An Embodied Companion for Visual Storytelling
arXiv:2603.05511v1 Announce Type: cross Abstract: As artificial intelligence shifts from pure tool for delegation toward agentic collaboration, its use in the arts can shift beyond the exploration of machine autonomy toward synergistic co-creation. While our earlier robotic works utilized automation...
When AI Levels the Playing Field: Skill Homogenization, Asset Concentration, and Two Regimes of Inequality
arXiv:2603.05565v1 Announce Type: cross Abstract: Generative AI compresses within-task skill differences while shifting economic value toward concentrated complementary assets, creating an apparent paradox: the technology that equalizes individual performance may widen aggregate inequality. We formalize this tension in a task-based...
On the Value of Tokeniser Pretraining in Physics Foundation Models
arXiv:2603.05598v1 Announce Type: cross Abstract: We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the...
Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective
arXiv:2603.05525v1 Announce Type: cross Abstract: Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need...
Autonomous Algorithm Discovery for Ptychography via Evolutionary LLM Reasoning
arXiv:2603.05696v1 Announce Type: cross Abstract: Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of regularization functions that largely remain manually designed. We introduce Ptychi-Evolve, an autonomous framework that uses...
The Rise of AI in Weather and Climate Information and its Impact on Global Inequality
arXiv:2603.05710v1 Announce Type: cross Abstract: The rapid adoption of AI in Earth system science promises unprecedented speed and fidelity in the generation of climate information. However, this technological prowess rests on a fragile and unequal foundation: the current trajectory of...
Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs
arXiv:2603.05618v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting improves LLM reasoning but can increase privacy risk by resurfacing personally identifiable information (PII) from the prompt into reasoning traces and outputs, even under policies that instruct the model not to restate...
NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories
arXiv:2603.05750v1 Announce Type: new Abstract: Existing scholarly information extraction (SIE) datasets focus on scientific papers and overlook implementation-level details in code repositories. README files describe datasets, source code, and other implementation-level artifacts, however, their free-form Markdown offers little semantic structure,...
RouteGoT: Node-Adaptive Routing for Cost-Efficient Graph of Thoughts Reasoning
arXiv:2603.05818v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Graph...
HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models
arXiv:2603.05828v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance in text generation and knowledge-intensive question answering. Nevertheless, they are prone to producing hallucinated content, which severely undermines their reliability in high-stakes application domains. Existing hallucination attribution...
Experiences Build Characters: The Linguistic Origins and Functional Impact of LLM Personality
arXiv:2603.06088v1 Announce Type: new Abstract: Human problem-solving is enriched by a diversity of styles and personality traits, yet the development of Large Language Models (LLMs) has largely prioritized uniform performance benchmarks that favour specific behavioural tendencies such as assertiveness. To...
Diffusion Language Models Are Natively Length-Aware
arXiv:2603.06123v1 Announce Type: new Abstract: Unlike autoregressive language models, which terminate variable-length generation upon predicting an End-of-Sequence (EoS) token, Diffusion Language Models (DLMs) operate over a fixed maximum-length context window for a predetermined number of denoising steps. However, this process...
MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue
arXiv:2603.06194v1 Announce Type: new Abstract: Subjective multi-turn dialogue tasks, such as emotional support, require conversational policies that adapt to evolving user states and optimize long-horizon interaction quality. However, reinforcement learning (RL) for such settings remains challenging due to the absence...
FlashPrefill: Instantaneous Pattern Discovery and Thresholding for Ultra-Fast Long-Context Prefilling
arXiv:2603.06199v1 Announce Type: new Abstract: Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, particularly during the compute-intensive prefilling phase. While various sparse attention mechanisms have been explored, they...
SPOT: Span-level Pause-of-Thought for Efficient and Interpretable Latent Reasoning in Large Language Models
arXiv:2603.06222v1 Announce Type: new Abstract: Explicit Chain-of-Thought improves the reasoning performance of large language models but often incurs high inference cost due to verbose token-level traces. While recent approaches reduce this overhead via concise prompting or step pruning, they largely...
PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations
arXiv:2603.06485v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a one-size-fits-all paradigm that neglects user differences in expertise, goals, and cognitive needs. Although Large Language...
FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design
arXiv:2603.05567v1 Announce Type: new Abstract: Dual-target structure-based drug design aims to generate a single ligand together with two pocket-specific binding poses, each compatible with a corresponding target pocket, enabling polypharmacological therapies with improved efficacy and reduced resistance. Existing approaches typically...
MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation
arXiv:2603.05760v1 Announce Type: new Abstract: Multi-objective reinforcement learning (MORL) is effective for multi-echelon combinatorial supply chain optimisation, where tasks involve high dimensionality, uncertainty, and competing objectives. However, its deployment in dynamic environments is hindered by the need for task-specific retraining...
Self-Auditing Parameter-Efficient Fine-Tuning for Few-Shot 3D Medical Image Segmentation
arXiv:2603.05822v1 Announce Type: new Abstract: Adapting foundation models to new clinical sites remains challenging in practice. Domain shift and scarce annotations must be handled by experts, yet many clinical groups do not have ready access to skilled AI engineers to...
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
arXiv:2603.05829v1 Announce Type: new Abstract: Test-time adaptation enables large language models (LLMs) to modify their behavior at inference without updating model parameters. A common approach is many-shot prompting, where large numbers of in-context learning (ICL) examples are injected as an...
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
arXiv:2603.06009v1 Announce Type: new Abstract: Plateaus, where an agent's performance stagnates at a suboptimal level, are a common problem in deep on-policy RL. Focusing on PPO due to its widespread adoption, we show that plateaus in certain regimes arise not...
Latent Diffusion-Based 3D Molecular Recovery from Vibrational Spectra
arXiv:2603.06113v1 Announce Type: new Abstract: Infrared (IR) spectroscopy, a type of vibrational spectroscopy, is widely used for molecular structure determination and provides critical structural information for chemists. However, existing approaches for recovering molecular structures from IR spectra typically rely on...
DC-Merge: Improving Model Merging with Directional Consistency
arXiv:2603.06242v1 Announce Type: new Abstract: Model merging aims to integrate multiple task-adapted models into a unified model that preserves the knowledge of each task. In this paper, we identify that the key to this knowledge retention lies in maintaining the...
The Office of Student Life
Holistically minded, the Office provides services and resources that reflect the diversity of needs and issues that law students encounter during their education. Its vision is to send the happiest, healthiest, and most professional law students out into the world...
Ph.D. in Law and Economics
Vanderbilt Law School's Ph.D. Program in Law and Economics is unlike any other. Dual-degree students pursue a J.D. and a Ph.D. concurrently in a fully integrated curriculum that combines economic theory and methodology with the study of law. The program...
A Critical View of Laws and Regulations of Artificial Intelligence in India and China
This research paper deals with the general understanding of AI technology and its laws and regulations in India and China. It examines this issue from developing countries perspective and focusing on India and China, as they represent around 40 %...
Anchored in Impact
Quick Guide Prospective Undergrads Prospective Grad & Professional Current Students Faculty & Staff Job Seekers Alumni & Friends Undergraduate Admissions Financial Aid & Scholarships Academics Find Your Major Schools & Colleges Student Life Plan a Visit Undergraduate Research Athletics -...
Computation of minimum-time feedback control laws for discrete-time systems with state-control constraints
The problem of finding a feedback law that drives the state of a linear discrete-time system to the origin in minimum-time subject to state-control constraints is considered. Algorithms are given to obtain facial descriptions of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</tex> -step...
AI-generated works and copyright law: towards a union of strange bedfellows
Journal Article AI-generated works and copyright law: towards a union of strange bedfellows Get access Emmanuel Salami Emmanuel Salami Email: Emmanuel.Salami@outlook.com. Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice,...
Information Theory and Statistical Mechanics
Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum-entropy estimate. It is the least biased estimate possible on the...