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cs.AI2026
How Fine-Tuning Objectives Shape Layer-Wise Information in LLM Hallucination Detection
Kaiyang Wan, Forrest Sheng Bao, Amin Ahmad +1
Under matched conditions, classifier-head fine-tuning (\textsc{cls}) paradigm consistently outperforms the LLM-as-Judge sequence-to-sequence (\textsc{s2s}) on binary hallucination…
cs.AI2026
ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
Tengfei Lyu, Zirui Yuan, Xu Liu +4
Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored da…
cs.AI2025
A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA
Kaiyang Wan, Lang Gao, Honglin Mu +3
Multi-Hop Question Answering (MHQA) requires integrating dispersed, interdependent evidence through sequential reasoning under noise. This task is challenging for LLMs as they have…