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The rapid advancements in Large Language Models (LLMs) have enabled their deployment as autonomous agents for handling complex tasks in dynamic environments. These LLMs demonstrate strong problem-solving capabilities and adaptability to multifaceted scenarios. However, their use as agents also introduces significant risks, including task-specific risks, which are identified by the agent administrator based on the specific task requirements and constraints, and systemic risks, which stem from vulnerabilities in their design or interactions, potentially compromising confidentiality, integrity, or availability (CIA) of information and triggering security risks. Existing defense agencies fail to adaptively and effectively mitigate these risks. In this paper, we propose AGrail, a lifelong agent guardrail to enhance LLM agent safety, which features adaptive safety check generation, effective safety check optimization, and tool compatibility &amp; flexibility. Extensive experiments demonstrate that AGrail not only achieves strong performance against task-specific and system risks but also exhibits transferability across different LLM agents&#39; tasks.

ACL 2025

AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection

risks on agent

agent safety

guardrail

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The rapid advancements in Large Language Models (LLMs) have enabled their deployment as autonomous agents for handling complex tasks in dynamic environments. These LLMs demonstrate strong problem-solving capabilities and adaptability to multifaceted scenarios. However, their use as agents also introduces significant risks, including task-specific risks, which are identified by the agent administrator based on the specific task requirements and constraints, and systemic risks, which stem from vulnerabilities in their design or interactions, potentially compromising confidentiality, integrity, or availability (CIA) of information and triggering security risks. Existing defense agencies fail to adaptively and effectively mitigate these risks. In this paper, we propose AGrail, a lifelong agent guardrail to enhance LLM agent safety, which features adaptive safety check generation, effective safety check optimization, and tool compatibility & flexibility. Extensive experiments demonstrate that AGrail not only achieves strong performance against task-specific and system risks but also exhibits transferability across different LLM agents' tasks.

technical paper

### 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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

Content analysis breaks down complex and unstructured texts into theory-informed numerical categories. Particularly, in social science, this process usually relies on multiple rounds of manual annotation, domain expert discussion, and rule-based refinement. In this paper, we introduce SCALE, a novel multi-agent framework that effectively $\underline{\textbf{S}}$imulates $\underline{\textbf{C}}$ontent $\underline{\textbf{A}}$nalysis via $\underline{\textbf{L}}$arge language model (LLM) ag$\underline{\textbf{E}}$nts. SCALE imitates key phases of content analysis, including text coding, collaborative discussion, and dynamic codebook evolution, capturing the reflective depth and adaptive discussions of human researchers. Furthermore, by integrating diverse modes of human intervention, SCALE is augmented with expert input to further enhance its performance. Extensive evaluations on real-world datasets demonstrate that SCALE achieves human-approximated performance across various complex content analysis tasks, offering an innovative potential for future social science research.

SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention

The Retrieval-Augmented Generation (RAG) framework introduces a retrieval module to dynamicaslly inject retrieved information into the input context of large language models (LLMs), and has demonstrated significant success in various NLP tasks. However, the current study points out that there is a preference gap between retrievers and LLMs in the RAG framework, which limit the further improvement of system performance. 
Some highly relevant passages may interfere with LLM reasoning because they contain complex or contradictory information; while some indirectly related or even inaccurate content may help LLM generate more accurate answers by providing suggestive information or logical clues. 
To solve this, we propose **GainRAG**, a novel approach that aligns the retriever’s and LLM’s preferences by defining a new metric, “gain’’, which measure how well an input passage contributes to correct outputs.
We then propose a method to estimate these gain signals and train a middleware that aligns the preferences of the retriever and the LLM using only limited data.
In addition, we introduce a pseudo-passage strategy to mitigate degradation.
The experimental results on 6 datasets verify the effectiveness of GainRAG.

GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis

This work investigates sentence-level models ($\textit{i.e.}$, models that operate at the sentence-level) to study how sentence representations from various encoders influence downstream task performance, and which syntactic, semantic, and discourse-level properties are essential for strong performance. Our experiments encompass encoders with diverse training regimes and pretraining domains, as well as various pooling strategies applied to multi-sentence input tasks (including sentence ordering, sentiment classification, and natural language inference) requiring coarse-to-fine-grained reasoning. We find that ''less mature'' representations ($\textit{e.g.}$, mean-pooled representations from BERT's first or last layer, or representations from encoders with limited fine-tuning) exhibit greater generalizability and adaptability to downstream tasks compared to representations from extensively fine-tuned models ($\textit{e.g.}$, SBERT or SimCSE). These findings are consistent across different pretraining seed initializations for BERT. Our probing analysis reveals that syntactic and discourse-level properties are stronger indicators of downstream performance than MTEB scores or decodability. Furthermore, the data and time efficiency of sentence-level models, often outperforming token-level models, underscores their potential for future research.

Less Mature is More Adaptable for Sentence-level Language Modeling

Sparse attention can effectively alleviate the significant demands on memory when large language models (LLMs) process long contexts. Existing methods typically apply the same sparse pattern across different attention heads and inputs. However, this uniform approach fails to capture the inherent diversity of attention patterns within LLMs — the intrinsic attention clustering. To address this, we propose ClusterAttn, a training-free sparse attention method that provides an efficient prompt cache compression scheme under intrinsic attention clustering for efficient LLM inference.

Our findings show that attention heads consistently focus on specific clusters of the prompt during decoding, a pattern detectable from an observation window at the prompt's end. ClusterAttn adaptively fits these clusters utilizing a density-based attention clustering algorithm, thus compressing the KV cache of the prompt. Evaluations on different models across various benchmarks demonstrate ClusterAttn's superior compression rates and efficiency. By utilizing only 1024 tokens, it can reduce memory usage by 10%–65%, resulting in a latency reduction of 12%–23% and a throughput increase of 2.6–4.8 times, all with nearly no accuracy loss. Additionally, ClusterAttn can handle up to 128k context on a single A100-80GB GPU, outperforming existing methods.

