United States

Retrieval shapes how language models access and cite knowledge in retrieval-augmented generation (RAG). In historical research, the goal is often to locate the exact record for a specific regnal month, where temporal alignment matters as much as topical relevance. This is especially challenging for Classical Chinese annals: time is encoded in terse, implicit, non-Gregorian reign phrases that are context-dependent, so semantically plausible evidence can still be temporally invalid. We introduce **ChunQiuTR**, a time-keyed retrieval benchmark built from the **Spring and Autumn Annals** and its exegetical tradition. It organizes records by month-level reign keys and includes chrono-near confounders that mimic real retrieval failures. We propose **CTD** (Calendrical Temporal Dual-encoder), a time-aware dual-encoder combining Fourier-based absolute context with relative offset biasing. Experiments show consistent gains over semantic dual-encoder baselines under time-keyed evaluation. We will release ChunQiuTR and code after the anonymity period.

ACL 2026

ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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Recent advancements in Generative Reward Models (GRMs) have demonstrated that scaling the length of Chain-of-Thought (CoT) reasoning considerably enhances the reliability of evaluation. However, current works predominantly rely on unstructured length scaling, ignoring the divergent efficacy of different reasoning mechanisms: Breadth-CoT (multi-dimensional principle coverage) and Depth-CoT (substantive judgment soundness). To address this, we introduce Mix-GRM, a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-CoT through a modular synthesis pipeline, subsequently employing Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR) to internalize and optimize these mechanisms. Comprehensive experiments demonstrate that Mix-GRM establishes a new state-of-the-art across five benchmarks, surpassing leading open-source RMs by an average of 8.2%. Our results reveal a clear divergence in reasoning: Breadth-CoT benefits subjective preference tasks, whereas Depth-CoT excels in objective correctness tasks. Consequently, misaligning the reasoning mechanism with the task directly degrades performance. Furthermore, we demonstrate that RLVR acts as a switching amplifier, inducing an emergent polarization where the model spontaneously allocates its reasoning style to match task demands.

Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models

Classical Chinese poetry is a treasured cultural heritage of humanity, attracting extensive research interest. However, the study of classical Chinese poetry is hindered by the lack of open, large-scale, and fine-grained multimodal datasets.Prior datasets are either limited by modality constraints, dataset size, or the level of dataset refinement, making them inadequate for effectively supporting studies and the development of applications in classical Chinese poetry.To address these issues, we propose a method for constructing a large-scale and fine-grained multimodal knowledge graph of classical Chinese poetry. We first design an informative ontology graph for classical Chinese poetry and comprehensively collect knowledge about poetry based on it. Furthermore, the method leverages knowledge augmentation, prompt optimization, and text-image alignment to acquire comprehensive, fine-grained knowledge. Both qualitative and quantitative evaluations are conducted on the Multimodal Knowledge Graph of Classical Chinese Poetry (CPMK), highlighting its comprehensiveness and high quality.We also conduct downstream evaluations on four tasks: poetry question answering, poetry theme classification, poetry-image retrieval, and rigid-formats poetry generation.Significant results are achieved across all four tasks, demonstrating CPMK's effectiveness in supporting research on Chinese poetry.CPMK will be released to promote research in Chinese culture.

Large-Scale Multimodal Knowledge Graph about Classical Chinese Poetry: Fine-grained Method and Comprehensive Evaluation

Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional SFT often ignores the $\textit{one-to-many}$
nature of language by forcing alignment with a single reference answer, leading to the model overfitting to non-core expressions. Although our empirical analysis suggests that introducing multiple reference answers can mitigate this issue, the prohibitive data and computational costs necessitate a strategic shift: prioritizing the mitigation of single-reference overfitting over the costly pursuit of answer diversity. To achieve this, we reveal the intrinsic connection between token probability and semantic importance: high-probability tokens carry the core logical framework, while low-probability tokens are mostly replaceable expressions. Based on this insight, we propose $\textbf{ProFit}$
, which selectively masks low-probability tokens to prevent surface-level overfitting. Extensive experiments confirm that ProFit consistently outperforms traditional SFT baselines on general reasoning and mathematical benchmarks

