A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance
arXiv:2603.22326v1 Announce Type: new Abstract: Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs)....
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...
First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution
arXiv:2603.22346v1 Announce Type: new Abstract: We isolate and empirically characterize first-mover bias -- a path-dependent concentration of feature importance caused by sequential residual fitting in gradient boosting -- as a specific mechanistic cause of the well-known instability of SHAP-based feature...
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,...
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...
Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure
arXiv:2603.22384v1 Announce Type: new Abstract: Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a lightweight adaptive temporal control system that learns the optimal interval between cognitive ticks from experience,...
Neural Structure Embedding for Symbolic Regression via Continuous Structure Search and Coefficient Optimization
arXiv:2603.22429v1 Announce Type: new Abstract: Symbolic regression aims to discover human-interpretable equations that explain observational data. However, existing approaches rely heavily on discrete structure search (e.g., genetic programming), which often leads to high computational cost, unstable performance, and limited scalability...
SkillRouter: Retrieve-and-Rerank Skill Selection for LLM Agents at Scale
arXiv:2603.22455v1 Announce Type: new Abstract: As LLM agent ecosystems grow, the number of available skills (tools, plugins) has reached tens of thousands, making it infeasible to inject all skills into an agent's context. This creates a need for skill routing...
Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
arXiv:2603.22530v1 Announce Type: new Abstract: Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This additional information can substantially improve model learning. However, due to their...
Decentring the governance of AI in the military: a focus on the postcolonial subject
Abstract The governance of emerging technologies with increased autonomy in the military has become a topical issue in recent years, especially considering the rapid advances in artificial intelligence and related innovations in computer science. Despite this hype, the postcolonial subject’s...
NeurIPS 2026 Evaluations & Datasets Track Call for Papers
KLDrive: Fine-Grained 3D Scene Reasoning for Autonomous Driving based on Knowledge Graph
arXiv:2603.21029v1 Announce Type: new Abstract: Autonomous driving requires reliable reasoning over fine-grained 3D scene facts. Fine-grained question answering over multi-modal driving observations provides a natural way to evaluate this capability, yet existing perception pipelines and driving-oriented large language model (LLM)...
Me, Myself, and $\pi$ : Evaluating and Explaining LLM Introspection
arXiv:2603.20276v1 Announce Type: new Abstract: A hallmark of human intelligence is Introspection-the ability to assess and reason about one's own cognitive processes. Introspection has emerged as a promising but contested capability in large language models (LLMs). However, current evaluations often...
ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting
arXiv:2603.20869v1 Announce Type: new Abstract: Financial time-series forecasting in real-world high-frequency markets is often hindered by delayed or partially stale observations caused by asynchronous data acquisition and transmission latency. To better reflect such practical conditions, we investigate a simulated delay...
GMPilot: An Expert AI Agent For FDA cGMP Compliance
arXiv:2603.20815v1 Announce Type: new Abstract: The pharmaceutical industry is facing challenges with quality management such as high costs of compliance, slow responses and disjointed knowledge. This paper presents GMPilot, a domain-specific AI agent that is designed to support FDA cGMP...
Reasoning Traces Shape Outputs but Models Won't Say So
arXiv:2603.20620v1 Announce Type: new Abstract: Can we trust the reasoning traces that large reasoning models (LRMs) produce? We investigate whether these traces faithfully reflect what drives model outputs, and whether models will honestly report their influence. We introduce Thought Injection,...
Compression is all you need: Modeling Mathematics
arXiv:2603.20396v1 Announce Type: new Abstract: Human mathematics (HM), the mathematics humans discover and value, is a vanishingly small subset of formal mathematics (FM), the totality of all valid deductions. We argue that HM is distinguished by its compressibility through hierarchically...
Locally Coherent Parallel Decoding in Diffusion Language Models
arXiv:2603.20216v1 Announce Type: new Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) models, offering sub-linear generation latency and bidirectional capabilities that are particularly appealing for code generation and editing. Achieving sub-linear latency in discrete...
DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation
arXiv:2603.20470v1 Announce Type: new Abstract: The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants of pretrained diffusion models specialized for diverse generative abilities. Yet, existing model merging methods remain...
Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models
arXiv:2603.20212v1 Announce Type: new Abstract: Reward models (RMs) are critical for aligning Large Language Models via Reinforcement Learning from Human Feedback (RLHF). While Generative Reward Models (GRMs) achieve superior accuracy through chain-of-thought (CoT) reasoning, they incur substantial computational costs. Conversely,...
Enhancing Safety of Large Language Models via Embedding Space Separation
arXiv:2603.20206v1 Announce Type: new Abstract: Large language models (LLMs) have achieved impressive capabilities, yet ensuring their safety against harmful prompts remains a critical challenge. Recent work has revealed that the latent representations (embeddings) of harmful and safe queries in LLMs...
RedacBench: Can AI Erase Your Secrets?
arXiv:2603.20208v1 Announce Type: new Abstract: Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information -- critical for data security. However, existing benchmarks for redaction typically focus on predefined categories...
Graph of States: Solving Abductive Tasks with Large Language Models
arXiv:2603.21250v1 Announce Type: new Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains significantly underexplored. Existing frameworks, predominantly designed for static deductive tasks, fail to generalize...
FinReflectKG -- HalluBench: GraphRAG Hallucination Benchmark for Financial Question Answering Systems
arXiv:2603.20252v1 Announce Type: new Abstract: As organizations increasingly integrate AI-powered question-answering systems into financial information systems for compliance, risk assessment, and decision support, ensuring the factual accuracy of AI-generated outputs becomes a critical engineering challenge. Current Knowledge Graph (KG)-augmented QA...
Attention in Space: Functional Roles of VLM Heads for Spatial Reasoning
arXiv:2603.20662v1 Announce Type: new Abstract: Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how attention heads within VLMs contribute to spatial reasoning by analyzing their functional roles through a...
LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling
arXiv:2603.20537v1 Announce Type: new Abstract: Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies struggle to meet. We study an LLM-driven heuristic synthesis framework for hot steel rolling, in which a language model iteratively...
ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture
arXiv:2603.21340v1 Announce Type: new Abstract: This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal reasoning, determinism, and architectural AI safety. We demonstrate that ARYA satisfies all canonical world model...
A Framework for Low-Latency, LLM-driven Multimodal Interaction on the Pepper Robot
arXiv:2603.21013v1 Announce Type: new Abstract: Despite recent advances in integrating Large Language Models (LLMs) into social robotics, two weaknesses persist. First, existing implementations on platforms like Pepper often rely on cascaded Speech-to-Text (STT)->LLM->Text-to-Speech (TTS) pipelines, resulting in high latency and...