Augmenting LLM search judges with historical QRI cards improves Spearman correlation with user preferences by ~5% overall (91% relative on disagreements) and 15% in multilingual settings, with better alignment to live A/B test outcomes.
Audit trails for accountability in large language models
8 Pith papers cite this work. Polarity classification is still indexing.
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Analysis of Canada's Federal AI Register reveals it frames AI as reliable internal tooling by obscuring sociotechnical elements like human discretion, turning transparency into performative compliance.
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.
This survey defines execution provenance as a typed graph of agent execution and evidence tracing as its projection onto evidence-support relations, then reviews methods, taxonomy, benchmarks, and challenges for auditable LLM agents.
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
A statistical survey of RLHF for LLM alignment that connects preference learning and policy optimization to models like Bradley-Terry-Luce while reviewing methods, extensions, and open challenges.
citing papers explorer
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Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures
Analysis of Canada's Federal AI Register reveals it frames AI as reliable internal tooling by obscuring sociotechnical elements like human discretion, turning transparency into performative compliance.
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Responsible Agentic AI Requires Explicit Provenance
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
- Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On
- Auditable Agents