paper_id stringlengths 15 18 | title stringlengths 15 142 | paper_url stringlengths 41 44 | authors listlengths 1 102 | abstract large_stringlengths 490 2.47k ⌀ | anthology_id stringlengths 15 18 | doi stringlengths 27 30 ⌀ | award stringclasses 0
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2026.acl-long.1 | OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning | https://aclanthology.org/2026.acl-long.1/ | [
"Pan Lu",
"Bowen Chen",
"Sheng Liu",
"Rahul Thapa",
"Joseph Boen",
"James Zou"
] | Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large language models (LLMs) with external tools but are restricted to specialized domains, limited tool types, or require additional training data. In ... | 2026.acl-long.1 | 10.18653/v1/2026.acl-long.1 | null | 2502.11271 | title_judge |
2026.acl-long.2 | No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand | https://aclanthology.org/2026.acl-long.2/ | [
"Jimin Jung",
"MyoungJin Kim",
"Jaehyung Seo",
"Heuiseok Lim"
] | The Plain Writing Act in the United States requires government documents to be written in clear and simple language. However, existing summarization systems struggle to address diverse linguistic and cognitive barriers among general readers. We propose NRLB (No Reader Left Behind), a unified multi-agent framework for p... | 2026.acl-long.2 | 10.18653/v1/2026.acl-long.2 | null | 2605.28836 | title_snapshot |
2026.acl-long.3 | Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 | https://aclanthology.org/2026.acl-long.3/ | [
"Chengwu Liu",
"Yichun Yin",
"Ye Yuan",
"Jiaxuan Xie",
"Botao Li",
"Siqi Li",
"Jianhao Shen",
"Yan Xu",
"Lifeng Shang",
"Ming Zhang"
] | Most ATP benchmarks embed the final answer within the formal statement — a convention we call "Easy Mode" — a design that simplifies the task relative to what human competitors face and may lead to optimistic estimates of model capability.We call the stricter, more realistic setting "Hard Mode": the system must indepen... | 2026.acl-long.3 | 10.18653/v1/2026.acl-long.3 | null | 2604.15839 | title_snapshot |
2026.acl-long.4 | Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models | https://aclanthology.org/2026.acl-long.4/ | [
"Chung-ju Huang",
"Huiqiang Zhao",
"Yuanpeng He",
"Lijian Li",
"Wenpin Jiao",
"Zhi Jin",
"Peixuan Chen",
"Leye Wang"
] | The increasing reliance on cloud-hosted Large Language Models (LLMs) exposes sensitive client data, such as prompts and responses, to potential privacy breaches by service providers.Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously.To address th... | 2026.acl-long.4 | 10.18653/v1/2026.acl-long.4 | null | 2603.00196 | title_snapshot |
2026.acl-long.5 | Rhetorical Questions in LLM Representations: A Linear Probing Study | https://aclanthology.org/2026.acl-long.5/ | [
"Louie Hong Yao",
"Vishesh Anand",
"Yuan Zhuang",
"Tianyu Jiang"
] | Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unclear. We analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts, and find that rhetorica... | 2026.acl-long.5 | 10.18653/v1/2026.acl-long.5 | null | 2604.14128 | title_snapshot |
2026.acl-long.6 | Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models | https://aclanthology.org/2026.acl-long.6/ | [
"Yifu Chen",
"Shengpeng Ji",
"Zhengqing Liu",
"Qian Chen",
"Wen Wang",
"Ziqing Wang",
"Yangzhuo Li",
"Tianle Liang",
"Zhou Zhao"
] | Achieving seamless, human-like interaction remains a key challenge for full-duplex spoken dialogue models (SDMs). Reinforcement learning (RL) has substantially enhanced text- and vision-language models, while well-designed reward signals are crucial for the performance of RL. We consider RL a promising strategy to addr... | 2026.acl-long.6 | 10.18653/v1/2026.acl-long.6 | null | 2604.14920 | title_snapshot |
2026.acl-long.7 | Different types of syntactic agreement recruit the same units within large language models | https://aclanthology.org/2026.acl-long.7/ | [
"Daria Kryvosheieva",
"Andrea Gregor de Varda",
"Evelina Fedorenko",
"Greta Tuckute"
] | Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the model remains an open question. We investigate whether different syntactic phenomena recruit shared or distinct components in LLMs. Using a functional localization appr... | 2026.acl-long.7 | 10.18653/v1/2026.acl-long.7 | null | 2512.03676 | title_snapshot |
2026.acl-long.8 | Empowering Tabular Data Preparation with Language Models: Why and How? | https://aclanthology.org/2026.acl-long.8/ | [
"Mengshi Chen",
"Yuxiang Sun",
"Tengchao Li",
"Jianwei Wang",
"Kai Wang",
"Xuemin Lin",
"Ying Zhang",
"Wenjie Zhang"
] | Data preparation is a critical step in enhancing the usability of tabular data and thus boosts downstream data-driven tasks. Traditional methods often face challenges in capturing the intricate relationships within tables and adapting to the tasks involved. Recent advances in Language Models (LMs), especially in Large ... | 2026.acl-long.8 | 10.18653/v1/2026.acl-long.8 | null | 2508.01556 | title_snapshot |
2026.acl-long.9 | Learning Diverse Responses with Prefix-Conditioned Supervised Fine-Tuning | https://aclanthology.org/2026.acl-long.9/ | [
"Zhiyuan Fan",
"Guanqiao Chen",
"Yanyi Huang",
"Mingkuan Zhao",
"Dadi Guo",
"Yi R. Fung"
] | Large language models (LLMs) have shown strong performance on hard reasoning and general instruction-following tasks. However, when sampling multiple outputs for the same prompt, they often produce highly homogeneous, repetitive responses, resulting in inefficient exploration. This limits the gains from test-time scali... | 2026.acl-long.9 | 10.18653/v1/2026.acl-long.9 | null | null | null |
2026.acl-long.10 | EASE: Entity-Aware Sub-table Generation for Real-world Multi-table QA | https://aclanthology.org/2026.acl-long.10/ | [
"Myunghoon Kang",
"Dahyun Jung",
"Suhyune Son",
"Seonmin Koo",
"Changwoo Chun",
"Daniel Rim",
"Haeyoung Kwon",
"Yuna Hur",
"Heuiseok Lim"
] | Recent advancements in table-based question answering (table QA) have been driven by the development of table-specific reasoning strategies for leveraging large language models. Previous works employ sub-table-based reasoning, which involves matching query-relevant table values and aggregating them into sub-tables for ... | 2026.acl-long.10 | 10.18653/v1/2026.acl-long.10 | null | null | null |
2026.acl-long.11 | Benchmarking LLM’s Capability in Reasoning over Conflicting Web References | https://aclanthology.org/2026.acl-long.11/ | [
"Yizhen Yuan",
"Rui Kong",
"Dongze Li",
"Yuanchun Li",
"Yunxin Liu"
] | Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have become a dominant framework for building intelligent assistants. In real-world applications such as ChatGPT with web search, the retrieved document often comes from diverse, potentially unreliable sources and may contain inconsistent... | 2026.acl-long.11 | 10.18653/v1/2026.acl-long.11 | null | null | null |
2026.acl-long.12 | Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation | https://aclanthology.org/2026.acl-long.12/ | [
"Qianli Wang",
"Van Bach Nguyen",
"Yihong Liu",
"Fedor Splitt",
"Nils Feldhus",
"Christin Seifert",
"Hinrich Schuetze",
"Sebastian Möller",
"Vera Schmitt"
] | Counterfactuals refer to minimally edited inputs that cause a model’s prediction to change, serving as a promising approach to explaining the model’s behavior. Large language models (LLMs) excel at generating English counterfactuals and demonstrate multilingual proficiency. However, their effectiveness in generating mu... | 2026.acl-long.12 | 10.18653/v1/2026.acl-long.12 | null | 2601.00263 | title_snapshot |
