Re²Math is a new benchmark that evaluates AI models on retrieving and verifying the applicability of theorems from math literature to advance steps in partial proofs, accepting any sufficient theorem while controlling for leakage.
arXiv preprint arXiv:2009.02252 (2020)
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A survey that defines Compound AI Systems, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, reviews four foundational paradigms, and identifies key challenges for future research.
BrowseComp-ZH is a new benchmark of 289 Chinese web questions where even the strongest LLM agents reach only 42.9% accuracy.
UMG-RAG improves long-document RAG by uncertainty-aware fusion of multi-granularity retrievals from complementary dense and sparse retrievers, plus a parent-promotion variant.
HieraRAG shows optimal RAG benchmark granularity varies by dimension, with complexity favoring fine-grained categories and a new Coherence Ratio measuring category structure.
ART automatically generates multi-step reasoning programs with tool integration for LLMs, yielding substantial gains over few-shot and auto-CoT prompting on BigBench and MMLU while matching hand-crafted CoT on most tasks.
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.
citing papers explorer
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Re$^2$Math: Benchmarking Theorem Retrieval in Research-Level Mathematics
Re²Math is a new benchmark that evaluates AI models on retrieving and verifying the applicability of theorems from math literature to advance steps in partial proofs, accepting any sufficient theorem while controlling for leakage.
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From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
A survey that defines Compound AI Systems, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, reviews four foundational paradigms, and identifies key challenges for future research.
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BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
BrowseComp-ZH is a new benchmark of 289 Chinese web questions where even the strongest LLM agents reach only 42.9% accuracy.
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Uncertainty-Aware Hybrid Retrieval for Long-Document RAG
UMG-RAG improves long-document RAG by uncertainty-aware fusion of multi-granularity retrievals from complementary dense and sparse retrievers, plus a parent-promotion variant.
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How Fine-Grained Should a RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation
HieraRAG shows optimal RAG benchmark granularity varies by dimension, with complexity favoring fine-grained categories and a new Coherence Ratio measuring category structure.
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ART: Automatic multi-step reasoning and tool-use for large language models
ART automatically generates multi-step reasoning programs with tool integration for LLMs, yielding substantial gains over few-shot and auto-CoT prompting on BigBench and MMLU while matching hand-crafted CoT on most tasks.
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Atlas: Few-shot Learning with Retrieval Augmented Language Models
Atlas reaches over 42% accuracy on Natural Questions with only 64 examples, outperforming a 540B-parameter model by 3% with 50x fewer parameters.