most citedLearning to Reason for Factuality

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cs.AI2026

World of SWECraft: Agent Collaboration at Scale via Simulated Software Worlds

Shannon Zejiang Shen, Yapei Chang, Qiyao Liang +13

As teams of AI agents take on larger projects and long-standing open problems, agent collaboration offers another dimension for scaling inference-time compute. To understand what m…

cs.AI2026

Agent Plasticity: Measuring Self-Improvement Through Experience

Harman Singh, Anton Bakhtin, Rulin Shao +8

AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a f…

cs.AI2026

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Sohyeon Kim, Yoonho Lee, Bo Liu +11

What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-gr…

cs.AI2026

Context Language Models

Rulin Shao, Shannon Zejiang Shen, Junjie Oscar Yin +10

We introduce Context Language Models (CLMs), language models that natively manage their own context. We implement this by treating the context as a file and allowing the model to m…

cs.AI2026

JobBench: Aligning Agent Work With Human Will

Yuetai Li, Yichen Feng, Zhangchen Xu +21

Current benchmarks for occupational AI agents are scoped primarily by economic values, telling a replacement story. We introduce JobBench, which evaluates AI agents on the workflow…

cs.AI2026

EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics

Shuyue Stella Li, Rui Xin, Teng Xiao +8

Language models encode substantial evaluative knowledge from pretraining, yet current post-training methods rely on external supervision (human annotations, proprietary models, or…