2 citations · 3 across the 26 of their papers we have counts for
12 papers · 1 filter
GeoBalance: Geometry-Aware Monitoring and Reconstruction with Asymmetric Optimization for Balanced Multimodal Learning
Zechang Xiong, Da Li, Rong Yin +7
Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of others. Existing balancing meth…
Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities
Liangjie Zhao, Jiaqing Lyu, Kexin Tang +5
Large Vision Language Models have integrated reasoning capabilities, elevating cognitive performance to new levels. However, existing evaluations either focus solely on perception…
Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation
Guo Yu, Wenlin Liu, Yulan Hu +3
On-policy distillation (OPD) has recently become a prominent post-training recipe by combining two desirable ingredients: on-policy student-generated trajectories and dense token-l…
GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning
Xiang Cheng, Yulan Hu, Lulu Zheng +4
Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single user, where the field is approachin…
Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation
Lulu Zheng, Wenjin Yang, Xiangwen Zhang +4
Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility aggregation, yielding unstable…
STEPS: Selective On-Policy Self-Distillation for Reasoning
Jiaxuan Wang, Xuan Ouyang, Zhiyu Chen +4
On-policy self-distillation (OPSD) uses a model as its own teacher under privileged context, providing token-level supervision on the model's own reasoning trajectories. However, p…
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
Jiaxuan Wang, Yulan Hu, Wenjin Yang +3
In classical Reinforcement Learning from Human Feedback (RLHF), Reward Models (RMs) serve as the fundamental signal provider for model alignment. As Large Language Models evolve in…
Beyond Forced Modality Balance: Intrinsic Information Budgets for Multimodal Learning
Zechang Xiong, Da Li, Kexin Tang +3
Multimodal models often converge to a dominant-modality solution, in which a stronger, faster-converging modality overshadows weaker ones. This modality imbalance causes suboptimal…
MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
Ziqiao Shang, Ling-Yue Ge, Zian Xu +10
Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI). Yet existing benchmarks remain inadequate for r…
Learn More with Less: Uncertainty Consistency Guided Query Selection for RLVR
Hao Yi, Yulan Hu, Xin Li +3
Large Language Models (LLMs) have recently improved mathematical reasoning through Reinforcement Learning with Verifiable Reward (RLVR). However, existing RLVR algorithms require l…
AMAP Agentic Planning Technical Report
AMAP AI Agent Team, Yulan Hu, Xiangwen Zhang +22
We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and…
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning
Zhicong Li, Lingjie Jiang, Yulan Hu +7
Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with natural-language feedback. However,…