arXiv subjects

Dongkyu Lee

Publications and source records attributed to Dongkyu Lee.

At least 19 recordsLinked to original sources

arXiv2026

Chiroptical Ternary Entropy Harvesting from Self-Assembled Block Copolymer Nanopatterns

Nanoscale fabrication inevitably produces local stochasticity that is commonly treated as a defect, but can instead be harnessed as a material resource for information security. Here we report a chiroptical platform for ternary entropy harvesting based on stochastic Au nanopatterns formed by block copolymer self-assembly. By transducing fabrication-induced stochastic microstates into handedness-dependent optical responses through chiroptical mapping, our platform enables native ternary digitization rather than conventional binary encoding, allowing physically harvested ternary random sequences to be used for key generation. This raises the information density to log2(3) = 1.585 bits per trit, approximately 58.5% higher than the binary limit, enabling more entropy to be harvested from a limited physical footprint. The harvested outputs exhibit near-balanced symbol populations, negligible spatial and inter-sample correlations, Shannon entropy approaching the ternary ideal, and resistance to statistical and machine-learning-based prediction. These results establish self-assembled chiroptical nanostructures as a scalable platform for cryptographic key generation, secure edge devices, and distributed Internet-of-Things platforms.

cond-mat.mtrl-sci↗

arXiv2026

Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents

Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack surface that spans websites and sessions. While existing security research on memory assumes attackers can directly inject into memory storage or exploit shared memory across users, we present a more realistic threat model: contamination through environmental observation alone. We introduce Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP), the first attack to achieve cross-session, cross-site compromise without requiring direct memory access. A single contaminated observation (e.g., viewing a manipulated product page) silently poisons an agent's memory and activates during future tasks on different websites, bypassing permission-based defenses. Our experiments on (Visual)WebArena reveal two key findings. First, eTAMP achieves substantial attack success rates: up to 32.5% on GPT-5-mini, 23.4% on GPT-5.2, and 19.5% on GPT-OSS-120B. Second, we discover Frustration Exploitation: agents under environmental stress become dramatically more susceptible, with ASR increasing up to 8 times when agents struggle with dropped clicks or garbled text. Notably, more capable models are not more secure. GPT-5.2 shows substantial vulnerability despite superior task performance. With the rise of AI browsers like OpenClaw, ChatGPT Atlas, and Perplexity Comet, our findings underscore the urgent need for defenses against environment-injected memory poisoning.

cs.CR↗

arXiv2026

DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space

Local navigation in cluttered environments often suffers from dense obstacles and frequent local minima. Conventional local planners rely on heuristics and are prone to failure, while deep reinforcement learning(DRL)based approaches provide adaptability but are constrained by limited onboard sensing. These limitations lead to navigation failures because the robot cannot perceive structures outside its field of view. In this paper, we propose DreamFlow, a DRL-based local navigation framework that extends the robot's perceptual horizon through conditional flow matching(CFM). The proposed CFM based prediction module learns probabilistic mapping between local height map latent representation and broader spatial representation conditioned on navigation context. This enables the navigation policy to predict unobserved environmental features and proactively avoid potential local minima. Experimental results demonstrate that DreamFlow outperforms existing methods in terms of latent prediction accuracy and navigation performance in simulation. The proposed method was further validated in cluttered real world environments with a quadrupedal robot. The project page is available at https://dreamflow-icra.github.io.

cs.RO↗

arXiv2026

R-WoM: Retrieval-augmented World Model For Computer-use Agents

Large Language Models (LLMs) can serve as world models to enhance agent decision-making in digital environments by simulating future states and predicting action outcomes, potentially eliminating costly trial-and-error exploration. However, this capability is fundamentally limited by LLMs' tendency toward hallucination and their reliance on static training knowledge, which can lead to compounding errors that inhibit long-horizon simulations. To systematically investigate whether LLMs are appropriate for world modeling, we probe two core capabilities of world models--future state prediction and reward estimation--through three tasks: next-state identification, full-procedure planning alignment, and milestone transition recognition. Our analysis shows that while LLMs effectively capture immediate next states and identify meaningful state transitions, their performance rapidly degrades in full-procedure planning. This highlights LLMs' limitations in reliably modeling environment dynamics over long horizons. To address these limitations, we propose the Retrieval-augmented World Model (R-WoM), which grounds LLM simulations by incorporating factual, up-to-date knowledge retrieved from external tutorials. Experiments show that R-WoM achieves relative improvements of up to 23.4% and 16.3% on the subsets of OSWorld and Webarena compared to baselines, with particular advantage in longer-horizon simulations.

