Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes
arXiv:2603.20418v1 Announce Type: new Abstract: Unidirectional tapes surface roughness determines the evolution of the degree of intimate contact required for ensuring the thermoplastic molecular diffusion and the associated inter-tapes consolidation during manufacturing of composite structures. However, usual characterization of rough...
Delightful Distributed Policy Gradient
arXiv:2603.20521v1 Announce Type: new Abstract: Distributed reinforcement learning trains on data from stale, buggy, or mismatched actors, producing actions with high surprisal (negative log-probability) under the learner's policy. The core difficulty is not surprising data per se, but \emph{negative learning...
Understanding Behavior Cloning with Action Quantization
arXiv:2603.20538v1 Announce Type: new Abstract: Behavior cloning is a fundamental paradigm in machine learning, enabling policy learning from expert demonstrations across robotics, autonomous driving, and generative models. Autoregressive models like transformer have proven remarkably effective, from large language models (LLMs)...
RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
arXiv:2603.20585v1 Announce Type: new Abstract: Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the system variables -- assumptions that fail to hold in many real-world...
Optimal low-rank stochastic gradient estimation for LLM training
arXiv:2603.20632v1 Announce Type: new Abstract: Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. Motivated by empirical evidence that many LLM gradient matrices are effectively low-rank during training, we...
Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity
arXiv:2603.20645v1 Announce Type: new Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data concentrated on low-dimensional structures -- remains incomplete. This paper investigates how diffusion models learn such structured...
Court reverses ruling on qualified immunity, denies review of death-row case and First Amendment challenge by citizen journalist
In a list of orders released on Monday morning, the Supreme Court reversed a ruling by a federal appeals court, holding that a Vermont police officer is entitled to qualified […]The postCourt reverses ruling on qualified immunity, denies review of...
Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
arXiv:2603.20046v1 Announce Type: new Abstract: Reinforcement Learning (RL) with rubric-based rewards has recently shown remarkable progress in enhancing general reasoning capabilities of Large Language Models (LLMs), yet still suffers from ineffective exploration confined to curent policy distribution. In fact, RL...
HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation
arXiv:2603.19260v1 Announce Type: cross Abstract: Sign Language Machine Translation (SLMT) aims to bridge communication between Deaf and hearing individuals. However, its progress is constrained by scarce datasets, limited signer diversity, and large domain gaps between sign motion patterns and pretrained...
The {\alpha}-Law of Observable Belief Revision in Large Language Model Inference
arXiv:2603.19262v1 Announce Type: cross Abstract: Large language models (LLMs) that iteratively revise their outputs through mechanisms such as chain-of-thought reasoning, self-reflection, or multi-agent debate lack principled guarantees regarding the stability of their probability updates. We identify a consistent multiplicative scaling...
On the Ability of Transformers to Verify Plans
arXiv:2603.19954v1 Announce Type: new Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited. We take important steps towards addressing this gap by analyzing the ability of decoder-only...
L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI)
arXiv:2603.19236v1 Announce Type: cross Abstract: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provides a rigorous foundation for evidence synthesis, yet the manual processes of data extraction and literature screening remain time-consuming and restrictive. Recent advances in...
Embodied Science: Closing the Discovery Loop with Agentic Embodied AI
arXiv:2603.19782v1 Announce Type: new Abstract: Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit governed by experimental cycles. Most current computational approaches are misaligned with this reality, framing discovery as...
When the Pure Reasoner Meets the Impossible Object: Analytic vs. Synthetic Fine-Tuning and the Suppression of Genesis in Language Models
arXiv:2603.19265v1 Announce Type: cross Abstract: This paper investigates the ontological consequences of fine-tuning Large Language Models (LLMs) on "impossible objects" -- entities defined by mutually exclusive predicates (e.g., "Artifact Alpha is a Square" and "Artifact Alpha is a Circle"). Drawing...
Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion
arXiv:2603.19266v1 Announce Type: cross Abstract: Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent advances, distilled models frequently suffer from superficial pattern memorization and subpar generalization. To overcome...
