Monotropic Artificial Intelligence: Toward a Cognitive Taxonomy of Domain-Specialized Language Models
arXiv:2603.00350v1 Announce Type: new Abstract: The prevailing paradigm in artificial intelligence research equates progress with scale: larger models trained on broader datasets are presumed to yield superior capabilities. This assumption, while empirically productive for general-purpose applications, obscures a fundamental epistemological...
Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning
arXiv:2603.00374v1 Announce Type: new Abstract: Offline learning of strategies takes data efficiency to its extreme by restricting algorithms to a fixed dataset of state-action trajectories. We consider the problem in a mixed-motive multiagent setting, where the goal is to solve...
NeuroHex: Highly-Efficient Hex Coordinate System for Creating World Models to Enable Adaptive AI
arXiv:2603.00376v1 Announce Type: new Abstract: \textit{NeuroHex} is a hexagonal coordinate system designed to support highly efficient world models and reference frames for online adaptive AI systems. Inspired by the hexadirectional firing structure of grid cells in the human brain, NeuroHex...
Optimizing In-Context Demonstrations for LLM-based Automated Grading
arXiv:2603.00465v1 Announce Type: new Abstract: Automated assessment of open-ended student responses is a critical capability for scaling personalized feedback in education. While large language models (LLMs) have shown promise in grading tasks via in-context learning (ICL), their reliability is heavily...
Why Not? Solver-Grounded Certificates for Explainable Mission Planning
arXiv:2603.00469v1 Announce Type: new Abstract: Operators of Earth observation satellites need justifications for scheduling decisions: why a request was selected, rejected, or what changes would make it schedulable. Existing approaches construct post-hoc reasoning layers independent of the optimizer, risking non-causal...
From Goals to Aspects, Revisited: An NFR Pattern Language for Agentic AI Systems
arXiv:2603.00472v1 Announce Type: new Abstract: Agentic AI systems exhibit numerous crosscutting concerns -- security, observability, cost management, fault tolerance -- that are poorly modularized in current implementations, contributing to the high failure rate of AI projects in reaching production. The...
LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks
arXiv:2603.00540v1 Announce Type: new Abstract: The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise state-transition objectives. However, this paradigm is bottlenecked by data scarcity, as existing tool-centric...
Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation
arXiv:2603.00546v1 Announce Type: new Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm across various domains. Evaluating the capability and reliability of MLLM-as-a-judge systems is therefore essential for...
Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs
arXiv:2603.00578v1 Announce Type: new Abstract: Long chain-of-thought~(CoT) has become a dominant paradigm for enhancing the reasoning capability of large reasoning models~(LRMs); however, the performance gains often come with a substantial increase in reasoning budget. Recent studies show that existing CoT...
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
arXiv:2603.00599v1 Announce Type: new Abstract: Hypergraphs are the natural description of higher-order interactions among objects, widely applied in social network analysis, cross-modal retrieval, etc. Hypergraph Neural Networks (HGNNs) have become the dominant solution for learning on hypergraphs. Traditional HGNNs are...
Machine Learning Grade Prediction Using Students' Grades and Demographics
arXiv:2603.00608v1 Announce Type: new Abstract: Student repetition in secondary education imposes significant resource burdens, particularly in resource-constrained contexts. Addressing this challenge, this study introduces a unified machine learning framework that simultaneously predicts pass/fail outcomes and continuous grades, a departure from...
TraceSIR: A Multi-Agent Framework for Structured Analysis and Reporting of Agentic Execution Traces
arXiv:2603.00623v1 Announce Type: new Abstract: Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, function calling, and coding. However, their long and intricate execution traces make failure diagnosis and...
LiTS: A Modular Framework for LLM Tree Search
arXiv:2603.00631v1 Announce Type: new Abstract: LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition, and RewardModel) that plug into algorithms like MCTS and BFS. A decorator-based registry...
InfoPO: Information-Driven Policy Optimization for User-Centric Agents
arXiv:2603.00656v1 Announce Type: new Abstract: Real-world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downstream decisions. However, current multi-turn GRPO-based methods often rely on trajectory-level reward computation, which leads to...
AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults
arXiv:2603.00691v1 Announce Type: new Abstract: The world is undergoing a major demographic shift as older adults become a rapidly growing share of the population, creating new challenges for driving safety. In car-dependent regions such as the United States, driving remains...
MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning
arXiv:2603.00730v1 Announce Type: new Abstract: Deep reinforcement learning (RL) has been applied extensively to solve complex decision-making problems. In many real-world scenarios, tasks often have several conflicting objectives and may require multiple agents to cooperate, which are the multi-objective multi-agent...
The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agents
arXiv:2603.00801v1 Announce Type: new Abstract: Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources. However, these sources can include unreliable or adversarial content, and the robustness of agents to adversarial ranking - where...
MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind
arXiv:2603.00808v1 Announce Type: new Abstract: A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons with collective awareness, without centralized supervision or explicit communication. In this...
MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains
arXiv:2603.00873v1 Announce Type: new Abstract: With the increasing demand for step-wise, cross-modal, and knowledge-grounded reasoning, multimodal large language models (MLLMs) are evolving beyond the traditional fixed retrieve-then-generate paradigm toward more sophisticated agentic multimodal retrieval-augmented generation (MM-RAG). Existing benchmarks, however, mainly...
HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents
arXiv:2603.00977v1 Announce Type: new Abstract: Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally limited in long-horizon tasks that require structured planning and reliable execution. Existing approaches predominantly rely on flat autoregressive...
Alien Science: Sampling Coherent but Cognitively Unavailable Research Directions from Idea Atoms
arXiv:2603.01092v1 Announce Type: new Abstract: Large language models are adept at synthesizing and recombining familiar material, yet they often fail at a specific kind of creativity that matters most in research: producing ideas that are both coherent and non-obvious to...
DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage
arXiv:2603.01106v1 Announce Type: new Abstract: Reinforcement learning (RL) with group relative policy optimization (GRPO) has become a widely adopted approach for enhancing the reasoning capabilities of multimodal large language models (MLLMs). While GRPO enables long-chain reasoning without a critic, it...
HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis
arXiv:2603.01121v1 Announce Type: new Abstract: While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primarily because the diagnostic process demands sophisticated multi-step logical reasoning, dynamic tool invocation, and...
Delaware Journal of Corporate Law
Delaware Journal of Corporate Law | 604 followers on LinkedIn. The Delaware Journal of Corporate Law continues to operate as a nationally recognized student-edited publication | The Delaware Journal of Corporate Law is a student-edited publication established in 1975 at...
FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning
arXiv:2603.01135v1 Announce Type: new Abstract: Large Language Models have achieved remarkable success in language understanding and reasoning, and their multimodal extensions enable comprehension of images, video, and audio. Inspired by this, foundation models for brain functional connectivity networks derived from...
AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
arXiv:2603.01145v1 Announce Type: new Abstract: In practical LLM applications, users repeatedly express stable preferences and requirements, such as reducing hallucinations, following institutional writing conventions, or avoiding overly technical wording, yet such interaction experience is seldom consolidated into reusable knowledge. Consequently,...
DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent
arXiv:2603.01152v1 Announce Type: new Abstract: Deep-research agents are capable of executing multi-step web exploration, targeted retrieval, and sophisticated question answering. Despite their powerful capabilities, deep-research agents face two critical bottlenecks: (1) the lack of large-scale, challenging datasets with real-world difficulty,...
How Well Does Agent Development Reflect Real-World Work?
arXiv:2603.01203v1 Announce Type: new Abstract: AI agents are increasingly developed and evaluated on benchmarks relevant to human work, yet it remains unclear how representative these benchmarking efforts are of the labor market as a whole. In this work, we systematically...
Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics
arXiv:2603.01209v1 Announce Type: new Abstract: Tool-augmented LLMs are increasingly deployed as agents that interleave natural-language reasoning with executable Python actions, as in CodeAct-style frameworks. In deployment, these agents rely on runtime state that persists across steps. By contrast, common training...
From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization
arXiv:2603.00021v1 Announce Type: new Abstract: This paper proposes a data-driven method to automatically construct graph-based document representations. Building upon the recent work of Bugue\~no and de Melo (2025), we leverage the dynamic sliding-window attention module to effectively capture local and...