Singapore

Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains. The effectiveness of MAS is critically dependent on its collaboration topology, which has become a focal point for automated design research. However, existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures, significantly limiting their adaptability to task-specific requirements. To address these limitations, we reframe MAS design as a conditional autoregressive graph generation task, where both the system composition and structure are designed jointly. We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch. Conditioned on a natural language task query, ARG-Designer sequentially and dynamically determines the required number of agents, selects their appropriate roles from an extensible pool, and establishes the optimal communication links between them. This generative approach creates a customized topology in a flexible and extensible manner, precisely tailored to the unique demands of different tasks. Extensive experiments across six diverse benchmarks demonstrate that ARG-Designer not only achieves state-of-the-art performance but also enjoys significantly greater token efficiency and enhanced extensibility.

AAAI 2026

Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation

mas: teamwork

mas: agent communication

graph generation

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

technical paper

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 purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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**Online Registration Form**: https://aaai.getregistered.net/conference-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.

Yaqing Wang’s research focuses on generalizing from a few
examples, aiming to build data-efficient, adaptive, and
explainable AI. Her early work established a unifying
framework for few-shot learning, which highlighted the
challenges of unreliable learning under sparse data and
articulated three canonical scenarios—scientific scarcity,
cold-start personalization, and annotation efficiency.
Building on this foundation, she has developed algorithms
addressing key real-world challenges: molecular property
prediction and drug–drug interaction under limited data in
drug discovery, recommendation models that overcome
cold-start issues and are deployed in large-scale
platforms, and efficient methods for intent recognition and
gesture sensing where annotation or interaction is costly.
Her recent work explores the synergy between meta-learning
and in-context learning, and introduces personalized agents
that adapt to user preferences with only a handful of
interactions. These contributions reflect her continued
efforts toward advancing few-shot learning in both theory
and practice, with growing impact in AI for science and
personalization.

From Few-Shot Learning to Data-Efficient Intelligence

The increased use of large language models (LLMs) in sensitive domains leads to growing interest in how their confidence scores correspond to fairness and bias. This study examines the alignment between LLM-predicted confidence and human-annotated bias judgments. Focusing on gender bias, the research investigates probability confidence calibration in contexts involving gendered pronoun resolution. The goal is to evaluate if calibration metrics based on predicted confidence scores effectively capture fairness-related disparities in LLMs. The results show that, among the six state-of-the-art models, Gemma-2 demonstrates the worst calibration according to the gender bias benchmark. The primary contribution of this work is a fairness-aware evaluation of LLMs confidence calibration, offering guidance for ethical deployment. In addition, we introduce a new calibration metric, Gender-ECE, designed to measure gender disparities in resolution tasks.

The Confidence Trap: Gender Bias and Predictive Certainty in LLMs

Multi-view clustering (MVC), which aims to separate the multi-view data into distinct clusters in an unsupervised manner, is a fundamental yet challenging task. To enhance its applicability in real-world scenarios, this paper addresses a more challenging task: MVC under multi-source noises, including missing noise and observation noise. To this end, we propose a novel framework, Reliability-Aware Contrastive Deep Multi-View Clustering (RAC-DMVC), which constructs a reliability graph to guide robust representation learning under noisy environments. Specifically, to address observation noise, we introduce a cross-view reconstruction to enhances robustness at the data level, and a reliability-aware noise contrastive learning to mitigates bias in positive and negative pairs selection caused by noisy representations. To handle missing noise, we design a dual-attention imputation to capture shared information across views while preserving view-specific features. In addition, a self-supervised cluster distillation module further refines the learned representations and improves the clustering performance. Extensive experiments on five benchmark datasets demonstrate that RAC-DMVC outperforms SOTA methods on multiple evaluation metrics and maintains excellent performance under varying ratios of noise.

RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering Under Multi-Source Noise

Large Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they tend to memorize unwanted information, such as private or copyrighted content, raising significant privacy and legal concerns. Unlearning has emerged as a promising solution, but existing methods face a significant challenge of over-forgetting. This issue arises because they indiscriminately suppress the generation of all the tokens in forget samples, leading to a substantial loss of model utility. To overcome this challenge, we introduce the Targeted Information Forgetting (TIF) framework, which consists of (1) a flexible targeted information identifier designed to differentiate between unwanted words (UW) and general words (GW) in the forget samples, and (2) a novel Targeted Preference Optimization approach that leverages Logit Preference Loss to unlearn unwanted information associated with UW and Preservation Loss to retain general information in GW, effectively improving the unlearning process while mitigating utility degradation. Extensive experiments on the TOFU and MUSE benchmarks demonstrate that the proposed TIF framework enhances unlearning effectiveness while preserving model utility and achieving state-of-the-art results.

Not All Tokens Are Meant to Be Forgotten

The rapid growth of Ethereum has made it more important to quickly and accurately detect smart contract vulnerabilities. While machine learning–based methods have shown some promises, many still rely on rule-based preprocessing designed by domain experts. Such rule-based preprocessing methods often discard crucial context from the source code, potentially causing certain vulnerabilities to be overlooked and limiting adaptability to newly emerging threats. We introduce \textit{BugSweeper}, an end-to-end deep learning framework that detects vulnerabilities directly from the source code without manual engineering. BugSweeper represents each Solidity function as a function-level abstract syntax graph (FLAG), a novel graph structure that combines its abstract syntax tree (AST) with enriched control-flow and data-flow semantics. Then, our two-stage graph neural network model analyzes these graphs. The first-stage GNN filters noise from syntax graphs, while the second-stage GNN conducts high-level reasoning to detect diverse vulnerabilities. Extensive experiments on real-world contracts show that BugSweeper significantly outperforms all state-of-the-art detection methods. By removing the need for handcrafted rules, our approach offers a robust, automated, and scalable solution for securing smart contracts without any dependence on security experts.

