Singapore

LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency through pretrained GNNs or greedy algorithms, but often isolate pre- and post-task optimization, lacking a unified strategy. To this end, we present SafeSieve, a progressive and adaptive multi-agent pruning algorithm that dynamically refines the inter-agent communication through a novel dual-mechanism. SafeSieve integrates initial LLM-based semantic evaluation with accumulated performance feedback, enabling a smooth transition from heuristic initialization to experience-driven refinement. Unlike existing greedy Top-k pruning methods, SafeSieve employs 0-extension clustering to preserve structurally coherent agent groups while eliminating ineffective links. Experiments across benchmarks (SVAMP, HumanEval, etc.) showcase that SafeSieve achieves 94.01\% average accuracy while reducing token usage by 12.4\%-27.8\%. Results further demonstrate robustness under prompt injection attacks (1.23\% average accuracy drop). In heterogeneous settings, SafeSieve reduces deployment costs by 13.3\% while maintaining performance. These results establish SafeSieve as a robust, efficient, and scalable framework for practical multi-agent systems. Our code can be found in the Supplementary Material.

AAAI 2026

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication

distributed problem solving

agent communication

coordination and collaboration

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

poster

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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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.

Conventional fairness in multi-tenant Large Language Model (LLM) inference services is typically defined by system-centric metrics, such as equitable resource allocation. This paper argues that this paradigm is fundamentally flawed, as it creates a gap between measured system performance and actual user-perceived quality. We challenge this notion by introducing and formalizing Experiential Fairness, a user-centric paradigm that shifts the objective from equality of opportunity (resource access) to equity of outcome (user experience). To operationalize this, we propose ExFairS, a lightweight scheduling framework that evaluates each user's state via a composite metric integrating SLO compliance with resource consumption, and then acts on this evaluation through a credit-based priority mechanism. Extensive experiments on an 8-GPU NVIDIA V100 node show that ExFairS reduces the SLO violation rate by up to 100% and improves system throughput by 14-21.9%, outperforming state-of-the-art schedulers and delivering a demonstrably higher degree of Experiential Fairness.

Experiential Fairness: Bridging the Gap Between User Experience and Resource-Centric Fairness in Online LLM Services

The rapid advancements in artificial intelligence have significantly accelerated the adoption of speech recognition technology, leading to its widespread integration across various applications. However, this surge in usage also highlights a critical issue: audio data is highly vulnerable to unauthorized exposure and analysis, posing significant privacy risks for businesses and individuals. This paper introduces an Information-Obfuscation Reversible Adversarial Example (IO-RAE) framework, the pioneering method designed to safeguard audio privacy using reversible adversarial examples. IO-RAE leverages large language models to generate misleading yet contextually coherent content, effectively preventing unauthorized eavesdropping by humans and Automatic Speech Recognition (ASR) systems. Additionally, we propose the Cumulative Signal Attack technique, which mitigates high-frequency noise and enhances attack efficacy by targeting low-frequency signals. Our approach ensures the protection of audio data without degrading its quality or usability. Experimental evaluations demonstrate the superiority of our method, achieving a targeted misguidance rate of 96.5% and a remarkable 100% untargeted misguidance rate in obfuscating target keywords across multiple ASR models, including a commercial black-box system from Google. Furthermore, the quality of the recovered audio, measured by the Perceptual Evaluation of Speech Quality score, reached 4.45, comparable to high-quality original recordings. Notably, the recovered audio processed by ASR systems exhibited an error rate of 0%, indicating nearly lossless recovery. These results highlight the practical applicability and effectiveness of our IO-RAE framework in protecting sensitive audio privacy.

