arXiv:2604.04281v1 Announce Type: new Abstract: Width expansion offers a practical route to reuse smaller causal-language-model checkpoints, but selecting a widened warm start is not solved by zero-step preservation alone. We study dense width growth as a candidate-selection problem over full training states, including copied weights, optimizer moments, and scheduler state. In a small-scale TinyStories proxy, […]
ReFinE: Streamlining UI Mockup Iteration with Research Findings
arXiv:2604.04353v1 Announce Type: cross Abstract: Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understanding technical jargon, the lack of contextualization, and limited actionability. To address these challenges, we present ReFinE, a Figma plugin that supports real-time design iteration by […]
Contextual Control without Memory Growth in a Context-Switching Task
arXiv:2604.03479v1 Announce Type: new Abstract: Context-dependent sequential decision making is commonly addressed either by providing context explicitly as an input or by increasing recurrent memory so that contextual information can be represented internally. We study a third alternative: realizing contextual dependence by intervening on a shared recurrent latent state, without enlarging recurrent dimensionality. To this […]
RoboPhD: Evolving Diverse Complex Agents Under Tight Evaluation Budgets
arXiv:2604.04347v1 Announce Type: new Abstract: 2026 has brought an explosion of interest in LLM-guided evolution of agentic artifacts, with systems like GEPA and Autoresearch demonstrating that LLMs can iteratively improve prompts, code, and agent architectures across diverse domains. As adoption accelerates, a central question emerges: given the same information, the same seed agent, and the […]
Cardinality Estimation for High Dimensional Similarity Queries with Adaptive Bucket Probing
arXiv:2604.04603v1 Announce Type: cross Abstract: In this work, we address the problem of cardinality estimation for similarity search in high-dimensional spaces. Our goal is to design a framework that is lightweight, easy to construct, and capable of providing accurate estimates with satisfying online efficiency. We leverage locality-sensitive hashing (LSH) to partition the vector space while […]
Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis
arXiv:2604.04386v1 Announce Type: new Abstract: Numerous math benchmarks exist to evaluate LLMs’ mathematical capabilities. However, most involve extensive manual effort and are difficult to scale. Consequently, they cannot keep pace with LLM development or easily provide new instances to mitigate overfitting. Some researchers have proposed automatic benchmark generation methods, but few focus on identifying the […]
Large Language Models Align with the Human Brain during Creative Thinking
arXiv:2604.03480v1 Announce Type: new Abstract: Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine. Large language models (LLMs) have recently demonstrated impressive performance on divergent thinking tests and prior work has shown that models with higher task performance […]
ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems
arXiv:2604.04426v1 Announce Type: new Abstract: Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on third-party tools and MCP servers, a new class of supply-chain threats has emerged, where malicious behaviors are embedded in seemingly benign tools, silently hijacking agent execution, leaking sensitive data, […]
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
arXiv:2604.04858v1 Announce Type: cross Abstract: Objective: Algorithmic fairness is essential for equitable and trustworthy machine learning in healthcare. Most fairness tools emphasize single-axis demographic comparisons and may miss compounded disparities affecting intersectional populations. This study introduces Fairlogue, a toolkit designed to operationalize intersectional fairness assessment in observational and counterfactual contexts within clinical settings. Methods: Fairlogue […]
What Makes a Sale? Rethinking End-to-End Seller–Buyer Retail Dynamics with LLM Agents
arXiv:2604.04468v1 Announce Type: new Abstract: Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through buyer-seller interaction to purchase decisions. However, existing retail simulators capture only partial aspects of this process and do not model cross-stage dependencies, making it difficult to assess how early decisions affect downstream […]
Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graphs from Complex Documents
arXiv:2604.03496v1 Announce Type: new Abstract: Knowledge graph construction typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas schema-free methods often produce fragmented graphs with weak global organization, especially in long technical documents with dense, context-dependent information. We propose TRACE-KG (Text-dRiven schemA […]
Receding-Horizon Control via Drifting Models
arXiv:2604.04528v1 Announce Type: new Abstract: We study the problem of trajectory optimization in settings where the system dynamics are unknown and it is not possible to simulate trajectories through a surrogate model. When an offline dataset of trajectories is available, an agent could directly learn a trajectory generator by distribution matching. However, this approach only […]