2 citations · 4 across the 16 of their papers we have counts for
17 papers
Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning
Yixin Ji, Fanghua Ye, Juntao Li +5
Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing…
LongFlow: Efficient KV Cache Compression for Reasoning Models
Yi Su, Zhenxu Tian, Dan Qiao +3
Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and code generation. However, this perform…
When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
Zhimin Lin, Yixin Ji, Jinpeng Li +5
Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable on challenging instances. Existing test-time scaling methods, such as…
Where Matters More Than What: Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries
Zhenxu Tian, Yi Su, Juntao Li +1
The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV cache memory footprint. Existing KV…
MemoryRewardBench: Benchmarking Reward Models for Long-Term Memory Management in Large Language Models
Zecheng Tang, Baibei Ji, Ruoxi Sun +7
Existing works increasingly adopt memory-centric mechanisms to process long contexts in a segment manner, and effective memory management is one of the key capabilities that enable…
Think Before You Prune: Selective Self-Generated Calibration for Pruning Large Reasoning Models
Yang Xiang, Yixin Ji, Juntao Li +1
Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex reasoning benchmarks. However, their long chain-of-thought reasoning processes incur significant i…
Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective
Wangjie You, Xusheng Wang, Xing Wang +4
While Large Language Models (LLMs) have demonstrated advanced reasoning capabilities, their comprehensive evaluation in general Chinese-language contexts remains understudied. To b…
FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning
Yuyang Ding, Chi Zhang, Juntao Li +2
Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models (LLMs). In this context,…
Efficient Reasoning for LLMs through Speculative Chain-of-Thought
Jikai Wang, Juntao Li, Jianye Hou +3
Large reasoning language models such as OpenAI-o1 and Deepseek-R1 have recently attracted widespread attention due to their impressive task-solving abilities. However, the enormous…
A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning
Yixin Ji, Juntao Li, Yang Xiang +6
The remarkable performance of the o1 model in complex reasoning demonstrates that test-time compute scaling can further unlock the model's potential, enabling powerful System-2 thi…
Revealing and Mitigating the Local Pattern Shortcuts of Mamba
Wangjie You, Zecheng Tang, Juntao Li +2
Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-co…
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
Zecheng Tang, Keyan Zhou, Juntao Li +3
Long-context models (LCMs) have made remarkable strides in recent years, offering users great convenience for handling tasks that involve long context, such as document summarizati…
MemLong: Memory-Augmented Retrieval for Long Text Modeling
Weijie Liu, Zecheng Tang, Juntao Li +2
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs…
Demonstration Augmentation for Zero-shot In-context Learning
Yi Su, Yunpeng Tai, Yixin Ji +3
Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations with…
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
Yixin Ji, Yang Xiang, Juntao Li +6
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating…
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
Zecheng Tang, Kaifeng Qi, Juntao Li +1
Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial e…
G-SPEED: General SParse Efficient Editing MoDel
Haoke Zhang, Yue Wang, Juntao Li +2
Large Language Models~(LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages. Through human-model interactions, LLMs can automati…