Therefore, we do not13 expect that alternativelearning algorithms likeA2C orDQN would reveal interesting insights into the dynamics of14 ourenvironment. R5: You state we did a reasonably good job in describing the tasks and environment to someone unfamiliar with48 NetHack andthatwesubmitted awellwrittenmanuscript. Without specifics astowhy,wecan only say that50 we patently disagree: Throughout the paper, we make a detailed argument as to the benefits of NLE over popular51 benchmarkenvironments.

Code will be madepublicuponpublication. Imagefeatureextractor. RC d, from the previously constructed time-augmented skeletal featuress 1:C,t RL C d. Inspired by The SparseConvNet consists in 3D sparse convolutions toprocess the input volume, diffusing the skeletal features into the nearby 3D space. The overview of the cross-attention between the sampled time-augmented skeletal features and time-specific pixel-aligned features is illustratedinFig.2. Wediscuss the additional details about the datasets used, including the train/test splits and license information. C.1 ZJU-MoCap We use the512 512 videos for the training and testing following the original Neural Body [7].

The left panelshows the energy profile for arotation around an O-C-C-C dihedral angle. In the right panel of Figure 4, we show energy predictions along a minimum energy path of an intramolecular hydrogen transfer reaction. A.2.2 3BPADataset The 3BPA dataset contains DFT train test splits of a flexible drug-like organic molecule sampled from different temperature molecular dynamics trajectories [33]. The first step of the algorithm is to contract the generalized Clebsch-Gordan coefficients with the weights of the product basis. Then, the last dimension of cν is contracted with theAi-features' last dimension resulting in the a-tensor with correlation orderν 1.

The base learning rate for SGDM and IA is set to 0.01 for a batch size of 256, and linearly rescaled for the remaining batch sizes.

Humans are black boxes -- we cannot observe their neural processes, yet society functions by evaluating verifiable arguments. AI explainability should follow this principle: stakeholders need verifiable reasoning chains, not mechanistic transparency. We propose using structured argumentation to provide a level of explanation and verification neither interpretability nor LLM-generated explanation is able to offer. Our pipeline achieves state-of-the-art 94.44 macro F1 on the AAEC published train/test split (5.7 points above prior work) and $0.81$ macro F1, $\sim$0.07 above previous published results with comparable data setups, for Argumentative MicroTexts relation classification, converting LLM text into argument graphs and enabling verification at each inferential step. We demonstrate this idea on multi-agent risk assessment using the Structured What-If Technique, where specialized agents collaborate transparently to carry out risk assessment otherwise achieved by humans alone. Using Bipolar Assumption-Based Argumentation, we capture support/attack relationships, thereby enabling automatic hallucination detection via fact nodes attacking arguments. We also provide a verification mechanism that enables iterative refinement through test-time feedback without retraining. For easy deployment, we provide a Docker container for the fine-tuned AMT model, and the rest of the code with the Bipolar ABA Python package on GitHub.

"NIPS 2013 Neural Information Processing Systems December 5 - 10, Lake Tahoe, Nevada, USA",,, "Paper ID:","1139" "Title:","Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization" Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a method for action detection (localization and classification of actions) using weakly supervised information (action labels + eye gaze information, no explicit definition of bounding boxes). Overall, the spatio-temporal search (a huge spatio-temporal space) is done using dynamic programming and a max-path algorithm. Gaze information is introduced into framework through a loss which acounts for gaze density at a given location. QUALITY: The paper seems technically sound and makes for a nice study given gaze information.

The base learning rate for SGDM and IA is set to 0.01 for a batch size of 256, and linearly rescaled for the remaining batch sizes. For FA and Adam across all models, this base learning rate is 0.001.