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This paper initiates the integration of Spiking Neural Networks (SNNs) into WiFi-based indoor sensing, aiming to enhance performance in challenging signal environments. WiFi signal-based pattern recognition enables a wide range of applications, including human activity recognition, gait analysis for human identification, fine-grained gesture recognition and etc. However, unlike cameras, radar, or LiDAR signals, WiFi measurements are particularly susceptible to noise due to the ubiquitous and interference-prone nature of indoor wireless communication. 
Biologically inspired SNNs, like the human brain, excel at processing information in noisy environments by leveraging stochastic neural dynamics. 
To address this, we propose a hybrid architecture that combines conventional Artificial Neural Networks (ANNs) with SNNs, leveraging the noise-resilient properties of spiking neurons. Our method demonstrates improved accuracy and faster convergence during training. To support this claim, we present a theoretical analysis comparing the noise-handling capabilities of ANN and SNN models in WiFi scenarios. Extensive experiments across three representative WiFi sensing tasks validate the effectiveness and robustness of the proposed ANN-SNN hybrid architecture. For reproducibility, we will release the code upon acceptance.

AAAI 2026

Spiking-Aided Neural Architecture for Efficient and Robust WiFi Sensing

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.<br><br>

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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.

Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms—traffic signals or local heuristics—are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns—demonstrating its potential for scalable, ML-driven traffic management.

Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks

Multiagent systems is a branch of artificial intelligence
(AI) that examines the behavior within communities of
rational actors, where the actions of one actor may have
consequences for another. Multiagent systems takes the idea
of individual incentives from economic game theory and
applies it to distributed computation and decentralized
mechanisms. It examines not only how certain overall
economic or computational goals can be accomplished, but
also why individual participants will choose to cooperate
in accomplishing that goal.
Pedagogy within multiagent system is rich in mathematical
rigor and theory. However, there is a gap in pedagogical
practices that ties that theoretical training with the
application of that theory to actual human agents. This is
especially important as AI is deployed in increasingly
sociotechnical domains.
This paper presents the first exploration of using large
participation activities to facilitate experiential
learning to bridge this gap. We conduct an in-person
game-like activity (a ``megagame'') where up to 43
participants engage in a day-long resource allocation
scenario, where learners can apply their theoretical
frameworks to analyze and solve emerging problems, while
motivated by and under the pressure of meaningful stakes.
We present these megagames as a potential pedagogical tool
for both multiagent systems and other domains.

Large Participation Experiential Learning Activity for Multiagent Systems

Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges due to the dramatically growing search space. Existing methods address this problem within a filtering-ordering-enumeration framework, in which the enumeration stage recursively matches the query graph against the candidate subgraphs of the data graph. However, the lack of awareness of subgraph structural patterns leads to a costly brute-force enumeration, thereby critically motivating the need for intelligent navigation in subgraph matching. To address this challenge, we propose Neural Graph Navigation (NeuGN), a neuro-heuristic framework that transforms brute-force enumeration into neural-guided search by integrating neural navigation mechanisms into the core enumeration process. By preserving heuristic-based completeness guarantees while incorporating neural intelligence, NeuGN significantly reduces the First Match Steps by up to 98.2\% compared to state-of-the-art methods across six real-world datasets.

Neural Graph Navigation for Intelligent Subgraph Matching

In this paper, we argue that current AI (alignment) research operates on a spectrum between two different underlying conceptions of intelligence: Intelligence Realism, which holds that intelligence represents a single, universal capacity measurable across all systems, and Intelligence Pluralism, which views intelligence as diverse, context-dependent capacities that cannot be reduced to a single universal measure. Through an analysis of current debates in AI research, we demonstrate how the conceptions remain largely implicit yet fundamentally shape how empirical evidence gets interpreted across a wide range of areas. More significantly, the underlying views generate fundamentally different research strands across three areas. Methodologically, they produce different approaches to model selection, benchmark design, and experimental validation. Interpretively, they lead to contradictory readings of scaling laws and system limitations. Regarding AI risk, they generate categorically different assessments of risk and alignment approaches: the ones viewing superintelligence as the biggest risk and searching for unified alignment solutions, the others seeing different threats in many different domains and searching for context-specific solutions. We argue that making explicit these underlying assumptions can contribute to a clearer understanding of the disagreements in this research space and, potentially, a more context-sensitive approach to alignment research.

Realist and Pluralist Conceptions of Intelligence and Their Implications on AI Research

Federated learning (FL) enables privacy-preserving model training across distributed Electronic Health Records (EHRs), but its deployment remains limited by data-view heterogeneity, where institutions maintain incompatible local schemas. Most existing methods address this by enforcing flat, aligned data views, which require extensive cross-site preprocessing and manual harmonisation that often discards client-specific features, or by projecting inputs into a shared latent space, which sacrifices interpretability. We propose a modelling shift from conventional FL with vectorised inputs to a symbolic, relation-centric framework, where each client organises its EHR data as a structured, type-aware relational graph. This enables client-specific inference without requiring schema alignment and supports FL across heterogeneous data views. To model over these symbolic structures, we introduce an architecture that combines relation-aware message passing with a learnable feature relevance mechanism, jointly enabling accurate local predictions and client-specific interpretability while supporting parameter sharing across clients. Beyond strong performance on three real-world EHR datasets exhibiting data-view heterogeneity, we further show that our framework supports multimodal FL under modality-level heterogeneity. Using MC-MED, a publicly available multimodal emergency department dataset, we demonstrate that our method accommodates clients with partially missing modalities, highlighting its robustness and scalability in real-world clinical settings.

Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling

Optimization modeling plays a critical role in supporting optimal decision-making across various domains. Previous works have demonstrated that large language models (LLMs) tailored for optimization modeling have significantly automated and simplified this process. However, these models typically employ a straightforward input-output paradigm and struggle with challenging instances. In contrast, recent advances in general-purpose reasoning LLMs (RLLMs), such as DeepSeek-R1, have shown impressive capabilities in complex domains like mathematics and coding. In this paper, we introduce DeepOR, the first RLLM specifically designed for optimization modeling. Instead of directly outputting solutions, DeepOR explicitly performs multiple intermediate reasoning steps. To adapt a base LLM into an RLLM, we begin by synthesizing long chain-of-thought (CoT) data guided by a flowchart, which is automatically generated using a self-exploration algorithm. Once the training data are prepared, we employ supervised fine-tuning on the base LLM to endow it with reasoning capabilities tailored for optimization modeling. To fully leverage the model's reasoning potential, we further apply reinforcement learning with reward-shaping derived from solver feedback. Experimental results on benchmarks confirm that DeepOR consistently and significantly outperforms existing state-of-the-art approaches.

DeepOR: A Deep Reasoning Foundation Model for Optimization Modeling

Fluid–structure interaction (FSI) systems involve distinct physical domains, fluid and solid, governed by different partial differential equations and coupled at a dynamic interface. While learning-based solvers offer a promising alternative to costly numerical simulations, existing methods struggle to capture the heterogeneous dynamics of FSI within a unified framework. This challenge is further exacerbated by inconsistencies in response across domains due to interface coupling and by disparities in learning difficulty across fluid and solid regions, leading to instability during prediction. To address these challenges, we propose the Heterogeneous Graph Attention Solver (HGATSolver). HGATSolver encodes the system as a heterogeneous graph, embedding physical structure directly into the model via distinct node and edge types for fluid, solid, and interface regions. This enables specialized message-passing mechanisms tailored to each physical domain. To stabilize explicit time stepping, we introduce a novel physics-conditioned gating mechanism that serves as a learnable, adaptive relaxation factor. Furthermore, an Inter-domain Gradient-Balancing Loss dynamically balances the optimization objectives across domains based on predictive uncertainty. Extensive experiments on two constructed FSI benchmarks and a public dataset demonstrate that HGATSolver achieves state-of-the-art performance, establishing an effective framework for surrogate modeling of coupled multi-physics systems.

HGATSolver: A Heterogeneous Graph Attention Solver for Fluid–Structure Interaction

Understanding the internal functional organization of Large Language Models (LLMs) is crucial for improving their trustworthiness and performance.
However, how LLMs organize different functions into modules remains highly unexplored.
To bridge this gap, we formulate a functional module discovery problem and propose an Unsupervised LLM Cross-layer MOdule Discovery (ULCMOD) framework that simultaneously disentangles the large set of neurons in the entire LLM into modules while discovering the topics of input samples related to these modules.
Our framework introduces a novel objective function and an efficient Iterative Decoupling (IterD) algorithm.
Extensive experiments show that our method discovers high-quality, disentangled modules that capture more meaningful semantic information and achieve superior performance in various downstream tasks.
Moreover, our qualitative analysis reveals that the discovered modules show semantic coherence, correspond to interpretable specializations, and a clear spatial and hierarchical organization within the LLM.
Our work provides a novel tool for interpreting the functional modules of LLMs, filling a critical blank in LLM’s interpretability research.

Discovering Decoupled Functional Modules in Large Language Models

Access to multiple predictive models trained for the same task, whether in regression or classification, is increasingly common in many applications. Aggregating their predictive uncertainties to produce reliable and efficient uncertainty quantification is therefore a critical but still underexplored challenge, especially within the framework of conformal prediction (CP). While CP methods can generate individual prediction sets from each model, combining them into a single, more informative set remains a challenging problem. To address this, we propose SACP (Symmetric Aggregated Conformal Prediction), a novel method that aggregates nonconformity scores from multiple predictors. SACP transforms these scores into e-values and combines them using any symmetric aggregation function. This flexible design enables a robust, data-driven framework for selecting aggregation strategies that yield sharper prediction sets. We also provide theoretical insights that help justify the validity and performance of the SACP approach. Extensive experiments on diverse datasets show that SACP consistently improves efficiency and often outperforms state-of-the-art model aggregation baselines.

Symmetric Aggregation of Conformity Scores for Efficient Uncertainty Sets

We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the integrity of their original documents. We show that the commonly used Probability Ranking Principle (PRP) ranking scheme results in an unstable environment where games often fail to reach pure Nash equilibrium. We propose two families of ranking functions that do not adhere to the PRP. We provide both theoretical and empirical evidence that these methods lead to a stable search ecosystem, by providing positive results on the learning dynamics convergence. We also define the publishers’ and users’ welfare, demonstrate a possible publisher-user trade-off, and provide means for a search system designer to control it. Finally, we show how instability harms long-term users’ welfare.

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Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks

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