6 papers
AInsteinBench: Benchmarking Coding Agents on Scientific Repositories
Titouan Duston, Shuo Xin, Yang Sun +26
We introduce AInsteinBench, a large-scale benchmark for evaluating whether large language model (LLM) agents can operate as scientific computing development agents within real rese…
SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories
Junhao Wang, Daoguang Zan, Shulin Xin +3
Creating large-scale verifiable training datasets for issue-resolving tasks is a critical yet notoriously difficult challenge. Existing methods on automating the Gym environment se…
Seed-Coder: Let the Code Model Curate Data for Itself
ByteDance Seed, Yuyu Zhang, Jing Su +24
Code data in large language model (LLM) pretraining is recognized crucial not only for code-related tasks but also for enhancing general intelligence of LLMs. Current open-source L…
FullStack Bench: Evaluating LLMs as Full Stack Coders
Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng +53
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most exist…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving
Daoguang Zan, Zhirong Huang, Wei Liu +16
The task of issue resolving is to modify a codebase to generate a patch that addresses a given issue. However, existing benchmarks, such as SWE-bench, focus almost exclusively on P…