United States

Guqin (古琴) Jianzi (減字) is an open and freely compositional tablature system that encodes performance actions rather than acoustic outcomes. Its automatic recognition remains largely unexplored, as conventional OCR assumes a closed and enumerable glyph set and struggles with Jianzi’s unbounded composition and manuscript-level variability.

We introduce Zero-shot Jianzi Recognition, which formulates Jianzi recognition as vision-to-sequence prediction of canonical component sequences under a zero-shot split. To enable scalable supervision, we construct Synthetic-JZ from aligned online composition metadata. We then synthesize manuscript-like training images via component-wise style recomposition and manuscript-domain noise modeling, and fine-tune a vision–language model for end-to-end component sequence recognition. At inference time, a lightweight legality-guided correction module re-ranks decoding candidates, suppressing structural hallucinations without modifying the backbone.

Experiments on two benchmarks show that our method achieves 63.02% sequence accuracy on Real-JZ, our manually annotated real-world Jianzi benchmark, surpassing Gemini-3-Pro by 35.11%. This result highlights the feasibility of reliable automated Jianzi recognition and its potential for large-scale digitization of historical Guqin Jianzi Pu manuscripts.

ACL 2026

Zero-shot Jianzi Recognition as Structured Visual Information Extraction in Open Compositional Symbolic Systems

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

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. <br>
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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.

Recently, activation steering has attracted considerable attention as a low-cost approach to improving the safety of large language models (LLMs). However, most existing methods apply interventions indiscriminately, often causing excessive refusal of benign queries. Although recent works have begun to explore selective intervention, their intervention decisions typically rely on residual stream activations where information is highly entangled, resulting in limited discriminative power and unreliable interventions. To address this issue, we propose FFN-Guided activation steering (FGAS). Motivated by the observation that feed-forward networks (FFNs) in LLMs serve as core modules for knowledge storage, we propose leveraging FFN output activations as more discriminative signals for intervention, since these activations more explicitly reflect the intent of a query. For a given query, FGAS projects the corresponding FFN output activation into a low-dimensional subspace that effectively separates harmful and benign queries, and then makes precise intervention decisions by assessing its similarity to pre-constructed prototype activations representing harmful and benign classes. Extensive experiments demonstrate that FGAS achieves state-of-the-art defense performance against various jailbreak attacks, while nearly preserving the model's original performance on benign tasks.

Detecting What Queries Seek: Steering LLM Safety with FFN Output Activation Monitoring

Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorly understood. To bridge this gap, we introduce CanMT, a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation, together with a theoretically grounded, multi-dimensional evaluation frame work for assessing cultural translation quality. Leveraging CanMT, we systematically evaluate a wide range of LLMs and translation systems under different translation strategy constraints. Our findings reveal substantial performance disparities across models and demonstrate that translation strategies exert a systematic influence on model behavior. Further analysis shows that translation difficulty varies across types of culture-specific items, and that a persistent gap remains between models’recognition of culture-specific knowledge and their ability to correctly operationalize it in translation outputs. In addition, incorporating reference translations is shown to substantially improve evaluation reliability in LLM-as-a-judge, underscoring their essential role in assessing culture-aware translation quality. The corpus and code are available at CanMT.

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation

We present a multilingual coreference dataset of 827k tokens of fiction in 7 languages: Bahasa Indonesia, Chinese, Dutch, English, Italian, Korean, and Spanish. The dataset includes full stories of diverse lengths, ranging from 500 to 17k words. We discuss our annotation scheme focusing on characters and language-specific challenges we encountered. Finally we present evaluation results of a neural coreference system trained on our dataset. We show that jointly training a system across all languages provides a strong improvement over monolingually trained models. The dataset is available under a creative commons license in CoNLL-2012 and CorefUD format at https://github.com/GOLEM-lab/GOLEMcoref/

GOLEMcoref: A Multilingual Coreference Dataset of Fiction

Cross-lingual chain-of-thought (XCoT) with self-consistency markedly enhances multilingual reasoning, yet existing methods remain costly due to extensive sampling of full trajectories across languages. Moreover, multilingual LLM representations vary strongly by language, hindering direct feature comparisons and effective pruning. To address this, we introduce UL-XCoT, the first efficient unified logic cross-lingual reasoning framework that minimizes redundancy in token usage and latency, yielding the greatest efficiency under limited sampling budgets during inference. Specifically, UL-XCoT (1) achieves less languages by selecting, per query, a small candidate language set in a language-invariant unified logic space, (2) enables less tokens by monitoring logic-space trajectory dynamics during decoding to prune low-quality reasoning paths, and (3) aggregates the remaining high-quality trajectories via voting. Experiments on PolyMath across 18 languages with DeepSeek-R1-Distill-Qwen-7B demonstrate that UL-XCoT achieves competitive accuracy while sharply cutting over 50% decoding token cost and latency versus prior sampling baselines. It also delivers more stable gains on low-resource languages, underscoring consistently superior robustness where standard XCoT self-consistency method fails.

Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework

Large Language Model-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in complex tasks. However, manually designing optimal communication topologies is labor-intensive, while automated expansion methods often result in bloated structures with redundant agents, leading to excessive token consumption. To address this problem, we introduce AgentSlimming, a plug-and-play compression framework for graph-structured multi-agent workflows. Motivated by the AgentPruner and AgentQuant in neural networks, AgentSlimming compresses workflows by firstly estimate the importance score of each agent with a hybrid mechanism, and then removing redundant agents or replacing them with low-cost ones, where each operation is then validated with a baseline-anchored acceptance rule to prevent performance collapse. Experiments show that AgentSlimming reduces average token cost by up to 78.9% with negligible performance degradation, and even sometimes improves accuracy, achieving a strong Pareto-optimal trade-off between cost and quality.

AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems

Existing jamming attacks on Retrieval-Augmented Generation (RAG) systems typically induce explicit refusals or denial-of-service behaviors, which are conspicuous and easy to detect. In this work, we formalize a subtler availability threat, termed soft failure, which degrades system utility by inducing fluent and coherent yet non-informative responses rather than overt failures. We propose Deceptive Evolutionary Jamming Attack (DEJA), an automated black-box attack framework that generates adversarial documents to trigger such soft failures by exploiting safety-aligned behaviors of large language models. DEJA employs an evolutionary optimization process guided by a fine-grained Answer Utility Score (AUS), computed via an LLM-based evaluator, to systematically undermine the certainty of answers while maintaining high retrieval success.
Extensive experiments across multiple RAG configurations and benchmark datasets show that DEJA consistently drives responses toward low-utility soft failures and that the resulting adversarial documents maintain high stealth and effectiveness, proving resilient against common mitigation strategies including perplexity-based detection and input perturbations.

Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented Generation

Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains. Inspired by human translators' ability to adapt reasoning effort based on difficulty, we propose TwT (Translation with Thought), a resource-rational framework that learns to modulate inference between intuitive and deliberate reasoning. TwT is trained in two stages: (1) supervised fine-tuning on difficulty-aware long chain-of-though traces distilled from DeepSeek-R1 and rewritten by GPT-4o to reflect human-like reasoning economy, and (2) reinforcement learning with a hybrid reward to optimize translation quality and reasoning efficiency. Evaluated on 15 benchmarks spanning in-domain and out-of-domain settings, as well as 3 seen and 59 unseen languages, with ablations across three backbone models, TwT-7B and TwT-14B outperform much larger SOTA reasoning models in translation quality, while reducing token usage by 32–60%. These results confirm that aligning translation behavior with cognitive principles enables robust generalization, high translation quality, and efficient reasoning in MDMT.

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation

While Reinforcement Learning (RL) has advanced LLM reasoning, applying it to long-context scenarios is hindered by sparsity of outcome rewards. This limitation fails to penalize ungrounded "lucky guesses," leaving the critical process of needle-in-a-haystack evidence retrieval largely unsupervised. To address this, we propose EAPO (Evidence-Augmented Policy Optimization). We first establish the Evidence-Augmented Reasoning paradigm, validating via Tree-Structured Evidence Sampling that precise evidence extraction is the decisive bottleneck for long-context reasoning. Guided by this insight, EAPO introduces a specialized RL algorithm where a reward model computes a Group-Relative Evidence Reward, providing dense process supervision to explicitly improve evidence quality. To sustain accurate supervision throughout training, we further incorporate an Adaptive Reward-Policy Co-Evolution mechanism. This mechanism iteratively refines the reward model using outcome-consistent rollouts, sharpening its discriminative capability to ensure precise process guidance. Comprehensive evaluations across eight benchmarks demonstrate that EAPO significantly enhances long-context reasoning performance compared to SOTA baselines.

Evidence-Augmented Policy Optimization with Reward Co-Evolution for Long-Context Reasoning

Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated relevance labels in isolation, decoupled from the downstream generation process. This isolation leads to a fundamental misalignment: documents identified as topically relevant by information retrieval metrics often fail to provide the actual utility required by the LLM for precise answer generation. To bridge this gap, we introduce ReRanking Preference Optimization (RRPO), a reinforcement learning framework that directly aligns reranking with the LLM's generation quality. By formulating reranking as a sequential decision-making process, RRPO optimizes for context utility using LLM feedback, thereby eliminating the need for expensive human annotations. To ensure training stability, we further introduce a reference-anchored deterministic baseline. Extensive experiments on knowledge-intensive benchmarks demonstrate that RRPO significantly outperforms strong baselines, including the powerful list-wise reranker RankZephyr. Further analysis highlights the versatility of our framework: it generalizes seamlessly to diverse readers (e.g., GPT-4o), integrates orthogonally with query expansion modules like Query2Doc, and remains robust even when trained with noisy supervisors.

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

Civil judicial cases are highly complicated, posing significant challenges for Large Language Models (LLMs) for Legal Judgment Prediction (LJP). While judges manage this complexity through the dispute focus—a mechanism distilling cases into core issues—existing research largely overlooks this tool in favor of generic reasoning frameworks that lack authentic judicial logic. To bridge this gap, we first introduce $\textbf{FocalLaw}$, the first dataset aligning full-process Chinese civil judicial data through the dispute focus, comprising 1,000 high-quality cases across six causes of action. 
Building on this dataset, we examine LLMs' capability to utilize the dispute focus and uncover a counter-intuitive phenomenon: LLMs fail to leverage the dispute focus even with CoT and SFT, which we identify as the "Clerk Trap".
To solve the problem, we propose $\textbf{FocalJudge}$, a novel framework that leverages the dispute focus to guide LLMs through a structured, judge-like cognitive workflow. Experimental results demonstrate the effectiveness of FocalJudge and offer valuable insights into the interpretability and reliability of LLMs in the legal domain.

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Detecting What Queries Seek: Steering LLM Safety with FFN Output Activation Monitoring

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