2026.acl-long.13 | CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training | https://aclanthology.org/2026.acl-long.13/ | [
"Seungyoon Lee",
"Minhyuk Kim",
"Seongtae Hong",
"Youngjoon Jang",
"Dongsuk Oh",
"Heuiseok Lim"
] | Existing multilingual embedding models often encounter challenges in cross-lingual scenarios due to imbalanced linguistic resources and less consideration of cross-lingual alignment during training. Although standardized contrastive learning approaches for cross-lingual adaptation are widely adopted, they may struggle ... | 2026.acl-long.13 | 10.18653/v1/2026.acl-long.13 | null | 2604.05821 | title_judge |
2026.acl-long.14 | Aligning Language Models with Real-time Knowledge Editing | https://aclanthology.org/2026.acl-long.14/ | [
"Chenming Tang",
"Yutong Yang",
"Kexue Wang",
"Yunfang Wu"
] | Knowledge editing aims to modify outdated knowledge in language models efficiently while retaining their original capabilities. Mainstream datasets for knowledge editing are predominantly static and fail to keep in pace with the evolving real-world knowledge. In this work, we introduce CRAFT, an ever-evolving real-worl... | 2026.acl-long.14 | 10.18653/v1/2026.acl-long.14 | null | 2508.01302 | title_snapshot |
2026.acl-long.15 | PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation | https://aclanthology.org/2026.acl-long.15/ | [
"Jiho Choi",
"Seojeong Park",
"Seongjong Song",
"Hyunjung Shim"
] | Automating scientific poster generation requires hierarchical document understanding and coherent content-layout planning.Existing methods often rely on flat summarization or optimize content and layout separately.As a result, they often suffer from information loss, weak logical flow, and poor visual balance.We presen... | 2026.acl-long.15 | 10.18653/v1/2026.acl-long.15 | null | 2508.21720 | title_snapshot |
2026.acl-long.16 | SLR: Automated Synthesis for Scalable Logical Reasoning | https://aclanthology.org/2026.acl-long.16/ | [
"Lukas Helff",
"Ahmad Omar",
"Felix Friedrich",
"Antonia Wüst",
"Hikaru Shindo",
"Rupert Mitchell",
"Tim Woydt",
"Patrick Schramowski",
"Wolfgang Stammer",
"Kristian Kersting"
] | We introduce SLR, an end-to-end framework for systematic evaluation and training of Large Language Models (LLMs) via Scalable Logical Reasoning. Given a user’s task specification, SLR automatically synthesizes (i) an instruction prompt for an inductive reasoning task, (ii) a validation program, executable on model outp... | 2026.acl-long.16 | 10.18653/v1/2026.acl-long.16 | null | 2506.15787 | title_snapshot |
2026.acl-long.17 | Can Continual Pretraining Bridge the Performance Gap between General-purpose and Specialized Language Models in the Medical Domain? | https://aclanthology.org/2026.acl-long.17/ | [
"Niclas Doll",
"Jasper Schulze Buschhoff",
"Shalaka Satheesh",
"Hammam Abdelwahab",
"Héctor Allende-Cid",
"Katrin Klug"
] | This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via continual pre-training and merging. We address the scarcity of specialized non-English data by constructing a high-quality German medical corpus (FineMed-de) from FineWe... | 2026.acl-long.17 | 10.18653/v1/2026.acl-long.17 | null | 2604.19394 | title_judge |
2026.acl-long.18 | MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Long-tail Knowledge | https://aclanthology.org/2026.acl-long.18/ | [
"Jie He",
"Nan Hu",
"Wanqiu Long",
"Jiaoyan Chen",
"Jeff Z. Pan"
] | Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, enabling them to tackle knowledge-intensive tasks. However, limited research has explored how LLMs effectively leverage RAG techniques for multi-hop question answering (QA), particularly when handling knowledge... | 2026.acl-long.18 | 10.18653/v1/2026.acl-long.18 | null | 2412.17032 | title_judge |
2026.acl-long.19 | Sense and Sensitivity: Examining the Influence of Semantic Recall on Long Context Code Understanding | https://aclanthology.org/2026.acl-long.19/ | [
"Adam Štorek",
"Mukur Gupta",
"Samira Hajizadeh",
"Prashast Srivastava",
"Suman Jana"
] | Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long code context or rely on pattern matching shortcuts remains unclear. We distinguish between lexical recall (retrieving code verbatim) and semantic recall (understanding oper... | 2026.acl-long.19 | 10.18653/v1/2026.acl-long.19 | null | 2505.13353 | title_judge |
2026.acl-long.20 | SenseRel: A Sense-Level Benchmark for Denotational and Connotational Meaning Relations | https://aclanthology.org/2026.acl-long.20/ | [
"Pierluigi Cassotti",
"Naomi Baes",
"Stefano De Pascale",
"Jáder Martins Camboim de Sá",
"Francesco Periti",
"Nick Haslam",
"Dirk Geeraerts",
"Nina Tahmasebi"
] | Polysemy enables a single word to convey multiple related meanings, reflecting the conceptual and emotional aspects of the evolution of the senses. We introduce the first sense-level benchmark, SenseRel, for modeling semantic relations between word senses, uniting denotational and connotational aspects of meaning. Sens... | 2026.acl-long.20 | 10.18653/v1/2026.acl-long.20 | null | null | null |
2026.acl-long.21 | WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering | https://aclanthology.org/2026.acl-long.21/ | [
"Yifei He",
"Pranit Chawla",
"Yaser Souri",
"Subhojit Som",
"Xia Song"
] | Computer use agents (CUAs) can operate real-world digital interfaces but remain difficult to train due to the high cost of graphical user interface (GUI) interaction and the scarcity of high-quality trajectory data. Existing datasets rely on human demonstrations, limiting scalability. A natural alternative is to synthe... | 2026.acl-long.21 | 10.18653/v1/2026.acl-long.21 | null | 2512.10962 | title_snapshot |
2026.acl-long.22 | PR-XAI: PageRank-Based Feature Attribution for Transformers | https://aclanthology.org/2026.acl-long.22/ | [
"Behrooz Azarkhalili",
"Linyi Li",
"Maxwell W. Libbrecht"
] | We introduce PR-XAI, a feature attribution method for transformer models based on the PageRank algorithm. The proposed PR-XAI models the attention mechanism as a directed graph, with weights derived from attention weights and their gradients. Evaluations across five well-known text classification datasets and three dif... | 2026.acl-long.22 | 10.18653/v1/2026.acl-long.22 | null | null | null |
2026.acl-long.23 | CAPruner: Conceptual-Adjacent Scene Graph Pruner for Enhancing 3D Spatial Reasoning of Large Language Models | https://aclanthology.org/2026.acl-long.23/ | [
"Shengli Zhou",
"Xiangchen Wang",
"Guanhua Chen",
"Feng Zheng"
] | Large language models (LLMs) have recently been applied to 3D vision-language (3D-VL) tasks, which require spatial reasoning to identify target objects relative to anchors. Scene graphs are commonly employed to represent such relations, but reasoning over complete graphs incurs high token costs and computational ineffi... | 2026.acl-long.23 | 10.18653/v1/2026.acl-long.23 | null | 2606.07529 | title_snapshot |
2026.acl-long.24 | MauBERT: Universal Phonetic Inductive Biases for Few-Shot Acoustic Units Discovery | https://aclanthology.org/2026.acl-long.24/ | [
"Angelo Ortiz Tandazo",
"Manel Khentout",
"Youssef Benchekroun",
"Thomas Hueber",
"Emmanuel Dupoux"
] | This paper introduces MauBERT, a multilingual extension of HuBERT that leverages articulatory features for robust cross-lingual phonetic representation learning. We continue HuBERT pre-training with supervision based on a phonetic-to-articulatory feature mapping in 55 languages. Our models learn from multilingual data ... | 2026.acl-long.24 | 10.18653/v1/2026.acl-long.24 | null | 2512.19612 | title_snapshot |
