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Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Agentic Proving for Program Verification MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts Solving the Aircraft Disassembly Scheduling Problem Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Naturalistic measure of social norms alignment Articulatory strategy as a source of variation in acoustic vowel dynamics When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning Cultural Adaptation in Large Language Models for Political Discourse Emotion Recognition in Sign Language Conversation ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning DART: Semantic Recoverability for Structured Tool Agents Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Parallel Context Compaction for Long-Horizon LLM Agent Serving
IORM: Hierarchical I/O Governance for Thousands of Consol...
Rajarshi Chowdhury, Akshay Shah, Zakaria Alrmaih, Chenhao Guo, A · 2026-05-28 · via cs updates on arXiv.org

Oracle Exadata consolidates thousands of tenant databases onto shared storage infrastructure deployed at hundreds of customer sites worldwide. Oracle Multitenant architecture enables this extreme density, with thousands of tenant databases sharing a single Exadata storage system -- but this creates a multi-level resource hierarchy (container databases, tenant databases, and workloads within tenants) that commodity block-layer schedulers cannot govern, as they lack visibility into database semantics and tenant boundaries. This paper presents the I/O Resource Manager (IORM), a storage-side scheduler built on three mechanisms: I/O Tagging, which propagates semantic context from the database kernel to the storage scheduler; Hierarchical Resource Profiles, which express compositional allocation policies across consolidation tiers using shares and limits; and Unified Storage Governance, which applies these policies consistently across all tiers of the storage hierarchy -- persistent memory, flash, and hard disk -- including cache placement decisions. IORM enables successful cloud deployments where thousands of tenants coexist on shared storage: production OLTP workloads run alongside concurrent analytical workloads from the same or different databases without noisy-neighbor interference. Evaluation on production Exadata systems demonstrates that IORM dramatically improves latency consistency, virtually eliminating tail latency outliers and delivering several-fold improvements in average read latency under mixed workloads. Hierarchical limits compose correctly across all three levels, and proportional share allocation tracks configured ratios closely even under highly skewed demand.