activity
20242026
most citedFullStack Bench: Evaluating LLMs as Full Stack Coders

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

collaborators

7 papers

cs.AI2026

SkillAlchemy: Open-World Agent Skill Creation

Hengjun Wang, Shuyue Wei, Boyi Liu +2

Agent skills are reusable procedural artifacts that extend language agents with specialized workflows, tool conventions, and domain behaviors at inference time. However, creating r…

cs.AI2026

SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents

Chang Liu, Yuqi Zhang, Yiman Zhong +3

Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing. Existing agent skills are primari…

cs.CL2025★ 1 cited

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…

cs.AI2024

Seed-CTS: Unleashing the Power of Tree Search for Superior Performance in Competitive Coding Tasks

Hao Wang, Boyi Liu, Yufeng Zhang +1

Competition-level code generation tasks pose significant challenges for current state-of-the-art large language models (LLMs). For example, on the LiveCodeBench-Hard dataset, model…

cs.AI2024★ 2 cited

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…

cs.LG2024

Reward-Augmented Data Enhances Direct Preference Alignment of LLMs

Shenao Zhang, Zhihan Liu, Boyi Liu +6

Preference alignment in Large Language Models (LLMs) has significantly improved their ability to adhere to human instructions and intentions. However, existing direct alignment alg…

cs.AI2024

BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data

Xuwu Wang, Qiwen Cui, Yunzhe Tao +16

Large language models (LLMs) have become increasingly pivotal across various domains, especially in handling complex data types. This includes structured data processing, as exempl…