arXiv:2603.06981v1 Announce Type: cross Abstract: Controllable diffusion generation often relies on various heuristics that are seemingly disconnected without a unified understanding. We bridge this gap with Diffusion Controller (DiffCon), a unified control-theoretic view that casts reverse diffusion sampling as state-only stochastic control within (generalized) linearly-solvable Markov Decision Processes (LS-MDPs). Under this framework, control acts by […]
Learning Concept Bottleneck Models from Mechanistic Explanations
arXiv:2603.07343v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approaches typically select which concepts to learn via human specification, open knowledge graphs, prompting an LLM, or using general CLIP concepts. However, concepts defined a-priori may not have sufficient […]
Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
arXiv:2603.07452v1 Announce Type: cross Abstract: Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism — the conditional activation of specific behaviors through input triggers — can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present […]
Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
arXiv:2603.07524v1 Announce Type: cross Abstract: Brain activity is intrinsically a neural dynamic process constrained by anatomical space. This leads to significant variations in spatial distribution patterns and correlation patterns of neural activity across variable and heterogeneous scenarios. However, dominant brain functional network construction methods, which relies on pre-defined brain atlases and linear assumptions, fails to […]
VoiceSHIELD-Small: Real-Time Malicious Speech Detection and Transcription
arXiv:2603.07708v1 Announce Type: cross Abstract: Voice interfaces are quickly becoming a common way for people to interact with AI systems. This also brings new security risks, such as prompt injection, social engineering, and harmful voice commands. Traditional security methods rely on converting speech to text and then filtering that text, which introduces delays and can […]
CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases
arXiv:2603.07886v1 Announce Type: cross Abstract: Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content […]
XAI and Few-shot-based Hybrid Classification Model for Plant Leaf Disease Prognosis
arXiv:2603.06676v1 Announce Type: cross Abstract: Performing a timely and accurate identification of crop diseases is vital to maintain agricultural productivity and food security. The current work presents a hybrid few-shot learning model that integrates Explainable Artificial Intelligence (XAI) and Few-Shot Learning (FSL) to address the challenge of identifying and classifying the stages of disease of […]
ProtAlign: Contrastive learning paradigm for Sequence and structure alignment
arXiv:2603.06722v1 Announce Type: cross Abstract: Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into consideration. Traditional methods treat sequence and structure separately, limiting the ability to exploit the alignment between the structure and protein sequence embeddings. In this paper, […]
Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention
arXiv:2603.06738v1 Announce Type: cross Abstract: Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representational capacity. However, most SR Transformers rely heavily on relative positional bias~(RPB), which prevents them from leveraging hardware-efficient attention kernels such as FlashAttention. This limitation imposes a prohibitive computational burden during both training and inference, severely […]
Robotic Foundation Models for Industrial Control: A Comprehensive Survey and Readiness Assessment Framework
arXiv:2603.06749v1 Announce Type: cross Abstract: Robotic foundation models (RFMs) are emerging as a promising route towards flexible, instruction- and demonstration-driven robot control, however, a critical investigation of their industrial applicability is still lacking. This survey gives an extensive overview over the RFM-landscape and analyses, driven by concrete implications, how industrial domains and use cases shape […]
SpatialMAGIC: A Hybrid Framework Integrating Graph Diffusion and Spatial Attention for Spatial Transcriptomics Imputation
arXiv:2603.06780v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) enables mapping gene expression with spatial context but is severely affected by high sparsity and technical noise, which conceals true biological signals and hinders downstream analyses. To address these challenges, SpatialMagic was proposed, which is a hybrid imputation model combining MAGIC-based graph diffusion with transformer-based spatial self-attention. […]
Twitch: Learning Abstractions for Equational Theorem Proving
arXiv:2603.06849v1 Announce Type: cross Abstract: Several successful strategies in automated reasoning rely on human-supplied guidance about which term or clause shapes are interesting. In this paper we aim to discover interesting term shapes automatically. Specifically, we discover abstractions : term patterns that occur over and over again in relevant proofs. We present our tool Twitch […]