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=
22 Pith papers cite this work, alongside 643 external citations. Polarity classification is still indexing.
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MetaBackdoor shows that LLMs can be backdoored using positional triggers like sequence length, enabling stealthy activation on clean inputs to leak system prompts or trigger malicious behavior.
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.
Proposes HCBF to benchmark four lightweight vision models on the MRL Eye Dataset across four dimensions, finding each excels in one area and that aggregate scores mask robustness vulnerabilities.
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.
citing papers explorer
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Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants
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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MetaBackdoor: Exploiting Positional Encoding as a Backdoor Attack Surface in LLMs
MetaBackdoor shows that LLMs can be backdoored using positional triggers like sequence length, enabling stealthy activation on clean inputs to leak system prompts or trigger malicious behavior.
-
Modeling Subjective Urban Perception with Human Gaze
Gaze data from eye-tracking carries predictive signals for subjective urban perception and improves accuracy when fused with image-based scene representations.
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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.
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MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection
MobileMold provides 4941 smartphone microscopy images and shows deep learning models reach 99.5% accuracy on mold detection and food classification tasks.
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Surrogate Fidelity: When Can Open LLMs Explain Closed Ones?
Prediction agreement between open and closed LLMs substantially overstates agreement on attributions and causal reasons.
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One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability
WorldModelLens defines a typed adapter with four core methods and a capability descriptor to unify interpretability tooling across diverse world model architectures.
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Human-Centered Benchmarking of Driver Monitoring Models
Proposes HCBF to benchmark four lightweight vision models on the MRL Eye Dataset across four dimensions, finding each excels in one area and that aggregate scores mask robustness vulnerabilities.
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AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
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: Making In-Context Learning Truly Learn
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 Shape Production of Language, not Processing
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.
-
Enabling Performant and Flexible Model-Internal Observability for LLM Inference
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 Does Not Consistently Improve Localisation-Based Explanation Quality
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.
-
Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models
Hallucinations in diffusion models are driven by local intrinsic dimension instabilities on the manifold, which Intrinsic Quenching corrects by deflating it.
-
X-SYS: A Reference Architecture for Interactive Explanation Systems
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: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Delta-XAI wraps existing XAI methods for online time series and introduces SWING to explain prediction changes while accounting for temporal dependencies.
-
Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models
Reliability and faithfulness of post-hoc explanations do not suffice to support claims about how a scientific phenomenon is structured.
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Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals
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).
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OPTIMUS-Prime: Minimal and Sufficient Concept Explanations for Deep Vision Models
OPTIMUS generates minimal and sufficient concept-based visual explanations for deep classifiers using prime implicant theory to enforce logical sufficiency and minimality.
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Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
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: An Explainable AI Framework for ECG models
ExECG is a Python framework providing Wrapper, Explainer, and Visualizer stages to unify XAI methods for ECG models and improve reproducibility.
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Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model
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.