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World models envision potential future states based on various ego actions. They embed extensive knowledge about the driving environment, facilitating safe and scalable autonomous driving. Most existing methods primarily focus on either data generation or the pretraining paradigms of world models. Unlike the aforementioned prior works, we propose Drive-OccWorld, which adapts a vision-centric 4D forecasting world model to end-to-end planning for autonomous driving. Specifically, we first introduce a semantic and motion-conditional normalization in the memory module, which accumulates semantic and dynamic information from historical BEV embeddings. These BEV features are then conveyed to the world decoder for future occupancy and flow forecasting, considering both geometry and spatiotemporal modeling. Additionally, we propose injecting flexible action conditions, such as velocity, steering angle, trajectory, and commands, into the world model to enable controllable generation and facilitate a broader range of downstream applications. Furthermore, we explore integrating the generative capabilities of the 4D world model with end-to-end planning, enabling continuous forecasting of future states and the selection of optimal trajectories using an occupancy-based cost function. Extensive experiments on the nuScenes dataset demonstrate that our method can generate plausible and controllable 4D occupancy, opening new avenues for driving world generation and end-to-end planning.

AAAI 2025

Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

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-25 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.



Computing an optimal classification tree that provably maximizes training performance within a given size limit, is NP-Hard and in practice, most state-of-the-art methods do not scale beyond computing optimal trees of depth three. Therefore, most methods rely on a coarse binarization of continuous features to maintain scalability. We propose a novel algorithm that directly optimizes on the continuous feature data using dynamic programming with branch-and-bound. We develop new lower-bounding techniques that eliminate many sub-optimal splits in the search when similar to previously computed splits and we provide an efficient subroutine for computing optimal depth-two trees. Our experiments demonstrate that these techniques yield a runtime improvement of two orders of magnitude over state-of-the-art optimal methods and improve test accuracy by 5% over greedy heuristics.

Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound

Understanding causal relationships among the variables of a system is paramount to explain and control its behaviour. For many real world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks. Existing metrics provide an *absolute* number of inconsistencies between the graph and the observed data, and without a baseline, practitioners are left to answer the hard question of how many such inconsistencies are acceptable or expected. Here, we propose a novel consistency metric by constructing a baseline through node permutations. By comparing the number of inconsistencies with those on the baseline, we derive an interpretable metric that captures whether the graph is significantly better than random. Evaluating on both simulated and real data sets from various domains, including biology and cloud monitoring, we demonstrate that the true graph is not falsified by our metric, whereas the wrong graphs given by a hypothetical user are likely to be falsified.

Toward Falsifying Causal Graphs Using a Permutation-Based Test

Large Language Models (LLMs) are known to hallucinate facts and make non-factual statements which can undermine trust in their output.
The essence of hallucination lies in the absence of metacognition in LLMs, namely the understanding of their own cognitive processes. However, there has been limited research on quantitatively measuring metacognition within LLMs. Drawing inspiration from cognitive psychology theories, we first quantify the metacognitive ability of LLMs as their ability to evaluate the correctness of responses through confidence. Subsequently, we introduce a general framework called DMC designed to decouple metacognitive ability and cognitive ability. This framework tackles the challenge of noisy quantification caused by the coupling of metacognition and cognition in current research, such as calibration-based metrics. Specifically, the DMC framework comprises two key steps. Initially, the framework tasks the LLM with failure prediction, aiming to evaluate the model's performance in predicting failures, a performance jointly determined by both cognitive and metacognitive abilities of the LLM. Following this, the framework disentangles metacognitive ability and cognitive ability based on the failure prediction performance, providing a quantification of the LLM's metacognitive ability independent of cognitive influences. Experiments conducted on eight datasets across five domains reveal that (1) Our proposed DMC framework effectively separates the metacognition and cognition of LLMs; (2) Various confidence elicitation methods impact the quantification of metacognitve ability differently; (3) Stronger metacognitive ability are exhibited by LLMs with better overall performance; (4) Enhancing metacognition holds promise for alleviating hallucination issues.

Decoupling Metacognition from Cognition: A Framework for Quantifying Metacognitive Ability in LLMs

Testing a hypothesized causal model against observational data is a key prerequisite for many causal inference tasks. A natural approach is to test whether the conditional independence relations (CIs) assumed in the model hold in the data. While a model can assume exponentially many CIs (with respect to the number of variables), testing all of them is both impractical and unnecessary. Causal graphs, which encode these CIs in polynomial space, give rise to local Markov properties that enable model testing with a significantly smaller subset of CIs. Model testing based on local properties requires an algorithm to list the relevant CIs. However, existing algorithms for realistic settings with hidden variables and non-parametric distributions can take exponential time to produce even a single CI constraint. In this paper, we introduce the c-component local Markov property (C-LMP) for causal graphs with hidden variables. Since C-LMP can still invoke an exponential number of CIs, we develop a polynomial delay algorithm to list these CIs in poly-time intervals. To our knowledge, this is the first algorithm that enables poly-delay testing of CIs in causal graphs with hidden variables against arbitrary data distributions. Experiments on real-world and synthetic data demonstrate the practicality of our algorithm.

