most citedMiMo-Audio: Audio Language Models are Few-Shot Learners

2 citations · 3 across the 5 of their papers we have counts for

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cs.CL2026

Reinforcement Learning for Chain of Thought Compression with One-Domain-to-All Generalization

Hanyu Li, Jiangshan Duo, Bofei Gao +4

Chain-of-thought reasoning in large language models can trigger an "overthinking trap": longer rollouts raise cost and latency yet often yield unreliable accuracy gains. Existing m…

cs.CL20261 cited

MiMo-V2-Flash Technical Report

Core Team, Bangjun Xiao, Bingquan Xia +123

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-…

cs.CL20252 cited

MiMo-Audio: Audio Language Models are Few-Shot Learners

Core Team, Dong Zhang, Gang Wang +97

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with…

cs.CL2025

Mitigating Overthinking through Reasoning Shaping

Feifan Song, Shaohang Wei, Bofei Gao +8

Large reasoning models (LRMs) boosted by Reinforcement Learning from Verifier Reward (RLVR) have shown great power in problem solving, yet they often cause overthinking: excessive,…

cs.CL2025

P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis

Feifan Song, Bofei Gao, Yifan Song +6

Large Language Models (LLMs) are expected to produce safe, helpful, and honest content during interaction with human users, but they frequently fail to align with such values when…