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From the 1 of 39 linked papers with an AI index.

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20242026
most citedMetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio

1 citations · 1 across the 19 of their papers we have counts for

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cs.CL20261 cited

MetaSyn: A Benchmark for LLM Agents on Meta-Analysis Articles from Nature Portfolio

Anzhe Xie, Weihang Su, Yujia Zhou +3

Systematic review and meta-analysis is an important method for scientific research. It comprehensively studies target research questions by combining evidence from multiple indepen…

cs.CL2026

Improve Large Language Model Systems with User Logs

Changyue Wang, Weihang Su, Qingyao Ai +4

Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality d…

cs.CL2026

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

Weihang Su, Jiacheng Kang, Jingyan Xu +7

Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate weight drift, potentially causing cat…

cs.CL2026

Decoupled Mixture-of-Experts for Parametric Knowledge Injection

Baoqing Yue, Weihang Su, Qingyao Ai +5

Knowledge injection aims to equip large language models (LLMs) with external, domain-specific, or time-sensitive knowledge. Existing approaches typically face a trade-off between f…

cs.CL2026

Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models

Changyue Wang, Weihang Su, Qingyao Ai +5

Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inj…

cs.CL2026

Skill Retrieval Augmentation for Agentic AI

Weihang Su, Jianming Long, Qingyao Ai +6

As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities…