FISformer proposes replacing the dot-product self-attention in Transformers with a Sugeno-type Fuzzy Inference System (FIS) for time series forecasting. Instead of computing query-key similarities, the model fuzzifies tokens using learnable Gaussian membership functions, applies fuzzy rules, and defuzzifies to produce interaction weights. The paper suggests this approach captures uncertainty and nonlinearity better than standard attention, reporting state-of-the-art results on benchmarks like ETT, ECL, and Weather.

This paper exposes a critical vulnerability in Multimodal Large Language Models (MLLMs): safety alignment fails when harmful intent is embedded in structured visual narratives. The authors introduce ComicJailbreak, a benchmark of 1,167 three-panel comics where panels 1–2 establish narrative context and panel 3 contains a blank speech bubble filled with a paraphrased harmful goal. The model is prompted to "complete the comic" by generating the fourth panel. Across 15 state-of-the-art MLLMs, comic-based attacks achieve ensemble success rates exceeding 90% on Gemini-family models and 85%+ on most open-source models—substantially outperforming plain-text and random-image baselines. The work also reveals that existing defenses (AdaShield, Attack as Defense) trigger severe over-refusal on benign prompts, and that automated safety judges are unreliable on sensitive-but-benign content.

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.

Vector Diffusion Maps (VDM) capture pairwise connection relationships in complex datasets via the Graph Connection Laplacian, but eigenvalue decomposition costs $O(n^{2.81})$, prohibiting large-scale applications. This paper proposes LA-VDM (Landmark Accelerated VDM), which constrains diffusion through landmark points and introduces a novel two-stage normalization scheme with parameters $\alpha$ and $\beta$ to handle non-uniform sampling densities in both data and landmarks. Under a manifold model with the frame bundle structure, the authors prove that LA-VDM asymptotically converges to the connection Laplacian while reducing complexity to $O(nm^2)$, enabling applications to datasets with millions of points.

This paper proposes a multi-UAV architecture for autonomous precision agriculture that combines centralized mission planning with decentralized execution control. It integrates coverage path planning, battery-aware task allocation, CNN-based image processing, and battery swapping stations to enable end-to-end farm monitoring. The work targets large-scale agricultural operations with minimal human intervention, claiming advantages in fault-tolerance, scalability, and user-friendliness.

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.

ALMAB-DC unifies Gaussian process active learning, multi-armed bandit scheduling, and asynchronous distributed computing to tackle expensive black-box optimization in sequential experimental design. The framework targets dose-finding, spatial field estimation, and ML/engineering tasks, claiming superior sample efficiency and near-linear parallel speedups up to $K=16$ agents. While the modular architecture and ablation analyses are rigorous, all empirical results derive from calibrated surrogate emulators rather than live systems, substantially limiting external validity.

Multi-agent trajectory prediction requires models to understand complex future interactions between agents. This paper proposes braid prediction, an auxiliary task where models classify the crossing relationships (below/over/no\_crossing) between every pair of agents using shared mode embeddings from a DETR-style decoder. By training jointly to predict these topological braid labels alongside trajectories, the model gains future-interaction awareness with negligible inference overhead.

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.

This paper tackles the challenge of multi-party consensus-building in travel planning, where agents must negotiate conflicting subjective preferences rather than converge on objective truths. MIND (Multi-agent Inference for Negotiation Dialogue) introduces a Theory-of-Mind-inspired framework where agents infer hidden preference intensities (willingness scores $w \in [1,10]$) from linguistic cues and dynamically adjust their tone between warmth and toughness. The work matters because it extends Multi-Agent Debate (MAD) from factual domains to social coordination problems requiring compromise.

This paper introduces SemEval-2026 Task 12, Abductive Event Reasoning (AER), a shared task requiring systems to identify the most plausible direct cause of a target event from noisy multi-document evidence. The task is cast as an evidence-grounded multiple-choice benchmark with multiple correct answers allowed, capturing challenges like distributed evidence, indirect background factors, and semantically related distractors. With 122 participants and 518 submissions, it represents a significant community effort to benchmark real-world causal reasoning in long-context settings.

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.

This paper studies nonparametric regression for learning degree-$k_0$ spherical polynomials on the unit sphere $\mathbb{S}^{d-1}$ using over-parameterized two-layer neural networks. The authors propose a novel Gradient Descent with Projection (GDP) algorithm that constrains learning to the top $r_0 = \Theta(d^{k_0})$ eigenspaces of the Neural Tangent Kernel (NTK). The main result establishes a nearly minimax optimal risk bound of order $\log(4/\delta) \cdot \Theta(d^{k_0}/n)$, improving the sample complexity from previous polynomial-in-$1/\varepsilon$ rates to linear $1/\varepsilon$ scaling.

This paper proposes the GenAI SECI model, an update to Nonaka & Takeuchi's classic SECI framework for knowledge creation, designed to leverage generative AI for managing workplace ("Gen-Ba") tacit knowledge. The central innovation is "Digital Fragmented Knowledge"—partial, fragmentary knowledge stored in cyberspace that generative AI aggregates, structures, and recommends to amplify human understanding without requiring full externalization into explicit knowledge. This addresses the urgent problem of transferring expert tacit knowledge as Japan's workforce ages, going beyond conventional KM systems that struggle with the effort required to formalize knowledge.

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.

The paper proposes AV-LR, a lightweight amortized variational inference framework for logistic regression with missing covariates that eliminates latent variables entirely. Unlike VAE-based competitors, it directly models the posterior over missing values using a single neural network coupled with a linear classification layer, enabling joint optimization of imputation and prediction. The approach extends naturally to MNAR settings and claims substantial computational speedups over EM-based methods while maintaining comparable statistical accuracy.

S2tc-bdd addresses Semi-Supervised Text Classification (SSTC) where pseudo-label accuracy suffers from "margin bias" caused by imbalanced label angle variances between classes. The core idea is to balance deep representation distributions by applying Gaussian linear transformations to Angular Margin (AM) loss, thereby eliminating decision boundary bias during self-training. This matters because it targets a fundamental distribution mismatch in SSL that particularly degrades performance when labeled data is scarce.

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.

ROM tackles overthinking in Large Reasoning Models, where models generate redundant reasoning after reaching correct answers. The core idea is a lightweight streaming detector—an 8.13M parameter head attached to late-layer hidden states of a frozen LLM—that predicts overthinking probability token-by-token and triggers early stopping. It matters because it promises 47% token reduction without full model retraining. We find the method empirically effective but note concerns regarding data scaling limits and labeling costs.

This paper presents Oph-Guid-RAG, a multimodal retrieval-augmented generation system for ophthalmic clinical decision support. Unlike conventional text-based RAG systems, it retrieves full guideline page images using ColQwen2.5, preserving tables, flowcharts, and layout information without OCR errors. The system introduces a controllable retrieval framework with routing and filtering to selectively introduce external evidence, evaluated on a specialized ophthalmology subset extracted from HealthBench.