World PulseNow
Newsletter

Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics

arXiv — cs.CL•Thursday, December 11, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
  • A new benchmark called WebDetective has been introduced to evaluate Retrieval-Augmented Generation (RAG) systems through hint-free multi-hop questions, addressing significant limitations in current evaluation practices. This benchmark allows for a more comprehensive assessment of model actions by ensuring full traceability and separating search sufficiency, knowledge utilization, and refusal behavior.
  • This development is crucial as it enhances the evaluation framework for RAG systems, which are increasingly relied upon for complex reasoning tasks. By addressing the shortcomings of existing benchmarks, WebDetective aims to improve the reliability and effectiveness of AI models in real-world applications.
  • The introduction of WebDetective reflects a growing trend in AI research to refine evaluation methodologies, particularly in multi-hop reasoning tasks. As RAG systems evolve, the need for robust evaluation frameworks becomes paramount, especially in light of advancements in related areas such as multi-agent systems and efficient web content extraction, which also seek to enhance the capabilities of AI in handling complex queries.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps

LucidQuery AI

Combines diffusion reasoning with autoregressive LLM for advanced AI analysis.

AI & DataView app details

HubRE AI

AI agents that boost user engagement, ensure compliance, and streamline knowledge management.

AI & DataView app details

Supametas.AI

Extract and structure unstructured data for seamless LLM RAG integration.

AI & DataView app details
Continue Readings
Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents
arXiv — cs.CL2 days ago
Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents
NeutralArtificial Intelligence
A recent survey and experiments have highlighted the potential of Large Language Models (LLMs) in detecting mental disorders through social media, emphasizing the importance of advanced techniques such as Retrieval-Augmented Generation (RAG) and Agentic systems to enhance reliability and reasoning in clinical settings. These methods aim to address the challenges posed by hallucinations and memory limitations in LLMs.
Read full article
via arXiv — cs.CL