ClusterAttn: KV Cache Compression under Intrinsic Attention Clustering

Recent advancements in Large Vision-Language Models have showcased remarkable capabilities. However, they often falter when confronted with complex reasoning tasks that humans typically address through visual aids and deliberate, step-by-step thinking. While existing methods have explored text-based slow thinking or rudimentary visual assistance, they fall short of capturing the intricate, interleaved nature of human visual-verbal reasoning processes. To overcome these limitations and inspired by the mechanisms of slow thinking in human cognition, we introduce VisuoThink, a novel framework that seamlessly integrates visuospatial and linguistic domains. VisuoThink facilitates multimodal slow thinking by enabling progressive visual-textual reasoning and incorporates test-time scaling through look-ahead tree search. Extensive experiments demonstrate that VisuoThink significantly enhances reasoning capabilities via inference-time scaling, even without fine-tuning, achieving state-of-the-art performance in tasks involving geometry and spatial reasoning.

VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search

The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI). Recent works demonstrate that various AI-assisted research methods can largely improve research efficiency by improving data analysis, accelerating computation, and fostering novel idea generation. To further move towards the ultimate goal (i.e., automatic scientific research), in this paper, we introduce Dolphin, a closed-loop LLM-driven framework to enhance the automation level of scientific research. Dolphin first generates novel ideas based on feedback from previous experiments and relevant papers ranked by the topic and task attributes. Then, the generated ideas can be implemented using a code template refined and debugged with the designed exception-traceback-guided local code structure. Finally, Dolphin automatically analyzes the results of each idea and feeds the results back to the next round of idea generation. Experiments are conducted on the benchmark datasets of different topics and a subset of MLE-bench. Results show that Dolphin can continuously improve the performance of the input topic in a loop. We highlight that Dolphin can automatically propose methods that are comparable to the state-of-the-art in some tasks such as 3D point classification.

Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedback

Recent advancements in large language models (LLMs) with extended context windows have significantly improved various tasks. To improve long-context capabilities, much work focuses on augmenting LLM's capabilities with synthetic data. Existing methods often leverage the Self-Instruct framework to generate long-context instruction-tuning data. However, our preliminary experiments show that fewer than 35% of samples generated by Qwen-2-72B are multi-hop, and over 40% exhibit poor quality, limiting comprehensive understanding and further research. To address this, we propose the Multi-agent Interactive Multi-hop Generation (MIMG) framework, which integrates a quality verification agent, a single-hop question generation agent, a multiple question sampling strategy, and a multi-hop question merger agent. This framework significantly improves data quality, with high-quality, multi-hop, and diverse data. Furthermore, we conduct a thorough analysis of document selection, question merging, and validation techniques through extensive experiments across various models. Our results demonstrate that synthetic high-quality long-context instruction data can enhance model performance, surpassing even models trained on larger amounts of human-annotated data.

What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices

Large language models (LLMs) have made significant strides in code acceleration (CA) tasks. Current works typically fine-tune LLMs using slow-fast code pairs mined from online programming platforms. Although these methods are widely recognized for their effectiveness, the training data often lack clear code acceleration patterns and offer only limited speed improvements. Moreover, existing training methods, such as direct instruction fine-tuning (IFT), tend to overlook the hierarchical relationships among acceleration patterns. In this work, we introduce BITE, a novel training paradigm designed to improve LLMs' CA capabilities through two key innovations: (1) Bidirectional tree editing, which generates high-quality training data by incrementally transforming given code into both its most efficient and least efficient variants, and (2) Progressive code acceleration learning, which enables LLMs to internalize multi-level CA strategies by learning increasingly sophisticated acceleration patterns. Additionally, we introduce a new CA evaluation benchmark and metric for comprehensive assessment of model performance on CA tasks. Extensive experiments on both our benchmark and existing benchmarks demonstrate the effectiveness of our approach. Notably, BITE enables Qwen-1.5B to outperform prompt-enhanced GPT-4 and current training-based methods on average across five programming languages.

Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing

Semantic Parsing aims to capture the meaning of a sentence and convert it into a logical, structured form. Previous studies show that semantic parsing enhances the performance of smaller models (e.g., BERT) on downstream tasks. However, it remains unclear whether the improvements extend similarly to LLMs. In this paper, our empirical findings reveal that, unlike smaller models, directly adding semantic parsing results into LLMs reduces their performance. To overcome this, we propose SENSE, a novel prompting approach that embeds semantic hints within the prompt. Experiments show that SENSE consistently improves LLMs' performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities.

Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints

Detecting deviant language such as sexism, or nuanced language such as metaphors or sarcasm, is crucial for enhancing the safety, clarity, and interpretation of social interactions. While existing classifiers deliver strong results on these tasks, they often come with significant computational cost and high data demands. In this work, we propose \textbf{Cla}ss \textbf{D}istillation (ClaD), a novel training paradigm that targets the core challenge: distilling a small, well-defined target class from a highly diverse and heterogeneous background. ClaD integrates two key innovations: (i) a loss function informed by the structural properties of class distributions, based on Mahalanobis distance, and (ii) an interpretable decision algorithm optimized for class separation. Across three benchmark detection tasks -- sexism, metaphor, and sarcasm -- ClaD outperforms competitive baselines, and even with smaller language models and orders of magnitude fewer parameters, achieves performance comparable to several large language models. These results demonstrate ClaD as an efficient tool for pragmatic language understanding tasks that require gleaning a small target class from a larger heterogeneous background.

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SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention

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