arXiv:2603.09172v2 Announce Type: replace-cross Abstract: We present improved lower bounds for five classical Ramsey numbers: $mathbfR(3, 13)$ is increased from $60$ to $61$, $mathbfR(3, 18)$ from $99$ to $100$, $mathbfR(4, 13)$ from $138$ to $139$, $mathbfR(4, 14)$ from $147$ to $148$, and $mathbfR(4, 15)$ from $158$ to $159$. These results were achieved using AlphaEvolve, an […]
A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification
arXiv:2603.10891v1 Announce Type: new Abstract: Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models (LLMs) to this zero-tolerance domain is untenable due to their inherent factual unreliability, lack of traceability, and weakness in complex reasoning. To address […]
CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
arXiv:2603.10745v1 Announce Type: cross Abstract: Accurate estimation of uncertainty in deep learning is critical for deploying models in high-stakes domains such as medical diagnosis and autonomous decision-making, where overconfident predictions can lead to harmful outcomes. In practice, understanding the reason behind a model’s uncertainty and the type of uncertainty it represents can support risk-aware decisions, […]
Macroscopic Dominance from Microscopic Extremes: Symmetry Breaking in Spatial Competition
arXiv:2603.10911v1 Announce Type: new Abstract: How do competing populations convert a spatial advantage into macroscopic dominance? We introduce a stochastic model for resource competition that decouples the transient discovery phase from monopolization. Initial symmetry breaking is governed by extreme value statistics of first-passage times: a linear spatial disadvantage requires an exponentially larger population to overcome. […]
BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation
arXiv:2603.10828v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) has revolutionized interactive segmentation through spatial prompting. While existing work primarily focuses on automating prompts in various settings, real-world annotation workflows involve iterative refinement where annotators observe model outputs and strategically place prompts to resolve ambiguities. Current pipelines typically rely on the annotator’s visual assessment […]
Digging Deeper: Learning Multi-Level Concept Hierarchies
arXiv:2603.10084v1 Announce Type: cross Abstract: Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent this, recent work has introduced Hierarchical Concept Embedding Models (HiCEMs) to explicitly model concept relationships, and Concept Splitting to discover sub-concepts using only coarse […]
Linear Readout of Neural Manifolds with Continuous Variables
arXiv:2603.10956v1 Announce Type: new Abstract: Brains and artificial neural networks compute with continuous variables such as object position or stimulus orientation. However, the complex variability in neural responses makes it difficult to link internal representational structure to task performance. We develop a statistical-mechanical theory of regression capacity that relates linear decoding efficiency of continuous variables […]
ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping
arXiv:2603.10088v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains computationally expensive as the full input context is processed at every iteration. In this work, […]
An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took “Use of Practical AI in Digital Libraries” seriously?
arXiv:2603.10876v1 Announce Type: cross Abstract: Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted […]
Execution Is the New Attack Surface: Survivability-Aware Agentic Crypto Trading with OpenClaw-Style Local Executors
arXiv:2603.10092v1 Announce Type: cross Abstract: OpenClaw-style agent stacks turn language into privileged execution: LLM intents flow through tool interception, policy gates, and a local executor. In parallel, skill marketplaces such as skills.sh make capability acquisition as easy as installing skills and CLIs, creating a growing capability supply chain. Together, these trends shift the dominant safety […]
Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
arXiv:2603.11032v1 Announce Type: new Abstract: Large-scale electrophysiological recordings now allow simultaneous monitoring of thousands of neurons across multiple brain regions, revealing structured variability in neural population activity. Understanding how these collective patterns emerge from microscopic neural interactions requires models that are scalable, predictive, and interpretable. Statistical physics provides principled frameworks to address this complexity, including […]
GroundCount: Grounding Vision-Language Models with Object Detection for Mitigating Counting Hallucinations
arXiv:2603.10978v1 Announce Type: cross Abstract: Vision Language Models (VLMs) exhibit persistent hallucinations in counting tasks, with accuracy substantially lower than other visual reasoning tasks (excluding sentiment). This phenomenon persists even in state-of-the-art reasoning-capable VLMs. Conversely, CNN-based object detection models (ODMs) such as YOLO excel at spatial localization and instance counting with minimal computational overhead. We […]