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Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across contexts. While usually associated with friendliness or playfulness, it is observed that emojis may trigger toxic content generation in LLMs. Motivated by such a observation, we aim to investigate: *(1) whether emojis can clearly enhance the toxicity generation in LLMs and (2) how to interpret this phenomenon.* We begin with a comprehensive exploration of emoji-triggered LLM toxicity generation by automating the construction of prompts with emojis to subtly express toxic intent. Experiments across 5 mainstream languages on 7 famous LLMs along with jailbreak tasks demonstrate that prompts with emojis could easily induce toxicity generation. To understand this phenomenon, we conduct model-level interpretations spanning semantic cognition, sequence generation and tokenization, suggesting that emojis can act as a heterogeneous semantic channel to bypass the safety mechanisms. To pursue deeper insights, we further probe the pre-training corpus and uncover potential correlation between the emoji-related data polution with the toxicity generation behaviors. Supplementary materials provide our implementation code and data. (Warning: This paper contains potentially sensitive contents)

AAAI 2026

When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs’ Toxicity

and evaluation of nlp models

nlp: interpretability

analysis

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ICWSM 2022

The International AAAI Conference on Web and Social Media (ICWSM) is a forum for researchers from multiple disciplines to come together to share knowledge, discuss ideas, exchange information, and learn about cutting-edge research in diverse fields with the common theme of online social media. This overall theme includes research in new perspectives in social theories, as well as computational algorithms for analyzing social media. ICWSM is a singularly fitting venue for research that blends social science and computational approaches to answer important and challenging questions about human social behavior through social media while advancing computational tools for vast and unstructured data.

technical paper

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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.

3D single object tracking (SOT) in LiDAR point clouds is a critical task in computer vision and autonomous driving. Despite great success having been achieved, the inherent sparsity of point clouds introduces a dual-redundancy challenge that limits existing trackers: (1) vast spatial redundancy from background noise impairs accuracy, and (2) informational redundancy within the foreground hinders efficiency. To tackle these issues, we propose **CompTrack**, a novel end-to-end framework that systematically eliminates both forms of redundancy in point clouds. First, CompTrack incorporates a Spatial Foreground Predictor (SFP) module to filter out irrelevant background noise based on information entropy, addressing spatial redundancy. Subsequently, its core is an Information Bottleneck-guided Dynamic Token Compression (IB-DTC) module that eliminates the informational redundancy within the foreground. Theoretically grounded in low-rank approximation, this module leverages an online SVD analysis to adaptively compress the redundant foreground into a compact and highly informative set of proxy tokens. Extensive experiments on KITTI, nuScenes and Waymo datasets demonstrate that CompTrack achieves top-performing tracking performance with superior efficiency, running at a real-time 90 FPS on a single RTX 3090 GPU.

CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking

Knowledge editing (KE) provides a scalable approach for updating factual knowledge in large language models without full retraining. While previous studies have demonstrated effectiveness in general domains and medical QA tasks, little attention has been paid to KE in multimodal medical scenarios. Unlike text-only settings, medical KE demands integrating updated knowledge with visual reasoning to support safe and interpretable clinical decisions. To address this gap, we propose MultiMedBench, the first benchmark tailored to
evaluating KE in clinical multimodal tasks. Our framework spans both understanding and reasoning task types, defines a three-dimensional metric suite (reliability, generality, and locality), and supports cross-paradigm comparisons across general and domain-specific models. We conduct extensive experiments under single-editing and lifelong-editing settings. Results suggest that current methods struggle with generalization and long-tail reasoning, particularly in complex clinical workflows. We further present an efficiency analysis (e.g., edit latency, memory footprint), revealing practical trade-offs in real-world deployment across KE paradigms. Overall, MultiMedBench not only reveals the limitations of current approaches but also provides a solid foundation for developing clinically robust knowledge editing techniques in the future.

MultiMedBench: A Scenario-Aware Benchmark for Evaluating Knowledge Editing in Medical VQA

Conversational Recommender Systems (CRS) aim to provide personalized recommendations by interacting with users through natural language dialogue. However, in scenarios requiring deep geospatial awareness, existing methods, including those based on Large Language Models (LLMs), still face significant challenges in effectively fusing heterogeneous, multimodal geographic information with dynamic dialogue context. Simple fusion strategies struggle to resolve the asymmetric dependencies between dynamic user intent and static geographic context and fail to bridge the semantic gap between LLMs and structured geospatial data. To address these issues, we propose a framework for geography-aware CRS, named GeoCRS. Our core idea is to empower a frozen LLM with powerful geospatial reasoning capabilities by conditioning it on a dynamic, multimodal guidance signal generated by an external fusion architecture, all without altering the LLM's internal parameters. Specifically, we first design a hierarchical geographical encoder to uniformly represent heterogeneous geographic data. Subsequently, we introduce a contextual feature modulation module that asymmetrically injects the geographic context into the user's dialogue intent via a novel modulation mechanism to improve conversational recommendation via both geographic and dialogue context. Extensive experiments on public benchmark datasets demonstrate that our proposed GeoCRS significantly outperforms state-of-the-art baselines on the geography-aware conversational recommendation task.