2026.acl-long.25 | Your Reasoning Model Knows What Counts: Self-Guided Chain-of-Thought Pruning for Efficient Reasoning | https://aclanthology.org/2026.acl-long.25/ | [
"Zi-Ao Ma",
"Xian-Ling Mao",
"Tian Lan",
"Chen Xu",
"Zhijing Wu"
] | Chain-of-Thought (CoT) reasoning is crucial for the performance of Large Reasoning Models (LRMs) but is often hindered by redundant and distracting segments, which incur excessive inference costs and degrade robustness. Existing approaches try to solve this problem by enforcing brevity through external supervision, suc... | 2026.acl-long.25 | 10.18653/v1/2026.acl-long.25 | null | null | null |
2026.acl-long.26 | ImReasoner: Improving Memory-based Language Models for Reasoning-in-a-Haystack Tasks | https://aclanthology.org/2026.acl-long.26/ | [
"Ching-Yun Ko",
"Payel Das",
"Sihui Dai",
"Georgios Kollias",
"Subhajit Chaudhury",
"Aurelie C. Lozano",
"Pin-Yu Chen"
] | Reasoning over long contexts remains a major challenge for language models, particularly when solving tasks that require integrating multiple facts in sequence or generalizing to new distributions. We argue that this difficulty stems from a lack of structural inductive bias. Recently, alternative frameworks have been p... | 2026.acl-long.26 | 10.18653/v1/2026.acl-long.26 | null | null | null |
2026.acl-long.27 | How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior | https://aclanthology.org/2026.acl-long.27/ | [
"Zidi Xiong",
"Yuping Lin",
"Wenya Xie",
"Pengfei He",
"Zirui Liu",
"Jiliang Tang",
"Himabindu Lakkaraju",
"Zhen Xiang"
] | Memory is a critical component in large language model (LLM)-based agents, enabling them to store and retrieve past executions to improve task performance over time. In this paper, we conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. Spe... | 2026.acl-long.27 | 10.18653/v1/2026.acl-long.27 | null | 2505.16067 | title_snapshot |
2026.acl-long.28 | SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification | https://aclanthology.org/2026.acl-long.28/ | [
"Tianyang Zhou",
"Ziyi Zhang",
"Haowen Lin",
"Somesh Jha",
"Mihai Christodorescu",
"Kirill Levchenko",
"Varun Chandrasekaran"
] | Translating software written in C to Rust has significant benefits in improving memory safety. However, manual translation is cumbersome, error-prone, and often produces unidiomatic code. Large language models (LLMs) have demonstrated promise in producing idiomatic translations, but offer no correctness guarantees. We ... | 2026.acl-long.28 | 10.18653/v1/2026.acl-long.28 | null | 2503.12511 | title_snapshot |
2026.acl-long.29 | IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review | https://aclanthology.org/2026.acl-long.29/ | [
"Fengbo Ma",
"Zixin Rao",
"Xiaoting Li",
"Zhetao Chen",
"Hongyue Sun",
"Yiping Zhao",
"Xianyan Chen",
"Zhen Xiang"
] | Scientific research relies on accurate information retrieval from literature to support analytical decisions.In this work, we introduce a new task, *INformation reTRieval through literAture reVIEW* (IntraView), which aims to automate fine-grained information retrieval *faithfully* grounded in the provided content in re... | 2026.acl-long.29 | 10.18653/v1/2026.acl-long.29 | null | 2604.22861 | title_snapshot |
2026.acl-long.30 | Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety | https://aclanthology.org/2026.acl-long.30/ | [
"Can Jin",
"Rui Wu",
"Tong Che",
"Qixin Zhang",
"Hongwu Peng",
"Jiahui Zhao",
"Zhenting Wang",
"Wenqi Wei",
"Ligong Han",
"Zhao Zhang",
"Yuan Cao",
"Ruixiang Tang",
"Dimitris N. Metaxas"
] | Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignment (DA) to enhance the safety of its o-series models through reasoning over detailed “code-like” safety rules, the effectiveness of this ap... | 2026.acl-long.30 | 10.18653/v1/2026.acl-long.30 | null | 2601.08000 | title_snapshot |
2026.acl-long.31 | Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production | https://aclanthology.org/2026.acl-long.31/ | [
"Maoxiao Ye",
"Xinfeng Ye",
"Sathiamoorthy Manoharan"
] | Earlier Sign Language Production (SLP) models typically relied on autoregressive methods that generate output tokens one by one, which inherently provide temporal alignment. Although techniques like Teacher Forcing can prevent model collapse during training, they still cannot solve the problem of error accumulation dur... | 2026.acl-long.31 | 10.18653/v1/2026.acl-long.31 | null | 2507.09105 | title_snapshot |
2026.acl-long.32 | Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models | https://aclanthology.org/2026.acl-long.32/ | [
"Zhiqing Cui",
"Binwu Wang",
"Qingxiang Liu",
"Yeqiang Wang",
"Zhengyang Zhou",
"Yuxuan Liang",
"Yang Wang"
] | Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approaches face notable limitations—such as marginalized role in model architectures, reliance on coarse statistical text prompts, and lack of inte... | 2026.acl-long.32 | 10.18653/v1/2026.acl-long.32 | null | 2510.07858 | title_snapshot |
2026.acl-long.33 | AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering | https://aclanthology.org/2026.acl-long.33/ | [
"Zheyuan Zhang",
"Kaiwen Shi",
"Zhengqing Yuan",
"Zehong Wang",
"Tianyi Ma",
"Keerthiram Murugesan",
"Vincent Galassi",
"Chuxu Zhang",
"Yanfang Ye"
] | Large language models (LLMs) and agent-based frameworks have advanced rapidly, enabling diverse applications. Yet, with the proliferation of models and agentic strategies, practitioners face substantial uncertainty in selecting the best configuration for a downstream task. Prior studies show that different agents and b... | 2026.acl-long.33 | 10.18653/v1/2026.acl-long.33 | null | 2510.05445 | title_snapshot |
2026.acl-long.34 | MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings | https://aclanthology.org/2026.acl-long.34/ | [
"Haonan Chen",
"Hong Liu",
"Yuping Luo",
"Liang Wang",
"Nan Yang",
"Furu Wei",
"Zhicheng Dou"
] | Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three limitations: causal attention in VLM backbones is suboptimal for embedding tasks; scalability issues due to reliance on high-quality labeled paired data for contrasti... | 2026.acl-long.34 | 10.18653/v1/2026.acl-long.34 | null | 2506.23115 | title_snapshot |
2026.acl-long.35 | ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL | https://aclanthology.org/2026.acl-long.35/ | [
"Yaxun Dai",
"Wenxuan Xie",
"Xialie Zhuang",
"Tianyu Yang",
"Ziyi Liu",
"Haiqin Yang",
"Yiying Yang",
"Yuhang Zhao",
"Pingfu Chao",
"Wenhao Jiang"
] | Current Text-to-SQL reasoning models often lack integrated execution feedback during generation, and most existing approaches utilize feedback only for post-hoc correction. This separation not only limits real-time error correction, but may also introduce mistakes by altering otherwise correct SQL queries. To address t... | 2026.acl-long.35 | 10.18653/v1/2026.acl-long.35 | null | 2505.12768 | title_snapshot |
2026.acl-long.36 | DecIF: Improving Instruction-Following through Decomposition | https://aclanthology.org/2026.acl-long.36/ | [
"Tingfeng Hui",
"Pengyu Zhu",
"Bowen Ping",
"Ling Tang",
"Guanting Dong",
"Yaqi Zhang",
"Sen Su"
] | We propose a novel data synthesis framework, DecIF, which automatically generates accurate and diverse instruction-following data from scratch for supervised fine-tuning (SFT) and reinforcement learning (RL), leveraging large language models (LLMs) and minimal external resources. By decomposing the data synthesis pipel... | 2026.acl-long.36 | 10.18653/v1/2026.acl-long.36 | null | 2505.13990 | title_judge |
2026.acl-long.37 | NavA^3: Understanding Any Instruction, Navigating Anywhere, Finding Anything | https://aclanthology.org/2026.acl-long.37/ | [
"Lingfeng Zhang",
"Xiaoshuai Hao",
"Yingbo Tang",
"Haoxiang Fu",
"Xinyu Zheng",
"Pengwei Wang",
"Zhongyuan Wang",
"Wenbo Ding",
"Shanghang Zhang"
] | Embodied navigation is a fundamental capability of embodied intelligence, enabling robots to move and interact within physical environments. However, existing navigation tasks primarily focus on predefined object navigation or instruction following, which significantly differs from human needs in real-world scenarios i... | 2026.acl-long.37 | 10.18653/v1/2026.acl-long.37 | null | 2508.04598 | title_snapshot |
2026.acl-long.38 | FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation | https://aclanthology.org/2026.acl-long.38/ | [
"Gen Li",
"Peiyu Liu"
] | Vision–Language Models (VLMs) excel at visual reasoning but still struggle with external knowledge integration. Retrieval-Augmented Generation(RAG) is a promising solution, but current methods remain inefficient and often fail to maintain high answer quality. To address these challenges, we propose VideoSpeculateRAG, a... | 2026.acl-long.38 | 10.18653/v1/2026.acl-long.38 | null | 2601.01513 | title_snapshot |
2026.acl-long.39 | Efficient Provably Secure Linguistic Steganography via Range Coding | https://aclanthology.org/2026.acl-long.39/ | [
"Ruiyi Yan",
"Yugo Murawaki"
] | Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is a long-standing goal and key motivation, has been extended to language-model-based steganography. Previous provably secure approaches have achieved perfect imperceptib... | 2026.acl-long.39 | 10.18653/v1/2026.acl-long.39 | null | 2604.08052 | title_snapshot |
2026.acl-long.40 | Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark | https://aclanthology.org/2026.acl-long.40/ | [
"Kai Zou",
"Ziqi Huang",
"Yuhao Dong",
"Shulin Tian",
"Dian Zheng",
"Hongbo Liu",
"Jingwen He",
"Bin Liu",
"Yu Qiao",
"Ziwei Liu"
] | Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat the two abilities in isolation or overlook tasks that inherently couple them. To address this gap, we present Uni-MMMU, a comprehensive and... | 2026.acl-long.40 | 10.18653/v1/2026.acl-long.40 | null | 2510.13759 | title_snapshot |
2026.acl-long.41 | MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search | https://aclanthology.org/2026.acl-long.41/ | [
"Sheng Zhang",
"Junyi Li",
"Yingyi Zhang",
"Pengyue Jia",
"Yichao Wang",
"Xiaowei Qian",
"Wenlin Zhang",
"Maolin Wang",
"Yong Liu",
"Xiangyu Zhao"
] | Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external knowledge to answer complex queries. However, the iterative think–search loop accumulates long system memories, leading to memory dilution prob... | 2026.acl-long.41 | 10.18653/v1/2026.acl-long.41 | null | 2604.17265 | title_snapshot |
2026.acl-long.42 | CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China | https://aclanthology.org/2026.acl-long.42/ | [
"Bolun Sun",
"Charles Chang",
"Yuen Yuen Ang",
"Ruotong Mu",
"Yuchen Xu",
"Zhengxin Zhang",
"Pingxu Hao"
] | We introduce CAPC-CG, the Chinese Adaptive Policy Communication (Central Government) Corpus, the first open dataset of Chinese policy directives annotated with a five-color typology of policy signals, capturing clarity and ambiguity, grounded in the theory of adaptive policy communication. Spanning 1949–2023, this corp... | 2026.acl-long.42 | 10.18653/v1/2026.acl-long.42 | null | 2510.08986 | title_snapshot |
2026.acl-long.43 | MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems | https://aclanthology.org/2026.acl-long.43/ | [
"Shuhang Chen",
"Hangjie Yuan",
"Yunqiu Xu",
"Pengwei Liu",
"Tao Feng",
"Jun Cen",
"Zeying Huang",
"Yi Yang"
] | Despite strong results on many tasks, multimodal large language models (MLLMs) still underperform on visual mathematical problem solving, especially in reliably perceiving and interpreting diagrams. Inspired by human problem-solving, we hypothesize that the ability to extract meaningful information from diagrams is piv... | 2026.acl-long.43 | 10.18653/v1/2026.acl-long.43 | null | 2503.16549 | title_snapshot |
2026.acl-long.44 | Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography | https://aclanthology.org/2026.acl-long.44/ | [
"Ruiyi Yan",
"Shiao Meng",
"Yugo Murawaki"
] | Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications. While previous work mitigates this fragility by limiting the context window, it significantly compromises text quality. In this paper, we pro... | 2026.acl-long.44 | 10.18653/v1/2026.acl-long.44 | null | 2604.09066 | title_snapshot |
2026.acl-long.45 | What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective | https://aclanthology.org/2026.acl-long.45/ | [
"Guangzeng Han",
"Xiaolei Huang"
] | Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an instruction data selection framework based on weighted in-context influence (wICI), which measures how effectively each candidate example reduces instruction-following ... | 2026.acl-long.45 | 10.18653/v1/2026.acl-long.45 | null | 2604.25132 | title_snapshot |
2026.acl-long.46 | RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention | https://aclanthology.org/2026.acl-long.46/ | [
"Xinyu Shi",
"Kairong Luo",
"Zhen Zheng",
"Wenguang Chen"
] | Large Language Models (LLMs) have rapidly advanced in recent years, scaling up in both parameter count and context length. However, as context windows extend from thousands to hundreds of thousands of tokens, attention computation becomes the dominant source of memory usage and runtime in decoding stages, severely limi... | 2026.acl-long.46 | 10.18653/v1/2026.acl-long.46 | null | null | null |
2026.acl-long.47 | XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration | https://aclanthology.org/2026.acl-long.47/ | [
"Nuo Chen",
"Andre Lin HuiKai",
"Jiaying Wu",
"Junyi Hou",
"Zining Zhang",
"Qian Wang",
"Xidong Wang",
"Bingsheng He"
] | Despite the growing adoption of large language models (LLMs) in academic workflows, their capabilities remain limited in supporting high-quality scientific writing. Most existing systems are designed for general-purpose scientific text generation and fail to meet the sophisticated demands of research communication beyo... | 2026.acl-long.47 | 10.18653/v1/2026.acl-long.47 | null | 2505.11336 | title_snapshot |
2026.acl-long.48 | Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution | https://aclanthology.org/2026.acl-long.48/ | [
"Beidan Liu",
"Zhengqiu Zhu",
"Chen Gao",
"Tianle Pu",
"Yong Zhao",
"Wei Qi",
"Quanjun Yin"
] | Large Language Model (LLM)-based optimization has recently shown promise for autonomous problem solving, yet most approaches still cast LLMs as passive constraint checkers rather than proactive strategy designers, limiting their effectiveness on complex Constraint Optimization Problems (COPs). To address this, we prese... | 2026.acl-long.48 | 10.18653/v1/2026.acl-long.48 | null | 2509.12643 | title_snapshot |
2026.acl-long.49 | A Game-Theoretica Negotiation Framework for Cross-Cultural Consensus | https://aclanthology.org/2026.acl-long.49/ | [
"Guoxi Zhang",
"Jiawei Chen",
"Tianzhuo Yang",
"Jiaming Ji",
"Yaodong Yang",
"Juntao Dai"
] | Large language models (LLMs) are shaping global values, yet they frequently exhibit a pronounced WEIRD (Western, Educated, Industrialized, Rich, Democratic) cultural bias, marginalizing diverse viewpoints and posing challenges for reconciling diverse populations with varying cultural backgrounds and value systems. In t... | 2026.acl-long.49 | 10.18653/v1/2026.acl-long.49 | null | 2506.13245 | title_judge |
2026.acl-long.50 | Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs | https://aclanthology.org/2026.acl-long.50/ | [
"Zifeng Cheng",
