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cs.AI updates on arXiv.org

Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method CocoaBench: Evaluating Unified Digital Agents in the Wild MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis Efficient Training for Cross-lingual Speech Language Models Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities Uncertainty-Aware Web-Conditioned Scientific Fact-Checking When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies When Verification Fails: How Compositionally Infeasible Claims Escape Rejection Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series TInR: Exploring Tool-Internalized Reasoning in Large Language Models Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models Learning and Enforcing Context-Sensitive Control for LLMs Efficient Process Reward Modeling via Contrastive Mutual Information Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models LLMs Should Incorporate Explicit Mechanisms for Human Empathy
Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trus...
Riddhi Mohan Sharma · 2026-05-18 · via cs.AI updates on arXiv.org

As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap. We present Ethical Hyper-Velocity (EHV), a governance-aware runtime enforcement architecture for agentic systems that combines Grammar-Constrained Decoding (GCD) for inline policy-constrained token generation, Causal Graph CRDT-based policy synchronization with vector-clock ordering, hardware-attested execution in Trusted Execution Environments (TEEs), and OSCAL-formatted machine-readable audit logging. Unlike retrospective auditing frameworks (ISO/IEC 42001, NIST AI RMF) that introduce 14-30 day policy latencies, EHV relocates the Policy Enforcement Point (PEP) into the inference pipeline via a Governance-Aware Just-In-Time (JIT) Compiler. Under explicitly stated assumptions, the architecture reduces enforcement latency, improves traceability, and supports formal verification of safety invariants in a bounded model. We demonstrate via TLA+ model checking that non-compliant agentic actions were unreachable in the verified bounded operating state space (1,738 states generated, 324 distinct, depth 8, zero violations). Under these conditions, O(1) runtime enforcement reduces the traditional trade-off between deployment velocity and governance integrity, targeting Governance Latency from O(days) toward O(1). EHV's differentiating contribution is the integration of GCD, Causal CRDT, TEE attestation caching, and bounded formal verification into a single, hardware-rooted enforcement architecture -- a combination not achieved by any contemporaneous system. The architecture is demonstrated through a pediatric oncology dosage use case, with applicability to regulated critical infrastructures including healthcare, financial compliance, and critical infrastructure control.