Geometry-Preserving Aggregation for Mixture-of-Experts Embedding Models
arXiv:2602.14039v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) embedding models combine expert outputs using weighted linear summation, implicitly assuming a linear subspace structure in the embedding space. This assumption is shown to be inconsistent with the geometry of expert representations. Geometric...
Mind the Boundary: Stabilizing Gemini Enterprise A2A via a Cloud Run Hub Across Projects and Accounts
arXiv:2602.17675v1 Announce Type: cross Abstract: Enterprise conversational UIs increasingly need to orchestrate heterogeneous backend agents and tools across project and account boundaries in a secure and reproducible way. Starting from Gemini Enterprise Agent-to-Agent (A2A) invocation, we implement an A2A Hub...
Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPO
arXiv:2602.17686v1 Announce Type: cross Abstract: Distilling Chain-of-Thought (CoT) reasoning from large language models into compact student models presents a fundamental challenge: teacher rationales are often too verbose for smaller models to faithfully reproduce. Existing approaches either compress reasoning into single-step,...
Robust and Efficient Tool Orchestration via Layered Execution Structures with Reflective Correction
arXiv:2602.18968v1 Announce Type: new Abstract: Tool invocation is a core capability of agentic systems, yet failures often arise not from individual tool calls but from how multiple tools are organized and executed together. Existing approaches tightly couple tool execution with...
Quantifying Automation Risk in High-Automation AI Systems: A Bayesian Framework for Failure Propagation and Optimal Oversight
arXiv:2602.18986v1 Announce Type: new Abstract: Organizations across finance, healthcare, transportation, content moderation, and critical infrastructure are rapidly deploying highly automated AI systems, yet they lack principled methods to quantify how increasing automation amplifies harm when failures occur. We propose a...
INSURE-Dial: A Phase-Aware Conversational Dataset \& Benchmark for Compliance Verification and Phase Detection
arXiv:2602.18448v1 Announce Type: new Abstract: Administrative phone tasks drain roughly 1 trillion USD annually from U.S. healthcare, with over 500 million insurance-benefit verification calls manually handled in 2024. We introduce INSURE-Dial, to our knowledge the first public benchmark for developing...
Asymptotic Semantic Collapse in Hierarchical Optimization
arXiv:2602.18450v1 Announce Type: new Abstract: Multi-agent language systems can exhibit a failure mode where a shared dominant context progressively absorbs individual semantics, yielding near-uniform behavior across agents. We study this effect under the name Asymptotic Semantic Collapse in Hierarchical Optimization....
Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook
arXiv:2602.20044v1 Announce Type: cross Abstract: Within twelve days of launch, an AI-native social platform exhibits extreme attention concentration, hierarchical role separation, and one-way attention flow, consistent with the hypothesis that stratification in agent ecosystems can emerge rapidly rather than gradually....
SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detection
arXiv:2602.23447v1 Announce Type: cross Abstract: Detection of rare lesions in whole-body CT is fundamentally limited by extreme class imbalance and low target-to-volume ratios, producing precision collapse despite high AUROC. Synthetic augmentation with diffusion models offers promise, yet pixel-space diffusion is...
SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks
arXiv:2603.00575v1 Announce Type: new Abstract: Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. This scarcity stems from three fundamental challenges in existing pipelines: environments are brittle and difficult...
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...
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...
TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation
arXiv:2603.00025v1 Announce Type: new Abstract: Direct Preference Optimization is an offline post-SFT method for aligning language models from preference pairs, with strong results in instruction following and summarization. However, DPO's sequence-level implicit reward can be brittle for token-critical structured prediction...
From Prerequisites to Predictions: Validating a Geometric Hallucination Taxonomy Through Controlled Induction
arXiv:2603.00307v1 Announce Type: new Abstract: We test whether a geometric hallucination taxonomy -- classifying failures as center-drift (Type~1), wrong-well convergence (Type~2), or coverage gaps (Type~3) -- can distinguish hallucination types through controlled induction in GPT-2. Using a two-level statistical design...
Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving
arXiv:2603.02214v1 Announce Type: new Abstract: Federated Inference (FI) studies how independently trained and privately owned models can collaborate at inference time without sharing data or model parameters. While recent work has explored secure and distributed inference from disparate perspectives, a...
