148 citations · 708 across the 141 of their papers we have counts for
148 papers
What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies
Duo Li, Haojun Pan, Zuhao Yang +4
Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating mismatched ones in a shared embed…
IndustryLLM: Failure-Driven LLM Training for Industrial Procurement
Liang Ding, Zhiang Xu, Yuyang Sheng +13
Industrial procurement requires language models to bridge informal buyer jargon, sparse marketplace attributes, and authoritative engineering standards under strict safety toleranc…
Incorporating Cognitive Load and Knowledge Transfer for Multi-Domain Knowledge Tracing
Haotian Zhang, Shucun Wang, Jinze Wu +6
Knowledge Tracing (KT) aims to assess students' dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable…
OmniPack: Unified Token Compression for Efficient Omni-modal Large Language Models
Wanshun Su, Yang Shi, Feihu Liu +10
Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly redundant visual and audio tok…
Diagnosing and Mitigating Thinking Collapse in On-Policy Self-Distillation
Keqin Peng, Chen Li, Yuanxin Ouyang +2
On-Policy Self-Distillation (OPSD) has emerged as a crucial paradigm for enhancing and aligning Large Language Models (LLMs). However, in complex reasoning tasks, OPSD paradoxicall…
Grounded Scaling: Why Agentic AI Needs Deterministic Environments
Liang Ding, Xintong Wang
Long-chain agent execution fails exponentially in environments designed for human tolerance: with per-step determinism , -step chain success degrades as . The AGI-to-…
Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports
Qingyu Lu, Ruochen Li, Liang Ding +3
Reliable evaluation of generated radiology reports requires strict clinical accuracy, as omitted critical findings or mischaracterized radiographic observations can directly affect…
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Haonan Qi, Jin Cao, Yongqi Zhang +9
Industrial products such as valves and circuit breakers are defined by dense technical specifications that govern procurement, compatibility, and safety across supply chains. These…
ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
Zheng Liu, Longxiang Zhang, Xintong Wang +8
LLM-based search agents are trained predominantly with outcome-only reward, leaving the search process itself unsupervised. This signal degenerates on outcome-homogeneous groups wh…
Heterogeneous Parallelism for Multimodal Large Language Model Training
Yashaswi Karnati, Kamran Jafari, Akash Mehra +10
Foundation model training is becoming multimodal, from post-training pipelines to large-scale pretraining. As modality coverage broadens, context windows grow, and encoder LLM scal…
VIDA: A Dataset for Visually Dependent Ambiguity in Multimodal Machine Translation
Jingheng Pan, Xintong Wang, Longyue Wang +3
Ambiguity resolution is a key challenge in multimodal machine translation (MMT), where models must genuinely leverage visual input to map an ambiguous expression to its intended me…
Better, Faster: Harnessing Self-Improvement in Large Reasoning Models
Qihuang Zhong, Liang Ding, Juhua Liu +3
Self-improvement training enables the large reasoning models (LRMs) to improve themselves by self-generating reasoning trajectories as training data without external supervision. H…
IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs
Songlin Bai, Xintong Wang, Linlin Yu +12
In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regula…
Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
Qihuang Zhong, Liang Ding, Wenjie Xuan +3
Post-training with explicit reasoning traces is common to improve the reasoning capabilities of Multimodal Large Language Models (MLLMs). However, acquiring high-quality reasoning…
Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
Fei Wang, Li Shen, Liang Ding +3
Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. However, its practical adoption…
Learning to Control Summaries with Score Ranking
Hongye Liu, Liang Ding, Ricardo Henao
Recent advances in summarization research focus on improving summary quality across multiple criteria, such as completeness, conciseness, and faithfulness, by jointly optimizing th…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
AdaRubric: Task-Adaptive Rubrics for Reliable LLM Agent Evaluation and Reward Learning
Liang Ding
Evaluating LLM agent trajectories is fundamentally task-specific: a code-debugging agent should be judged on Correctness and Error Handling, not on Fluency or Safety. Yet the domin…
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling
Liang Ding
LLM-agent training pipelines routinely discard failed trajectories even though GPT-4o achieves only 14-20% on WebArena and below 55% pass@1 on ToolBench; even specialised systems a…
Think Dense, Not Long: Dynamic Decoupled Conditional Advantage for Efficient Reasoning
Keqin Peng, Yuanxin Ouyang, Xuebo Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) can elicit strong multi-step reasoning, yet it often encourages overly verbose traces. Moreover, naive length penalties in gro…
MASRubric: Auditing Information Flow in Multi-Agent Systems with Failure-Distilled Pitfall Rubrics
Yutong Wang, Siyuan Xiong, Xuebo Liu +4
While multi-agent systems (MAS) excel at complex reasoning, they are vulnerable to errors that intermediate agents introduce and downstream agents build upon. Auditing intermediate…
The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check
Qingyu Lu, Liang Ding, Kanjian Zhang +2
The pursuit of real-time agentic interaction has driven interest in Diffusion-based Large Language Models (dLLMs) as alternatives to auto-regressive backbones, promising to break t…
EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce
Rui Min, Zile Qiao, Ze Xu +18
Foundation agents have rapidly advanced in their ability to reason and interact with real environments, making the evaluation of their core capabilities increasingly important. Whi…
REVEALER: Reinforcement-Guided Visual Reasoning for Element-Level Text-Image Alignment Evaluation
Fulin Shi, Wenyi Xiao, Bin Chen +2
Evaluating the alignment between textual prompts and generated images is critical for ensuring the reliability and usability of text-to-image (T2I) models. However, most existing e…