The paper introduces the Mirror Agent Model, a Bayesian architecture designed to produce interpretable agent behavior and explanations. It defines an observer model that mirrors the agent’s internal state, enabling explicit and implicit communication of intentions. The architecture incorporates off‑the‑shelf saliency methods to generate explanations, and the authors present preliminary qualitative results demonstrating its potential.
arXiv:2508.08966v2 Announce Type: replace Abstract: The attention mechanism lies at the core of the transformer architecture, providing an interpretable model-internal signal that has motivated a gro...
arXiv:2607. 21209v1 Announce Type: cross Abstract: In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal.
The paper discusses how Large Language Models can produce natural language self‑explanations that appear plausible but may not accurately reflect the model’s reasoning. It critiques current evaluation methods for such explanations and offers practical guidelines to assess their plausibility and faithfulness. Additionally, it argues that evaluation should also consider the actionability of these explanations, showing how they can aid decision‑making for various stakeholders.
The paper argues that as Large Language Models transition from chatbots to agentic systems, the current post-hoc interpretability paradigm is insufficient for safe deployment because it cannot audit or intervene before an output is produced. It proposes a shift to generative interpretability, where a model’s inference process inherently exposes semantically meaningful checkpoints that are human-understandable and can be causally intervened upon. The authors illustrate the advantages of this approach and introduce Neuro‑Symbolic Models as a concrete implementation.
arXiv:2607. 25570v1 Announce Type: cross Abstract: The development of autonomous vehicles (AVs) usually relies heavily on data-driven artificial intelligence (AI) models that require large volumes of sensor data with ground-truth annotations.
arXiv:2606. 16786v1 Announce Type: new Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short.
arXiv:2508. 04427v2 Announce Type: replace-cross Abstract: Multimodal learning has witnessed remarkable advancements in recent years, particularly with the integration of attention-based models, leading to significant performance gains across a variety of tasks.
The paper introduces a diagnostic test for class sensitivity in saliency methods, revealing that many popular techniques produce nearly identical explanations regardless of the predicted class. This limitation appears across different architectures and datasets, indicating a structural issue. To address this, the authors propose CASE, a contrastive explanation method that isolates features uniquely discriminative for the predicted class, and demonstrate its improved fidelity and class specificity through experiments.
arXiv:2606. 14838v1 Announce Type: new Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs.
arXiv:2606. 10942v1 Announce Type: cross Abstract: As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust.
A survey paper titled "A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs" reviews over 250 studies in the sub‑domain of eXplainable AI focused on reinforcement learning (XRL). The authors introduce a taxonomy based on the questions "What" (the target of explanation) and "How" (the method of explanation) and use it to organize the state‑of‑the‑art literature. They also highlight related domains that warrant further attention and outline key needs for advancing the field of XRL.
arXiv:2609.06063v1 Announce Type: new Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ov...