Normal Guidance: Bell-Curve Regularization for Attention-Based MIL in 3D Medical Imaging
This paper addresses weakly supervised classification of 3D medical images where only volume-level binary labels are available. The authors identify that a simple center-focused baseline outperforms attention-based and transformer-based multiple instance learning (MIL) at slice-level classification across brain, thoracic, and abdominal CT datasets. They propose Normal Guidance, a regularization technique that constrains learned attention distributions to follow a bell-shaped curve, achieving superior slice-level localization over state-of-the-art MIL methods across datasets totaling over 4 million 2D slices.
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RefRad2D dataset and RadGrounder model enable spatially grounded radiology VLMs without manual annotations
Researchers introduce RefRad2D, a 1.2M-pair bilingual (German/English) CT and MR image-text dataset generated automatically via LLM curation and automated segmentation, requiring no manual spatial annotations. The accompanying RadGrounder model jointly performs report generation, VQA, and spatial grounding via bounding-box or segmentation outputs. On external benchmarks Slake and VQA-RAD, RadGrounder matches specialized medical VLMs while adding grounding supervision without degrading language quality. The work demonstrates that large-scale automatically curated clinical data can transfer to downstream medical VQA tasks.
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
This paper addresses miscalibration in Gaussian process predictive distributions used for Bayesian optimization, focusing specifically on the lower tail relevant to minimization objectives. The authors introduce a framework for 'goal-oriented' spatial calibration below a threshold t, defining occurrence calibration and thresholded μ-calibration on sublevel sets. They propose tcGP, a post-hoc calibration method, and prove the resulting EI-based optimizer remains dense in the design space. Experiments on standard benchmarks show tcGP improves both lower-tail calibration and overall BO performance compared to standard and globally calibrated GP models.
PGT: Procedurally Generated Tasks for Improving Visual Grounding in MLLMs
This paper introduces Procedurally Generated Tasks (PGT), a data-driven framework that overlays geometric primitives on images to create dense supervision signals for fine-grained visual grounding in multimodal large language models. PGT serves both as a training augmentation method and a diagnostic tool to isolate perception failures from semantic priors. Instruction tuning on LLaVA-v1.5-Instruct augmented with PGT data yields gains of up to +20% on the What'sUp benchmark and +13.3% on CV-Bench-2D. The results suggest that spatial reasoning deficits in MLLMs stem primarily from inadequate supervision rather than architectural or resolution constraints.
The Matching Principle: A Geometric Theory Unifying Robustness, Domain Adaptation, and Alignment via Nuisance Covariance
This paper proposes the 'matching principle': a unified geometric framework arguing that robustness methods (CORAL, IRM, adversarial training, augmentation, metric learning, Jacobian penalties, alignment constraints) are all estimators of the same object—the covariance of label-preserving deployment nuisance—and that regularizing the encoder Jacobian along this covariance's range is the core statistical problem. The authors prove closed-form optimality results in a linear-Gaussian model, introduce the Trajectory Deviation Index (TDI) as a label-free embedding sensitivity probe, and validate predictions across 13 pre-registered experimental blocks including Qwen2.5-7B. At 7B scale, matched style-PMH improves selective honesty while standard DPO degrades Style TDI, connecting the theory to alignment safety.
LLM-guided MAP-Elites evolution improves medical decision pipelines at inference time
Researchers propose using LLM-guided MAP-Elites evolutionary search as an inference-time alternative to fine-tuning for adapting LLMs to clinical workflows, formulating triage, consultation, and image classification as evolutionary searches over executable artifacts. Across three medical settings, evolved programs substantially outperform manually designed baselines: triage accuracy improves from 77.3% to 87.1% and emergency recall from 0.60 to 0.97, with gains also shown on MIMIC-ESI, iCRAFTMD, and PneumoniaMNIST. The approach works across Llama-3, Qwen-3.5, and Gemma-4 backbones and produces interpretable program-level mechanisms rather than superficial prompt changes.
MAGIC: Multimodal Alignment & Grounding-aware Instruction Coreset for Vision-Language Models
MAGIC is a training-free coreset selection method for multimodal instruction tuning that uses three intrinsic signals—Multimodal Gain, Bridging Relevance, and Skill-Neuron Signatures—to identify compact, behaviorally faithful training subsets without backpropagation. The method operates in a three-stage pipeline: filtering low-gain examples, ranking by a quality objective, and bucket-wise budget allocation over neuron signatures. On LLaVA-665K and Vision-Flan datasets with 20% data budgets, MAGIC matches or slightly exceeds full fine-tuning performance (100.3% and 101.6% relative) while reducing wall-clock training time by 73.7%. Results transfer to LLaVA-1.5-7B and -13B target models.
Gaze Heads: Attention heads in VLMs that track and control image region description
Researchers identify a small set of attention heads in vision-language model backbones, called 'gaze heads', whose attention patterns track the image region currently being described. Using comic strips as a controlled testbed, they show that intervening on the top-100 gaze heads (fewer than 9% of all heads) can steer the model to describe any chosen region at 83.1% accuracy, without retraining. The mechanism generalizes across model sizes from 2B to 32B parameters and to natural images (COCO), establishing a practical inference-time control lever for multimodal models via mechanistic analysis.
Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment
This paper introduces a framework for evaluating alignment between artificial vision models and the human visual cortex that goes beyond scalar prediction accuracy. Using repeated fMRI data from the Natural Scenes Dataset, the authors decompose brain response spaces into reproducible dimensions and measure which of these dimensions are recovered by model predictions. A key finding is that pretrained and randomly initialized models can achieve similar prediction accuracy while showing distinct recovery profiles, revealing that accuracy alone can mask fundamental model-brain mismatches. The framework also enables brain-to-brain comparisons as a diagnostic human reference baseline.