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Mingyuan Ma

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5 papers

2 author rows

ICML Conference 2025 Conference Paper

CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models

Qinsi Wang
Hancheng Ye
Ming-Yu Chung
Yudong Liu
Yueqian Lin
Martin Kuo
Mingyuan Ma
Jianyi Zhang

Vision-Language Models (VLMs) excel across diverse tasks but suffer from high inference costs in time and memory. Token sparsity mitigates inefficiencies in token usage, while neuron sparsity reduces high-dimensional computations, both offering promising solutions to enhance efficiency. Recently, these two sparsity paradigms have evolved largely in parallel, fostering the prevailing assumption that they function independently. However, a fundamental yet underexplored question remains: Do they truly operate in isolation, or is there a deeper underlying interplay that has yet to be uncovered? In this paper, we conduct the first comprehensive investigation into this question. By introducing and analyzing the matching mechanism between Core Neurons and Core Tokens, we found that key neurons and tokens for inference mutually influence and reinforce each other. Building on this insight, we propose CoreMatching, a co-adaptive sparse inference framework, which leverages the synergy between token and neuron sparsity to enhance inference efficiency. Through theoretical analysis and efficiency evaluations, we demonstrate that the proposed method surpasses state-of-the-art baselines on ten image understanding tasks and three hardware devices. Notably, on the NVIDIA Titan Xp, it achieved 5$\times$ FLOPs reduction and a 10$\times$ overall speedup. Code is released at https: //github. com/wangqinsi1/2025-ICML-CoreMatching/tree/main.

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NeurIPS Conference 2025 Conference Paper

KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems

Hancheng Ye
Zhengqi Gao
Mingyuan Ma
Qinsi Wang
Yuzhe Fu
Ming-Yu Chung
Yueqian Lin
Zhijian Liu

Multi-agent large language model (LLM) systems are increasingly adopted for complex language processing tasks that require communication and coordination among agents. However, these systems often suffer substantial overhead from repeated reprocessing of overlapping contexts across agents. In typical pipelines, once an agent receives a message from its predecessor, the full context-including prior turns-must be reprocessed from scratch, leading to inefficient processing. While key-value (KV) caching is an effective solution for avoiding redundant computation in single-agent settings where prefixes remain unchanged, it cannot be directly reused in multi-agent scenarios due to diverging prefixes introduced by agent-specific context extensions. We identify that the core challenge lies in the offset variance of KV-caches across agents. To address this, we propose KVCOMM, a training-free framework that enables efficient prefilling in multi-agent inference by reusing KV-caches and aligning cache offsets of overlapping contexts under diverse prefix contexts. KVCOMM estimates and adjusts KV-caches for shared content by referencing a pool of cached examples—termed anchors —that store observed cache deviations under varying prefixes. The anchor pool is maintained and updated online, allowing dynamic adaptation to distinct user requests and context structures. KVCOMM achieves over 70% reuse rate across diverse multi- agent workloads, including retrieval-augmented generation, math reasoning, and collaborative coding tasks, all without quality degradation. Particularly, when each fully-connected agent receives 1K input tokens with 512 prefix tokens and 512 output tokens under a five-agent setting, KVCOMM achieves up to 7. 8× speedup compared to the standard prefill pipeline, reducing TTFT from ∼430ms to ∼55ms. Code is available at https: //github. com/FastMAS/KVCOMM.

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IROS Conference 2025 Conference Paper

Multi-Cali Anything: Dense Feature Multi-Frame Structure-from-Motion for Large-Scale Camera Array Calibration

Jinjiang You
Hewei Wang 0001
Yijie Li 0003
Mingxiao Huo
Long Van Tran Ha
Mingyuan Ma
Jinfeng Xu 0003
Jiayi Zhang

Calibrating large-scale camera arrays, such as those in dome-based setups, is time-intensive and typically requires dedicated captures of known patterns. While extrinsics in such arrays are fixed due to the physical setup, intrinsics often vary across sessions due to factors like lens adjustments or temperature changes. In this paper, we propose a dense-feature-driven multi-frame calibration method that refines intrinsics directly from scene data, eliminating the necessity for additional calibration captures. Our approach enhances traditional Structure-from-Motion (SfM) pipelines by introducing an extrinsics regularization term to progressively align estimated extrinsics with ground-truth values, a dense feature reprojection term to reduce keypoint errors by minimizing reprojection loss in the feature space, and an intrinsics variance term for joint optimization across multiple frames. Experiments on the Multiface dataset show that our method achieves nearly the same precision as dedicated calibration processes, and significantly enhances intrinsics and 3D reconstruction accuracy. Fully compatible with existing SfM pipelines, our method provides an efficient and practical plug-and-play solution for large-scale camera setups. Our code is publicly available at: https://github.com/YJJfish/Multi-Cali-Anything

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ICLR Conference 2025 Conference Paper

Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solver

Zhenting Qi
Mingyuan Ma
Jiahang Xu
Li Lyna Zhang
Fan Yang 0024
Mao Yang 0004

This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self-play mutual generation-discrimination process. First, a target SLM augments the Monte Carlo Tree Search (MCTS) with a rich set of human-like reasoning actions to construct higher quality reasoning trajectories. Next, another SLM, with capabilities similar to the target SLM, acts as a discriminator to verify each trajectory generated by the target SLM. The mutually agreed reasoning trajectories are considered mutual consistent, thus are more likely to be correct. Extensive experiments across five SLMs demonstrate rStar can effectively solve diverse reasoning problems, including GSM8K, GSM-Hard, MATH, SVAMP, and StrategyQA. Remarkably, rStar boosts GSM8K accuracy from 12.51\% to 63.91\% for LLaMA2-7B, from 36.46\% to 81.88\% for Mistral-7B, from 74.53\% to 91.13\% for LLaMA3-8B-Instruct. Code is available at https://github.com/zhentingqi/rStar.

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TCS Journal 2022 Journal Article

Total coloring of recursive maximal planar graphs

Yangyang Zhou
Dongyang Zhao
Mingyuan Ma
Jin Xu

The recursive maximal planar graphs can be obtained from K 4, by embedding a 3-vertex in a triangular face continuously. A total k-coloring of a graph G is a coloring of its vertices and edges such that no two adjacent or incident elements receive the same color. The Total Coloring Conjecture, in short, TCC, states that every simple graph G is totally ( Δ + 2 ) -colorable, where Δ is the maximum degree of G. In this paper, we prove that TCC holds for recursive maximal planar graphs, especially, a main class of recursive maximal planar graphs, named (2, 2)-recursive maximal planar graphs, are totally ( Δ + 1 ) -colorable. Moreover, we give linear time algorithms for total coloring of recursive maximal planar graphs and (2, 2)-recursive maximal planar graphs, respectively.

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