LegalHalluLens introduces a framework to audit AI hallucinations in legal contexts by analyzing typed hallucination profiles across four claim categories. It reveals a 38-40 point gap between obligation/numeric and temporal claims, and shows two systems with identical 52% hallucination rates can have opposite risk directions. The framework uses a Risk Direction Index and calibrated debate pipelines to reduce fabricated detections by 45% and improve accountability in legal AI deployment.
LegalHalluLens: Auditing Hallucinations in Legal AI
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
LedgerAgent introduces a structured ledger to maintain task states separately in tool-calling agents. It renders states into prompts and enforces policy constraints before tool execution, reducing policy violations and improving performance across customer-service domains.
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
LedgerAgent introduces a structured ledger to maintain task states separately in tool-calling agents. It renders these states into prompts and enforces policy constraints before tool execution, reducing policy violations and improving performance across customer-service domains.
Trajectory Mining Reveals Skill Structure but Fails to Improve Policies
A three-stage pipeline mines skill libraries from GUI interaction data, achieving high purity in five of eight clusters against InteraSkill labels. However, the method only slightly improves skill-step accuracy on IW and fails to advance performance on BrowseComp+ or key metrics, indicating limitations in cross-domain policy transfer.
LegalHalluLens: Auditing Hallucinations in Legal AI
LegalHalluLens introduces a framework to audit AI hallucinations in legal contexts by analyzing typed hallucination profiles across four claim categories. It reveals a 38-40 point gap between obligation/numeric and temporal claims, and shows two systems with identical 52% hallucination rates can have opposite risk directions. The framework uses a Risk Direction Index and calibrated debate pipelines to reduce fabricated detections by 45%, offering actionable diagnostics for trustworthy legal AI deployment.
SkillWeaver: Compositional Skill Routing for LLM Agents
SkillWeaver introduces a decompose-retrieve-compose framework for LLM agents, formalizing the Compositional Skill Routing problem. It achieves 67.7% decomposition accuracy via Iterative Skill-Aware Decomposition (SAD), improving from 51.0% with a p-value of less than 10^-6, and reduces context window usage by over 99%.