"Lingyun Qian",
"Zhiwei Jiang",
"Cong Wang",
"Yafeng Yin",
"Fei Shen",
"Ao Zhou",
"Qing Gu"
] | Extracting conditional text embeddings from large language models (LLMs) is a promising paradigm, as it requires neither additional data nor fine-tuning. Existing methods incorporate conditions into prompts to guide LLMs to focus on specific aspects and elicit conditional text embeddings. However, relying solely on pro... | 2026.acl-long.50 | 10.18653/v1/2026.acl-long.50 | null | null | null |
2026.acl-long.51 | Exploring Attention Attractors in Large Language Models | https://aclanthology.org/2026.acl-long.51/ | [
"Ziheng Wang",
"Zihao Yue",
"Wenxuan Wang",
"Qin Jin"
] | This paper explores attention attractors, tokens that draw significantly high attention, in large language models. We analyze them from three perspectives: (1) Functionality: We demonstrate their role in aggregating information from preceding contexts to facilitate future predictions. (2) Distribution: Through layer-wi... | 2026.acl-long.51 | 10.18653/v1/2026.acl-long.51 | null | null | null |
2026.acl-long.52 | Identifying the Periodicity of Information in Natural Language | https://aclanthology.org/2026.acl-long.52/ | [
"Yulin OU",
"Yu Wang",
"Yang Xu",
"Hendrik Buschmeier"
] | Recent theoretical advancement of information density in natural language has brought the following question on desk: To what degree does natural language exhibit periodicity pattern in its encoded information? We address this question by introducing a new method called AutoPeriod of Surprisal (APS). APS adopts a canon... | 2026.acl-long.52 | 10.18653/v1/2026.acl-long.52 | null | 2510.27241 | title_snapshot |
2026.acl-long.53 | EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User’s Internal World | https://aclanthology.org/2026.acl-long.53/ | [
"Jing Ye",
"Lu Xiang",
"Yaping Zhang",
"Chengqing Zong"
] | Current evaluation paradigms for emotional support conversations tend to reward generic empathetic responses, yet they fail to assess whether the support is genuinely personalized to users’ unique psychological profiles and contextual needs. We introduce EmoHarbor, an automated evaluation framework that adopts a User-a... | 2026.acl-long.53 | 10.18653/v1/2026.acl-long.53 | null | 2601.01530 | title_snapshot |
2026.acl-long.54 | ReasonEmbed: Enhanced Text Embeddings for Reasoning-Intensive Document Retrieval | https://aclanthology.org/2026.acl-long.54/ | [
"Jianlyu Chen",
"Junwei Lan",
"Chaofan Li",
"Defu Lian",
"Zheng Liu"
] | In this paper, we introduce **ReasonEmbed**, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. First, we propose **ReMixer**, a new data synthesis method that overcomes the triviality problem prevalent in previous synthetic datasets, ... | 2026.acl-long.54 | 10.18653/v1/2026.acl-long.54 | null | 2510.08252 | title_snapshot |
2026.acl-long.55 | SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models | https://aclanthology.org/2026.acl-long.55/ | [
"Binxian Su",
"Haoye Lou",
"Shucheng Zhu",
"Weikang Wang",
"Ying Liu",
"Dong Yu",
"Pengyuan Liu"
] | Large language models (LLMs) are being increasingly used in urban planning, but since gendered space theory highlights how gender hierarchies are embedded in spatial organization, there is concern that LLMs may reproduce or amplify such biases. We introduce SPAGBias — the first systematic framework to evaluate spatial ... | 2026.acl-long.55 | 10.18653/v1/2026.acl-long.55 | null | 2604.14672 | title_snapshot |
2026.acl-long.56 | VALU: A Benchmark for Video Anomaly Temporal Localization and Understanding at Multiple Semantic Levels | https://aclanthology.org/2026.acl-long.56/ | [
"Yixiao He",
"Menghao Zhang",
"Haifeng Sun",
"Jing Wang",
"Kangheng Lin",
"Jinghan Wang",
"Chenye Xu",
"Pengfei Ren",
"Qi Qi",
"Jingyu Wang"
] | Video anomaly understanding (VAU) is critical for real-world scenarios. Recent advances in Video Large Language Models (Video-LLMs) enhance the ability of VAU models to describe and interpret anomalies. However, progress in anomaly localization is still limited by two key issues. First, most existing video anomaly data... | 2026.acl-long.56 | 10.18653/v1/2026.acl-long.56 | null | null | null |
2026.acl-long.57 | CoreGaze: Core Subgraph-Driven Visual Gaze Diffusion for Training-Free Referring Multimodal Large Language Models | https://aclanthology.org/2026.acl-long.57/ | [
"Xiaoyang Yi",
"Jing Chen",
"Yuru Bao",
"Jian Zhang"
] | Referring multimodal large language models enable users to ground queries to specific image regions via spatial prompts, supporting fine-grained referring dialogue. However, existing methods rely on extensive fine-tuning to mitigate attention distraction, which incurs high computational costs and limits adaptability. W... | 2026.acl-long.57 | 10.18653/v1/2026.acl-long.57 | null | null | null |
2026.acl-long.58 | RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification | https://aclanthology.org/2026.acl-long.58/ | [
"Shakhrul Iman Siam",
"Tiantian Feng",
"Jiankun Zhang",
"Shrikanth Narayanan",
"Mi Zhang"
] | Respiratory diseases remain a leading cause of global mortality, where timely and accurate diagnosis is critical to improving patient outcomes and reducing healthcare burdens. While prior work has explored audio-based models for respiratory disease detection, such unimodal approaches often suffer from limited generaliz... | 2026.acl-long.58 | 10.18653/v1/2026.acl-long.58 | null | 2606.09966 | title_snapshot |
2026.acl-long.59 | Beyond Detection: Evaluating Fallacy Awareness of LLMs in Interactive Scenarios | https://aclanthology.org/2026.acl-long.59/ | [
"Conghui Niu",
"Ningxin Wu",
"Ziran Zhao",
"Dong Yu",
"Chen Kang",
"Pengyuan Liu"
] | Large Language Models (LLMs) often fail to recognize fallacious reasoning in real-world interactions, despite strong performance on static fallacy detection tasks. We define this ability as fallacy awareness, the capacity to autonomously perceive and resist fallacies in dynamic, pragmatic contexts. To study this, we in... | 2026.acl-long.59 | 10.18653/v1/2026.acl-long.59 | null | null | null |
2026.acl-long.60 | Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining | https://aclanthology.org/2026.acl-long.60/ | [
"Deniz Bayazit",
"Aaron Mueller",
"Antoine Bosselut"
] | Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, because traditional evaluation methods (e.g., benchmarking) fail to reveal how models acquire these concepts and capabilities, it is not well understood when and how these specific ... | 2026.acl-long.60 | 10.18653/v1/2026.acl-long.60 | null | 2509.05291 | title_snapshot |
2026.acl-long.61 | Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark | https://aclanthology.org/2026.acl-long.61/ | [
"Zihan Zhang",
"Yu Bao",
"Xiao Ding",
"Tianyi Jiang",
"Kai Xiong"
] | Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG). Electroencephalography (EEG) offers a non-invasive alternative, and EEG-to-text (EEG2Text) has been widely explored. Interestingly, howev... | 2026.acl-long.61 | 10.18653/v1/2026.acl-long.61 | null | null | null |
2026.acl-long.62 | ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling | https://aclanthology.org/2026.acl-long.62/ | [
"Yuxuan Jiang",
"Zehua Chen",
"Zeqian Ju",
"Yusheng Dai",
"Weibei Dou",
"Jun Zhu"
] | Recent efforts on text-to-audio (TTA) generation are starting to explore fine-grained controllability, e.g., precise timing control, with innovations on conditioning techniques or training-free latent manipulations. However, constrained by data scarcity, their generation performance at scale is still limited. In this s... | 2026.acl-long.62 | 10.18653/v1/2026.acl-long.62 | null | 2510.08878 | title_snapshot |
