29 citations · 31 across the 32 of their papers we have counts for
10 papers · 1 filter
Instella-MoE Technical Report
Jiang Liu, Sudhanshu Ranjan, Prakamya Mishra +10
In this work, we introduce Instella-MoE, a fully open Mixture-of-Experts (MoE) language model with 16 billion total parameters and 2.8 billion active parameters per token, trained…
AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection
Pretam Ray, Pratik Prabhanjan Brahma, Zicheng Liu +1
Evolutionary agentic systems intensify the trade-off between computational efficiency and reasoning capability by repeatedly invoking large language models (LLMs) during inference.…
Reliable Use of Lemmas via Eligibility Reasoning and SectionAware Reinforcement Learning
Zhikun Xu, Xiaodong Yu, Ben Zhou +6
Recent large language models (LLMs) perform strongly on mathematical benchmarks yet often misapply lemmas, importing conclusions without validating assumptions. We formalize lemma$…
CD4LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models
Yihao Liang, Ze Wang, Hao Chen +7
Autoregressive large language models achieve strong results on many benchmarks, but decoding remains fundamentally latency-limited by sequential dependence on previously generated…
Instella: Fully Open Language Models with Stellar Performance
Jiang Liu, Jialian Wu, Xiaodong Yu +10
Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially ope…
Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks
Jianghui Wang, Vinay Joshi, Saptarshi Majumder +7
The demand for AI-generated GPU kernels is rapidly growing, influenced by the need for scalable, hardware-optimized solutions in both industry and academia. As deep learning worklo…
SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers
Chaitanya Manem, Pratik Prabhanjan Brahma, Prakamya Mishra +2
The demand for Large Language Models (LLMs) at multiple scales, capable of sophisticated and sound mathematical reasoning, continues to grow. However, the development of performant…
TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games
Prakamya Mishra, Jiang Liu, Jialian Wu +3
Large reasoning models (LRMs) have demonstrated impressive reasoning capabilities across a broad range of tasks including Olympiad-level mathematical problems, indicating evidence…
TaDA: Training-free recipe for Decoding with Adaptive KV Cache Compression and Mean-centering
Vinay Joshi, Pratik Prabhanjan Brahma, Zicheng Liu +1
The key-value (KV) cache in transformer models is a critical component for efficient decoding or inference, yet its memory demands scale poorly with sequence length, posing a major…
Self-Taught Agentic Long Context Understanding
Yufan Zhuang, Xiaodong Yu, Jialian Wu +7
Answering complex, long-context questions remains a major challenge for large language models (LLMs) as it requires effective question clarifications and context retrieval. We prop…