most citedSeed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model

Chenghao Fan, Wen Heng, Bo Li +6

Diffusion-based language models (DLLMs) offer non-sequential, block-wise generation and richer data reuse compared to autoregressive (AR) models, but existing code DLLMs still lag…

cs.SD2026

FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning

Tanyu Chen, Tairan Chen, Kai Shen +4

Recent end-to-end spoken dialogue systems leverage speech tokenizers and neural audio codecs to enable LLMs to operate directly on discrete speech representations. However, these m…

cs.CL20251 cited

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…

cs.CL20251 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.SE2025

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…

cs.AI2025

BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving

Ran Xin, Chenguang Xi, Jie Yang +6

Recent advancements in large language models (LLMs) have spurred growing interest in automatic theorem proving using Lean4, where effective tree search methods are crucial for navi…