EvoSkill: Automated Skill Discovery for Multi-Agent Systems
arXiv:2603.02766v1 Announce Type: new Abstract: Coding agents are increasingly used as general-purpose problem solvers, but their flexibility does not by itself confer the domain expertise needed for specialized tasks. Recent work addresses this through \textit{agent skills}: reusable workflows, and code,...
Agentified Assessment of Logical Reasoning Agents
arXiv:2603.02788v1 Announce Type: new Abstract: We present a framework for evaluating and benchmarking logical reasoning agents when assessment itself must be reproducible, auditable, and robust to execution failures. Building on agentified assessment, we use an assessor agent to issue tasks,...
Retrievit: In-context Retrieval Capabilities of Transformers, State Space Models, and Hybrid Architectures
arXiv:2603.02874v1 Announce Type: new Abstract: Transformers excel at in-context retrieval but suffer from quadratic complexity with sequence length, while State Space Models (SSMs) offer efficient linear-time processing but have limited retrieval capabilities. We investigate whether hybrid architectures combining Transformers and...
AI Space Physics: Constitutive boundary semantics for open AI institutions
arXiv:2603.03119v1 Announce Type: new Abstract: Agentic AI deployments increasingly behave as persistent institutions rather than one-shot inference endpoints: they accumulate state, invoke external tools, coordinate multiple runtimes, and modify their future authority surface over time. Existing governance language typically specifies...
Universal Conceptual Structure in Neural Translation: Probing NLLB-200's Multilingual Geometry
arXiv:2603.02258v1 Announce Type: new Abstract: Do neural machine translation models learn language-universal conceptual representations, or do they merely cluster languages by surface similarity? We investigate this question by probing the representation geometry of Meta's NLLB-200, a 200-language encoder-decoder Transformer, through...
ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model
arXiv:2603.04589v1 Announce Type: new Abstract: Electrocardiography (ECG) analysis is crucial for cardiac diagnosis, yet existing foundation models often fail to capture the periodicity and diverse features required for varied clinical tasks. We propose ECG-MoE, a hybrid architecture that integrates multi-model...
AegisUI: Behavioral Anomaly Detection for Structured User Interface Protocols in AI Agent Systems
arXiv:2603.05031v1 Announce Type: new Abstract: AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are becoming common in production systems. The trouble is that a payload can pass every schema...
Jagarin: A Three-Layer Architecture for Hibernating Personal Duty Agents on Mobile
arXiv:2603.05069v1 Announce Type: new Abstract: Personal AI agents face a fundamental deployment paradox on mobile: persistent background execution drains battery and violates platform sandboxing policies, yet purely reactive agents miss time-sensitive obligations until the user remembers to ask. We present...
Bidirectional Curriculum Generation: A Multi-Agent Framework for Data-Efficient Mathematical Reasoning
arXiv:2603.05120v1 Announce Type: new Abstract: Enhancing mathematical reasoning in Large Language Models typically demands massive datasets, yet data efficiency remains a critical bottleneck. While Curriculum Learning attempts to structure this process, standard unidirectional approaches (simple-to-complex) suffer from inefficient sample utilization:...
AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments
arXiv:2603.04718v1 Announce Type: new Abstract: In oral arguments, judges probe attorneys with questions about the factual record, legal claims, and the strength of their arguments. To prepare for this questioning, both law schools and practicing attorneys rely on moot courts:...
FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learning
arXiv:2603.04422v1 Announce Type: new Abstract: Federated learning (FL) often degrades when clients hold heterogeneous non-Independent and Identically Distributed (non-IID) data and when some clients behave adversarially, leading to client drift, slow convergence, and high communication overhead. This paper proposes FedEMA-Distill,...
Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation
arXiv:2603.04478v1 Announce Type: new Abstract: Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG...
Invariant Causal Routing for Governing Social Norms in Online Market Economies
arXiv:2603.04534v1 Announce Type: new Abstract: Social norms are stable behavioral patterns that emerge endogenously within economic systems through repeated interactions among agents. In online market economies, such norms -- like fair exposure, sustained participation, and balanced reinvestment -- are critical...
FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation
arXiv:2603.04890v1 Announce Type: new Abstract: Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving framework that leverages complementary cross-modal information. However, existing methods often overlook personalized client performance...
EVMbench: Evaluating AI Agents on Smart Contract Security
arXiv:2603.04915v1 Announce Type: new Abstract: Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is...