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From the 2 of 45 linked papers with an AI index.

most citedExternalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering

5 citations · 5 across the 35 of their papers we have counts for

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14 papers · 1 filter

cs.CL2026

BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation

Ning Li, Zixuan Guo, Yan Xu +7

Hallucinations remain a major obstacle to deploying large language models (LLMs) in knowledge-intensive settings, where generated responses must be faithfully grounded in provided…

cs.CL2026

Retrospective Progress-Aware Self-Refinement for LLM Agent Training

Xinbei Ma, Congmin Zheng, Jiyang Qiu +10

LLM-based agents trained with reinforcement learning optimize step-wise action prediction but lack metacognitive awareness of task progress, inducing a gap that hinders long-horizo…

cs.CL2026

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

Aofan Yu, Chenyu Zhou, Tianyi Xu +8

Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and e…

cs.CL2026

Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents

Haoyi Hu, Qirong Lyu, Xianghan Kong +7

While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This par…

cs.CL2026

Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents

Jingxing Wang, Chenyu Zhou, Zhihui Fu +4

Additional test-time compute can give LLM agents access to more past experience, yet expanding the context or adding rollouts does not necessarily yield greater agent capability. W…

cs.CL2026

Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering

Jiachen Zhu, Zhuoying Ou, Congmin Zheng +9

Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existing methods predominantly trea…