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Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due the extensive knowledge demands involved. Knowledge editing, which aims to precisely modify the LLMs to incorporate specific knowledge without negatively impacting other unrelated knowledge, offers a potential solution for addressing MHQA challenges with LLMs. However, current solutions struggle to effectively resolve issues of knowledge conflicts. Most parameter-preserving editing methods are hindered by inaccurate retrieval and overlook secondary editing issues, which can introduce noise into the reasoning process of LLMs. In this paper, we introduce KEDKG, a novel knowledge editing method that leverages a dynamic knowledge graph for MHQA, designed to ensure the reliability of answers. KEDKG involves two primary steps: dynamic knowledge graph construction and knowledge graph augmented generation. Initially, KEDKG autonomously constructs a dynamic knowledge graph to store revised information while resolving potential knowledge conflicts. Subsequently, it employs a fine-grained retrieval strategy coupled with an entity and relation detector to enhance  the accuracy of graph retrieval for LLM generation. Experimental results on benchmarks show that KEDKG surpasses previous state-of-the-art models, delivering more accurate and reliable answers in environments with dynamic information.

AAAI 2025

Knowledge Editing with Dynamic Knowledge Graphs for Multi-hop Question Answering

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.



We present EvHDR-NeRF to recover a High Dynamic Range (HDR) radiance field from event streams and a set of low dynamic range (LDR) views with single exposures. Using the EvHDR-NeRF, we can generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the new relationship between events streams and LDR images, which considers both the Camera Response Function (CRF) and exposure time.  Based on this relationship, we categorize events into inter-frame events and intra-exposure. The former is utilized for building HDR radiance field and the latter is used to deblur potentially blurred images. Compared to existing methods, this method can effectively reconstruct the HDR radiance field even when the input images are degraded. Experimental results demonstrate that our method achieves state-of-the-art performance in HDR reconstruction, providing a more adaptable and accurate solution for complex imaging applications.

EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events

Molecular dynamics (MD) has long been the \emph{de facto} choice for simulating intricate physical systems from first principles. Recent efforts utilize the implicit neural representation (INR) to directly learn surface point clouds' signed distance function (SDF) with promising outcomes. However, INR's temporal generalization to unexplored molecular systems remains limited, which poses a significant barrier to applying INR to a broader range of real-world scenarios. This study introduces MoE-DSR, an enhanced version of dynamic surface representations (DSR) that effectively integrates the mixture-of-experts (MoE) strategy. Specifically, the router employs a novel geometric surface cloud network to extract the structural information from the initial static protein conformation as the prior knowledge. Meanwhile, experts compromising a team of equivariant implicit neural networks (E-INNs), each responsible for distinct protein families, ensure precise SDF estimation across varied protein data landscapes. We showcase the ability of MoE-DSR to model dynamic protein surface shapes using ensembles from ATLAS, the largest available protein MD simulations database. Extensive experiments validate its effectiveness in analyzing complex molecular systems across continuous space and time domains.

Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling

Compared to conventional long-tail learning, which focuses on addressing class-wise imbalances, generalized long-tail (GLT) learning considers that samples within each class still conform to long-tailed distributions due to varying attributes, known as attribute imbalance. In the presence of such imbalance, the assumption of equivalence between the class-conditional probability densities of the training and testing sets is no longer tenable. Existing GLT approaches typically employ regularization techniques to avoid directly modeling the class-conditional probability density (CCPD) ratio between training and test data, leading to suboptimal performance. This study aims to directly estimate this ratio, for which a novel class-attribute aware logit-adjusted (CALA) loss incorporating both the CCPD ratio and the class priors is presented. Two new GLT learning methods, named Heuristic-CALA and Meta-CALA, are then proposed, which estimate the CCPD ratio in the CALA loss by leveraging the neighborhood information of samples. Extensive experiments across diverse scenarios susceptible to class and attribute imbalances showcase the state-of-the-art performance of Meta-CALA. Furthermore, while Heuristic-CALA exhibits inferior performance compared to Meta-CALA, it incurs only negligible additional training time compared to the Cross-Entropy loss, yet surpasses existing methods by a significant margin.

Class and Attribute-Aware Logit Adjustment for Generalized Long-Tail Learning

Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives.

ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data

Uncertainty quantification remains a difficult challenge in reinforcement learning. Several algorithms exist that successfully quantify uncertainty in a practical setting. However it is unclear whether these algorithms are theoretically sound and can be expected to converge. Further, they seem to treat the uncertainty in the target parameters in different ways. In this work, we unify several practical algorithms into one theoretical framework by defining a new Bellman operator on distributions, and show that this Bellman operator is a contraction. We highlight use cases of our framework by analyzing an existing Bayesian Q-learning algorithm, and also introduce a novel uncertainty-aware variant of PPO that adaptively sets its clipping hyperparameter.

