Reliable Classroom AI via Neuro-Symbolic Multimodal Reasoning
arXiv:2603.22793v1 Announce Type: new Abstract: Classroom AI is rapidly expanding from low-level perception toward higher-level judgments about engagement, confusion, collaboration, and instructional quality. Yet classrooms are among the hardest real-world settings for multimodal vision: they are multi-party, noisy, privacy-sensitive, pedagogically...
PhySe-RPO: Physics and Semantics Guided Relative Policy Optimization for Diffusion-Based Surgical Smoke Removal
arXiv:2603.22844v1 Announce Type: new Abstract: Surgical smoke severely degrades intraoperative video quality, obscuring anatomical structures and limiting surgical perception. Existing learning-based desmoking approaches rely on scarce paired supervision and deterministic restoration pipelines, making it difficult to perform exploration or reinforcement-driven...
Functional Component Ablation Reveals Specialization Patterns in Hybrid Language Model Architectures
arXiv:2603.22473v1 Announce Type: new Abstract: Hybrid language models combining attention with state space models (SSMs) or linear attention offer improved efficiency, but whether both components are genuinely utilized remains unclear. We present a functional component ablation framework applied to two...
Towards Automated Community Notes Generation with Large Vision Language Models for Combating Contextual Deception
arXiv:2603.22453v1 Announce Type: new Abstract: Community Notes have emerged as an effective crowd-sourced mechanism for combating online deception on social media platforms. However, its reliance on human contributors limits both the timeliness and scalability. In this work, we study the...
Reddit After Roe: A Computational Analysis of Abortion Narratives and Barriers in the Wake of Dobbs
arXiv:2603.22566v1 Announce Type: new Abstract: The 2022 U.S. Supreme Court decision in Dobbs v. Jackson Women's Health Organization reshaped the reproductive rights landscape, introducing new uncertainty and barriers to abortion access. We present a large-scale computational analysis of abortion discourse...
Session Risk Memory (SRM): Temporal Authorization for Deterministic Pre-Execution Safety Gates
arXiv:2603.22350v1 Announce Type: new Abstract: Deterministic pre-execution safety gates evaluate whether individual agent actions are compatible with their assigned roles. While effective at per-action authorization, these systems are structurally blind to distributed attacks that decompose harmful intent across multiple individually-compliant...
Span Modeling for Idiomaticity and Figurative Language Detection with Span Contrastive Loss
arXiv:2603.22799v1 Announce Type: new Abstract: The category of figurative language contains many varieties, some of which are non-compositional in nature. This type of phrase or multi-word expression (MWE) includes idioms, which represent a single meaning that does not consist of...
When AI Shows Its Work, Is It Actually Working? Step-Level Evaluation Reveals Frontier Language Models Frequently Bypass Their Own Reasoning
arXiv:2603.22816v1 Announce Type: new Abstract: Language models increasingly "show their work" by writing step-by-step reasoning before answering. But are these reasoning steps genuinely used, or decorative narratives generated after the model has already decided? Consider: a medical AI writes "The...
Quality Over Clicks: Intrinsic Quality-Driven Iterative Reinforcement Learning for Cold-Start E-Commerce Query Suggestion
arXiv:2603.22922v1 Announce Type: new Abstract: Existing dialogue systems rely on Query Suggestion (QS) to enhance user engagement. Recent efforts typically employ large language models with Click-Through Rate (CTR) model, yet fail in cold-start scenarios due to their heavy reliance on...
DariMis: Harm-Aware Modeling for Dari Misinformation Detection on YouTube
arXiv:2603.22977v1 Announce Type: new Abstract: Dari, the primary language of Afghanistan, is spoken by tens of millions of people yet remains largely absent from the misinformation detection literature. We address this gap with DariMis, the first manually annotated dataset of...
Beyond Hate: Differentiating Uncivil and Intolerant Speech in Multimodal Content Moderation
arXiv:2603.22985v1 Announce Type: new Abstract: Current multimodal toxicity benchmarks typically use a single binary hatefulness label. This coarse approach conflates two fundamentally different characteristics of expression: tone and content. Drawing on communication science theory, we introduce a fine-grained annotation scheme...
Knowledge Access Beats Model Size: Memory Augmented Routing for Persistent AI Agents
arXiv:2603.23013v1 Announce Type: new Abstract: Production AI agents frequently receive user-specific queries that are highly repetitive, with up to 47\% being semantically similar to prior interactions, yet each query is typically processed with the same computational cost. We argue that...
