5 citations · 11 across the 14 of their papers we have counts for
16 papers
Graph Sensitivity of Cartesian Products with Matched Bridges
Zhen-Mu Hong, Zi-Yi Wu, Zheng-Jiang Xia
For a graph , let denote the minimum of the maximum degree of an induced subgraph with vertices, where is the independence number, and write $f(G)=f_1(G…
OPD Before RL: Warm-Starting Rubric-Based RL with On-Policy Distillation
Xinpeng Wang, Wei Shi, Yu-Chia Chen +3
Many useful language-model tasks cannot be evaluated by exact outcome verification. Rubric-based reinforcement learning (RL) addresses this issue by scoring open-ended responses ag…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+
Sherzod Hakimov, Karl Osswald, Jelle Psurek +3
We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU languages plus six others. Unl…
Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
Tong Zheng, Chengsong Huang, Runpeng Dai +9
Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-traj…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
A Unified 3D Object Perception Framework for Real-Time Outside-In Multi-Camera Systems
Yizhou Wang, Sameer Pusegaonkar, Yuxing Wang +10
Accurate 3D object perception and multi-target multi-camera (MTMC) tracking are fundamental for the digital transformation of industrial infrastructure. However, transitioning "ins…
LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents
Yun He, Francesco Pittaluga, Ziyu Jiang +3
LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D…
AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
Yun He, Wenzhe Li, Hejia Zhang +22
Recent progress in large language models (LLMs) has led to impressive performance on a range of tasks, yet advanced instruction following (IF)-especially for complex, multi-turn, a…
Bradley-Terry Policy Optimization for Generative Preference Modeling
Shengyu Feng, Yun He, Shuang Ma +12
Reinforcement learning (RL) has recently proven effective at scaling chain-of-thought (CoT) reasoning in large language models for tasks with verifiable answers. However, extending…
Generalized Parallel Scaling with Interdependent Generations
Harry Dong, David Brandfonbrener, Eryk Helenowski +5
Parallel LLM inference scaling involves sampling a set of responses for a single input prompt. However, these parallel responses tend to be generated independently from e…
Boosting LLM Reasoning via Spontaneous Self-Correction
Xutong Zhao, Tengyu Xu, Xuewei Wang +11
While large language models (LLMs) have demonstrated remarkable success on a broad range of tasks, math reasoning remains a challenging one. One of the approaches for improving mat…
Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization
Zishun Yu, Tengyu Xu, Di Jin +9
Solving mathematics problems has been an intriguing capability of large language models, and many efforts have been made to improve reasoning by extending reasoning length, such as…
Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback
Yen-Ting Lin, Di Jin, Tengyu Xu +11
Large language models (LLMs) have recently demonstrated remarkable success in mathematical reasoning. Despite progress in methods like chain-of-thought prompting and self-consisten…
Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
Yun He, Di Jin, Chaoqi Wang +16
Large Language Models (LLMs) have demonstrated impressive capabilities in various tasks, including instruction following, which is crucial for aligning model outputs with user expe…
The Perfect Blend: Redefining RLHF with Mixture of Judges
Tengyu Xu, Eryk Helenowski, Karthik Abinav Sankararaman +17
Reinforcement learning from human feedback (RLHF) has become the leading approach for fine-tuning large language models (LLM). However, RLHF has limitations in multi-task learning…