arXiv subjects

Donghao Wu

Publications and source records attributed to Donghao Wu.

5 recordsLinked to original sources

arXiv2026

Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models. At industrial scale, embedding tables constituted by such vectors of all users/items demand a vast amount of parameters and impose heavy compute and memory overhead during training and inference, hindering model deployment under resource constraints. Existing solutions towards embedding compression either suffer from severely compromised recommendation accuracy or incur considerable computational costs. To mitigate these issues, this paper presents BACO, a fast and effective framework for compressing embedding tables. Unlike traditional ID hashing, BACO is built on the idea of exploiting collaborative signals in user-item interactions for user and item groupings, such that similar users/items share the same embeddings in the codebook. Specifically, we formulate a balanced co-clustering objective that maximizes intra-cluster connectivity while enforcing cluster-volume balance, and unify canonical graph clustering techniques into the framework through rigorous theoretical analyses. To produce effective groupings while averting codebook collapse, BACO instantiates this framework with a principled weighting scheme for users and items, an efficient label propagation solver, as well as secondary user clusters. Our extensive experiments comparing BACO against full models and 18 baselines over benchmark datasets demonstrate that BACO cuts embedding parameters by over 75% with a drop of at most 1.85% in recall, while surpassing the strongest baselines by being up to 346X faster.

cs.IR↗

arXiv2026

Efficient Coordination with the System-Level Shared State: An Embodied-AI Native Modular Framework

As Embodied AI systems move from research prototypes to real world deployments, they tend to evolve rapidly while remaining reliable under workload changes and partial failures. In practice, many deployments are only partially decoupled: middleware moves messages, but shared context and feedback semantics are implicit, causing interface drift, cross-module interference, and brittle recovery at scale. We present ANCHOR, a modular framework that makes decoupling and robustness explicit system-level primitives. ANCHOR separates (i) Canonical Records, an evolvable contract for the standardized shared state, from (ii) a communication bus for many-to-many dissemination and feedback-oriented coordination, forming an inspectable end-to-end loop. We validate closed-loop feasibility on a de-identified workflow instantiation, characterize latency distributions under varying payload sizes and publish rates, and demonstrate automatic stream resumption after hard crashes and restarts even with shared-memory loss. Overall, ANCHOR turns ad-hoc integration glue into explicit contracts, enabling controlled degradation under load and self-healing recovery for scalable deployment of closed-loop AI systems.

cs.RO↗

arXiv2025

EmbodiedAgent: A Scalable Hierarchical Approach to Overcome Practical Challenge in Multi-Robot Control

This paper introduces EmbodiedAgent, a hierarchical framework for heterogeneous multi-robot control. EmbodiedAgent addresses critical limitations of hallucination in impractical tasks. Our approach integrates a next-action prediction paradigm with a structured memory system to decompose tasks into executable robot skills while dynamically validating actions against environmental constraints. We present MultiPlan+, a dataset of more than 18,000 annotated planning instances spanning 100 scenarios, including a subset of impractical cases to mitigate hallucination. To evaluate performance, we propose the Robot Planning Assessment Schema (RPAS), combining automated metrics with LLM-aided expert grading. Experiments demonstrate EmbodiedAgent's superiority over state-of-the-art models, achieving 71.85% RPAS score. Real-world validation in an office service task highlights its ability to coordinate heterogeneous robots for long-horizon objectives.

cs.RO↗

arXiv2025

GenTe: Generative Real-world Terrains for General Legged Robot Locomotion Control

Developing bipedal robots capable of traversing diverse real-world terrains presents a fundamental robotics challenge, as existing methods using predefined height maps and static environments fail to address the complexity of unstructured landscapes. To bridge this gap, we propose GenTe, a framework for generating physically realistic and adaptable terrains to train generalizable locomotion policies. GenTe constructs an atomic terrain library that includes both geometric and physical terrains, enabling curriculum training for reinforcement learning-based locomotion policies. By leveraging function-calling techniques and reasoning capabilities of Vision-Language Models (VLMs), GenTe generates complex, contextually relevant terrains from textual and graphical inputs. The framework introduces realistic force modeling for terrain interactions, capturing effects such as soil sinkage and hydrodynamic resistance. To the best of our knowledge, GenTe is the first framework that systemically generates simulation environments for legged robot locomotion control. Additionally, we introduce a benchmark of 100 generated terrains. Experiments demonstrate improved generalization and robustness in bipedal robot locomotion.

cs.RO↗

arXiv2018

Past and future of the central double-degenerate core of Henize 2-428

It has been suggested that SNe Ia could be produced in the condition of the violent merger scenario of the double-degenerate model, in which a thermonuclear explosion could be produced when the merging of double carbon-oxygen white dwarfs (CO WDs) is still ongoing. It has been recently found that the nucleus of the bipolar planetary nebula Henize 2-428 consists of double CO WDs that have a total mass of ~1.76Msun, a mass ratio of ~1 and an orbital period of ~4.2 hours, which is the first and only discovered progenitor candidate of SNe Ia predicted by the violent merger scenario. In this work, we aim to reproduce the evolutionary history of the central double CO WDs of Henize 2-428. We find that the planetary nebula Henize 2-428 may originate from a primordial binary that have a ~5.4Msun primary and a ~2.7Msun secondary with an initial orbital period of ~15.9 days. The double CO WDs are formed after the primordial binary experiencing two Roche-lobe overflows and two common-envelope ejection processes. According to our calculations, it takes about ~840 Myr for the double CO WDs to merge and form an SN Ia driven by the gravitational wave radiation after their birth. To produce the current status of Henize 2-428, a large common-envelope parameter is needed. We also estimate that the rate of SNe Ia from the violent merger scenario is at most 2.9*10-4 yr-1, and that the delay time is in the range of ~90 Myr to the Hubble time.

astro-ph.SR↗