cs.CL↗

arXiv2026

DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning

Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating base configuration makes them vulnerable to real-world uncertainties, yielding substantial challenges in their locomotion control. Deep reinforcement learning has become one of the plausible alternatives for realizing a robust locomotion controller. However, the approaches that rely solely on proprioception sacrifice collision-free locomotion because they require front-feet contact to detect the presence of stairs to adapt the locomotion gait. Meanwhile, incorporating exteroception necessitates a precisely modeled map observed by exteroceptive sensors over a period of time. Therefore, this work proposes a novel method to fuse proprioception and exteroception featuring a resilient multi-modal reinforcement learning. The proposed method yields a controller that showcases agile locomotion performance on a quadrupedal robot over a myriad of real-world courses, including rough terrains, steep slopes, and high-rise stairs, while retaining its robustness against out-of-distribution situations.

cs.RO↗

arXiv2026

LocoVLM: Grounding Vision and Language for Adapting Versatile Legged Locomotion Policies

Recent advances in legged locomotion learning are still dominated by the utilization of geometric representations of the environment, limiting the robot's capability to respond to higher-level semantics such as human instructions. To address this limitation, we propose a novel approach that integrates high-level commonsense reasoning from foundation models into the process of legged locomotion adaptation. Specifically, our method utilizes a pre-trained large language model to synthesize an instruction-grounded skill database tailored for legged robots. A pre-trained vision-language model is employed to extract high-level environmental semantics and ground them within the skill database, enabling real-time skill advisories for the robot. To facilitate versatile skill control, we train a style-conditioned policy capable of generating diverse and robust locomotion skills with high fidelity to specified styles. To the best of our knowledge, this is the first work to demonstrate real-time adaptation of legged locomotion using high-level reasoning from environmental semantics and instructions with instruction-following accuracy of up to 87% without the need for online query to on-the-cloud foundation models.

cs.RO↗

arXiv2025

Translating Chirality into Multidirectional Motion through Broadband Chiroptical MXenes

The integration of chirality into functional materials enables control of light-matter interactions beyond binary illumination (on/off). Conventional photoactuators rely on binary modulation, limiting them to unidirectional motion. In contrast, we introduce a ternary optical logic paradigm where actuation direction is encoded by the handedness of circularly polarized light (CPL). Here, we establish a chiral Ti$_{3}$C$_{2}$T$_{x}$ MXene platform bridging molecular chirality and mechanical actuation. Phenylalanine enantiomers are covalently anchored onto MXene nanoflakes via chiral nanopainting. The 2D confinement forces ligands into vertically aligned supramolecular networks. Interlayer-spacing analysis and simulations corroborate that such supramolecular networks unlock exceptionally broadband circular dichroism from the ultraviolet to the near-infrared. This supramolecular chirality synergizes with MXene's plasmonic properties to drive handedness-dependent photothermal conversion, with a 30\% differential temperature rise between matched and mismatched CPL. Embedding this chiral MXene into thermoresponsive hydrogels realizes, to the best of our knowledge, the first CPL-driven soft actuator that implements LCP/RCP/off as a ternary input to program multidirectional deformation based on a photothermal mechanism. This molecular-to-macroscopic translation demonstrates a new paradigm for chirality-encoded soft robotics and adaptive photonics.

cond-mat.mtrl-sci↗

arXiv2025

Self-consistent tight-binding calculations with extended Hubbard interactions in rhombohedral multilayer graphene

We study the mean-field broken symmetry phases of charge neutral multilayer rhombohedral graphene within tight-binding approximations including self-consistent extended Hubbard interactions. We used on-site and inter-site Hubbard interactions obtained from a newly developed first-principles calculation method. Our calculations for systems up to eight layers give rise to electron-hole asymmetries, band flatness, band gaps, and layer anti-ferromagnetic ground states in keeping with available experiments. By including the intersite Hubbard interactions up to the next-nearest neighboring sites, the band gaps are shown to open when the number of layers is larger than three, while the trilayer system maintains its metallic nature with two low energy density of state peaks near the Fermi energy whose separation increases with the range of inter-site Hubbard parameters. Within our framework, the calculated band gaps reflect mean-field ground states with extended Hubbard interactions, in closer agreement with experimental estimates. The tight-binding formulation further enables efficient treatment of large rhombohedral chiral systems, including twisted multilayer graphene.

cond-mat.mes-hall↗

arXiv2025

DreamFLEX: Learning Fault-Aware Quadrupedal Locomotion Controller for Anomaly Situation in Rough Terrains

