8 citations · 23 across the 57 of their papers we have counts for
62 papers
RoLaQ: Joint Rotation and Range Calibration for 2-Bit KV Caches in Multi-Head Latent Attention
Shiyu Ji, Peng Ling, Chen Lin +4
Multi-head latent attention (MLA) compresses the content of a key-value cache into a shared latent per token, making low-bit storage attractive for long-context inference. At two b…
FormalTCS: Benchmarking End-to-End Frontier Formal Theoretical Computer Science Research of Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far from realistic research sett…
SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL
Keyan Xu, Dingzirui Wang, Xuanliang Zhang +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While memory-based agent system improves complex SQL generation, existing memory design neglect…
CurateEvo: Data-Curation Evolving for Agentic Post-Training
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pip…
Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards
Tianhao Niu, Ziyu Han, Qiguang Chen +5
Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactiv…
What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation
Tianhao Niu, Qingfu Zhu, Wanxiang Che
Chart-to-code generation is commonly trained through supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. However,…
From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape
Hao Chen, Ziyu Han, Yukun Yan +3
As Large Language Models (LLMs) advance toward open-ended autonomous agents, the mechanisms used to evaluate and guide their behavior must evolve accordingly. This work introduces…
Scaling Laws for Agent Harnesses via Effective Feedback Compute
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wal…
When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models
Xuan Dong, Zhe Han, Tianhao Niu +2
Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understo…
ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring
Tianhao Niu, Ziyu Han, Xuan Dong +2
Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision and language models, yet most p…
EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents
Xuan Dong, Huanyang Zheng, Tianhao Niu +7
Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across paper…
EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction
Shiyu Ji, Yixuan Wang, Yijun Liu +2
The increasing memory demand of the Key-Value (KV) cache poses a significant bottleneck for Large Language Models (LLMs) in long-context applications. Existing low-rank KV compress…
How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
While Large Language Models (LLMs) are increasingly deployed for table-related tasks, the internal mechanisms enabling them to process linearized two-dimensional structured tables…
When Does Context Help? Error Dynamics of Contextual Information in Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Contextual information at inference time, such as demonstrations, retrieved knowledge, or interaction history, can substantially improve large language models (LLMs) without parame…
Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
Hao Chen, Ye He, Yuchun Fan +5
Knowledge augmentation has significantly enhanced the performance of Large Language Models (LLMs) in knowledge-intensive tasks. However, existing methods typically operate on the s…
CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability
Xianzhen Luo, Jingyuan Zhang, Shiqi Zhou +8
Evaluating and improving the security capabilities of code agents requires high-quality, executable vulnerability tasks. However, existing works rely on costly, unscalable manual r…
MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
Chuanzhe Guo, Jingjing Wu, Sijun He +10
The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity o…
Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling
Shiyu Ji, Yixuan Wang, Yijun Liu +2
Test-time scaling improves the inference performance of Large Language Models (LLMs) but also incurs substantial computational costs. Although recent studies have reduced token con…
Automated Snippet-Alignment Data Augmentation for Code Translation
Zhiming Zhang, Qingfu Zhu, Xianzhen Luo +3
Code translation aims to translate the code from its source language to the target language and is used in various software development scenarios. Recent developments in Large Lang…
How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective
Xianzhen Luo, Jinyang Huang, Wenzhen Zheng +5
Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, u…
Scaling Laws for Code: A More Data-Hungry Regime
Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu +5
Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantly analyzed on Natural Language (…
Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Existing research indicates that the output of Chain-of-Thought (CoT) is significantly affected by input perturbations. Although many methods aim to mitigate such impact by optimiz…
ProxyAttn: Guided Sparse Attention via Representative Heads
Yixuan Wang, Huang He, Siqi Bao +4
The quadratic complexity of attention mechanisms limits the efficiency of Large Language Models (LLMs) on long-text tasks. Recently, methods that dynamically estimate block importa…
Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction
Yijun Liu, Yixuan Wang, Yuzhuang Xu +4
Large language models (LLMs) utilize key-value (KV) cache to store historical information during sequence processing. The size of KV cache grows linearly as the length of the seque…