From Dialogue to Destination: Geography-Aware Large Language Models with Multimodal Fusion for Conversational Recommendation

Graph neural networks (GNNs) face dual challenges of limited structural expressiveness and opaque decision-making processes. Recent research on Subgraph Neural Networks (SGNNs) enhance model expressiveness through subgraph ensembles. However, their reliance on predefined sampling strategies leads to poor interpretability and computational inefficiency. Meanwhile, post-hoc GNN explainers enhance model interpretability but still struggle to translate their explanations into model improvements. This paper presents a novel framework that fundamentally bridges this gap by developing SGNNs with intrinsic interpretability. Our key innovation lies in constructing a self-interpretable architecture where the explanation generation mechanism is organically integrated with the prediction process. Our proposed Self-Interpretable SGNN introduces a reinforcement walk exploration (RWE-SGNN) as its data-driven sampling strategy, which can dynamically extract discriminative substructures during model training. This reinforcement walk exploration module not only provides inherent interpretability, but also enables: (1) Efficient substructure extraction via walk-based exploration with less candidate number and simper embedding than subgraph generation; (2) Provable equivalence to traditional subgraph enumeration methods with polynomial complexity reduction. Our numerical evaluations on molecular property prediction and social network analysis tasks show accuracy improvements over state-of-the-art GNNs, with case studies validating that the automatically identified subgraphs align with domain-specific knowledge.

Self-Interpretable Subgraph Neural Network with Deep Reinforcement Walk Exploration

Embodied navigation is a fundamental capability for intelligent agents, yet remains challenging in partially observable environments where navigation instructions can be difficult to interpret. However, existing tasks only provide unimodal instructions, which are ambiguous in complex multimodal environments with multiple similar objects, and may result in misinterpretation and navigation failure. To overcome these limitations, we introduce MINav, a novel task where the navigation path is precisely described by a multimodal instruction. The instruction provides multimodal cues, including object categories, RGB images, language descriptions, and auditory descriptions, which help the agent to disambiguate and ground objects in the environment and navigate effectively. We further construct a large-scale dataset of 43.9K navigation episodes using a two-stage pipeline that first annotates multimodal references of objects and then synthesizes diverse multimodal instructions. We find that existing methods struggle on MINav task, indicating substantial room for improvement in agents' multimodal grounding. To address this, we propose NaVLA$^2$, a vision-language-audio-action model that additionally integrates spatial audio and employs a CoThinkAct module to jointly generate high-level reasoning and consistent low-level actions. Experimental results demonstrate that NaVLA$^2$ significantly outperforms competitive baselines on MINav benchmark. We hope that our proposed MINav and NaVLA$^2$ will facilitate future research toward agents with stronger multimodal understanding and grounding capabilities for navigation.

NaVLA$^2$: A Vision-Language-Audio-Action Model for Multimodal Instruction Navigation

Recent research reveals that a minority of high-entropy tokens significantly influences the reasoning quality of large language models (LLMs). Inspired by this, we propose Prototype Entropy Alignment (PEA), a reinforcement learning framework that models effective reasoning not as a single path, but as a collection of learnable "entropy signatures." PEA identifies these signatures by clustering the uncertainty patterns of expert trajectories into a dynamic set of prototypes. It then rewards the model for aligning its own reasoning process with these evolving targets, creating a self-improving loop. Instead of replacing traditional outcome-based rewards, PEA provides a complementary, process-oriented signal. Our experiments show this synergy is crucial: PEA substantially boosts performance on creative and general reasoning tasks, and when combined with outcome rewards, achieves state-of-the-art results on structured tasks like mathematics. By rewarding alignment with diverse and evolving reasoning structures, PEA offers a robust, verifier-free pathway to enhance the depth and adaptability of LLM reasoning.

