A two-stage alignment framework that first fuses visual modalities (RGB, flow, skeleton) then introduces text, achieving 21% SRCC improvement on a new clinical AQA dataset and gains on two public benchmarks.
Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
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abstract
Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated translations. In this paper, we introduce Seed-X, a family of open-source LLMs comprising instruct and reasoning models, pushing the limits of translation capability with 7B parameter size. The base model is pre-trained on a diverse, high-quality dataset encompassing both monolingual and bilingual content across 28 languages, harnessing the full potential of multilingual data. The instruct model is then finetuned to translate by Chain-of-Thought (CoT) reasoning and further enhanced through reinforcement learning (RL) to achieve better generalization across diverse language pairs. Seed-X achieves performance comparable to leading closed-source models, including Gemini-2.5 and GPT-4o, across 28 languages, and significantly outperforms larger open-source models in both automatic metrics and human evaluations. We share the best practices through our optimization process, and make the parameter public available for advancing translation research and applications.
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Token pruning of non-Korean vocabulary in LLMs improves generation stability and often boosts machine translation on Korean tasks while cutting vocabulary size substantially.
GLM-4.5, a 355B-parameter MoE model with hybrid reasoning, scores 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified while ranking 3rd overall and 2nd on agentic benchmarks.
citing papers explorer
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Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment
A two-stage alignment framework that first fuses visual modalities (RGB, flow, skeleton) then introduces text, achieving 21% SRCC improvement on a new clinical AQA dataset and gains on two public benchmarks.
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Optimizing Korean-Centric LLMs via Token Pruning
Token pruning of non-Korean vocabulary in LLMs improves generation stability and often boosts machine translation on Korean tasks while cutting vocabulary size substantially.
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GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
GLM-4.5, a 355B-parameter MoE model with hybrid reasoning, scores 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified while ranking 3rd overall and 2nd on agentic benchmarks.