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection

Dunhuang art, a cornerstone of global heritage, demands fine-grained visual perception anchored by specialized cultural knowledge. Given the strong performance of multimodal large language models (MLLMs) on generic multimodal benchmarks, to what extent can they understand artifacts from Dunhuang art that are grounded in cultural context? To this end, we construct Dunhuang-Bench, a large-scale benchmark comprising 486 images and 22,970 QA pairs. It incorporates diverse task formats to evaluate MLLMs' cultural understanding: Question Answering with Text Description, Multi-turn Dialogue, and Question Answering with Choices. Guided by Panofsky’s theory of iconology, we design two tasks including visual perception and knowledge reasoning for the evaluation of content understanding. In addition, we follow the theory of formal analytic tradition to design another task of artistic appreciation in our Dunhuang-Bench. Extensive evaluations of 20 mainstream MLLMs on Dunhuang-Bench reveal a consistent performance drop from perception and appreciation to reasoning. Moreover, CoT and few-shot prompting show marginal or negative impact, highlighting the limits of prompting-based improvements. Dunhuang-Bench thus provides a challenging benchmark for advancing multimodal cultural understanding. Data and code will be publicly available.

Dunhuang-Bench: How Well Do MLLMs Understand Cultural Heritage?

Consistency models (CMs) have shown promise in the efficient generation of both image and text. 
This raises the natural question of whether we can learn a unified CM for efficient multimodal generation (e.g., text-to-image) and understanding (e.g., image-to-text). 
Intuitively, such a model could be acquired by applying the consistency distillation (CD) to existing unified multimodal models. 
However, the key challenge is establishing a unified denoising perspective for both image and text generation, which is essential for establishing the consistency mapping. 
To tackle this, at the representation level, we advocate for discrete tokens for both modalities to best preserve language modeling capabilities. 
Critically, instead of defining the text denoising trajectory via recent discrete diffusion language modeling principles, we specify it using the parallel decoding trace of an autoregressive language model, benefiting from the latter's superior performance in general text generation tasks. 
The denoising trajectory of image tokens adheres to standard discrete diffusion. 
We train our unified consistency models (UniCMs) on these combined multimodal trajectories simultaneously with a unified objective. 
We introduce a trajectory segmentation strategy to improve the training convergence. 
Empirically, in text-to-image generation, UniCMs outperform SD3 on GenEval and Image Reward, while requiring only approximately ${1}/{8}$ of the sampling time. 
Meanwhile, in image-to-text generation, 
UniCMs surpass Show-o on the MMMU benchmark while being $1.5 \times$ faster at long-sequence generating speed.

UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding

Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerability inherent to this paradigm: lowering image resolution inadvertently catalyzes jailbreaking. Our experiments reveal that the safety defenses of SOTA models deteriorate sharply as resolution degrades, surprisingly persisting even when text remains legible. We attribute this to ``Cognitive Overload``, hypothesizing that the effort required to decipher degraded inputs diverts attentional resources from safety auditing. This phenomenon is consistent across various visual perturbations, including noise and geometric distortion. To address this, we propose a simple ``Structured Cognitive Offloading'' strategy that mitigates these risks by enforcing a serialized pipeline to decouple visual transcription from safety assessment. Our work exposes a significant risk in vision-based compression and provides critical insights for the secure design of future MLLMs.

Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment

Optimizing communication topology is fundamental to the efficiency and effectiveness of Large Language Model (LLM)-based Multi-Agent Systems (MAS). While recent approaches utilize reinforcement learning to dynamically construct task-specific graphs, they typically rely on single-sample policy gradients with absolute rewards (e.g., binary correctness). This paradigm suffers from severe gradient variance and the credit assignment problem: simple queries yield non-informative positive rewards for suboptimal structures, while difficult queries often result in failures that provide no learning signal. To address these challenges, we propose Graph-GRPO, a novel topology optimization framework that integrates Group Relative Policy Optimization. Instead of evaluating a single topology in isolation, Graph-GRPO samples a group of diverse communication graphs for each query and computes the advantage of specific edges based on their relative performance within the group. By normalizing rewards across the sampled group, our method effectively mitigates the noise derived from task difficulty variance and enables fine-grained credit assignment. Extensive experiments on reasoning and code generation benchmarks demonstrate that Graph-GRPO significantly outperforms state-of-the-art baselines, achieving superior training stability and identifying critical communication pathways previously obscured by reward noise.

Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization

With the rapid progress of large language models (LLMs), aligning a general-purpose model with downstream tasks through fine-tuning has become a central research focus. Selecting only high-quality examples for training has been shown to be one of the most effective ways to improve fine-tuning performance. However, prior work concentrates almost exclusively on data preprocessing: filtering and cleaning data before training begins. While the order and composition of training data during training have received little fine-grained attention. To fill this gap, our work proposed Fine-Grained Order Fine-Tuning, a fine-grained scheduling method of data order in epochs. Drawing on curriculum-learning principles, FOT defines data difficulty based on the relevance between the data and the model, and then performs dynamic scheduling of the training order in each epoch according to the difficulty. On both large-scale continued pre-training and small-scale supervised fine-tuning experiments, FOT has achieved an average 2.4% improvement over baselines. Our study offers a new perspective on data governance in the fine-tuning phase.

Fine-Grained Data Ordering Improves Fine-Tuning for Large Language Models

Autoregressive (AR) language modeling remains the dominant paradigm due to its dense supervision signal and highly optimized serving infrastructure, but its strictly causal, token-by-token decoding limits parallelism and non-causal modeling. While masked diffusion offers a promising path toward parallel generation, it faces two critical bottlenecks: training inefficiency stemming from sparse masked objectives, and high latency caused by iterative whole-sequence denoising. We present a systematic study of blockwise discrete diffusion, a pragmatic middle ground that preserves AR-compatible serving while enabling parallel intra-block generation. Our study proceeds in four steps: (i) a \textbf{controlled, compute- and scale-matched comparison} revealing that AR is a more effective backbone for blockwise hybrids than masked diffusion objectives; (ii) a \textbf{scalable conversion recipe, \textsc{SDAR}}, validating that AR models spanning 1.7B to 30B parameters can be adapted into block diffusion models with minimal compute while preserving backbone capabilities; and (iii) a \textbf{systematic characterization of decoding dynamics}, which reveals a virtuous cycle where larger models enable more aggressive parallel decoding, achieving {theoretical speedups over 5$\times$} and {wall-clock speedups of 2.3$\times$} on H200 GPUs in latency-critical regimes; and (iv) an \textbf{investigation of local non-causal modeling capabilities}, showing that SDAR's local bidirectional attention overcomes causal bottlenecks in scientific domains (e.g., chemistry) and enables robust test-time scaling. We release the full model suite, the training framework, and our inference engines for further innovation in non-autoregressive generative paradigms.

SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation

Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts are equally forgettable and largely ignores whether the forgotten knowledge originates from pretraining or supervised fine-tuning (SFT). In this paper, we introduce DUAL (Dual Unlearning Evaluation across Training Stages), a benchmark of 28.6k Wikidata-derived triplets annotated with fact popularity using Wikipedia link counts and LLM-based salience scores. Our experiments show that pretrained and SFT models respond differently to unlearning. An SFT step on the forget data yields smoother forgetting, more stable tuning, and 10–50% higher retention, while direct unlearning on pretrained models remains unstable and prone to relearning or catastrophic forgetting.

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Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models

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