Parametric Knowledge and Retrieval Behavior in RAG Fine-Tuning for Electronic Design Automation
arXiv:2603.23047v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering and often rely on standard NLP metrics that can obscure factual differences. We evaluate RAG fine-tuning for...
AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing
arXiv:2603.23069v1 Announce Type: new Abstract: The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original text. Existing style transfer methods train a single model on large corpora...
UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities
arXiv:2603.23160v1 Announce Type: new Abstract: Benchmarking AI systems in multi-turn interactive scenarios is essential for understanding their practical capabilities in real-world applications. However, existing evaluation protocols are highly heterogeneous, differing significantly in dataset formats, model interfaces, and evaluation pipelines, which...
Scaling Attention via Feature Sparsity
arXiv:2603.22300v1 Announce Type: new Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce this cost along the sequence axis through local windows, kernel approximations, or token-level sparsity, but these approaches consistently...
Mitigating Premature Discretization with Progressive Quantization for Robust Vector Tokenization
arXiv:2603.22304v1 Announce Type: new Abstract: Vector Quantization (VQ) has become the cornerstone of tokenization for many multimodal Large Language Models and diffusion synthesis. However, existing VQ paradigms suffer from a fundamental conflict: they enforce discretization before the encoder has captured...
Emergency Preemption Without Online Exploration: A Decision Transformer Approach
arXiv:2603.22315v1 Announce Type: new Abstract: Emergency vehicle (EV) response time is a critical determinant of survival outcomes, yet deployed signal preemption strategies remain reactive and uncontrollable. We propose a return-conditioned framework for emergency corridor optimization based on the Decision Transformer...
ST-GDance++: A Scalable Spatial-Temporal Diffusion for Long-Duration Group Choreography
arXiv:2603.22316v1 Announce Type: new Abstract: Group dance generation from music requires synchronizing multiple dancers while maintaining spatial coordination, making it highly relevant to applications such as film production, gaming, and animation. Recent group dance generation models have achieved promising generation...
Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
arXiv:2603.22317v1 Announce Type: new Abstract: Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a single Riemannian manifold. While emerging mixed-curvature methods attempt to capture such diversity, they often rely on implicit, task-driven routing that...
Sparsely-Supervised Data Assimilation via Physics-Informed Schr\"odinger Bridge
arXiv:2603.22319v1 Announce Type: new Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in...
Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting
arXiv:2603.22343v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. Local specialized models are efficient for routine conditions but often degrade under rare ramp events...
WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement
arXiv:2603.22352v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improvement of language models, but existing methods face a key trade-off: endogenous self-play can drift over iterations, while corpus-grounded approaches rely...
MCLR: Improving Conditional Modeling in Visual Generative Models via Inter-Class Likelihood-Ratio Maximization and Establishing the Equivalence between Classifier-Free Guidance and Alignment Objectives
arXiv:2603.22364v1 Announce Type: new Abstract: Diffusion models have achieved state-of-the-art performance in generative modeling, but their success often relies heavily on classifier-free guidance (CFG), an inference-time heuristic that modifies the sampling trajectory. From a theoretical perspective, diffusion models trained with...
Rethinking Multimodal Fusion for Time Series: Auxiliary Modalities Need Constrained Fusion
arXiv:2603.22372v1 Announce Type: new Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific...
Three Creates All: You Only Sample 3 Steps
arXiv:2603.22375v1 Announce Type: new Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics,...
Instruction-Tuned, but Not More Verifiable Instruction-Following: A Cross-Task Diagnosis for LoRA Adapters
arXiv:2603.22379v1 Announce Type: new Abstract: Adapters are often selected and deployed based on nominal labels (e.g., instruction-tuned), which implicitly suggest what capability improves after adaptation. We test whether nominal training objectives reliably align with realized cross-task capability gains by evaluating...
Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data
arXiv:2603.22380v1 Announce Type: new Abstract: Data-driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws from observational data. However, in practical scenarios, measurements are often contaminated by noise and limited by sparse sampling, which...
A Theoretical Framework for Energy-Aware Gradient Pruning in Federated Learning
arXiv:2603.22465v1 Announce Type: new Abstract: Federated Learning (FL) is constrained by the communication and energy limitations of decentralized edge devices. While gradient sparsification via Top-K magnitude pruning effectively reduces the communication payload, it remains inherently energy-agnostic. It assumes all parameter...
Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
arXiv:2603.22525v1 Announce Type: new Abstract: Operator learning models are rapidly emerging as the predictive core of digital twins for nuclear and energy systems, promising real-time field reconstruction from sparse sensor measurements. Yet their robustness to adversarial perturbations remains uncharacterized, a...