PACE: Towards Surfacing Hidden Conflicts in User Requests

arXiv:2609.03293 · cs.CL · Submitted 2026-09-03 · Read on arXiv

cs.CL

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/p2chp2t/pacemaker

Terminology

• Core — 20× in this paper · explained in 2 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• RAG — 11× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Redundancy — 8× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Personalization — 7× in this paper · explained in 2 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Benchmarking — 5× in this paper · explained in 3 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Knowledge Graph — 4× in this paper · explained in 7 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Multi-Agent Framework — 3× in this paper · explained in 3 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• Multi-hop Reasoning — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Weighted Reciprocal Rank Fusion — 1× in this paper

Sources

• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory
• Learning to Extract Context for Context-Aware LLM Inference
• Personalized Language Modeling from Personalized Human Feedback
• Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
• Learning Personalized Agents from Human Feedback
• A Survey of Personalized Large Language Models: Progress and Future Directions
• Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions
• Qwen3 Technical Report
• PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
• Agentic Information Retrieval
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval

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