arXiv subjects

Jiayu Shi

Publications and source records attributed to Jiayu Shi.

8 recordsLinked to original sources

arXiv2026

Paritok-4B: Intent-Conditioned Context Compression for Coding Agents

Coding agents re-send large file reads and tool outputs to a frontier LLM every turn, and this context dominates their token bill. General-purpose prompt compressors are trained on prose and suit code poorly: they paraphrase identifiers and drop the exact spans an agent needs to edit. We present Paritok-4B, a 4B LoRA compressor for coding-agent trajectories built on two commitments. It is extractive: it selects spans rather than rewriting them, and 96.0% of the identifiers, paths, and numbers it emits already appear in its input, holding at 96.2% on held-out SWE-bench Lite output. It is intent-conditioned: told the agent's current task, it acts chiefly inside a retained segment, selecting which lines survive (retained lines are +0.067 more intent-relevant than removed ones, paired 95% CI [+0.056, +0.078]) rather than changing how much is retained. We distil a gpt-4.1-mini teacher over 67,074 real OpenHands trajectories into 40,606 validated examples and fine-tune Qwen3-4B. On all 300 SWE-bench Lite instances, Paritok-4B compresses agent context to 25.7% of its size, 2.0x harder than a gpt-4.1-mini compressor (50.2%) and 2.4x harder than gpt-5 (61.9%), while retaining 86.5% of uncompressed single-shot solve quality. Fed the cat -n line-numbered input real agents produce, it compresses slightly less (27.8%) and retains more (89.3%); there the paired test is informative, with 30 instances solved only uncompressed and 17 only compressed, an exact McNemar p=0.079, so at this sample size compressing context to roughly a quarter of its size does not significantly reduce the solve rate. The model is a 264 MB adapter that self-hosts on one 24 GB GPU with no per-token compressor fee, which at list prices decides the economics: gpt-5 as a compressor is net-negative, costing more than the downstream tokens it saves. Weights, data, and evaluation scripts are open (Apache 2.0).

cs.AI↗

arXiv2026

Probing mesoscopic nonlocal screening in van der Waals heterostructures with polaritons

Predictive optical modelling of van der Waals (vdW) heterostructures is critical for meta-optics, near-field photonics and quantum technologies. At their buried interfaces, charge transfer and spatially extended screening challenge local descriptions based on layer-by-layer stacking of fixed permittivity tensors. However, such nonlocal corrections have been established mainly for plasmonic systems at ångström-nanometre scales and are often assumed negligible on optical-wavelength scales. Here we challenge this view by uncovering a mesoscopic nonlocal screening regime, extending up to ~140 nm, at buried charge-transfer interfaces in transition-metal dichalcogenide/α-molybdenum trioxide (TMDC/α-MoO3) phonon-polaritonic heterostructures. Using phonon polaritons as an ultrasensitive probe, we quantify charge transfer from polariton-wavelength shifts and find a thickness-independent saturated response as α-MoO3 is thinned. Rather than merely complicating optical modelling, this nonlocal saturation turns a design-level correction into an opportunity by yielding a transferable cross-material metric. Across more than 120 devices, this metric scales linearly with the work-function difference between the TMDC and α-MoO3. We further identify a lattice-mismatch-set energy threshold for charge transfer, revising Anderson-type band alignment for vdW interfaces.

physics.optics↗

arXiv2025

Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain

Building robots that can perceive, reason, and act in dynamic, unstructured environments remains a central challenge. Recent embodied systems often follow a dual-system paradigm, where System 2 performs high-level reasoning and System 1 handles low-level control. We refer to System 2 as the embodied brain, the cognitive core for decision-making in manipulation. Although evaluating this embodied brain is crucial, existing benchmarks mainly measure execution success or cover only limited aspects of high-level cognition and task realism. We introduce RoboBench, a benchmark for evaluating multimodal large language models (MLLMs) as embodied brains. RoboBench covers five dimensions: Instruction Comprehension, Perception Reasoning, Generalized Planning, Affordance Prediction, and Failure Analysis. It spans 14 capabilities, 25 tasks, and 6,092 QA pairs. To improve realism, it draws from large-scale real robotic data and in-house collection across diverse embodiments, attribute-rich objects, multi-view scenes, and memory-driven navigation. For planning, RoboBench introduces an MLLM-as-world-simulator framework that assesses whether predicted plans can achieve critical object-state changes under physical and visual constraints, enabling more faithful evaluation of long-horizon reasoning than symbolic matching. Experiments on 18 state-of-the-art MLLMs reveal persistent limitations in implicit instruction understanding, spatiotemporal reasoning, cross-scenario planning, fine-grained affordance understanding, and failure diagnosis. We further analyze how embodied cognitive abilities relate to downstream robotic control. RoboBench offers a comprehensive scaffold for quantifying high-level cognition and guiding next-generation MLLMs toward more robust robotic intelligence.

