activity
20242026
most citedAgent-as-a-Judge: Evaluate Agents with Agents

3 citations · 7 across the 13 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

DiG-bench: Discovery in Games

Ruairidh M. Battleday, Kai Sandbrink, Jimi Cullen-Drohan +13

Discovery---formulating novel generalizations---is a central part of the scientific process. Despite its importance, there is a gap in the current AI benchmark landscape, with few…

cs.AI2026

Self-Improvements in Modern Agentic Systems: A Survey

Zhe Ren, Yimeng Chen, Dandan Guo +9

Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or…

cs.AI2025

Huxley-Gödel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine

Wenyi Wang, Piotr Piękos, Li Nanbo +5

Recent studies operationalize self-improvement through coding agents that edit their own codebases. They grow a tree of self-modifications through expansion strategies that favor h…

cs.AI2025★ 1 cited

Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models

Ruibin Xiong, Yimeng Chen, Dmitrii Khizbullin +2

Long-form writing agents require flexible integration and interaction across information retrieval, reasoning, and composition. Current approaches rely on predefined workflows and…

cs.AI2024★ 3 cited

Agent-as-a-Judge: Evaluate Agents with Agents

Mingchen Zhuge, Changsheng Zhao, Dylan Ashley +10

Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes -- ignoring the step-by-step nature of agentic sy…

cs.AI2024

FACTS: A Factored State-Space Framework For World Modelling

Li Nanbo, Firas Laakom, Yucheng Xu +2

World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such…