Epistemic Bellman Operators

Algebraic model counting unifies many inference tasks on logic formulas by exploiting semirings. Rather than focusing on inference, we consider learning, especially in statistical-relational and neurosymbolic AI, which combine logical, probabilistic and neural representations. Concretely, we show that the very same semiring perspective of algebraic model counting also applies to learning.  This allows us to unify various learning algorithms by generalizing gradients and backpropagation to different semirings. Furthermore, we show how cancellation and ordering properties of a semiring can be exploited for more memory-efficient backpropagation. This allows us to obtain some interesting variations of state-of-the-art gradient-based optimisation methods for probabilistic logical models. We also discuss why algebraic model counting on tractable circuits does not lead to more efficient second-order optimization. Empirically, our algebraic backpropagation exhibits considerable speed-ups as compared to existing approaches.

The Gradient of Algebraic Model Counting

Reliable failure detection holds paramount importance in safety-critical applications.
Yet, neural networks are known to produce overconfident predictions for misclassified samples. 
As a result, it remains a problematic matter as existing confidence score functions rely on category-level signals, the logits, to detect failures. 
This research introduces an innovative strategy, leveraging human-level concepts for a dual purpose: to reliably detect $\textit{when}$ a model fails and to transparently interpret $\textit{why}$.
By integrating a nuanced array of signals for each category, our method enables a finer-grained assessment of the model's confidence.
We present a simple yet highly effective approach based on the ordinal ranking of concept activation to the input image. 
Without bells and whistles, our method is able to significantly reduce the false positive rate across diverse real-world image classification benchmarks, specifically by $3.7$% on $\textit{ImageNet}$ and $9.0$% on $\textit{EuroSAT}$.

Interpretable Failure Detection with Human-Level Concepts

The accurate assessment of sperm morphology is crucial in andrological diagnostics, where the segmentation of sperm images presents significant challenges. Existing approaches frequently rely on large annotated datasets and often struggle with the segmentation of overlapping sperm and the presence of dye impurities. To address these challenges, this paper first analyzes the issue of overlapping sperm tails from a geometric perspective and introduces a novel clustering algorithm, Con2Dis, which effectively segments overlapping tails by considering three essential factors: CONnectivity, CONformity, and DIStance. Building on this foundation, we propose an unsupervised method, SpeHeaTal, designed for the comprehensive segmentation of the SPErm HEAd and TAiL. SpeHeaTal employs the Segment Anything Model (SAM) to generate masks for sperm heads while filtering out dye impurities, utilizes Con2Dis to segment tails, and then applies a tailored mask splicing technique to produce complete sperm masks. Experimental results underscore the superior performance of SpeHeaTal, particularly in handling images with overlapping sperm.

SpeHeaTal: A Cluster-Enhanced Segmentation Method for Sperm Morphology Analysis

Self-supervised stereo matching has drawn attention due to its ability to estimate disparity without needing ground-truth data.
However, existing self-supervised stereo matching methods heavily rely on the photo-metric consistency assumption, which is vulnerable to natural disturbances, resulting in ambiguous supervision and inferior performance compared to the supervised ones.
To relax the limitation of the photo-metric consistency assumption and even bypass this assumption, we propose a novel self-supervised framework named \textbf{\textit{DualNet}}, which consists of two key steps: robust self-supervised teacher learning and pseudo-label supervised student training.
Specifically, the teacher model is first trained in a self-supervised manner with a focus on feature-metric consistency and data augmentation consistency.
Then, the output of the teacher model is geometrically constrained to obtain high-quality pseudo labels. 
Benefiting from these high-quality pseudo labels, the student model can outperform its teacher model by a large margin.
With the two well-designed steps, the proposed framework
\textbf{\textit{DualNet}} ranks $1^{st}$ among all self-supervised methods on multiple benchmarks, surprisingly even outperforming several supervised counterparts.

DualNet: Robust Self-Supervised Stereo Matching with Pseudo-Label Supervision

Logistics and transportation networks require a large amount of resources to realise necessary connections between locations and minimizing these resources is a vital aspect of planning research. 
Since such networks have dynamic connections that are available only at specific times, intricate models are needed to portray them accurately. 
In this paper, we study the problem of minimizing the number of resources needed to realise a dynamic network, using the temporal graphs model in which edges appear only at specific points in time.
Given a temporal graph and a natural number $k$, we ask whether we can cover every temporal edge exactly once using at most $k$ temporal journeys; in a temporal journey consecutive edges have to adhere to the order of time.
We conduct a thorough investigation of the complexity of the problem with respect to four dimensions: 
(a) whether the type of the temporal journeys is a walk, a trail, or a path; 
(b) whether the chronological order of edges in the journey is strict or non-strict; 
(c) whether the temporal graph is directed or undirected; 
(d) whether the start and end points of each journey is given or not.
We almost completely resolve the complexity of all these problems and provide dichotomies for each one of them with respect to $k$.

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