MetaSyn is a stage-level benchmark of 442 meta-analyses showing LLM agents retrieve up to 90.9% of eligible studies but include at most 52.7% in their final reports.
Dynamic parametric retrieval augmented generation for test-time knowledge enhancement
7 Pith papers cite this work. Polarity classification is still indexing.
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RWGBench measures related-work generation by citation choices, and shows citation-focused metrics expose failures that text-similarity and LLM-judge scores miss.
Judge-R1 improves LLM judgment document generation by combining agentic legal information retrieval with GRPO-based rubric-guided optimization, outperforming baselines on the JuDGE benchmark.
Introduces SRA paradigm and SRA-Bench benchmark (5,400 tasks, 26,262 skills) showing retrieval improves performance but LLMs fail to selectively incorporate retrieved skills.
Document LoRA acts as decoding-time parametric memory that recovers 13-21 ROUGE-L points under heavy KV cache compression in QA, performing best when the base model encodes the document and the adapter is used only at generation with QA supervision.
Introduces Parametric Memory Law as power law for LoRA memory capacity and MemFT threshold-guided optimization for better memory fidelity.
MemOS introduces a unified memory management framework for LLMs using MemCubes to handle and evolve different memory types for improved controllability and evolvability.
citing papers explorer
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MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio
MetaSyn is a stage-level benchmark of 442 meta-analyses showing LLM agents retrieve up to 90.9% of eligible studies but include at most 52.7% in their final reports.
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RWGBench: Evaluating Scholarly Positioning in Related Work Generation
RWGBench measures related-work generation by citation choices, and shows citation-focused metrics expose failures that text-similarity and LLM-judge scores miss.
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Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization
Judge-R1 improves LLM judgment document generation by combining agentic legal information retrieval with GRPO-based rubric-guided optimization, outperforming baselines on the JuDGE benchmark.
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Skill Retrieval Augmentation for Agentic AI
Introduces SRA paradigm and SRA-Bench benchmark (5,400 tasks, 26,262 skills) showing retrieval improves performance but LLMs fail to selectively incorporate retrieved skills.
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Rethinking LoRA Memory Through the Lens of KV Cache Compression
Document LoRA acts as decoding-time parametric memory that recovers 13-21 ROUGE-L points under heavy KV cache compression in QA, performing best when the base model encodes the document and the adapter is used only at generation with QA supervision.
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How LoRA Remembers? A Parametric Memory Law for LLM Finetuning
Introduces Parametric Memory Law as power law for LoRA memory capacity and MemFT threshold-guided optimization for better memory fidelity.
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MemOS: A Memory OS for AI System
MemOS introduces a unified memory management framework for LLMs using MemCubes to handle and evolve different memory types for improved controllability and evolvability.