Although recent large multimodal models (LMMs) demonstrate impressive progress on vision language tasks, their alignment with human centered (HC) principles, such as fairness, ethics, inclusivity, empathy, and robustness; remains poorly understood. We present HumaniBench, a unified evaluation framework designed to characterize HC alignment across realistic, socially grounded visual contexts. HumaniBench contains 32,000 expert-verified image question pairs derived from real world news imagery and spanning seven evaluation tasks: scene understanding, instance identity, multiple-choice visual question answering (VQA), multilinguality, visual grounding, empathetic captioning, and image resilience testing. Each task is mapped to one or more HC principles through a principled operationalization of metrics covering accuracy, harmful content detection, hallucination and faithfulness, coherence, cross lingual quality, empathy, and robustness.We evaluate 15 state-of-the-art LMMs under this framework and observe consistent cross model trade offs: proprietary systems achieve the strongest performance on ethics, reasoning, and empathy, while open-source models exhibit superior visual grounding and resilience. All models, however, show persistent gaps in fairness and multilingual inclusivity. We further analyze the effect of inference-time techniques, finding that chain of thought prompting and test-time scaling yield 8 to 12 % improvements on several HC dimensions. HumaniBench provides a reproducible, extensible foundation for systematic HC evaluation of LMMs and enables fine-grained analysis of alignment trade-offs that are not captured by conventional multimodal benchmarks. https://vectorinstitute.github.io/humanibench/

Physics Informed Neural Networks (PINNs) often exhibit failure modes in which the PDE residual loss converges while the solution error stays large, a phenomenon traditionally blamed on local optima separated from the true solution by steep loss barriers. We challenge this understanding by demonstrate that the real culprit is insufficient arithmetic precision: with standard FP32, the LBFGS optimizer prematurely satisfies its convergence test, freezing the network in a spurious failure phase. Simply upgrading to FP64 rescues optimization, enabling vanilla PINNs to solve PDEs without any failure modes. These results reframe PINN failure modes as precision induced stalls rather than inescapable local minima and expose a three stage training dynamic unconverged, failure, success whose boundaries shift with numerical precision. Our findings emphasize that rigorous arithmetic precision is the key to dependable PDE solving with neural networks.

Sequential knowledge editing techniques aim to continuously update knowledge in large language models at low cost, preventing models from generating outdated or incorrect information. However, existing sequential editing methods suffer from a significant decline in editing success rates after long-term editing. Through theoretical analysis and experiments, our findings reveal that as the number of edits increases, the model's output increasingly deviates from the desired target, leading to a drop in editing success rates. We refer to this issue as the superimposed noise accumulation problem. Our further analysis demonstrates that the problem is related to the erroneous activation of irrelevant knowledge and conflicts between activated knowledge. Based on this analysis, a method named DeltaEdit is proposed that reduces conflicts between knowledge through dynamic orthogonal constraint strategies. Experiments show that DeltaEdit significantly reduces superimposed noise, achieving a 16.8% improvement in editing performance over the strongest baseline.

Concept-based explanations have emerged as an effective approach within Explainable Artificial Intelligence, enabling interpretable insights by aligning model decisions with human-understandable concepts. However, existing methods rely on computationally intensive procedures and struggle to efficiently capture complex, semantic concepts. This work introduces the Concept Directions via Latent Clustering (CDLC), which extracts global, class-specific concept directions by clustering latent difference vectors derived from factual and diffusion-generated counterfactual image pairs. CDLC reduces storage requirements by 4.6 and accelerates concept discovery by 5.3 compared to the baseline method, while requiring no GPU for clustering, thereby enabling efficient extraction of multidimensional semantic concepts across latent dimensions. This approach is validated on a real-world skin lesion dataset, demonstrating that the extracted concept directions align with clinically recognized dermoscopic features and, in some cases, reveal dataset-specific biases or unknown biomarkers. These results highlight that CDLC is interpretable, scalable, and applicable across high-stakes domains and diverse data modalities. Introduction In high-stakes applications, such as medical diagnosis, financial risk assessment, and autonomous driving, understanding the rationale behind a neural network's decision is often as important as the decision itself. Explainable Artificial Intelligence (XAI) [1, 2] has emerged as a critical research area, aiming to bridge the gap between high-performing black-box models and human interpretability. Among the various XAI paradigms, concept-based explanations [3, 4] have gained particular attention due to their ability to express model behavior in terms of high-level, semantically meaningful concepts, rather than low-level feature weights or pixel-based saliency maps [5, 6]. By aligning explanations with concepts recognized by domain experts, these methods facilitate trust [7, 8], debugging [9], and regulatory compliance [10, 11].

