This paper introduces BHDD, the first public benchmark dataset for handwritten Burmese digits. Myanmar script's distinctive circular letterforms—originally developed for writing on palm leaves—create recognition challenges distinct from Latin digits, with pairs like 0 and 1 differing only by whether a circle is closed. The authors release 87,561 verified images (28×28 grayscale, MNIST-compatible format) from over 150 contributors, with writer-independent train/test splits and baseline models reaching up to 99.83% accuracy.

HamVision proposes using damped harmonic oscillator dynamics as a structured inductive bias for medical image analysis. The core idea is that phase-space decomposition yields three representations—position $q$ (features), momentum $p$ (gradients), and energy $H = rac{1}{2}|z|^2$ (saliency)—that serve both segmentation and classification tasks without modifying the shared bottleneck. This physics-constrained approach aims to replace generic learned transformations with interpretable, dynamics-based feature extraction across diverse medical imaging modalities.

This paper addresses a subtle but critical issue in latent diffusion models (LDMs): VAE tokenizers tend to collapse latent variance toward zero to minimize reconstruction error, creating overly compact manifolds that are brittle against sampling perturbations. The authors propose a Variance Expansion (VE) loss that adaptively counteracts this collapse via an inverse-variance term $\mathcal{L}_{\text{var}} = 1/(\sigma^2 + \delta)$, allowing the latent space to absorb stochastic diffusion noise while maintaining reconstruction fidelity. The work achieves state-of-the-art FID 1.18 on ImageNet 256$\times$256 and provides both theoretical grounding and empirical validation across multiple architectures.

This paper tackles the visual perception gap in automated text layout generation. While existing Multimodal Large Language Models (MLLMs) generate layout code (SVG/JSON) to render text on images, they operate blind to the actual rendered output, producing layouts with overlapping text, poor contrast, or misalignment. The authors propose Visual Feedback Layout Model (VFLM), which closes the loop by rendering generated SVGs and feeding the visual results back to the model for iterative reflection and refinement. The framework uses a two-stage pipeline—cold-start supervised fine-tuning followed by reinforcement learning with GRPO—and introduces a specialized layout reward model trained on fine-grained quality hierarchies. A surprising finding is that simple outcome-based rewards outperform complex process-oriented rewards that explicitly encode step-wise incentives.

Brain tumor segmentation from MRI scans faces challenges because the three target sub-regions—Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET)—have ambiguous visual boundaries. This paper proposes TextCSP, a hierarchical framework that integrates radiological reports by replacing the standard single global text embedding with sub-region-aware prompts and a soft cascade decoder that enforces the anatomical hierarchy $ET \subset TC \subset WT$. The method builds on the TextBraTS baseline and achieves modest gains on its paired MRI-text dataset.

dynActivation addresses the rigidity of fixed activation functions by introducing per-layer trainable scalars that interpolate between a base nonlinearity and a linear path. The method adds only two parameters per layer ($\alpha_i$ and $\beta_i$) via $f_i(x) = \text{BaseAct}(x)(\alpha_i - \beta_i) + \beta_i x$, allowing adaptive nonlinearity allocation across depth. Results show strong vision benchmarks (+14% on CIFAR-10), robustness to extreme depth scaling (95%+ accuracy on 75-layer MNIST), and faster convergence (24% AUC reduction), though LLM perplexity gains vanish in long-run training.

Autonomous vehicles struggle with adverse weather perception. This paper proposes LRC-WeatherNet, a lightweight fusion network combining LiDAR, RADAR, and camera via early BEV fusion and mid-level gating to classify weather conditions in real-time. The approach achieves $86.66\%$ accuracy on the MSU-4S dataset with $7.13\,\mathrm{ms}$ inference, demonstrating that adaptive multi-modal fusion outperforms unimodal baselines, though dataset limitations restrict generalization to rare weather events.

This paper tackles parameter-efficient multi-task learning (PEFT-MTL), where the challenge is to share parameters across tasks without interference while maintaining the efficiency of methods like LoRA. The core idea is Free Sinewich: it modulates a shared low-rank convolutional adapter (Sine-AWB) using task-specific sinusoidal frequencies generated by a lightweight Clock Net, achieving task specialization without duplicating parameters. This frequency-switching mechanism is inspired by biological oscillatory multiplexing and aims to decorrelate task weights while boosting effective rank.

SSAM tackles the problem of merging independently trained multimodal large language models (e.g., vision-language and audio-language specialists) into a single model capable of processing arbitrary modality combinations without any paired multimodal training data. The core idea is to project language-specific parameter updates (task vectors) onto a shared low-rank subspace identified via SVD, thereby aligning consistent update directions while filtering conflicting ones before merging. This is significant because it offers a training-free alternative to expensive joint multimodal training, achieving state-of-the-art results on four benchmarks.

JANUS addresses jailbreaking of text-to-image models by reframing the discrete prompt search as optimization over a structured distribution. The framework mixes two Gaussian-anchored prompt distributions—one around the target harmful prompt and one around a sanitized 'clean' version—and uses policy gradient on a single scalar mixing parameter $\alpha$ to maximize end-to-end reward. This avoids both proxy-loss optimization and costly LLM-based generators, achieving substantial efficiency gains while exposing weaknesses in current safety pipelines.

