1 citations · 2 across the 18 of their papers we have counts for
19 papers
One Recipe, Many Harnesses: What Self-Evolution Encodes Across Languages and Models
Siqi Yang, Qianlan Yang, Yu-Xiong Wang +2
Self-evolving harnesses are closed-loop systems in which an agent inspects its own rollouts and edits its prompts, tools, and memory. They reliably improve coding agents in evaluat…
Path-Coupled Bellman Flows for Distributional Reinforcement Learning
Boyang Xu, Qing Zou, Siqin Yang +1
Distributional reinforcement learning (DRL) models the full return distribution, but existing finite-support or quantile-based methods rely on projections, while recent flow-based…
Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence
Xuanle Zhao, Qiushi Sun, Jingyu Xiao +16
While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual artifacts such as screenshots, cha…
Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
Hanxuan Chen, Jie Zheng, Siqi Yang +9
Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high…
Latent Wasserstein Adversarial Imitation Learning
Siqi Yang, Kai Yan, Alexander G. Schwing +1
Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality dem…
ProofVerifier: A Scalable, Diversity-Driven Framework for Natural-Language Proof Verification
Haotong Yang, Zitong Wang, Shijia Kang +7
While large language models (LLMs) have achieved strong performance on mathematical problems with verifiable answers, many advanced problems are proof-based and require evaluating…
Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR
Fanfan Liu, Youyang Yin, Peng Shi +3
Recent applications of Reinforcement Learning with Verifiable Rewards (RLVR) to Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated significant success…
Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective
Kun Chen, Peng Shi, Fanfan Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Language Models (LLMs). However, continuous…
V-FAT: Benchmarking Visual Fidelity Against Text-bias
Ziteng Wang, Yujie He, Guanliang Li +3
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive performance on standard visual reasoning benchmarks. However, there is growing concern…
Learning When to Look: A Disentangled Curriculum for Strategic Perception in Multimodal Reasoning
Siqi Yang, Zilve Gao, Haibo Qiu +5
Multimodal Large Language Models (MLLMs) demonstrate significant potential but remain brittle in complex, long-chain visual reasoning tasks. A critical failure mode is "visual forg…
Reading or Reasoning? Format Decoupled Reinforcement Learning for Document OCR
Yufeng Zhong, Lei Chen, Zhixiong Zeng +8
Reading text from images or scanned documents via OCR models has been a longstanding focus of researchers. Intuitively, text reading is perceived as a straightforward perceptual ta…
Metis-HOME: Hybrid Optimized Mixture-of-Experts for Multimodal Reasoning
Xiaohan Lan, Fanfan Liu, Haibo Qiu +4
Inspired by recent advancements in LLM reasoning, the field of multimodal reasoning has seen remarkable progress, achieving significant performance gains on intricate tasks such as…
Perceptual-Evidence Anchored Reinforced Learning for Multimodal Reasoning
Chi Zhang, Haibo Qiu, Qiming Zhang +6
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs) and is now being applied to Vision-Langu…
Metis-SPECS: Decoupling Multimodal Learning via Self-distilled Preference-based Cold Start
Kun Chen, Peng Shi, Haibo Qiu +4
Reinforcement learning (RL) with verifiable rewards has recently catalyzed a wave of "MLLM-r1" approaches that bring RL to vision language models. Most representative paradigms beg…
STAGE: Stable and Generalizable GRPO for Autoregressive Image Generation
Xiaoxiao Ma, Haibo Qiu, Guohui Zhang +4
Reinforcement learning has recently been explored to improve text-to-image generation, yet applying existing GRPO algorithms to autoregressive (AR) image models remains challenging…
VITRIX-CLIPIN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions
Ziteng Wang, Siqi Yang, Limeng Qiao +1
Despite the success of Vision-Language Models (VLMs) like CLIP in aligning vision and language, their proficiency in detailed, fine-grained visual comprehension remains a key chall…
DocTron-Formula: Generalized Formula Recognition in Complex and Structured Scenarios
Yufeng Zhong, Zhixiong Zeng, Lei Chen +5
Optical Character Recognition (OCR) for mathematical formula is essential for the intelligent analysis of scientific literature. However, both task-specific and general vision-lang…
UniViTAR: Unified Vision Transformer with Native Resolution
Limeng Qiao, Yiyang Gan, Bairui Wang +4
Conventional Vision Transformer simplifies visual modeling by standardizing input resolutions, often disregarding the variability of natural visual data and compromising spatial-co…
A Method for Parsing and Vectorization of Semi-structured Data used in Retrieval Augmented Generation
Hang Yang, Jing Guo, Jianchuan Qi +5
This paper presents a novel method for parsing and vectorizing semi-structured data to enhance the functionality of Retrieval-Augmented Generation (RAG) within Large Language Model…