: | They propose a framework analogous to semi-supervised learning that uses self-generated annotations instead of ground truth labels. |
| Outcome: | The proposed framework outperforms ground truth ICL under zero-shot, few-shot and many-shot settings. |
Multi-LLM Collaborative Search for Complex Problem Solving (2026.findings-acl)
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| Challenge: | Large language models (LLMs) often struggle with complex reasoning tasks due to the vast reasoning space inherent in the complexity and inherent ambiguities of natural languages. |
| Approach: | They propose a mixture-of-search-agents paradigm that integrates diverse reasoning pathways by combining independent exploration and iterative refinement among multiple LLMs. |
| Outcome: | The proposed approach improves performance over single-agent and multi-agend baselines in complex mathematical and commonsense reasoning tasks. |
SceneLM: 3D-Aware Language Models for Editable 3D Scene Synthesis (2026.findings-acl)
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| Challenge: | Existing methods for synthesising 3D scenes from a single image are text-driven and lack precise metric understanding from images. |
| Approach: | They propose a language-model-based framework that grounds 3D scene synthesis in visual evidence by recovering an executable metric 3D layout directly from a single image. |
| Outcome: | The proposed framework recovers an executable metric 3D layout directly from an RGB image and instantiates, places, and edits objects for iterative refinement. |