2026.acl-long.63 | PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise | https://aclanthology.org/2026.acl-long.63/ | [
"Sapir Harary",
"Eran Hirsch",
"Aviv Slobodkin",
"David Wan",
"Mohit Bansal",
"Ido Dagan"
] | Natural Language Inference (NLI) models have been used in various ways to improve the factuality of LLM outputs. This is typically done by applying an NLI model to judge whether the model output is entailed from the supposed evidence, triggering some corrective actions, such as beam reranking at inference time or RL re... | 2026.acl-long.63 | 10.18653/v1/2026.acl-long.63 | null | 2511.01359 | title_snapshot |
2026.acl-long.64 | RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection | https://aclanthology.org/2026.acl-long.64/ | [
"Ziyang Zhou",
"Ziqi Liu",
"Yan Wang",
"Yiming Lin",
"Yangbin Chen"
] | Sarcasm detection remains a significant challenge due to its reliance on nuanced contextual understanding, world knowledge, and multi-faceted linguistic cues that vary substantially across different sarcastic expressions. Existing approaches, from fine-tuned transformers to large language models, apply a uniform reason... | 2026.acl-long.64 | 10.18653/v1/2026.acl-long.64 | null | 2601.17002 | title_snapshot |
2026.acl-long.65 | On the Emergence and Test-Time Use of Structural Information in Large Language Models | https://aclanthology.org/2026.acl-long.65/ | [
"Michelle Chao Chen",
"Moritz Miller",
"Bernhard Schölkopf",
"Siyuan Guo"
] | Learning structural information from observational data is central to producing new knowledge outside the training corpus. This holds for mechanistic understanding in scientific discovery as well as flexible test-time compositional generation. We thus study how language models learn abstract structures and utilize the ... | 2026.acl-long.65 | 10.18653/v1/2026.acl-long.65 | null | 2601.17869 | title_snapshot |
2026.acl-long.66 | SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA | https://aclanthology.org/2026.acl-long.66/ | [
"Sher Badshah",
"Ali Emami",
"Hassan Sajjad"
] | As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness. Meanwhile, using LLMs themselves as evaluators without external grounding remains unreliable for objective... | 2026.acl-long.66 | 10.18653/v1/2026.acl-long.66 | null | null | null |
2026.acl-long.67 | J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization | https://aclanthology.org/2026.acl-long.67/ | [
"Austin Xu",
"Yilun Zhou",
"Xuan-Phi Nguyen",
"Caiming Xiong",
"Shafiq Joty"
] | To keep pace with the increasing velocity of large language models (LLM) development, model output evaluation has transitioned away from time-consuming human evaluation to automatic evaluation, where LLMs themselves are tasked with assessing and critiquing other model outputs. LLM-as-judge models are a class of generat... | 2026.acl-long.67 | 10.18653/v1/2026.acl-long.67 | null | 2505.13346 | title_snapshot |
2026.acl-long.68 | A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains | https://aclanthology.org/2026.acl-long.68/ | [
"Xianren Zhang",
"Shreyas Prasad",
"Di Wang",
"Qiuhai Zeng",
"Suhang Wang",
"Wenbo Yan",
"Mat Hans"
] | Web agents have shown great promise in performing many tasks on e-commerce websites. To assess their capabilities, several benchmarks have been introduced. However, current benchmarks in the e-commerce domain face two major problems. First, they primarily focus on product search tasks (e.g., "Find an Apple Watch"), fai... | 2026.acl-long.68 | 10.18653/v1/2026.acl-long.68 | null | 2508.15832 | title_snapshot |
2026.acl-long.69 | Reinforcement Learning for Self-Improving Agent with Skill Library | https://aclanthology.org/2026.acl-long.69/ | [
"Jiongxiao Wang",
"Qiaojing Yan",
"Yawei Wang",
"Yijun Tian",
"Soumya Smruti Mishra",
"Zhichao Xu",
"Megha Gandhi",
"Panpan Xu",
"Lin Lee Cheong"
] | Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new ... | 2026.acl-long.69 | 10.18653/v1/2026.acl-long.69 | null | 2512.17102 | title_snapshot |
2026.acl-long.70 | CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing | https://aclanthology.org/2026.acl-long.70/ | [
"Rui Wang",
"Junda Wu",
"Yu Xia",
"Tong Yu",
"Ruiyi Zhang",
"Ryan A. Rossi",
"Subrata Mitra",
"Lina Yao",
"Julian McAuley"
] | Large Language Models (LLMs) are susceptible to indirect prompt injection attack, where the model inadvertently responds to instructions injected into the prompt context. This vulnerability stems from LLMs’ inability to distinguish between data and instructions within a prompt. We propose CachePrune that defends agains... | 2026.acl-long.70 | 10.18653/v1/2026.acl-long.70 | null | 2504.21228 | title_snapshot |
2026.acl-long.71 | Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs | https://aclanthology.org/2026.acl-long.71/ | [
"Parsa Hejabi",
"Elnaz Rahmati",
"Alireza Salkhordeh Ziabari",
"Morteza Dehghani"
] | Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. In this paper, we propose Flip-Flop Consistency (F^2C), an unsupervised training method that improves robustness to such perturbations. F^2C is composed of two key components. The first, Consensus Cro... | 2026.acl-long.71 | 10.18653/v1/2026.acl-long.71 | null | 2510.14242 | title_snapshot |
2026.acl-long.72 | Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection | https://aclanthology.org/2026.acl-long.72/ | [
"Zedian Shao",
"Hongbin Liu",
"Yuepeng Hu",
"Neil Zhenqiang Gong"
] | Multi-modal large language models (MLLMs) have emerged as powerful tools for analyzing Internet-scale image data, offering significant benefits but also raising critical safety and societal concerns. In particular, these models may be misused to extract sensitive information from personal images, such as identifying in... | 2026.acl-long.72 | 10.18653/v1/2026.acl-long.72 | null | 2604.09024 | title_snapshot |
2026.acl-long.73 | Logic Matters in Lightweight Hallucination Classification for RAG System | https://aclanthology.org/2026.acl-long.73/ | [
"Ningyuan Yang",
"Kaizhu Huang"
] | We propose a lightweight, modular framework for hallucination detection in Retrieval-Augmented Generation (RAG) systems, addressing the critical challenge where logical dependencies span across fragmented retrieval results. To address the inherent limitations of compact models in processing long-context information and... | 2026.acl-long.73 | 10.18653/v1/2026.acl-long.73 | null | null | null |
2026.acl-long.74 | Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning | https://aclanthology.org/2026.acl-long.74/ | [
"Shuyao Xu",
"Cheng Peng",
"Jiangxuan Long",
"Weidi Xu",
"Wei Chu",
"Yuan Qi"
] | Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. However, standard practices discard incorrect reasoning traces—valuable, yet underutilized data. This paper addresses the critical question: How can both positive and negative distilled reas... | 2026.acl-long.74 | 10.18653/v1/2026.acl-long.74 | null | 2505.24850 | title_snapshot |
2026.acl-long.75 | Grammar Search for Multi-Agent Systems | https://aclanthology.org/2026.acl-long.75/ | [
"Mayank Singh",
"Vikas Yadav",
"Shiva Krishna Reddy Malay",
"Shravan Nayak",
"Sai Rajeswar",
"Sathwik Tejaswi Madhusudhan",
"Eduardo Blanco"
] | Automatic search for Multi-Agent Systems has recently emerged as a key focus in agentic AI research. Several prior approaches have relied on LLM-based free-form search over the code space. In this work, we propose a more structured framework that explores the same space through a fixed set of composable components. We ... | 2026.acl-long.75 | 10.18653/v1/2026.acl-long.75 | null | 2512.14079 | title_snapshot |
