arXiv subjects

Jinkai Zhang

Publications and source records attributed to Jinkai Zhang.

8 recordsLinked to original sources

arXiv2026

Diffractive Sail H-Reversal Trajectory: Theoretical Feasibility, Design Strategies, and Applications

With the growing threat of near-Earth asteroid, planetary defense serves as a vital shield against catastrophic disasters. Kinetic impact utilizing an angular momentum reversal (H-reversal) trajectory of a solar sail is a highly advantageous defense approach. However, traditional reflective sails (RS) are constrained during these maneuvers by attitude-thrust coupling and a degradation of solar radiation pressure utilization at the high cone angles required for transverse acceleration. To enhance impact performance and simplify control, this paper proposes an H-reversal impact scheme utilizing a Sun-facing diffractive sail (SFDS) under a one-stage diffraction angle θd strategy and a two-stage θd strategy. The feasible parameter spaces for both strategies were mapped using the hodograph method. Tailored trajectory design methods were established for both strategies based on the feasibility analysis. Apophis impact scenario was considered, and the corresponding trajectories were constructed. Simulations demonstrate that the one-stage θd SFDS outperforms RS through a 21% increase in the impact velocity and a 35% decrease in the mission duration. Furthermore, the proposed two-stage θd strategy yields an additional 9km/s gain in impact velocity. By utilizing SFDS H-reversal trajectories, this research establishes an emergency planetary defense framework characterized by rapid response and high kinetic energy.

astro-ph.EP↗

arXiv2026

Diffractive-Sail Single-Impulse Reachable Set for Interplanetary Transfer Design

Interest in planetary exploration has renewed, and the design of interplanetary transfers has attracted remarkable attention. This paper considers the interplanetary transfer design using a diffractive sail. Considering a nonzero departure hyperbolic excess velocity, the interplanetary transfer problem is transformed into the problem of computing single-impulse reachable sets. Then, based on previous work, a complementary computational method for reachable sets under arbitrary dynamics is proposed using differential algebra combined with adaptive grid refinement. The adaptive grid refinement considers two types of merit scores that reveal dynamical properties and the truncation error of the differential algebra propagation. The proposed method is applied to compute the diffractive-sail reachable sets, and the results verify the effectiveness of the method and merit scores. Finally, a preliminary design of the interplanetary transfers, specified as the Earth-Mars transfers, is performed based on the diffractive-sail reachable sets. The design results are presented. The effects of the corresponding parameters, including transfer time, diffractive angle, and type of diffractive sails, on transfer characteristics are analyzed, providing further insight into parameter selection for interplanetary transfer design.

astro-ph.EP↗

arXiv2026

From Scaffolding to Internalization: Enhancing CPR Training with In-Situ Visualization and Kinesthetic Feedback

CPR training requires learners to not only understand explicit procedural targets, such as compression depth and rate, but also to internalize these targets as stable psychomotor skills. However, existing CPR training systems often rely on feedback presented outside the action space, which divides learners' attention between performing compressions and monitoring external guidance. This separation weakens the coupling between action and bodily sensation and may lead to an over-reliance on external feedback, compromising skill retention once support is removed. To address this challenge, we conducted a formative study with novice trainees and certified BLS instructors, from which we derived three design goals: embedding feedback within the task space, providing active kinesthetic guidance, and gradually fading assistance based on learning phases. Informed by these insights, we designed Kinesthetic-CPR, a stage-adaptive multimodal mixed reality CPR training system, and evaluated it in a controlled user study across two sub-studies (N = 60). This work offers design implications for CPR training systems that aim to better support skill retention.

cs.HC↗

arXiv2026

Reflective-Sail Weak Stability Boundary Structure with the Locally Optimal Control Law

Escaping from the Earth is the first step of interplanetary transfers. Traditional ballistic escape trajectories in the Sun-Earth circular restricted three-body problem face limitations in relatively long time of flight and low hyperbolic excess velocity. To augment the construction of escape trajectories from the Earth, this Note proposes the concept of reflective-sail weak stability boundary structures and accordingly constructs and analyzes escape trajectories from the Earth in the context of the Sun-Earth planar circular restricted three-body problem with a reflective sail. Using an ideal reflective sail, the locally optimal control law to maximize the time derivative of the Keplerian energy with respect to the Earth is adopted. Levi-Civita regularization about the Earth is derived to address the singularity caused by the Earth. The configurations of reflective-sail weak stability boundary structures are calculated to provide initial states for constructing escape trajectories and information about regions where escape is facilitated. Then, the escape trajectories using a reflective sail are constructed based on the proposed weak stability boundary structures. The escape performance, including time of flight and estimated hyperbolic excess velocity, is analyzed. Comparison with ballistic escape trajectories in the Sun-Earth PCR3BP is also performed, indicating improved escape performance characterized by shorter time of flight and higher hyperbolic excess velocity.

astro-ph.EP↗

arXiv2026

ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment

Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complicated training process. To address this, we propose a training-free inference method to sample directly from the optimal RL policy. The transition probability applied to Masked Language Modeling (MLM) consists of a reference policy model and an energy term. Based on this, our algorithm, Energy-Guided Test-Time Scaling (ETS), estimates the key energy term via online Monte Carlo, with a provable convergence rate. Moreover, to ensure practical efficiency, ETS leverages modern acceleration frameworks alongside tailored importance sampling estimators, substantially reducing inference latency while provably preserving sampling quality. Experiments on MLM (including autoregressive models and diffusion language models) across reasoning, coding, and science benchmarks show that our ETS consistently improves generation quality, validating its effectiveness and design. The code is available at https://github.com/sheriyuo/ETS.

cs.LG↗

arXiv2026

ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation

Long-horizon robotic manipulation requires dense feedback that reflects how a task advances through its procedural stages, not merely whether the final outcome is successful. Existing reward models often rely on trajectory-level success labels or time-based interpolation, which can conflate elapsed time with true task progress and therefore fail to capture unfinished steps, stagnation, and failure states. We present ProcVLM, a progress-aware vision-language model that learns procedure-grounded progress as a dense reward signal for manipulation. Rather than deriving progress from terminal outcomes or temporal proxies, ProcVLM grounds progress estimation in procedural structure and intra-stage visual change, and further adopts a reasoning-before-estimation paradigm that infers the remaining atomic actions before estimating task progress. Specifically, we construct this supervision by synthesizing frame-level subtask-semantic annotations, assigning progress budgets according to subtask structure, and distributing each budget based on intra-subtask visual change. To train ProcVLM at scale, we build a standardized procedural supervision synthesis pipeline and construct ProcCorpus-60M from 30 embodied datasets with 60M annotated frames, from which we derive ProcVQA for procedure-aware pretraining, with progress estimation as the central task alongside action segmentation and future planning. Experiments on ProcVQA and reward-model benchmarks show that ProcVLM improves embodied procedural reasoning and yields more discriminative trajectory-internal progress estimates than representative baselines, supporting its use as a dense reward model for downstream reward-guided policy optimization. Project page: https://procvlm.github.io/

cs.RO↗

arXiv2026

RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference

Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and bandwidth demands. This is because the key-value (KV) cache, an intermediate structure storing token representations, grows linearly with context length and requires an iterative linear scan for attention computation. A promising direction to accelerate long-context inference is to exploit attention's inherent sparsity by offloading the KV cache to CPU memory and retrieving only a small subset of tokens important to the current generation step. However, prior sparse attention approaches struggle to balance accuracy and retrieval cost due to varying sparsity patterns and inefficient GPU-CPU memory management. We present RetroInfer, a vector storage engine that realizes a sparsity-based KV cache for long-context inference. RetroInfer introduces an Attention-aWare VEctor index (wave index), which fundamentally improves the tradeoff between attention accuracy and retrieval cost through tripartite attention approximation, accuracy-bound attention estimation, and segmented clustering. We also design the wave buffer, a GPU-CPU buffer manager that assigns computation and manages data across heterogeneous hardware. We evaluate RetroInfer across a range of models and workloads, demonstrating up to 4.4X decoding throughput over full attention at 120K context and up to 12.2X over sparse attention baselines at 1 million tokens -- all while preserving full-attention-level accuracy.

cs.LG↗

arXiv2025

SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking

Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts. However, current LLM API-using methods struggle with complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM API-using methodology for academic information seeking. It uses code with a solution as the reasoning method, where a solution is a pre-constructed API calling sequence. The addition of the solution reduces the difficulty for the model to understand the complex relationships between APIs. Code improves the efficiency of reasoning. To evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99\% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://github.com/RUCKBReasoning/SoAy.

cs.CL↗