arXiv:2603.19677v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the underlying communication topology that coordinates agent interactions. Within these systems, successful problem-solving often necessitates task-specific group structures to divide and conquer subtasks. However, most existing approaches generate communication […]
Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation
arXiv:2603.04803v2 Announce Type: replace-cross Abstract: The limited understanding capacity of the visual encoder in Contrastive Language-Image Pre-training (CLIP) has become a key bottleneck for downstream performance. This capacity includes both Discriminative Ability (D-Ability), which reflects class separability, and Detail Perceptual Ability (P-Ability), which focuses on fine-grained visual cues. Recent solutions use diffusion models to enhance […]
AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation
arXiv:2603.19710v1 Announce Type: cross Abstract: Pre-search query recommendation, widely known as HintQ on Taobao’s homepage, plays a vital role in intent capture and demand discovery, yet traditional methods suffer from shallow semantics, poor cold-start performance and low serendipity due to reliance on ID-based matching and co-click heuristics. To overcome these challenges, we propose AIGQ (AI-Generated […]
Prompt Injection as Role Confusion
arXiv:2603.12277v2 Announce Type: replace-cross Abstract: Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models infer roles from how text is written, not where it comes from. We design novel role probes to capture how models internally identify “who is speaking.” These reveal why prompt […]
FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
arXiv:2603.16513v2 Announce Type: replace-cross Abstract: Structured data is foundational to healthcare, finance, e-commerce, and scientific data management. Large structured-data models (LDMs) extend the foundation model paradigm to unify heterogeneous datasets for tasks such as classification, regression, and decision support. However, existing LDMs face major limitations. First, most rely on sample-wise self-attention, whose O(N^2) complexity limits […]
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
arXiv:2603.19722v1 Announce Type: cross Abstract: Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in distinguishing noisy samples from the dataset for label correction by leveraging loss values. However, noisy samples recognition relying on scalar loss lacks reliability for FL under heterogeneous […]
VirPro: Visual-referred Probabilistic Prompt Learning for Weakly-Supervised Monocular 3D Detection
arXiv:2603.17470v2 Announce Type: replace-cross Abstract: Monocular 3D object detection typically relies on pseudo-labeling techniques to reduce dependency on real-world annotations. Recent advances demonstrate that deterministic linguistic cues can serve as effective auxiliary weak supervision signals, providing complementary semantic context. However, hand-crafted textual descriptions struggle to capture the inherent visual diversity of individuals across scenes, limiting […]
Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer’s and Dementia Caregivers
arXiv:2506.15047v2 Announce Type: replace-cross Abstract: Family caregivers of individuals with Alzheimer’s Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI — particularly large language models (LLMs) — offer new opportunities to support mental health, little is […]
ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models
arXiv:2603.19676v1 Announce Type: cross Abstract: Text-to-image diffusion models achieve high visual fidelity but surprisingly exhibit systematic failures in numerical control when prompts specify explicit object counts. To address this limitation, we introduce ATHENA, a model-agnostic, test-time adaptive steering framework that improves object count fidelity without modifying model architectures or requiring retraining. ATHENA leverages intermediate representations […]
Analytically tractable model of synaptic crowding explains emergent small-world structure and network dynamics
arXiv:2603.19320v1 Announce Type: new Abstract: Neural circuits must balance local connectivity constraints against the need for global integration. Here we introduce a minimal wiring rule motivated by synaptic crowding: as a neuron accumulates incoming connections, each additional synapse becomes progressively harder to form. This single-parameter model admits an exact finite-size solution for the induced in-degree […]
CIRCUS: Circuit Consensus under Uncertainty via Stability Ensembles
arXiv:2603.00523v2 Announce Type: replace-cross Abstract: Every mechanistic circuit carries an invisible asterisk: it reflects not just the model’s computation, but the analyst’s choice of pruning threshold. Change that choice and the circuit changes, yet current practice treats a single pruned subgraph as ground truth with no way to distinguish robust structure from threshold artifacts. We […]
Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation
arXiv:2603.19757v1 Announce Type: cross Abstract: Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype-based methods typically construct compact and deterministic prototypes from the support set to guide query segmentation. However, such rigid representations are unable to capture the intrinsic uncertainty introduced […]