arXiv:2604.16710v1 Announce Type: cross Abstract: Linear-threshold networks (LTNs) capture the mesoscale behavior of interacting populations of neurons and are of particular interest to control theorists due to their dynamical richness and relative ease of analysis. The aim of this paper is to advance the study of global asymptotic stability in LTNs with asymmetric neural interactions […]
Machine individuality: Separating genuine idiosyncrasy from response bias in large language models
arXiv:2604.16755v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into daily life, in roles ranging from high-stakes decision support to companionship, understanding their behavioral dispositions becomes critical. A growing literature uses psychometric inventories and cognitive paradigms to profile LLM dispositions. However, these approaches cannot determine whether behavioral differences reflect stable, stimulus-specific […]
Frozen Vision Transformers for Dense Prediction on Small Datasets: A Case Study in Arrow Localization
arXiv:2604.16758v1 Announce Type: cross Abstract: We present a system for automated detection, localization, and scoring of arrow punctures on 40,cm indoor archery target faces, trained on only 48 annotated photographs (5,084 punctures). Our pipeline combines three components: a color-based canonical rectification stage that maps perspective-distorted photographs into a standardized coordinate system where pixel distances correspond […]
ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition
arXiv:2503.21248v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we introduce the first large-scale benchmark for evaluating LLMs on a sufficient set of scientific discovery sub-tasks-inspiration […]
When Informal Text Breaks NLI: Tokenization Failure, Distribution Shift, and Targeted Mitigations
arXiv:2604.16787v1 Announce Type: cross Abstract: We study how informal surface forms degrade NLI accuracy in ELECTRA-small (14M) and RoBERTa-large (355M) across four transforms applied to SNLI and MultiNLI: slang substitution, emoji replacement, Gen-Z filler tokens, and their combination. Slang substitution (replacing formal words with informal equivalents, e.g., “going to” -> “gonna”, “friend” -> “homie”) causes […]
SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention
arXiv:2604.16776v1 Announce Type: new Abstract: Modeling single-cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high-level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framework based on conditional Transformers for […]
enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways
arXiv:2604.16838v1 Announce Type: cross Abstract: We present enclawed, a hard-fork hardening framework built on top of the OpenClaw single-user personal artificial intelligence (AI) assistant gateway. enclawed targets deployments that need attestable peer trust, deny-by-default external connectivity, signed-module loading, and a tamper-evident audit trail typically regulated industries such as financial services, healthcare, defense contracting, regulated R&D, […]
Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations
arXiv:2507.20409v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting helps models think step by step. But naive CoT breaks down in visually grounded social tasks, where models must perceive, understand, and judge all at once; bridging perception with norm-grounded reasoning. Recent work has introduced structured reasoning for multi-turn agent planning and visual QA, decomposing tasks into […]
Physics-Informed Tracking (PIT)
arXiv:2604.16895v1 Announce Type: cross Abstract: We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder localizes a particle as a heatmap peak (landmark) and a differentiable physics module embedded in the autoencoder constrains several landmarks over time (a trajectory) to satisfy known dynamics. The novel […]
Introspection Adapters: Training LLMs to Report Their Learned Behaviors
arXiv:2604.16812v1 Announce Type: new Abstract: When model developers or users fine-tune an LLM, this can induce behaviors that are unexpected, deliberately harmful, or hard to detect. It would be far easier to audit LLMs if they could simply describe their behaviors in natural language. Here, we study a scalable approach to rapidly identify learned behaviors […]
Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach
arXiv:2604.16953v1 Announce Type: cross Abstract: Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This paper presents a novel Hybrid Quantum Neural Network (HQNN) architecture that integrates quantum computing principles with classical convolutional neural networks for enhanced […]
CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit
arXiv:2510.06133v2 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms only high-confidence positions while remasking the others. By analyzing dLLM denoising traces, we uncover a key inefficiency: models often predict the correct target token several steps before its confidence becomes high […]