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Triple-based Iterative Retrieval-Augmented Generation (iRAG) mitigates document-level noise for multi-hop question answering. However, existing methods still face limitations: (i) greedy single-path expansion, which propagates early errors and fails to capture parallel evidence from different reasoning branches, and (ii) granularity–demand mismatch, where a single evidence representation struggles to balance noise control with contextual sufficiency. In this paper, we propose the Construction–Integration Retrieval and Adaptive Generation model, CIRAG. It introduces an Iterative Construction–Integration module that constructs candidate triples and history-conditionally integrates them to distill core triples and generate the next-hop query. This module mitigates the greedy trap by preserving multiple plausible evidence chains. Besides, to address the granularity–demand mismatch, we propose an Adaptive Cascaded Multi-Granularity Generation module that progressively expands contextual evidence based on the problem requirements, from triples to supporting sentences and full passages. Moreover, we introduce Trajectory Distillation, which distills the teacher model’s integration policy into a lightweight student, enabling efficient and reliable long-horizon reasoning. Extensive experiments demonstrate that CIRAG achieves superior performance compared to existing iRAG methods.

ACL 2026

CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

technical paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Efficiently aligning visual features with Large Language Models (LLMs) remains a critical bottleneck in Multimodal LLMs. Existing query-based alignment modules (e.g., Q-Former) rely on randomly initialized queries, resulting in an inefficient cold start exploration process. Furthermore, they enforce uniform cross-attention across all layers, leading to computational redundancy. Our empirical analysis reveals that query tokens initialized with language priors can rapidly capture global semantics, leading to early representation convergence after only a few layers. In this paper, we propose **Cat-MoD**, a **Ca**ption **t**oken Guided Asymmetric **M**ixture-**o**f-**D**epths framework. It incorporates a **Hybrid Query Construction** module where Guide Tokens initialized from coarse-grained linguistic priors rapidly anchor global semantic context, and randomly initialized Explorer Tokens remain active to capture fine-grained visual details. Exploiting this early convergence, we introduce an **Asymmetric Mixture-of-Depths** mechanism, where a similarity-aware router dynamically prunes redundant tokens from expensive cross-attention layers while preserving their context in self-attention. Experiments on multiple benchmarks demonstrate that Cat-MoD matches or surpasses baseline performance, while substantially reducing alignment FLOPs by approximately 37% during both training and inference, offering a highly efficient solution for multimodal alignment. Code: https://github.com/JasonOrange0726/Cat-MoD.

Cat-MoD: Accelerating Multimodal Alignment via Caption Token Guided Asymmetric Mixture-of-Depths

Large Language Model (LLM) personalization aims to align model behaviors with individual user preferences.
Existing methods often focus on isolated user histories, neglecting the essential role of inter-user differences.
We propose C-BPO, a framework that personalizes LLMs via preference-calibrated binary signals.
By treating target user data as positive feedback and other users' data as an auxiliary set of implicit negative signals, C-BPO captures distinct inter-user differences.
To mitigate the preference overlap issue, where shared task knowledge is erroneously penalized, we derive an objective grounded in Positive-Unlabeled (PU) learning theory.
This approach purifies negative signals by subtracting ``positive bias'', ensuring alignment with unique idiosyncrasies without compromising general helpfulness.
Empirical experiments across various personalization tasks and backbone LLMs show C-BPO consistently outperforms baselines, demonstrating the efficacy of preference-calibrated binary signals in modeling inter-user differences.

Personalizing LLMs with Binary Feedback: A Preference-Calibrated Optimization Framework

First-order logic (FOL) is a fundamental formalism for factual reasoning over knowledge graphs (KGs), e.g. in researches of KG-based fact verification and logical consistency or reasoning of large language models (LLM). However, existing benchmarks and approaches insufficiently capture many claims that require comparison or counting, and lack support for several FOL quantifiers and connectives. To address these challenges and expand the expressive capacity of FOL for KG-based reasoning, we introduce FOLX-KG, a novel extended FOL $\sigma$-structure over KGs that incorporates comparison predicates and counting quantifiers. Using this extended logic, we construct Fact-FOLX-KG, a fact verification dataset consisting of 43,821 KG-based claim–formula pairs designed to enable systematic study of richer logical forms and reasoning types. We further propose FOLX Prover, an executable program-guided logic reasoning pipeline adapted for KG-based factual reasoning under the extended FOL. Experimental results show that our method achieves state-of-the-art performance on Fact-FOLX-KG, while previous methods experience performance drop on claims requiring comparison and counting. These findings demonstrate the importance of extended logical expressiveness for robust factual reasoning over KGs.

Extending First-Order Logic for Factual Reasoning over Knowledge Graphs

Comprehending long visual documents, where information is distributed across extensive pages of text and visual elements, is a critical but challenging task for modern Vision-Language Models (VLMs). Existing approaches falter on a fundamental challenge: evidence localization. They struggle to retrieve relevant pages and overlook fine-grained details within visual elements, leading to limited performance and model hallucination. To address this, we propose DocLens, a tool-augmented multi-agent framework that effectively ``zooms in'' on evidence like a lens. It first navigates from the full document to specific visual elements on relevant pages, then employs a sampling-adjudication mechanism to generate a single, reliable answer. Paired with Gemini-2.5-Pro, DocLens achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts. The framework's superiority is particularly evident on vision-centric and unanswerable queries, demonstrating the power of its enhanced localization capabilities.

DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding

Standardized patients (SPs) are indispensable for clinical skills training but remain expensive and difficult to scale. Although large language model (LLM)-based virtual standardized patients (VSPs) have been proposed as an alternative, their behavior remains unstable and lacks rigorous comparison with human standardized patients. We propose EasyMED, a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior. We also introduce SPBench, a human-grounded benchmark with eight expert-defined criteria for interaction-level evaluation. Experiments show that EasyMED more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure. A four-week controlled study further demonstrates learning outcomes comparable to human SP training, with stronger early gains for novice learners and improved flexibility, psychological safety, and cost efficiency.

Human or LLM as Standardized Patients? A Comparative Study in Medical Education

The increasing context window greatly extends the capabilities of large language models, but on the other hand, it incurs an unaffordable memory overhead and computational latency due to the increasing Key-Value (KV) cache size. Recent KV cache compression methods manage to reduce the cache size by dropping irrelevant KVs. However, these methods often fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. To address this gap, we propose **IntentKV**, an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user's goal and determines which part of the context is relevant. The consistency between the semantics and attention distribution is further substantiated through meticulously designed experiments. On this basis, IntentKV first distinguishes intention tokens from the vanilla context tokens based on their attention distribution distances. Then, the block-wise cumulative attention is calculated via aggregating the intention token attention. Finally, blocks that acquire high cumulative attention are picked and stored in KV cache. We evaluate our method across diverse long-context tasks and models. Results demonstrate that IntentKV can effectively maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting.

Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression

Discrete Diffusion Language Models have emerged as a compelling paradigm for unified multimodal generation, yet their deployment is hindered by high inference latency arising from iterative decoding. Existing acceleration strategies often require expensive re-training or fail to leverage the 2D spatial redundancy inherent in visual data. To address this, we propose Locality-Aware Dynamic Rescue (LADR), a training-free method that expedites inference by exploiting the spatial Markov property of images. LADR prioritizes the recovery of tokens at the ``generation frontier'', regions spatially adjacent to observed pixels, thereby maximizing information gain. Specifically, our method integrates morphological neighbor identification to locate candidate tokens, employs a risk-bounded filtering mechanism to prevent error propagation, and utilizes manifold-consistent inverse scheduling to align the diffusion trajectory with the accelerated mask density. Extensive experiments on four text-to-image generation benchmarks demonstrate that our LADR achieves an approximate 4 $\times $ speedup over standard baselines. Remarkably, it maintains or even enhances generative fidelity, particularly in spatial reasoning tasks, offering a state-of-the-art trade-off between efficiency and quality.

LADR: Locality-Aware Dynamic Rescue for Efficient Text-to-Image Generation with Diffusion Large Language Models

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in interpreting single medical images. However, real-world clinical diagnosis is intrinsically a multi-view process, requiring the synthesis of information across volumetric slices, temporal sequences, and comparative modalities. Existing benchmarks fail to capture this complexity, limiting the assessment of models in realistic clinical workflows. To bridge this gap, we introduce MedMultiBench, the first large-scale benchmark specifically designed for medical multi-image understanding. Comprising 11,392 expert-curated samples, MedMultiBench evaluates MLLMs across four distinct dimensions: Joint Reasoning, Comparative Analysis, Comprehensive Perception, and In-Context Learning. We benchmark 13 state-of-the-art MLLMs, revealing that while current models excel in single-view tasks, they struggle significantly with multi-image contexts. Our experiments identify a performance degradation in open-source models when processing increased visual loads, whereas closed-source models demonstrate better scalability. MedMultiBench provides a robust framework to facilitate the development of MLLMs capable of holistic clinical reasoning.

Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding

LLM-based Multi-agent systems (MAS) have shown strong capabilities across a wide range of domains. 
Their success largely hinges on the collaboration topology design, which has emerged as a central research focus in the automated MAS design.
However, existing approaches are fundamentally constrained by their reliance on homogeneous LLMs, which significantly limits overall system intelligence.
In response to this limitation, we for the first time propose the concept of **Automated Design of Heterogeneous-LLMs-based MAS (ADHM)**.
ADHM sheds light on a promising avenue for advancing collective intelligence, which focuses on the automated design of cost-effective MAS composed of diverse LLMs
and roles to suit various queries.
Toward this challenging goal, we propose **Hetero-Designer**, a novel pipeline that efficiently encodes intricate dependencies among queries, LLMs and roles through a novel Binary-Star Transformer and constructs Hetero-MAS in an autoregressive graph generation process. Extensive experiments demonstrate that **Hetero-Designer** is: (1) high-performing on various benchmarks, (2) economical in reducing overhead, (3) extensible to unseen LLMs and roles.

Hetero-Designer: Automated Design of Multi-Agent Systems with Heterogeneous LLMs

Financial numerical reasoning demands rigorous adherence to domain-specific logic and precise evidence foundation. However, large language models (LLMs) are prone to forced generation when confronting ambiguous evidence or complex recursive dependencies, often hallucinating values to bridge information gaps. To address this, we propose graph-bounded financial reasoning (GBFR), a neuro-symbolic framework that imposes semantic and structural constraints via a financial metric knowledge graph (FMKG). Unlike sequential generation paradigms, our approach employs a parallel graph-constrained reasoning algorithm that orchestrates specialized operators to simultaneously explore heterogeneous derivation paths of complex financial metrics. Through cross-path verification, the framework aggregates only semantically consistent results, ensuring reasoning is bounded by available context. Crucially, this approach enables safe abstention by distinguishing genuine data absence from retrieval failure, thereby preventing ungrounded fabrication. To evaluate this capability, we further construct counterfactual samples by perturbing entities, times, and metrics to synthesize unanswerable scenarios. Empirical evaluations on standard benchmarks demonstrate that GBFR significantly outperforms state-of-the-art baselines.

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