2026.acl-long.76 | ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval | https://aclanthology.org/2026.acl-long.76/ | [
"David H. Yang",
"Yuxuan Zhu",
"Mohammad Mohammadi Amiri",
"Keerthiram Murugesan",
"Tejaswini Pedapati",
"Subhajit Chaudhury",
"Pin-Yu Chen"
] | Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During generation, LLMs rely on a key-value (KV) cache for autoregressive decoding. However, the memory footprint of the KV cache grows with output... | 2026.acl-long.76 | 10.18653/v1/2026.acl-long.76 | null | 2604.10898 | title_snapshot |
2026.acl-long.77 | Explicit Trait Inference for Multi-Agent Coordination | https://aclanthology.org/2026.acl-long.77/ | [
"Suhaib Abdurahman",
"Etsuko Ishii",
"Katerina Margatina",
"Divya Bhargavi",
"Monica Sunkara",
"Yi Zhang"
] | LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misaligned behaviors. We propose Explicit Trait Inference (ETI), a psychologically grounded method for improving coordination. ETI enables agents to infer and track partner... | 2026.acl-long.77 | 10.18653/v1/2026.acl-long.77 | null | 2604.19278 | title_snapshot |
2026.acl-long.78 | SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe | https://aclanthology.org/2026.acl-long.78/ | [
"Yuxin Xiao",
"Shujian Zhang",
"Marzyeh Ghassemi",
"Wenxuan Zhou"
] | To acquire instruction-following capabilities, large language models (LLMs) undergo instruction tuning, where they are trained on instruction-response pairs using next-token prediction (NTP). Efforts to improve instruction tuning often focus on higher-quality supervised fine-tuning (SFT) datasets, typically requiring d... | 2026.acl-long.78 | 10.18653/v1/2026.acl-long.78 | null | 2410.05248 | title_snapshot |
2026.acl-long.79 | Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness | https://aclanthology.org/2026.acl-long.79/ | [
"Amin Banayeeanzade",
"Ala N. Tak",
"Fatemeh Bahrani",
"Anahita Bolourani",
"Leonardo Blas",
"Emilio Ferrara",
"Jonathan Gratch",
"Sai Praneeth Karimireddy"
] | The ability to control LLMs’ emulated emotional states and personality traits is an essential step in enabling rich, human-centered interactions in socially interactive settings. We introduce PsySET, a Psychologically-informed benchmark to evaluate LLM Steering Effectiveness and Trustworthiness across the emotion and p... | 2026.acl-long.79 | 10.18653/v1/2026.acl-long.79 | null | 2510.04484 | title_snapshot |
2026.acl-long.80 | Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech | https://aclanthology.org/2026.acl-long.80/ | [
"Siqi Ouyang",
"Shuoyang Ding",
"Oleksii Hrinchuk",
"Vitaly Lavrukhin",
"Brian Yan",
"Boris Ginsburg",
"Lei Li"
] | Simultaneous speech translation (SST) generates translations while receiving partial speech input. Recent advances show that large language models (LLMs) can substantially improve SST quality, but at the cost of high computational overhead. To reduce this cost, prior work reformulates SST as a multi-turn dialogue task,... | 2026.acl-long.80 | 10.18653/v1/2026.acl-long.80 | null | 2604.21045 | title_snapshot |
2026.acl-long.81 | Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering | https://aclanthology.org/2026.acl-long.81/ | [
"Songtao Jiang",
"Yuan Wang",
"Ruizhe Chen",
"Yan Zhang",
"Ruilin Luo",
"Bohan Lei",
"Yeying Jin",
"Sibo Song",
"ZhiBo Yang",
"Jimeng Sun",
"Jian Wu",
"Zuozhu Liu"
] | While reinforcement learning from verifiable rewards (RLVR) has been proven highly effective for enhancing reasoning, its application to medical visual question answering (Med-VQA) is hampered by models producing reasoning inconsistent with either the visual evidence or the final answer. Our analysis reveals a critical... | 2026.acl-long.81 | 10.18653/v1/2026.acl-long.81 | null | 2506.12849 | title_judge |
2026.acl-long.82 | CRISP: Persistent Concept Unlearning via Sparse Autoencoders | https://aclanthology.org/2026.acl-long.82/ | [
"Tomer Ashuach",
"Dana Arad",
"Aaron Mueller",
"Martin Tutek",
"Yonatan Belinkov"
] | As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-ba... | 2026.acl-long.82 | 10.18653/v1/2026.acl-long.82 | null | 2508.13650 | title_snapshot |
2026.acl-long.83 | LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification | https://aclanthology.org/2026.acl-long.83/ | [
"Penghui Yang",
"Cunxiao Du",
"Fengzhuo Zhang",
"Haonan Wang",
"Tianyu Pang",
"Chao Du",
"Bo An"
] | As Large Language Models (LLMs) can now process extremely long contexts, efficient inference over these extended inputs has become increasingly important, especially for emerging applications like LLM agents that highly depend on this capability. Speculative decoding (SD) offers a promising lossless acceleration techni... | 2026.acl-long.83 | 10.18653/v1/2026.acl-long.83 | null | 2502.17421 | title_snapshot |
2026.acl-long.84 | Using Perspectival Words Is Harder Than Vocabulary Words for Humans —and Even More So for Multimodal Language Models | https://aclanthology.org/2026.acl-long.84/ | [
"Dota Tianai Dong",
"Yifan Luo",
"Po-Ya Angela Wang",
"Asli Ozyurek",
"Paula Rubio-Fernandez"
] | Multimodal language models (MLMs) increasingly demonstrate human-like communication, yet their use of everyday perspectival words remains poorly understood. To address this gap, we compare humans and MLMs in their use of three word types, which we predict impose increasing cognitive demands: vocabulary (e.g., ’boat’ or... | 2026.acl-long.84 | 10.18653/v1/2026.acl-long.84 | null | 2506.00065 | title_snapshot |
2026.acl-long.85 | CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation | https://aclanthology.org/2026.acl-long.85/ | [
"Noy Sternlicht",
"Tom Hope"
] | A hallmark of human innovation is recombination—the creation of novel ideas by integrating elements from existing concepts and mechanisms. In this work, we introduce CHIMERA, the first large-scale Knowledge Base (KB) of recombination examples automatically mined from the scientific literature. CHIMERA enables empirical... | 2026.acl-long.85 | 10.18653/v1/2026.acl-long.85 | null | 2505.20779 | title_snapshot |
2026.acl-long.86 | Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning | https://aclanthology.org/2026.acl-long.86/ | [
"Houxing Ren",
"Mingjie Zhan",
"Zimu Lu",
"Ke Wang",
"Yunqiao Yang",
"Haotian Hou",
"Hongsheng Li"
] | Spreadsheets are central to real-world applications such as enterprise reporting, auditing, and scientific data management. Despite their ubiquity, existing large language model based approaches typically treat tables as plain text, overlooking critical layout cues and visual semantics. Moreover, real-world spreadsheet... | 2026.acl-long.86 | 10.18653/v1/2026.acl-long.86 | null | 2604.12282 | title_snapshot |
2026.acl-long.87 | GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks | https://aclanthology.org/2026.acl-long.87/ | [
"Jianwen Luo",
"Yiming Huang",
"Jinxiang Meng",
"Fangyu Lei",
"Shizhu He",
"Xiao Liu",
"Shanshan Jiang",
"Bin Dong",
"Jun Zhao",
"Kang Liu"
] | Large Language Models (LLMs) have shown great promise in tool-making, yet existing frameworks often struggle to efficiently construct reliable toolsets and are limited to single-task settings. To address these challenges, we propose GATE (Graph-based Adaptive Tool Evolution), an adaptive framework that dynamically cons... | 2026.acl-long.87 | 10.18653/v1/2026.acl-long.87 | null | 2502.14848 | title_snapshot |
2026.acl-long.88 | Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models | https://aclanthology.org/2026.acl-long.88/ | [
"Huazheng Wang",
"Yongcheng Jing",
"Haifeng Sun",