BugSweeper: Function-Level Detection of Smart Contract Vulnerabilities Using Graph Neural Networks

The Minimum Dominating Set (MDS) problem is a well-established combinatorial optimization problem with numerous real-world applications. Its NP-hard nature makes it increasingly difficult to obtain exact solutions as the graph size grows. This paper introduces ParDS, an exact algorithm developed to address the MDS problem within the branch-and-bound framework. ParDS features two key innovations: an advanced linear programming technique that yields tighter lower bounds and a set of novel reduction rules that dynamically simplify instances throughout the solving process. Compared to the leading exact algorithms presented at IJCAI 2023 and 2024, ParDS demonstrates theoretically superior lower-bound quality. Experimental results on standard benchmark datasets highlight several significant advantages of ParDS: it achieves the fastest solving times in 70% of graph categories, especially on large, sparse graphs, delivers a speed-up of up to 3,411 times on the fastest individual instance, and successfully solves 16 out of 43 instances that other algorithms were unable to resolve within the 5-hour time limit. 
These findings establish ParDS as a state-of-the-art solution for exactly solving the MDS problem.

Exact Optimization for Minimum Dominating Sets

We study fair allocations of {\em indivisible} goods among agents with heterogeneous monotone valuations. As fair we consider the allocations that are envy-free-up-to-any-good (EFX). Finding if EFX allocations always exist, even for agents with additive valuations, is a major open problem in Fair Division. 
Christodoulou et al. (2023) introduced the (multi-hyper)graph setting, where agents and goods are represented by vertices and edges of a graph, respectively, and only the endpoints of an edge may have non-zero marginal value for it. We show that for \textit{hypergraphs} with girth at least 4 and agents with \textit{general monotone} valuations there always exists an EFX allocation and can be constructed in polynomial time. We generalize our approach to also show that multi-hypergraphs with girth (on the simple hypergraph) at least 4 always admit an EFX allocation, as long as there exists a single vertex whose adjacent edges have multiplicity at most the size of that edge minus 2; our construction in this case needs pseudo-polynomial time.

EFX Allocation in (Multi)Hypergraphs

Public health experts need scalable methods to monitor large volumes of health data (e.g., human-reported cases, hospitalizations, deaths). These methods must identify individual data points that may indicate significant events, such as outbreaks, or reveal data quality issues. Identifying, triaging, and analyzing these data points in real-time is critical for preventing downstream errors in forecasting or policy. Traditional alert-based data monitoring systems, used for decades in practice, fail to identify relevant data events for several reasons. For example, these systems may not output real-time results from large data volumes, or they may return tens of thousands of unhelpful alerts. 

We introduce a human-in-the-loop AI system for public health data monitoring that uses a ranking-based AI anomaly detection method. This system was developed through a multi-year interdisciplinary collaboration with participatory design from researchers, engineers, and public health data experts. From this process, we identified system goals, such as user control and efficiency and designed a system that balances these goals. This system has since been deployed at a national public health organization and analyzes up to 5 million data points daily. A three-month longitudinal deployment evaluation revealed a significant improvement in system goals, including a 54x increase in data reviewer efficiency and increased engagement compared to traditional alert-based methods.

Reducing Alert Fatigue Through AI Ranking: A Deployed Public Health Data Monitoring System

Human trafficking, affecting over 50 million people globally, is a complex criminal enterprise in which traffickers actively conceal and distribute information across fragmented and often illicit online platforms. Traditional investigative tools are ill-suited for detecting patterns across such obfuscated, heterogeneous data. This paper presents Domain-specific Insight Graphs (DIG), an investigative AI search engine designed to operate at web scale and enable non-technical decision-makers, such as law enforcement and prosecutors, to rapidly uncover actionable leads in human trafficking investigations. DIG employs a novel AI pipeline that ingests large, diverse web corpora (including trafficking-relevant advertisements), cleans and normalizes extracted information, and links entities into a semantic knowledge graph. A domain-optimized search layer allows investigators to traverse these graphs to identify potential victims, perpetrators, and trafficking networks. Unlike commercial alternatives, DIG was released free of charge, open-sourced, and deployed to over 200 U.S. state and local law enforcement agencies through the DARPA Memex program. Deployment results demonstrate measurable impact: in New York, agencies using DIG reported a drop in sex worker arrests and an increase in trafficking-related arrests from <1% to over 60%, disrupting cycles of victim re-victimization. The system has been credited in high-profile prosecutions and received endorsements from District Attorneys. This paper details the problem context, AI approach, deployment process, operational challenges, and lessons learned from maintaining DIG post-federal funding, including navigating intellectual property for open release and sustaining the system via philanthropic support. DIG exemplifies how AI-driven investigative tools can deliver lasting societal benefit through targeted, innovative application in high-stakes domains.

A Deployed Investigative AI Search Engine for Combating Human Trafficking at Web Scale

Poaching poses significant threats to wildlife and biodiversity. A valuable step in reducing poaching is to forecast poacher behavior, which can inform patrol planning and other conservation interventions. Existing poaching prediction methods based on linear models or decision trees lack the expressivity to capture complex, nonlinear spatiotemporal patterns. Recent advances in generative modeling, particularly flow matching, offer a more flexible alternative. However, training such models on real-world poaching data faces two central obstacles: imperfect detection of poaching events and limited data. To address imperfect detection, we integrate flow matching with an occupancy-based detection model and train the flow in latent space to infer the underlying occupancy state. To mitigate data scarcity, we adopt a composite flow initialized from a linear-model prediction rather than random noise which is the standard in diffusion models, injecting prior knowledge and improving generalization. Evaluations on datasets from two national parks in Uganda show consistent gains in predictive accuracy.

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