This section contains supplementary material that provides additional details for the main paper and further experimental analysis. In this section, we provide detailed hyperparameters (Table 4) used to train each of the architectures on which results are reported in the main paper. Note that if the batchsize is reduced, the learning rate should be linearly scaled accordingly. Note that for error injection experiments, we perform single-bit flips only in the convolutional and linear layers of the neural network, in line with other work in this field. The primary motivation is that these two layer types are the most computationally intensive, consuming 90% 95%of a DNN's computations.

We present a brief proof about the local optimality of one-hot encodings in the decision-theoretic framework presented in Section 3.2. We seek to prove that, under assumptions of an identity reward matrix, tokens constrained to a unit hypercube, and gaussian additive noise, one-hot tokens are an optimally robust communication strategy. We only seek to prove local optimality, as one many trivially generate multiple, equally optimal tokens by, for example, flipping all bits. The following derivation uses Karush-Kuhn-Tucker (KKT) conditions, a generalization of Lagrange multipliers [17]. We maximize the function, subject to constraints. T>j Ti Ti + ||Tj||2 Ti # ~µi + ~λi = ~0 (13) (14) We seek to show that one-hot vectors are an optimum, so we now show that one-hot vectors indeed respect the constraints and set the derivatives to zero.

Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AIAgents, enabling them to harness expert models for complex task-solving towards Artificial General Intelligence (AGI). Large Language Models (LLMs) show promising learning and reasoning abilities, and can effectively use external models, tools, plugins, or APIs to tackle complex problems. In this work, we introduce OpenAGI, an open-source AGI research and development platform designed for solving multi-step, real-world tasks. Specifically, OpenAGI uses a dual strategy, integrating standard benchmark tasks for benchmarking and evaluation, and open-ended tasks including more expandable models, tools, plugins, or APIs for creative problem-solving. Tasks are presented as natural language queries to the LLM, which then selects and executes appropriate models. We also propose a Reinforcement Learning from Task Feedback (RLTF) mechanism that uses task results to improve the LLM's task-solving ability, which creates a self-improving AI feedback loop. While we acknowledge that AGI is a broad and multifaceted research challenge with no singularly defined solution path, the integration of LLMs with domain-specific expert models, inspired by mirroring the blend of general and specialized intelligence in humans, offers a promising approach towards AGI.

Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models. While the challenges of applying GNNs for node classification when class labels display strong heterophily are well understood, it is unclear how heterophily affects GNN performance in other important graph learning tasks where class labels are not available. In this work, we focus on the link prediction task and systematically analyze the impact of heterophily in node features on GNN performance. We first introduce formal definitions of homophilic and heterophilic link prediction tasks, and present a theoretical framework that highlights the different optimizations needed for the respective tasks. We then analyze how different link prediction encoders and decoders adapt to varying levels of feature homophily and introduce designs for improved performance. Based on our definitions, we identify and analyze six real-world benchmarks spanning from homophilic to heterophilic link prediction settings, with graphs containing up to 30M edges. Our empirical analysis on a variety of synthetic and real-world datasets confirms our theoretical insights and highlights the importance of adopting learnable decoders and GNN encoders with ego-and neighbor-embedding separation in message passing for link prediction tasks beyond homophily.

Similarly,thetexture ofacloth mayconfuse classifying clothing categories (shirts vs. skirts), but it is crucial for classifying fashion attributes (spottedvs.

Note that for error injection experiments, we perform single-bit flips only in the convolutional and linear layers of the neural network, in line with other work in this field. In this section, we provide visualizations of additional backbones. Figure 9 and Figure 10 extend from Figure 3 for more networks. The Y -axis shows the absolute value of the max neuron value observed per layer on the X-axis. Next, Figure 11 and Figure 12 are extensions for Figure 4, showcasing the impact of our proposed technique on the end-to-end network accuracy.

Seq2Seq AP - 44.8 (e) Large vocabulary object detection.