Prototype Entropy Alignment: Reinforcing Structured Uncertainty in LLM Reasoning

We consider a problem of offline reinforcement learning from human feedback (RLHF) with pairwise comparisons proposed by Zhu et al. (2023), where the implicit reward is a linear function of an unknown parameter. Given an offline dataset, our objective consists in ascertaining the optimal action for each state, with the ultimate goal of minimizing the {\em simple regret}. We propose an algorithm, \underline{RL} with \underline{L}ocally \underline{O}ptimal \underline{W}eights or {\sc RL-LOW}, which yields an exponential form of simple regret of $\exp ( - \Omega(n/H) )$ where $n$ is the number of data samples and $H$ denotes an instance-dependent hardness quantity that depends explicitly on the suboptimality gap of each action. Furthermore, we derive a first-of-its-kind instance-dependent lower bound in offline RLHF with pairwise comparisons. Interestingly, we observe that the lower and upper bounds on the simple regret match order-wise in the exponent, demonstrating order-wise optimality of our {\sc RL-LOW}. 
In view of privacy considerations in practical applications, we also extend {\sc RL-LOW} to the setting of $(\varepsilon,\delta)$-differential privacy and show, somewhat surprisingly, that the hardness parameter $H$ is unchanged in the asymptotic regime as $n$ tends to infinity; this underscores the inherent efficiency of {\sc RL-LOW} in terms of preserving the privacy of the observed rewards. Given our focus on establishing instance-dependent bounds of exponential convergence, our research fills the research gap in existing studies that concentrate on establishing worst-case regrets of {\em inverse polynomial convergence} (e.g., $\widetilde{O}(\frac{1}{\sqrt{n}})$) for offline RLHF with pair-wise comparison.

On the Exponential Convergence for Offline RLHF with Pairwise Comparisons

Given a non-negative integer $\ell$, the $k$-median with outliers problem extends the standard $k$-median problem by allowing the removal of up to $\ell$ points and minimizing the clustering cost over the remaining ones. Algorithmic development in this setting remains an active area of research due to its relevance in processing noisy data. In this paper, we present a sampling-based reduction from the $k$-median with outliers problem to its outlier-free counterpart. The reduction incurs a multiplicative overhead of $(k\ell^{-1} + \varepsilon^{-1})^{O(\ell)}$ in the running time and an arbitrarily small loss in the approximation ratio. This improves upon previously known reductions with overheads of $((k + \ell)\varepsilon^{-1})^{O(\ell)}n^{O(1)}$ and $((k + \ell)\varepsilon^{-1})^{O(\ell)}$, given by Agrawal et al. (AAAI 2023) and Jaiswal and Kumar (ISAAC 2023), respectively. As applications, we obtain faster fixed-parameter tractable (FPT) algorithms with tight approximation guarantees for the $k$-median with outliers problem under various metric spaces. Furthermore, our approach naturally generalizes to constrained variants of the problem where additional constraints are imposed on the cluster sizes, and yields similar improvements in their FPT approximations.

A More Efficient Reduction from Outlier-Aware to Outlier-Free k-Median

Food rescue organizations simultaneously tackle food insecurity and waste by working with volunteers to redistribute food from donors who have excess to recipients who need it. Volunteer feedback allows food rescue organizations to identify issues early and ensure volunteer satisfaction. However, food rescue organizations monitor feedback manually, which can be cumbersome and labor-intensive, making it difficult to prioritize which issues are most important. In this work, we investigate how large language models (LLMs) assist food rescue organizers in understanding and taking action based on volunteer experiences. We work with 412 Food Rescue, a large food rescue organization based in Pittsburgh, Pennsylvania, to design RescueLens, an LLM-powered tool that automatically categorizes volunteer feedback, suggests donors and recipients to follow up with, and updates volunteer directions based on feedback. We evaluate the performance of RescueLens on an annotated dataset, and show that it can recover 96% of volunteer issues at 71% precision. Moreover, by ranking donors and recipients according to their rates of volunteer issues, RescueLens allows organizers to focus on 0.5% of donors responsible for more than 30% of volunteer issues. RescueLens is now deployed at 412 Food Rescue and through semi-structured interviews with organizers, we find that RescueLens streamlines the feedback process so organizers better allocate their time.

RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue

Industrial data scientists modeling an asset's condition need to build domain understanding by asking questions about a given asset. Some example asset questions are what failure modes can it experience, under which operating conditions, and how the manufacturer and weather affect.
Traditionally, the main source of domain information comes from Subject Matter Experts (SMEs) and Failure Modes and Effects Analysis (FMEA) documents which are not always available and may not be detailed enough to cover different external factors (e.g., operating mode, manufacturer, weather). 
Now that Large Language Models (LLMs) have became a commodity, this gives us a big opportunity to leverage them to bridge this gap. 
Inspired by other's work on LLM knowledge probing, we present a Multi-Agent System (MAS) specialized on aiding industrial data scientists guide their modeling decisions. One challenge we address is the generated linguistic diversity and question relevance, which we optimize by using popular information diversity metrics and a grounded relevancy classifier. 
We continuously monitor the set of newly generated instruction sets at the end of each round, compare the linguistic diversity against common baselines and show high generated knowledge coverage on the downstream FMEA task.
We also conduct user studies to validate the quality of the questions. 
We finally present the real-world implications of providing diverse asset specific information to aid data scientist's modeling decisions through our deployed MAS. 
Through the deployed system, we show its generalizability to different assets and extendibility to more downstream tasks like work order scheduling, failure mode sensor analysis and machine learning model recipes generation.

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CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking

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