IO-RAE: Information-Obfuscation Reversible Adversarial Example for Audio Privacy Protection

Sparse Urban CrowdSensing (Sparse UCS) is a practical paradigm for completing full sensing maps from limited observations. However, existing methods typically rely on a time-discrete assumption, where data is considered static within fixed intervals. This simplification introduces significant errors as real-world data changes continuously. To address this, we propose a framework for time-continuous data completion. Our approach, Time-Aware Mamba-based Deep Matrix Factorization (TIME-DMF), leverages the Mamba architecture as a powerful temporal encoder. Crucially, we enhance Mamba with a novel time-aware mechanism that explicitly incorporates the actual, often irregular, physical time intervals between observations into its state transitions. This allows our model to accurately capture true temporal dynamics and generate high-fidelity data for any queried moment in time through a query-generate mechanism. Extensive experiments on five diverse sensing tasks demonstrate that TIME-DMF significantly outperforms state-of-the-art methods, validating the superiority of the time-continuous paradigm for Sparse UCS. Our code is available at https://anonymous.4open.science/r/Time-DMF-B373/.

Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing

Structured sparsity has emerged as a popular model pruning technique, widely adopted in various architectures, including CNNs, Transformer models, and especially large language models (LLMs) in recent years. A promising direction to further improve post-pruning performance is weight permutation, which reorders model weights into patterns more amenable to pruning. However, the exponential growth of the permutation search space with the scale of Transformer architectures forces most methods to rely on greedy or heuristic algorithms, limiting the effectiveness of reordering.


In this work, we propose a novel end-to-end learnable permutation framework. Our method introduces a learnable permutation cost matrix to quantify the cost of swapping any two input channels
of a given weight matrix, a differentiable bipartite matching solver to obtain the optimal binary permutation matrix given a cost matrix, and a sparsity optimization loss function to directly optimize the permutation operator.
We extensively validate our approach on vision and language Transformers, demonstrating that our method achieves state-of-the-art permutation results for structured sparsity.

Learnable Permutation for Structured Sparsity on Transformer Models

Recent advances in model compression have highlighted the potential of low-bit precision techniques, with Binary Neural Networks (BNNs) attracting attention for their extreme efficiency. However, extreme quantization in BNNs limits representational capacity and destabilizes training, posing significant challenges for lightweight architectures with depth-wise convolutions.
To address this, we propose a 1.58-bit convolution to enhance expressiveness and a pre-BN residual connection to stabilize optimization by improving the Hessian condition number. These innovations enable the first successful binarization of depth-wise convolutions in BNNs.
Our method achieves 32M OPs on ImageNet with MobileNet V1, establishing a new state-of-the-art in BNNs by outperforming prior methods with comparable OPs. Moreover, it consistently outperforms existing methods on various datasets, including CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet, and Oxford Flowers 102, with accuracy improvements of up to 9.3 percentage points.

BD-Net: Has Depth-Wise Convolution Ever Been Applied in Binary Neural Networks?

Economic decision‑making depends not only on structured signals—such as prices and taxes—but also on unstructured language, including peer dialogue and media narratives. While multi‑agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP (Language‑Augmented Multi‑Agent Policy), the first framework to integrate language into economic decision‑making, narrowing the gap to real‑world settings.
LAMP follows a Think–Speak–Decide pipeline:
(1) Think interprets numerical observations to extract short‑term shocks and long‑term trends, caching high‑value reasoning trajectories.
(2) Speak crafts and exchanges strategic messages based on the reasoning, updating beliefs by parsing peer communications.
(3) Decide fuses numerical data, reasoning, and reflections into a MARL policy to optimize language‑augmented decision‑making.
Experiments in economic simulation show that LAMP outperforms both MARL and LLM‑only baselines in cumulative return (+63.5%, +34.0%), robustness (+18.8%, +59.4%), and interpretability. These results demonstrates the potential of language‑augmented policies to deliver more effective and robust economic strategies.

Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making

Reconstructing high dynamic range (HDR) images from low dynamic range (LDR) bursts plays an essential role in the computational photography. Impressive progress has been achieved by learning-based algorithms which require LDRHDR image pairs. However, these pairs are hard to be obtained, which motivates researchers to delve into the problem of annotation-efficient HDR image reconstructing: how to achieve comparable performance with limited HDR ground truths (GTs). This work attempts to address this problem from the view of semi-supervised learning where a teacher model generates pseudo HDR GTs for the LDR samples without GTs and a student model learns from pseudo GTs. Nevertheless, the confirmation bias, i.e., the student may learn from the artifacts in pseudo HDR GTs, presents an impediment. Trying to remove this impediment, an uncertainty-based masking process is proposed to discard unreliable parts of pseudo GTs on both pixel and patch levels, then trusted area can be learned by the student. With this novel masking process, our semi-supervised HDR reconstructing method not only outperforms previous annotation-efficient algorithms, but also achieves comparable performance with up-to-date fully-supervised methods by using only 6.7% HDR GTs.

Semi-Supervised High Dynamic Range Image Reconstructing via Bi-Level Uncertain Area Masking

Hypergraph neural networks (HNNs) have emerged as powerful tools for modeling high-order relationships in complex systems. However, most existing HNNs are designed under the assumption of homophily, which does not hold in many real-world scenarios where connected nodes often exhibit diverse semantics, i.e., heterophily. This inconsistency leads to suboptimal aggregation and degraded performance, especially in low-label regimes. While a few recent methods have attempted to enhance heterophilic hypergraph learning, they often rely heavily on label supervision and overlook the potential of self-supervised techniques. In this paper, we propose HeroCL, a heterophily-aware contrastive learning framework that improves hypergraph representation under both structural heterogeneity and label scarcity. Specifically, HeroCL integrates a multi-hop neighbor encoding module to capture informative higher-order context and incorporates two complementary contrastive objectives, label-aware and structure-aware, to guide representation learning from both semantic and relational perspectives. A multi-granularity contrastive strategy is introduced to exploit latent signals across multiple neighborhood levels. Extensive experiments on several benchmark datasets against 11 existing baselines demonstrate that HeroCL achieves consistent and significant performance gains, particularly under strong heterophily and limited supervision, validating its robustness and effectiveness.

Heterophily-aware Contrastive Learning for Heterophilic Hypergraphs

The rapid development of large language models (LLMs) has highlighted the need for efficient and reliable methods to evaluate their performance. Traditional evaluation methods often face challenges like high costs, limited task formats, dependence on human references, and systematic biases. To address these limitations, we propose Auto-PRE, an automatic LLM evaluation framework inspired by the peer review process. Unlike previous approaches that rely on human annotations, Auto-PRE automatically selects evaluator LLMs based on three core traits: consistency, pertinence, and self-confidence, which correspond to the instruction, content, and response stages, respectively, and collectively cover the entire evaluation process. Experiments on three representative tasks, including summarization, non-factoid QA, and dialogue generation, demonstrate that Auto-PRE achieves state-of-the-art performance while significantly reducing evaluation costs. Furthermore, the structured and scalable design of our automatic qualification exam framework provides valuable insights into automating the evaluation of LLMs-as-judges, paving the way for more advanced LLM-based evaluation frameworks.

Auto-PRE: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation

Most existing RGB-Event trackers rely on strictly aligned datasets, overlooking the asynchronous spatio-temporal resolutions common in real-world scenarios. 
This methodological limitation impedes effective RGB-Event feature alignment and ultimately degrades tracking performance.
To overcome this limitation, we propose AlignTrack, a novel tracking framework built upon a Top-Down Alignment (TDA) strategy inspired by the human visual system. 
Our TDA framework follows an encode-decode-align paradigm: it first encodes multimodal features to generate target-related priors, which are then progressively decoded to guide a subsequent feature alignment pass. 
Within this framework, we introduce two key innovations: (1) a Cross-Prior Attention (CPA) module that effectively generates and integrates cross-modal priors, and (2) a Cross-Modal Semantic Alignment (CSA) loss that maximizes mutual information to enforce semantic consistency between modalities. 
Extensive experiments show that AlignTrack achieves state-of-the-art performance on four challenging RGB-Event tracking benchmarks, demonstrating its robustness in both aligned and unaligned scenarios. 
Ablation studies further validate the significant contribution of each proposed component.

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Experiential Fairness: Bridging the Gap Between User Experience and Resource-Centric Fairness in Online LLM Services

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