InChIfied Invariants based on InChI achieve 99.62% identical representations for chemically equivalent molecular graphs versus 0.35% for standard Daylight invariants on one million PubChem molecules, while preserving predictive performance and enforcing consistent attributions.
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arXiv preprint arXiv:2009.07896 , year=
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Gaze data from eye-tracking carries predictive signals for subjective urban perception and improves accuracy when fused with image-based scene representations.
FASS benchmark shows post-hoc attributions remain unstable under geometric perturbations even after filtering for unchanged predictions, with Grad-CAM exhibiting the highest stability across ImageNet, COCO, and CIFAR-10.
MobileMold provides 4941 smartphone microscopy images and shows deep learning models reach 99.5% accuracy on mold detection and food classification tasks.
Prediction agreement between open and closed LLMs substantially overstates agreement on attributions and causal reasons.
WorldModelLens defines a typed adapter with four core methods and a capability descriptor to unify interpretability tooling across diverse world model architectures.
AIM is a new evaluation framework for explainability in GNNs that combines accuracy, instance-level, and model-level measures, applied to graph kernel networks to create an improved model xGKN.
Many-shot CoT-ICL improves when demonstrations are ordered for smooth conceptual progression, with CDS delivering up to 5.42 percentage-point gains on math tasks using 64 examples.
Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
DMI-Lib delivers 0.4-6.8% overhead for offline batch LLM inference and ~6% for moderate online serving while exposing rich internal signals across backends, cutting latency overhead 2-15x versus prior observability baselines.
Scaling vision models by depth and parameter count does not consistently improve localisation-based explanation quality across architectures, datasets, and post-hoc methods; smaller models often perform comparably or better.
Hallucinations in diffusion models are driven by local intrinsic dimension instabilities on the manifold, which Intrinsic Quenching corrects by deflating it.
X-SYS is a reference architecture for interactive explanation systems organized around STAR quality attributes and five service components, demonstrated via SemanticLens for vision-language models.
Delta-XAI wraps existing XAI methods for online time series and introduces SWING to explain prediction changes while accounting for temporal dependencies.
Reliability and faithfulness of post-hoc explanations do not suffice to support claims about how a scientific phenomenon is structured.
Fine-tuning a global DL model on patient-specific ECG segments raises AUROC for 5-minute AF prediction from 0.614 to 0.711 (ICENTIA11K) and 0.585 to 0.686 (MobiCARE).
OPTIMUS generates minimal and sufficient concept-based visual explanations for deep classifiers using prime implicant theory to enforce logical sufficiency and minimality.
eXTC learns a natural-language SOP via structured prompt optimization, distills it into a compact LM, and extends it with RL to deliver fast inference plus global rules and local traces while claiming benchmark gains over prior paradigms.
ExECG is a Python framework providing Wrapper, Explainer, and Visualizer stages to unify XAI methods for ECG models and improve reproducibility.
A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.
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Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?
FASS benchmark shows post-hoc attributions remain unstable under geometric perturbations even after filtering for unchanged predictions, with Grad-CAM exhibiting the highest stability across ImageNet, COCO, and CIFAR-10.