Papers with sequential
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
| Approach: | They propose a model that combines sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. |
| Outcome: | The proposed model produces fluent translations with better reordering than previous models. |
How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains (2026.eacl-long)
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Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur, Charese Smiley, Ivan Brugere, Kundan S Thind, Mohammad M. Ghassemi
| Challenge: | Large Reasoning Models often struggle with confidence calibration, authors say . authors: accurate confidence scores are essential to build trustworthy systems . |
| Approach: | They propose a Reasoning Model Confidence estimation benchmark to assess LRM confidence . the benchmark is constructed from 347,496 reasoning traces from six popular LRMs . |
| Outcome: | The proposed benchmark compares ten different representation-based methods on a wide range of architectures. |
Improving Backchannel Prediction Leveraging Sequential and Attentive Context Awareness (2024.findings-eacl)
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| Challenge: | Backchannels are short and often affirmative or empathetic responses from a listener during a conversation . et al. (2010) showed that timely backchanneling can enhance storytelling ability . |
| Approach: | They propose a context-aware backchannel prediction approach that leverages a pretrained wav2vec model to enhance backchannel performance. |
| Outcome: | The proposed approach improves performance in Korean and English datasets . it leverages the pretrained wav2vec model for encoding audio signal . |
Recurrent Attention Networks for Long-text Modeling (2023.findings-acl)
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| Challenge: | Existing approaches to encoding long documents using self-attention have been limited by quadratic computational complexities and limited application in long text processing. |
| Approach: | They propose a long-document encoding model that allows the recurrent operation of self-attention. |
| Outcome: | The proposed model extracts global semantics in token-level and document-level representations, making it inherently compatible with both sequential and sequential tasks. |
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (P18-1)
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| Challenge: | Analyzing human multimodal language is emerging area of research in NLP. |
| Approach: | They propose a multimodal fusion technique to exploit how modalities interact in multimodal language. |
| Outcome: | The proposed technique exploits how modalities interact with each other in human multimodal language. |
EDC: Effective and Efficient Dialog Comprehension For Dialog State Tracking (2024.naacl-long)
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| Challenge: | Existing methods for dialog state tracking face trade-offs between accuracy and efficiency . effective and efficient dialog comprehension (EDC) predicts domains, slot names and slot values of dialog state step-by-step for better accuracy . |
| Approach: | They propose an alternative method that leverages the tree structure of the dialog state. |
| Outcome: | The proposed approach achieves state-of-the-art JGA accuracy and is more efficient than previous models. |
Learning Sequential and Structural Information for Source Code Summarization (2021.findings-acl)
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| Challenge: | Existing models that learn both sequential and structural features of source code are limited in their use. |
| Approach: | They propose a model that learns both the sequential and the structural features of code for source code summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art for source code summarization by experiments and human evaluations. |
Efficient Sentence Embedding using Discrete Cosine Transform (D19-1)
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| Challenge: | Modern NLP systems rely on word embeddings as input units to encode statistical semantic and syntactic properties of words. |
| Approach: | They propose to use discrete cosine transform to compress word sequences in order-preserving manner. |
| Outcome: | The proposed model preserves syntactic information in semantic probing tasks . it is comparable to vector averaging but mediocre in performance. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
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Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
Generating Summaries with Topic Templates and Structured Convolutional Decoders (P19-1)
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| Challenge: | Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure . |
| Approach: | They propose a structured convolutional decoder that is guided by the content structure of target summaries. |
| Outcome: | The proposed model outperforms existing decoders on three datasets representing different domains. |
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. |
Consecutive Batch Model Editing with HooK Layers (2024.emnlp-main)
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| Challenge: | Existing models that retrain are time- and resource-consuming, but they lack the memory to support sequential and batch editing. |
| Approach: | They propose a model editing method that supports sequential and batch editing . they use a small amount of memory to store several hook layers that remain unchanged over time . |
| Outcome: | The proposed method is memory-friendly and can store hook layers that remain unchanged over time. |
Rationales for Sequential Predictions (2021.emnlp-main)
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| Challenge: | Sequence models produce accurate predictions, but their decision making processes are hard to explain. |
| Approach: | They propose an efficient algorithm to approximate sequential objective by identifying the most faithful rationales. |
| Outcome: | The proposed algorithm is best at optimizing the sequential objective and provides the most faithful rationales. |
Neuron-Level Sequential Editing for Large Language Models (2025.acl-long)
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Houcheng Jiang, Junfeng Fang, Tianyu Zhang, Baolong Bi, An Zhang, Ruipeng Wang, Tao Liang, Xiang Wang
| Challenge: | Existing model editing methods focus on single-round editing and often face significant challenges in sequential model editing. |
| Approach: | They propose a model editing method that optimizes the target layer’s hidden states using the model’s original weights to prevent model failure. |
| Outcome: | The proposed method outperforms existing model editing methods and is available on the open-source platform 4open.science. |
Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are revolutionizing mobile intelligence, but their implementation on mobile devices is severely bottlenecked by the prohibitive resource demands of LLMs. |
| Approach: | They propose a paradigm that forgoes end-to-end updates in favor of a sequential, layer-by-layer manner. |
| Outcome: | Extensive experiments on multiple benchmarks demonstrate the superiority of ChainFed over existing methods, boosting average accuracy by up to 46.46%. |
Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive. |
| Approach: | They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%. |
TPS-Bench: Evaluating AI Agents’ Tool Planning & Scheduling Abilities in Compounding Tasks (2026.acl-long)
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| Challenge: | Large language model (LLM) agents have demonstrated strong problem-solving competence across domains like research and coding. |
| Approach: | They propose to use a tool repository to analyze the ability of large language model agents to solve complex problems. |
| Outcome: | The proposed model outperforms open-source and closed-source models in task completion rate and efficiency. |
JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal Practice (2026.acl-long)
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Ziang Chen, Guannan Li, Fanlin Ji, Yipeng Kang, Jiaqi Li, Muhan Zhang, Yangtao Zhang, Li Tianjiao, Jiannan Wang, Xin Guo, Song-Chun Zhu, Bin Ling
| Challenge: | Existing legal benchmarks evaluate isolated tasks or exam-style questions, failing to capture the procedural interdependencies and adjudicative rigor inherent in professional practice. |
| Approach: | They propose a vertical, depth-oriented, domain-specific benchmark to evaluate Large Language Models (LLMs) in Chinese civil litigation. |
| Outcome: | The proposed benchmarks show that large language models exhibit an "illusion of competence" the results highlight a critical gap between fluent linguistic output and judicial reliability . |
MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)
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| Challenge: | Multi-turn, long-horizon tasks require dozens of sequential model calls per episode. |
| Approach: | They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility. |
| Outcome: | The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks. |