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Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

Study of label leakage and anatomical grounding in multimodal MRI models for Alzheimer's staging shows cognitive-score fusion accuracy of 87.3% is leakage-driven.

The authors train a ResNet18 slice-based encoder with a one-layer Transformer on 1,075 ADNI-1 T1 MRI scans, using FastSurfer segmentations and YOLOv8 localization (mAP_50 above 0.96) as anatomical reference. Grad-CAM shows the image-only classifier often attends to skull and background rather than disease-relevant structures. A CLIP-style image-tabular contrastive framework organized along a label-leakage spectrum yields 87.3% three-way accuracy with cognitive scores versus 73.0% with regional volumes, and cropping to the medial temporal lobe raises image-only accuracy from 58.7% to 65.1%. Results come from single runs on a small balanced test set with reported confidence intervals.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research1

Probabilistic Linear Explanations

Researchers introduce a unified probabilistic explainability framework using sparse anchored linear models that outperforms LIME and MAPLE on relevance error.

The paper proposes probabilistic explanations based on sparse, anchored linear models applicable to both binary classification and continuous regression. It proves that minimizing relevance error for neural-network models is NP-hard and relates it to a tractable fidelity-error surrogate. Solutions are computed via a mixed integer programming formulation with provably optimal empirical solutions and a polynomial-time iterative hard thresholding algorithm with approximation guarantees. Empirical evaluations show lower relevance error than LIME and MAPLE while satisfying anchoring and sparsity constraints by construction.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments

When LLM judges agree, should we believe them?

Amazon ICML paper uses Ising models to correct correlated LLM-judge votes, beating accuracy-weighted panels by 9-14%.

Amazon Science describes an ICML paper, "Dependence-aware label aggregation for LLM-as-a-judge via Ising models," addressing how correlated judge outputs inflate majority-vote confidence. The unsupervised method models pairwise dependence between judges, learning both reliability and similarity without human reference labels. Tested on relevance, toxicity, and summarization tasks with 10 judge models at temperature zero, it outperformed accuracy-weighted voting by 9% to 14%.

Hacker News · AI · 2d agoAI researchHN 41↑ · 23 comments

Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion

VyPER framework reconstructs collider events using hypergraph representation learning and graph-conditioned diffusion, outperforming existing reconstruction techniques across Standard Model processes.

Researchers present VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology for particle event reconstruction. It combines supervised hyperedge classification for assigning measured jets and charged leptons to parent particles with a graph-conditioned diffusion model predicting unmeasured neutrino kinematics, optimized with a joint loss. Evaluated across several proton-proton collision processes, it demonstrates accurate reconstruction across Higgs, electroweak, and top-quark sectors.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection

Det-LIME extends LIME to multi-instance object detection explanations, improving attribution for harbor seal aerial surveys.

Det-LIME adapts LIME to object detection by combining per-detection weighting, a proximity kernel emphasizing box-adjacent regions, and IoU-based matching to track instances across perturbations. It was evaluated on aerial drone imagery for harbor seal detection plus a seabird case study, and compared against vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient-based attribution. Using Attribution Ratio and Max Saliency Hit Rate metrics, it consistently improved multi-instance attribution and produced box-aligned explanations useful for debugging and data augmentation.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

Federated learning framework combining dynamic differential privacy, homomorphic encryption, and local DP retains 82.6% accuracy at epsilon 0.1 while cutting communication 21.3%.

The paper proposes a privacy-enhanced federated learning framework integrating Dynamic Differential Privacy, lightweight Homomorphic Encryption, and Local Differential Privacy during training. An asynchronous aggregation strategy with version control supports distributed training in asynchronous environments. On CIFAR-10 and Purchase-100, the method maintains up to 82.6% classification accuracy under stringent privacy constraints (epsilon = 0.1) and reduces communication overhead by 21.3% versus FedAvg.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Domain-Specific Hallucination Detection in Large Language Models

A multi-signal pipeline detects LLM hallucinations, reaching F1 0.915 on HaluEval and cutting Qwen2.5-0.5B hallucination rates from 85.5% to 37.7% via DPO.

The paper presents a hallucination detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty, and temperature-scaled calibration. It achieves F1 0.915 and AUROC 0.977 on general-domain HaluEval tasks, with MC Dropout inference raising accuracy to 93.2%. Applying DPO to a Qwen2.5-0.5B generator reduces its hallucination rate from 85.5% to 37.7%, while cross-domain evaluation shows poor general-domain transfer to SciFact (F1 0.52) and PubMedBERT fine-tuning as the strongest adaptation (F1 0.63).

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1