"Yingjie Wang",
"Jingyu Wang",
"Jianxin Liao",
"Dacheng Tao"
] | In this paper, we investigate knowledge forgetting in large language models with a focus on its generalisation—ensuring that models forget not only specific training samples but also related implicit knowledge. To this end, we begin by identifying a broader unlearning scope that includes both target data and logically ... | 2026.acl-long.88 | 10.18653/v1/2026.acl-long.88 | null | 2502.19982 | title_snapshot |
2026.acl-long.89 | Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation | https://aclanthology.org/2026.acl-long.89/ | [
"Xingyu Zhu",
"Junfeng Fang",
"Shuo Wang",
"Beier Zhu",
"Zhicai Wang",
"Yonghui Yang",
"Xiangnan He"
] | Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine tuning on annotated data devoid of hallucinations offers the most direct solution, while its high computational cost motivates recent representation-based methods,... | 2026.acl-long.89 | 10.18653/v1/2026.acl-long.89 | null | 2604.20366 | title_snapshot |
2026.acl-long.90 | APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention | https://aclanthology.org/2026.acl-long.90/ | [
"Yuxiang Huang",
"Mingye Li",
"Xu Han",
"Chaojun Xiao",
"Weilin Zhao",
"Ao Sun",
"Ziqi Yuan",
"Hao Zhou",
"Fandong Meng",
"Zhiyuan Liu"
] | The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply sparse attention on a single GPU, yielding limited acceleration or degraded performance and restri... | 2026.acl-long.90 | 10.18653/v1/2026.acl-long.90 | null | 2601.21444 | title_snapshot |
2026.acl-long.91 | AraVQA: Building a New Arabic Factoid Visual Question Answering Dataset from Wikipedia | https://aclanthology.org/2026.acl-long.91/ | [
"Sultan Alrowili",
"Younes Samih",
"Abed Alhakim Freihat",
"Mathan Kumar Eswaran"
] | The development of large-scale Visual Question Answering (VQA) datasets has traditionally relied on resource-intensive manual annotation. In addition, most of the existing Arabic VQA datasets focus on culturally-specific and dialect-aware domains. To address these limitations, we propose a new pipeline that leverages W... | 2026.acl-long.91 | 10.18653/v1/2026.acl-long.91 | null | null | null |
2026.acl-long.92 | What is a protest anyway? Codebook conceptualization is still a first-order concern in LLM-era classification | https://aclanthology.org/2026.acl-long.92/ | [
"Andrew Halterman",
"Katherine A. Keith"
] | Generative large language models (LLMs) are now used extensively for text classification in computational social science (CSS). In this work, we focus on the steps before and after LLM prompting: conceptualization of the categories to classify and using LLM predictions in downstream statistical inference. We argue thes... | 2026.acl-long.92 | 10.18653/v1/2026.acl-long.92 | null | 2510.03541 | title_snapshot |
2026.acl-long.93 | When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms | https://aclanthology.org/2026.acl-long.93/ | [
"Chaewan Chun",
"Delvin Ce Zhang",
"Dongwon Lee"
] | Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of shows and hundreds of millions of listeners, audio platforms are now a major channel for misinformation. Yet existing fact-checking pipeline... | 2026.acl-long.93 | 10.18653/v1/2026.acl-long.93 | null | 2604.16767 | title_snapshot |
2026.acl-long.94 | Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking | https://aclanthology.org/2026.acl-long.94/ | [
"Jerry Huang",
"Siddarth Madala",
"Cheng Niu",
"Julia Hockenmaier",
"Tong Zhang"
] | Reranking algorithms have made progress in improving document retrieval quality by efficiently aggregating relevance judgments generated by large language models (LLMs). However, identifying relevant documents for queries that require in-depth reasoning remains a major challenge. Reasoning-intensive queries often exhib... | 2026.acl-long.94 | 10.18653/v1/2026.acl-long.94 | null | 2511.01208 | title_snapshot |
2026.acl-long.95 | Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence | https://aclanthology.org/2026.acl-long.95/ | [
"Jinseok Chung",
"Minkyoung Song",
"Hyunji Jung",
"Namhoon Lee"
] | In-Context Learning (ICL) allows large language models to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model’s ability to understand the context, obscuring whether failures arise from data properties or model limitati... | 2026.acl-long.95 | 10.18653/v1/2026.acl-long.95 | null | 2606.19353 | title_snapshot |
2026.acl-long.96 | SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs | https://aclanthology.org/2026.acl-long.96/ | [
"Yanxiao Zhao",
"Yaqian Li",
"Zi-Hao Bo",
"Rinyoichi Takezoe",
"Haojia Hui",
"Mo Guang",
"Renlei",
"Xiaolin Qin",
"Kaiwen Long"
] | Large language models (LLMs) exhibit strong general reasoning, yet the community lacks controllable, scalable, and verifiable tools to analyze and improve these abilities. We present SATQuest, a verifier that generates diverse SAT-based reasoning tasks directly from Conjunctive Normal Form (CNF) instances and checks an... | 2026.acl-long.96 | 10.18653/v1/2026.acl-long.96 | null | 2509.00930 | title_snapshot |
2026.acl-long.97 | Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation | https://aclanthology.org/2026.acl-long.97/ | [
"Xi Xiao",
"Chenrui Ma",
"Yunbei Zhang",
"Chen Liu",
"Zhuxuanzi Wang",
"Yanshu Li",
"Lin Zhao",
"Guosheng Hu",
"Tianyang Wang",
"Hao Xu"
] | Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: \textit{semantic drift}, arising from treating all update directions with equal importance, and \textit{structural incoherence}, due to adapting layers independent... | 2026.acl-long.97 | 10.18653/v1/2026.acl-long.97 | null | 2603.14228 | title_snapshot |
2026.acl-long.98 | ReContraster: Making Your Posters Stand Out with Regional Contrast | https://aclanthology.org/2026.acl-long.98/ | [
"Peixuan Zhang",
"Zijian Jia",
"Ziqi Cai",
"Shuchen Weng",
"Si Li",
"Boxin Shi"
] | Effective poster design requires rapidly capturing attention and clearly conveying messages. Inspired by the “contrast effects” principle, we propose ReContraster, the first training-free model to leverage regional contrast to make posters stand out. By emulating the cognitive behaviors of a poster designer, ReContrast... | 2026.acl-long.98 | 10.18653/v1/2026.acl-long.98 | null | 2604.10442 | title_snapshot |
2026.acl-long.99 | Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders | https://aclanthology.org/2026.acl-long.99/ | [
"Xiangchen Song",
"Aashiq Muhamed",
"Yujia Zheng",
"Lingjing Kong",
"Zeyu Tang",
"Mona T. Diab",
"Virginia Smith",
"Kun Zhang"
] | Sparse Autoencoders (SAEs) are a prominent tool in mechanistic interpretability (MI) for decomposing neural network activations into interpretable features. However, the aspiration to identify a canonical set of features is challenged by the observed inconsistency of learned SAE features across different training runs,... | 2026.acl-long.99 | 10.18653/v1/2026.acl-long.99 | null | 2505.20254 | title_judge |
2026.acl-long.100 | EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning | https://aclanthology.org/2026.acl-long.100/ | [
"Jiawei Liu",
"Qisi Chen",
"Jianshu Zhang",
"Quan Liu",
"Defu Lian"
] | Large Language Models (LLMs) excel at complex reasoning through search algorithms, yet current strategies often suffer from massive token consumption due to redundant exploration of semantically equivalent steps. Existing semantic similarity methods struggle to accurately identify such equivalence in domain-specific co... | 2026.acl-long.100 | 10.18653/v1/2026.acl-long.100 | null | 2505.16312 | title_snapshot |
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