From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG
arXiv:2603.19276v1 Announce Type: cross Abstract: Automated short answer grading (ASAG) is critical for scaling educational assessment, yet large language models (LLMs) often struggle with hallucinations and strict rubric adherence due to their reliance on generalized pre-training. While Rretrieval-Augmented Generation (RAG)...
Framing Effects in Independent-Agent Large Language Models: A Cross-Family Behavioral Analysis
arXiv:2603.19282v1 Announce Type: cross Abstract: In many real-world applications, large language models (LLMs) operate as independent agents without interaction, thereby limiting coordination. In this setting, we examine how prompt framing influences decisions in a threshold voting task involving individual-group interest...
A Visualization for Comparative Analysis of Regression Models
arXiv:2603.19291v1 Announce Type: cross Abstract: As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very...
Spelling Correction in Healthcare Query-Answer Systems: Methods, Retrieval Impact, and Empirical Evaluation
arXiv:2603.19249v1 Announce Type: new Abstract: Healthcare question-answering (QA) systems face a persistent challenge: users submit queries with spelling errors at rates substantially higher than those found in the professional documents they search. This paper presents the first controlled study of...
From Comprehension to Reasoning: A Hierarchical Benchmark for Automated Financial Research Reporting
arXiv:2603.19254v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate financial research reports, shifting from auxiliary analytic tools to primary content producers. Yet recent real-world deployments reveal persistent failures--factual errors, numerical inconsistencies, fabricated references, and shallow...
From Tokens To Agents: A Researcher's Guide To Understanding Large Language Models
arXiv:2603.19269v1 Announce Type: new Abstract: Researchers face a critical choice: how to use -- or not use -- large language models in their work. Using them well requires understanding the mechanisms that shape what LLMs can and cannot do. This...
Automated Motif Indexing on the Arabian Nights
arXiv:2603.19283v1 Announce Type: new Abstract: Motifs are non-commonplace, recurring narrative elements, often found originally in folk stories. In addition to being of interest to folklorists, motifs appear as metaphoric devices in modern news, literature, propaganda, and other cultural texts. Finding...
Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs
arXiv:2603.19313v1 Announce Type: new Abstract: A core challenge for faithful LLM role-playing is sustaining consistent characterization throughout long, open-ended dialogues, as models frequently fail to recall and accurately apply their designated persona knowledge without explicit cues. To tackle this, we...
Prompt-tuning with Attribute Guidance for Low-resource Entity Matching
arXiv:2603.19321v1 Announce Type: new Abstract: Entity Matching (EM) is an important task that determines the logical relationship between two entities, such as Same, Different, or Undecidable. Traditional EM approaches rely heavily on supervised learning, which requires large amounts of high-quality...
Scalable Prompt Routing via Fine-Grained Latent Task Discovery
arXiv:2603.19415v1 Announce Type: new Abstract: Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools scale to include dozens of frontier models with narrow...
Is Evaluation Awareness Just Format Sensitivity? Limitations of Probe-Based Evidence under Controlled Prompt Structure
arXiv:2603.19426v1 Announce Type: new Abstract: Prior work uses linear probes on benchmark prompts as evidence of evaluation awareness in large language models. Because evaluation context is typically entangled with benchmark format and genre, it is unclear whether probe-based signals reflect...
EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Models
arXiv:2603.19532v1 Announce Type: new Abstract: Large Language Models (LLMs) are fluent but prone to hallucinations, producing answers that appear plausible yet are unsupported by available evidence. This failure is especially problematic in high-stakes domains where decisions must be justified by...
BEAVER: A Training-Free Hierarchical Prompt Compression Method via Structure-Aware Page Selection
arXiv:2603.19635v1 Announce Type: new Abstract: The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference latency and information utilization. Existing compression methods often suffer from high training costs or semantic...
Ternary Gamma Semirings: From Neural Implementation to Categorical Foundations
arXiv:2603.19317v1 Announce Type: new Abstract: This paper establishes a theoretical framework connecting neural network learning with abstract algebraic structures. We first present a minimal counterexample demonstrating that standard neural networks completely fail on compositional generalization tasks (0% accuracy). By introducing...