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies

Anomaly detection on graphs has garnered considerable attention due to its critical applications, such as detecting money laundering in financial systems and identifying fake reviews on social networks. Traditional fraud detection methods typically focus on either fraudsters (node-level) or criminal activities (graph-level) in isolation, while availing the latent information associated with transactions (edge-level). Similarly, fake review detection often considers either individual fake reviews (node-level) or collaborative reviewer groups (graph-level) separately, based on designed relationships (edge-level). However, this separation neglects the interconnections and frequent co-occurrences across different levels, limiting the effective use of complementary information to identify community anomalies that emerge from the collective behavior of individual anomalies. Additionally, the inherent imbalance in anomaly detection, where anomalous instances are outnumbered by normal samples, exacerbates these challenges. To address these issues, we propose UniFORM, a unified self-supervised anomaly detection framework, that integrates node, edge, and graph-level tasks. First, we extract centralized and decentralized communities as multi-grained contexts and employ an energy-based GNN to reveal anomalous properties. Then, we construct the Phantom Pool, Query Pool, and Support Pool by the enhancement of meta-learning with contrastive learning. Finally, we design unified loss in intra-inter perspectives. Comprehensive experiments on real-world datasets substantiate that our framework markedly surpasses state-of-the-art methods across these multi-grained levels.

UniFORM: Towards Unified Framework for Anomaly Detection on Graphs

In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. When only a limited number of views are available as input, the performance of monocular priors deteriorates due to scale ambiguity, leading to the collapse of the reconstructed scene geometry. In this paper, we propose a new method, named Sparis, for indoor surface reconstruction from sparse views. Specifically, we investigate the impact of monocular priors on sparse scene reconstruction, introducing a novel prior based on inter-image matching information. Our prior offers more accurate depth information while ensuring cross-view matching consistency. Additionally, we employ an angular filter strategy and an epipolar matching weight function, aiming to reduce errors due to view matching inaccuracies, thereby refining the inter-image prior for improved reconstruction accuracy. The experiments conducted on widely used benchmarks demonstrate superior performance in sparse-view scene reconstruction.

Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from Sparse Views

Applying Reinforcement Learning (RL) to Restless Multi-Arm Bandits (RMABs) offers a promising avenue for addressing allocation problems with resource constraints and temporal dynamics. However, classic RMAB models largely overlook the challenges of (systematic) data errors—a common occurrence in real-world scenarios due to factors like varying data collection protocols and intentional noise for differential privacy. We demonstrate that conventional RL algorithms used to train RMABs can struggle to perform well in such settings. To solve this problem, we propose the first communication learning approach in RMABs, where we study which arms, when involved in communication, are most effective in mitigating the influence of such systematic data errors. 
%To solve this problem, we propose a novel communication learning approach that enables arms to identify and mitigate the influence of such systematic data errors by learning from other arms via communication. 
In our setup, the arms receive Q-function parameters from similar arms as messages to guide behavioral policies, steering Q-function updates. We learn communication strategies by considering the joint utility of messages across all pairs of arms and using a Q-network architecture that decomposes the joint utility. Both theoretical and empirical evidence validate the effectiveness of our method in significantly improving RMAB performance across diverse problems.

The Bandit Whisperer: Communication Learning for Restless Bandits

Person re-identification (Re-ID) is crucial for intelligent surveillance systems, facilitating the identification of individuals across multiple camera views. While significant advancements have been made for daytime scenarios, ensuring reliable Re-ID performance during nighttime remains a significant challenge. Given the cost and limited accessibility of infrared cameras, we investigate a critical question: Can RGB cameras be effectively utilized for accurate Re-ID during nighttime? To address this, we introduce NightReID, a large-scale RGB Re-ID dataset collected from a real-world nighttime surveillance system. NightReID includes 1,500 identities and over 53,000 images, capturing diverse scenes with complex lighting and adverse weather conditions. This rich dataset provides a valuable benchmark for advancing nighttime Re-ID research. Moreover, we propose two novel modules to enhance nighttime Re-ID performance. First, an unsupervised Image Enhancement and Denoising (IED) method is designed to improve the quality of nighttime images, preserving critical details while removing noise without requiring paired ground truth. Second, we introduce Data Distribution Alignment (DDA) through statistical priors, aligning the distributions between pre-training data and nighttime data to mitigate domain shift. Extensive experiments on multiple nighttime Re-ID datasets demonstrate the significance of NightReID and validate the efficacy, flexibility, and applicability of our proposed methods.

NightReID: A Large-Scale Nighttime Person Re-Identification Benchmark

The ability to reason at multiple levels of temporal abstraction is a fundamental aspect of intelligence. In reinforcement learning (RL), this attribute is often modelled through temporally extended courses of actions called options (Sutton et al. 1999). In this talk, I will introduce a general framework for option discovery, which uses the agent's representation to discover useful options (Machado et al., 2023). By leveraging these options to generate a rich stream of experience, the agent can improve its representations and learn more effectively. This representation-driven option discovery approach creates a virtuous cycle of refinement, continuously improving both the representation and options, and it is particularly effective for problems where agents need to operate at varying levels of abstraction to succeed.

Representation-driven Option Discovery in Reinforcement Learning

Representation learning constructs low-dimensional representations to
summarize essential features of high-dimensional data. This learning
problem is often approached by describing various desiderata
associated with learned representations; e.g., that they be
non-spurious, efficient, or disentangled. It can be challenging,
however, to turn these intuitive desiderata into formal criteria that
can be measured and enhanced based on observed data. In this paper, we
take a causal perspective on representation learning, formalizing
desiderata like non-spuriousness and demonstrating their practical utility.

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Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound

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