cs.RO↗

arXiv2025

RoboBrain 2.0 Technical Report

We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.

cs.RO↗

arXiv2025

Enhanced and modulable induced superconducting gap and effective Landé g-factor in Pb-InSb hybrid devices

The hybrid system of a conventional superconductor (SC) on a semiconductor (SM) nanowire with strong spin-orbit coupling (SOC) represents a promising platform for achieving topological superconductivity and Majorana zero modes (MZMs) towards topological quantum computation. While aluminum (Al)-based hybrid nanowire devices have been widely utilized, their limited superconducting gap and intrinsic weak SOC as well as small Landé g-factor may hinder future experimental advancements. In contrast, we demonstrate that lead (Pb)-based hybrid quantum devices exhibit a remarkably large and hard proximity-induced superconducting gap, exceeding that of Al by an order of magnitude. By exploiting electrostatic gating to modulate wavefunction distribution and SC-SM interfacial coupling, this gap can be continuously tuned from its maximum value (~1.4 meV, matching the bulk Pb gap) down to nearly zero while maintaining the hardness. Furthermore, magnetic-field-dependent measurements reveal a radial evolution of the gap structure with anti-crossing feature, indicative of strong SOC and huge effective g-factors up to 76. These findings underscore the superior functionality of Pb-based hybrid systems, significantly advancing their potential for realizing and stabilizing MZMs and the further scalable topological quantum architectures.

cond-mat.mes-hall↗

arXiv2025

RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete

Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabilities: Planning Capability, which involves decomposing complex manipulation instructions into manageable sub-tasks; Affordance Perception, the ability to recognize and interpret the affordances of interactive objects; and Trajectory Prediction, the foresight to anticipate the complete manipulation trajectory necessary for successful execution. To enhance the robotic brain's core capabilities from abstract to concrete, we introduce ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. ShareRobot's diversity and accuracy have been meticulously refined by three human annotators. Building on this dataset, we developed RoboBrain, an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities. Extensive experiments demonstrate that RoboBrain achieves state-of-the-art performance across various robotic tasks, highlighting its potential to advance robotic brain capabilities.

cs.RO↗

arXiv2024

Versatile Method of Engineering the Band Alignment and the Electron Wavefunction Hybridization of Hybrid Quantum Devices

With the development of quantum technology, hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the possibility of engineering structures that benefit from the integration of the properties of both materials. However, until now, none of the experiments have reported good control of band alignment at the interface, which determines the strength of S-Sm coupling and the proximitized superconducting gap. Here, we fabricate hybrid devices in a generic way with argon milling to modify the interface while maintaining its high quality. First, after the milling the atomically connected S-Sm interfaces appear, resulting in a large induced gap, as well as the ballistic transport revealed by the multiple Andreev reflections and quantized above-gap conductance plateaus. Second, by comparing transport measurement with Schrödinger-Poisson (SP) calculations, we demonstrate that argon milling is capable of varying the band bending strength in the semiconducting wire as the electrons tend to accumulate on the etched surface for longer milling time. Finally, we perform nonlocal measurements on advanced devices to demonstrate the coexistence and tunability of crossed Andreev reflection (CAR) and elastic co-tunneling (ECT) -- key ingredients for building the prototype setup for realization of Kitaev chain and quantum entanglement probing. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems.

cond-mat.mes-hall↗

arXiv2024

Room-temperature tunable tunneling magnetoresistance in Fe3GaTe2/WSe2/Fe3GaTe2 van der Waals heterostructures

The exceptional properties of two-dimensional (2D) magnet materials present a novel approach to fabricate functional magnetic tunnel junctions (MTJ) by constructing full van der Waals (vdW) heterostructures with atomically sharp and clean interfaces. The exploration of vdW MTJ devices with high working temperature and adjustable functionalities holds great potential for advancing the application of 2D materials in magnetic sensing and data storage. Here, we report the observation of highly tunable room-temperature tunneling magnetoresistance through electronic means in a full vdW Fe3GaTe2/WSe2/Fe3GaTe2 MTJ. The spin valve effect of the MTJ can be detected even with the current below 1 nA, both at low and room temperatures, yielding a tunneling magnetoresistance (TMR) of 340% at 2 K and 50% at 300 K, respectively. Importantly, the magnitude and sign of TMR can be modulated by a DC bias current, even at room temperature, a capability that was previously unrealized in full vdW MTJs. This tunable TMR arises from the contribution of energy-dependent localized spin states in the metallic ferromagnet Fe3GaTe2 during tunnel transport when a finite electrical bias is applied. Our work offers a new perspective for designing and exploring room-temperature tunable spintronic devices based on vdW magnet heterostructures.

cond-mat.mes-hall↗