RAG-Reflect achieves F1=0.78 on valid comment-edit prediction using retrieval-augmented reasoning and self-reflection, outperforming baselines and approaching fine-tuned models without retraining.
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The Decision Event Schema (DES) is a unified JSON schema that records governance evidence from four infrastructure layers in a single per-decision event structure with tiered completeness options.
Asteria is a runtime system that enables second-order optimization for LLMs by dynamically distributing optimizer state across GPU, CPU, and NVMe while using asynchronous inverse-root computations and bounded-staleness synchronization.
Pilot study shows agent decision reconstructability varies by vendor SDK regime, with completeness scores from 42.9% to 85.7% and consistent gaps in reasoning traces.
Hard distractors trigger a nonlinear 'First Drop of Ink' performance collapse in long-context LLM reasoning, with most damage from the initial small fraction via disproportionate attention.
Autonomous excavator controller achieves 1.8 cm RMSE in heavy-duty grading across different hydraulic architectures, outperforming commercial solutions by a factor of 2.6 in precision while better utilizing machine pressure.
Dual-Guard embeds complementary watermarks in diffusion image generation to verify provenance and localize tampering with low error rates on a 2400-sample benchmark under reprompting and editing attacks.
Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.
KAPPS is a knowledge-based CPPS architecture that uses an ontology-grounded knowledge graph as the unifying data backbone and authoritative write-time state for handling uncertainty in circular manufacturing, demonstrated via anomaly detection and constraint enforcement use cases.
Synthesizes a governance evidence framework revealing a coverage gradient from full auditability in rule engines to structural breaks in agentic AI, with a cascade of uncertainty and four formal propositions.
DEMM defines four executable evidence-sufficiency categories plus a conflicting category for agentic AI decisions and rolls per-property verdicts into a five-level maturity rubric.
A human-in-control LLM architecture translates natural language to OpenSearch DSL queries using hybrid lexical and semantic search in a secure private-cloud setup, shown via prototype on the Enron dataset.
Rule-based annotation generation for ACSL outperforms LLM-based methods in achieving successful formal verification of C programs.
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Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
Asteria is a runtime system that enables second-order optimization for LLMs by dynamically distributing optimizer state across GPU, CPU, and NVMe while using asynchronous inverse-root computations and bounded-staleness synchronization.
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A Cloud-Native Architecture for Human-in-Control LLM-Assisted OpenSearch in Investigative Settings
A human-in-control LLM architecture translates natural language to OpenSearch DSL queries using hybrid lexical and semantic search in a secure private-cloud setup, shown via prototype on the Enron dataset.