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Focus Shifts to Structured and Verifiable AI Development

The observed trend moves beyond simple model capability improvements toward building highly reliable systems that function within complex, real-world constraints. This is evidenced by the focus on using composable architecture frameworks or subjecting AI to rigorous validation through high-difficulty benchmarks like the Ramanujan Challenge. Furthermore, securing efficiency and robustness has become a critical concern across diverse domains, from optimizing RAG performance to streamlining embedded system security patches.

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arXivAugust 5, 2026

When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills

English summary not available yet. You can still read the original source.

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페르소나 스킬을 통한 에이전트 개인화 과정에서 발생하는 개인정보 유출 및 사칭 위험을 분석한 연구입니다. 새로운 벤치마크인 AntiSkillBench를 통해 스킬 증류 전략에 따른 보안 취약점과 기존 방어 체계의 한계를 체계적으로 평가합니다.

ai-agents
privacy
security
benchmark
persona-skills

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Focus Shifts to Structured and Verifiable AI Development

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Focus Shifts to Structured and Verifiable AI Development

When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills

How Closely Do LLM Reviews Align with Human Peer Review?

When Outputs Disperse, Does Epistemic Revision Follow? A Black-Box Coupling Diagnostic for Machine Collectives

Detecting Hallucinations and Recovering Verified Answers in Arabic Islamic Question Answering

VetScore: Risk-Weighted Fact Verification for Veterinary Long-Form QA with Citations

An Actionable Diagnosis of Multilingual, Multi-Agent Planning Failures

Predicting Deep Neural Network Training Outcomes from Early Training Telemetry

CausalOPD: First-Wrong-Step Supervision for Distilling Causal Chain Reasoning

MissClick: Exploiting Digit-Serialized Coordinates to Attack GUI Grounding Models

AgenticECO: An Agentic Framework for ECO on 3D Integrated Circuits

Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation

GPTKB 2.0: Direct Construction of Disambiguated Knowledge Bases from Large Language Models

CARE-Bench: Benchmarking Patient-Facing LLM Triage

Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement

DiagLoop: A Counterfactual Data Flywheel with Stage-Localized Reinforcement for Diagnostic LLMs

Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs

Agents Catching Agents: Shortcut Cascades and Benchmark Gaming in Clinical Multi-Agent Systems

LAEF: A Lead-Agnostic ECG Foundation Model Towards Point-of-Care Diagnostics

Risky Business: Measuring The Faithfulness-Safety Tension

GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks