arXiv subjects

Sheng Wang

Publications and source records attributed to Sheng Wang.

3 recordsLinked to original sources

arXiv2026

LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory

Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose LookStep, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7\% success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at https://github.com/kunyang-YU/LookStep.

cs.CV↗

arXiv2026

CacheBridge: Efficient Cross-Model KV Cache Transfer

Sharing context between LLMs in a multi-model system requires the receiving model to prefill the shared prefix because KV caches are model-specific. Recent closed-form cross-model KV transfer, hereafter Full-Head Mapping, avoids this replay by fitting a training-free affine mapper from source to target caches. However, its full-head design maps each target KV head from every source KV head in the selected layers, making transfer quality sensitive to architectural differences and causing mapper storage and application cost to grow with layer support. To this end, we introduce CacheBridge, which co-designs architecture-indexed mapper support, attention-aligned calibration, and bounded mapper construction while retaining a closed-form affine interface for online deployment. CacheBridge restricts each target head to a matched source head, weights reconstruction errors by causal attention sensitivity, and uses a fused GPU kernel to construct weighted sufficient statistics without materializing full observation tensors. Across three transfer directions, CacheBridge recovers the two Ministral 3 transfer directions where Full-Head Mapping loses substantial accuracy while preserving 99.83\% mean target retention on Qwen3. On Qwen3 $14\mathrm{B}\to32\mathrm{B}$, it reduces mapper storage by $8\times$, accelerates application by up to $3.0\times$, matches \fullhead with one tenth of the calibration data, and reduces 500-sequence construction from 92.63 to 8.63 seconds ($10.7\times$).

cs.AI↗

arXiv2026

GRBench: A Comprehensive Benchmark Evaluation for Graph-relational Data Management

Modern data-intensive applications increasingly require database systems to manage structured records and graph data. This demand gives rise to graph-relational data management, spanning storage, query processing, and optimization across relational and graph data. In response, relational database extensions, multi-model databases, and dedicated graph-relational systems have emerged with diverse architectures. However, evaluation methodologies have not kept pace. Existing relational and graph benchmarks assess the two models largely in isolation, while multi-model benchmarks provide limited coverage of graph-relational workloads. Available graph-relational workloads mainly support functional validation and end-to-end latency measurement, revealing little about how storage, operator, and optimization designs affect performance. To evaluate system capabilities in graph-relational data management, we present GRBench. First, GRBench constructs a linked graph-relational schema from the real-world SciSciNet-v2 dataset and derives scalable instances through consistency-preserving subset extraction. Second, it organizes purpose-built query series for controlled evaluation of query processing and system components. Third, GRBench provides semantically equivalent native query formulations and evaluates representative system architectures through a unified, multidimensional methodology. Based on this evaluation, we analyze design trade-offs and identify open challenges to guide future system design and optimization.

cs.DB↗