BRIGHT-Pro and RTriever-Synth advance reasoning-intensive retrieval by adding multi-aspect evidence evaluation and aspect-decomposed synthetic training, with the fine-tuned RTriever-4B showing gains over its base model.
Litsearch: A retrieval benchmark for scientific literature search
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Lacuna is an LLM-powered research map for ML that outperforms OpenScholar on retrieval benchmarks and GPT-Researcher on multi-stage report generation tasks.
H-MAPS uses a three-layered hierarchical memory to infer a reader's background and intent from implicit behaviors, generating profile-specific questions and on-device literature retrieval, as shown when NLP and HCI researchers receive different recommendations for the same paper.
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
citing papers explorer
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Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems
BRIGHT-Pro and RTriever-Synth advance reasoning-intensive retrieval by adding multi-aspect evidence evaluation and aspect-decomposed synthetic training, with the fine-tuned RTriever-4B showing gains over its base model.
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Lacuna: A Research Map for Machine Learning
Lacuna is an LLM-powered research map for ML that outperforms OpenScholar on retrieval benchmarks and GPT-Researcher on multi-stage report generation tasks.
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H-MAPS: Hierarchical Memory-Augmented Proactive Search Assistant for Scientific Literature
H-MAPS uses a three-layered hierarchical memory to infer a reader's background and intent from implicit behaviors, generating profile-specific questions and on-device literature retrieval, as shown when NLP and HCI researchers receive different recommendations for the same paper.
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.