This paper addresses vision-only UAV navigation in GNSS-denied environments by moving beyond the standard "matching-to-tile" (M2T) paradigm. Instead of retrieving discrete satellite tiles, the proposed Bearing-UAV method jointly regresses continuous position and heading from four neighboring satellite tiles and a UAV view patch, enabling sub-tile localization accuracy while maintaining a lightweight model. The work also introduces Bearing-UAV-90K, a multi-city dataset with heading annotations designed for unaligned cross-view scenarios.

This paper introduces ChronoCon, a self-supervised method that repurposes Rank-N-Contrast learning to use temporal ordering of longitudinal medical scans instead of expert severity labels. By assuming monotonic progression in irreversible diseases, the method learns progression-aware representations from routinely archived clinical metadata. The core finding is that under few-shot scenarios—using labels from only 5 patients—the model achieves an ICC of 86% for disease severity prediction on rheumatoid arthritis radiographs, potentially reducing reliance on costly expert annotations.

This paper tackles the instability of Group Relative Policy Optimization (GRPO) when applied to video generation. The core problem is that converting deterministic ODE samplers to SDE for exploration injects excess noise in high-noise regimes, causing off-manifold drift that degrades rollout quality and destabilizes reward updates. SAGE-GRPO introduces a precise SDE with logarithmic curvature correction to keep exploration closer to the flow trajectory, plus a Dual Trust Region mechanism combining periodic moving anchors with stepwise KL constraints to prevent long-horizon drift. The method is evaluated on HunyuanVideo1.5 using VideoAlign rewards, showing improvements over DanceGRPO, FlowGRPO, and CPS.

Hyperbolic Vision-Language Models (VLMs) improve hierarchical structure preservation over Euclidean counterparts, yet existing approaches treat all part-whole relationships as equally informative. This paper proposes UNCHA (UNcertainty-guided Compositional Hyperbolic Alignment), which leverages the hyperbolic radius as an uncertainty measure to quantify the varying semantic representativeness of image parts to the whole scene. By incorporating this uncertainty into adaptive temperature scaling for contrastive learning and an entropy-regularized entailment loss, UNCHA achieves state-of-the-art performance on zero-shot classification, retrieval, and fine-grained compositional benchmarks, demonstrating that modeling heterogeneous part-whole strength is critical for complex multi-object understanding.

Adversarial Camouflage proposes a wearable privacy defense against facial recognition by optimizing simple face paint patterns (stripes or chevrons) to adversarially minimize embedding similarities across multiple recognition models. The core idea is to restrict the attack space to low-dimensional, user-reproducible geometric parameters (color, angle, width) that can be painted onto semantically valid facial regions, enabling protesters and privacy-conscious individuals to evade automated surveillance without specialized equipment.

This paper tackles camera-agnostic pruning of 3D Gaussian splats for standardized interchange settings like MPEG I-3DGS, where training images, camera parameters, and gradients are unavailable. The authors propose BetaDescPrune, a one-shot post-training method that computes Hybrid Splat Feature Histogram (HSFH) descriptors to capture local geometric and appearance consistency, then models pruning decisions via Beta-distributed evidence with uncertainty-aware confidence scoring. The core insight is that reliable splat importance can be inferred from intrinsic neighborhood structure alone without rendering supervision.

SteelDefectX introduces a vision-language dataset for steel defect detection that aggregates 7,778 images from four existing sources with novel coarse-to-fine textual annotations—ranging from class-level defect descriptions to sample-level attributes (shape, size, depth, position, contrast) generated via GPT-4o. The paper establishes a four-task benchmark showing that rich textual supervision improves cross-material transfer, though it reveals a tension where fine-grained annotations unexpectedly hurt few-shot performance.

SpatialReward addresses the persistent problem of spatial inconsistencies in text-to-image generation, where models produce globally plausible images with incorrect object positioning and relationships. The paper proposes a three-stage verifiable reward model that decomposes free-form prompts into structured constraints, verifies object attributes via expert detectors, and employs vision-language chain-of-thought reasoning to assess complex spatial layouts. Integrated into Flow-GRPO reinforcement learning for Stable Diffusion and FLUX, the approach significantly improves spatial consistency while maintaining overall image quality.

Ctrl-A addresses automated data augmentation by framing it as a control problem, dynamically adjusting per-operation augmentation strengths via a feedback loop that balances training and validation loss ratios. The method introduces Relative Operation Response (ROR) curves to individually tune transformation distributions without manual initialization or expensive search phases. While it achieves competitive results on CIFAR and SVHN benchmarks with minimal computational overhead (~10% vs. TrivialAugment), the evaluation relies on a modified training setup with extended epochs, raising questions about separability of algorithmic gains from training protocol changes.

Video diffusion models suffer from prohibitive inference costs, but standard image distillation techniques like DMD cause severe oversaturation and temporal collapse when naively extended to video. This work introduces a video-specific distillation framework featuring an adaptive regression loss that dynamically reweights real-data supervision to prevent color artifacts, a temporal variance regularizer to combat static output, and an inference-time frame interpolation module that halves sequence length during high-noise steps to accelerate generation. Applied to Wan2.1, the method enables stable 4-step synthesis with state-of-the-art VBench scores.