Personal use of this material is permitted. Abstract -- Recently, many humanoid robots have been increasingly deployed in various facilities, including hospitals and assisted living environments, where they are often remotely controlled by human operators. Their kinematic redundancy enhances reachability and manipulability, enabling them to navigate complex, cluttered environments and perform a wide range of tasks. However, this redundancy also presents significant control challenges, particularly in coordinating the movements of the robot's macro-micro structure (torso and arms). Therefore, we propose various human-robot collaborative (HRC) methods for coordinating the torso and arm of remotely controlled mobile humanoid robots, aiming to balance autonomy and human input to enhance system efficiency and task execution. The proposed methods include human-initiated approaches, where users manually control torso movements, and robot-initiated approaches, which autonomously coordinate torso and arm based on factors such as reachability, task goal, or inferred human intent. We conducted a user study with N=17 participants to compare the proposed approaches in terms of task performance, manipulability, and energy efficiency, and analyzed which methods were preferred by participants. Human-robot collaborative (HRC) control enables humans and robot autonomy to complement each other and improve overall robotic manipulation performance.

In this work, we conduct an extensive empirical study of several deep reinforcement learning algorithms on two challenging combinatorial optimization problems: the job-shop and flexible job-shop scheduling problems, both fundamental challenges with multiple industrial applications. Broadly, deep reinforcement learning algorithms fall into two categories: policy-gradient and value-based. While value-based algorithms have achieved notable success in domains such as the Arcade Learning Environment, the combinatorial optimization community has predominantly favored policy-gradient algorithms, often overlooking the potential of value-based alternatives. From our results, value-based algorithms demonstrated a lower variance and a more stable convergence profile compared to policy-gradient ones. Moreover, they achieved superior cross-size and cross-distribution generalization, that is, effectively solving instances that are substantially larger or structurally distinct from those seen during training. Finally, our analysis also suggests that the relative performance of each category of algorithms may be dependent on structural properties of the problem, such as problem flexibility and instance size. Overall, our findings challenge the prevailing assumption that policy-gradient algorithms are inherently superior for combinatorial optimization. We show instead that value-based algorithms can match or even surpass the performance of policy-gradient algorithms, suggesting that they deserve greater attention from the combinatorial optimization community. Our code is openly available at: https://github.com/AJ-Correa/Unraveling-the-Rainbow

Multi-label classification (MLC) often suffers from performance disparities across labels. We propose \textbf{FairPO}, a framework combining preference-based loss and group-robust optimization to improve fairness by targeting underperforming labels. FairPO partitions labels into a \textit{privileged} set for targeted improvement and a \textit{non-privileged} set to maintain baseline performance. For privileged labels, a DPO-inspired preference loss addresses hard examples by correcting ranking errors between true labels and their confusing counterparts. A constrained objective maintains performance for non-privileged labels, while a Group Robust Preference Optimization (GRPO) formulation adaptively balances both objectives to mitigate bias. We also demonstrate FairPO's versatility with reference-free variants using Contrastive (CPO) and Simple (SimPO) Preference Optimization.

We develop pre-trained estimators for structural econometric models. The estimator uses a neural net to recognize the structural model's parameter from data patterns. Once trained, the estimator can be shared and applied to different datasets at negligible cost and effort. Under sufficient training, the estimator converges to the Bayesian posterior given the data patterns. As an illustration, we construct a pretrained estimator for a sequential search model (available at pnnehome.github.io). Estimation takes only seconds and achieves high accuracy on 12 real datasets. More broadly, pretrained estimators can make structural models much easier to use and more accessible.

Abstract--The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satellite imagery for detecting digestate application, a practice that enhances soil fertility but poses environmental risks like mi-croplastic contamination and nitrogen losses. In the first instance, Sentinel-2 satellite image time series (SITS) analysis of specific indices (EOMI, NDVI, EVI) was used to characterize EOM's spectral behavior after application on the soils of four different crop types in Thessaly, Greece. Furthermore, Machine Learning (ML) models (namely Random Forest, k-NN, Gradient Boosting and a Feed-Forward Neural Network), were used to investigate digestate presence detection, achieving F1-scores up to 0.85. Agricultural systems can benefit from the application of Exogenous Organic Matter (EOM), which not only enhances soil fertility but also supports waste recycling and promotes circular economies [1], [2].

Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed management methods are essential for policymakers to assess farmer practices, evaluate impacts on vegetation health, biodiversity, and climate, as well as ensure compliance with policies and subsidies. However, monitoring weed management methods is challenging as they commonly rely on ground-based field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage earth observation data and Machine Learning (ML). Specifically, we developed separate ML models using Sentinel-2 and PlanetScope satellite time series data, respectively, to classify four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards. The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.