Recent advances in quadrupedal robots have demonstrated impressive agility and the ability to traverse diverse terrains. However, hardware issues, such as motor overheating or joint locking, may occur during long-distance walking or traversing through rough terrains leading to locomotion failures. Although several studies have proposed fault-tolerant control methods for quadrupedal robots, there are still challenges in traversing unstructured terrains. In this paper, we propose DreamFLEX, a robust fault-tolerant locomotion controller that enables a quadrupedal robot to traverse complex environments even under joint failure conditions. DreamFLEX integrates an explicit failure estimation and modulation network that jointly estimates the robot's joint fault vector and utilizes this information to adapt the locomotion pattern to faulty conditions in real-time, enabling quadrupedal robots to maintain stability and performance in rough terrains. Experimental results demonstrate that DreamFLEX outperforms existing methods in both simulation and real-world scenarios, effectively managing hardware failures while maintaining robust locomotion performance.

cs.RO↗

arXiv2025

TRG-planner: Traversal Risk Graph-Based Path Planning in Unstructured Environments for Safe and Efficient Navigation

Unstructured environments such as mountains, caves, construction sites, or disaster areas are challenging for autonomous navigation because of terrain irregularities. In particular, it is crucial to plan a path to avoid risky terrain and reach the goal quickly and safely. In this paper, we propose a method for safe and distance-efficient path planning, leveraging Traversal Risk Graph (TRG), a novel graph representation that takes into account geometric traversability of the terrain. TRG nodes represent stability and reachability of the terrain, while edges represent relative traversal risk-weighted path candidates. Additionally, TRG is constructed in a wavefront propagation manner and managed hierarchically, enabling real-time planning even in large-scale environments. Lastly, we formulate a graph optimization problem on TRG that leads the robot to navigate by prioritizing both safe and short paths. Our approach demonstrated superior safety, distance efficiency, and fast processing time compared to the conventional methods. It was also validated in several real-world experiments using a quadrupedal robot. Notably, TRG-planner contributed as the global path planner of an autonomous navigation framework for the DreamSTEP team, which won the Quadruped Robot Challenge at ICRA 2023. The project page is available at https://trg-planner.github.io .

cs.RO↗

arXiv2024

TRIP: Terrain Traversability Mapping With Risk-Aware Prediction for Enhanced Online Quadrupedal Robot Navigation

Accurate traversability estimation using an online dense terrain map is crucial for safe navigation in challenging environments like construction and disaster areas. However, traversability estimation for legged robots on rough terrains faces substantial challenges owing to limited terrain information caused by restricted field-of-view, and data occlusion and sparsity. To robustly map traversable regions, we introduce terrain traversability mapping with risk-aware prediction (TRIP). TRIP reconstructs the terrain maps while predicting multi-modal traversability risks, enhancing online autonomous navigation with the following contributions. Firstly, estimating steppability in a spherical projection space allows for addressing data sparsity while accomodating scalable terrain properties. Moreover, the proposed traversability-aware Bayesian generalized kernel (T-BGK)-based inference method enhances terrain completion accuracy and efficiency. Lastly, leveraging the steppability-based Mahalanobis distance contributes to robustness against outliers and dynamic elements, ultimately yielding a static terrain traversability map. As verified in both public and our in-house datasets, our TRIP shows significant performance increases in terms of terrain reconstruction and navigation map. A demo video that demonstrates its feasibility as an integral component within an onboard online autonomous navigation system for quadruped robots is available at https://youtu.be/d7HlqAP4l0c.

cs.RO↗

arXiv2024

Conditional Synthesis of 3D Molecules with Time Correction Sampler

Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation remains a fundamental challenge due to an intrinsic trade-off between targeting specific chemical properties and generating meaningful samples from the data distribution. In this work, we present Time-Aware Conditional Synthesis (TACS), a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties while maintaining validity and stability. A key component of our algorithm is our new type of diffusion sampler, Time Correction Sampler (TCS), which is used to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process at the same time. Our proposed method demonstrates significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields.

cs.LG↗

arXiv2024

Direct view of gate-tunable miniband dispersion in graphene superlattices near the magic twist angle

Superlattices from twisted graphene mono- and bi-layer systems give rise to on-demand many-body states such as Mott insulators and unconventional superconductors. These phenomena are ascribed to a combination of flat bands and strong Coulomb interactions. However, a comprehensive understanding is lacking because the low-energy band structure strongly changes when the electron filling is varied. Here, we gain direct access to the filling-dependent low energy bands of twisted bilayer graphene (TBG) and twisted double bilayer graphene (TDBG) by applying micro-focused angle-resolved photoemission spectroscopy to in situ gated devices. Our findings for the two systems are in stark contrast: The doping dependent dispersion for TBG can be described in a simple model, combining a filling-dependent rigid band shift with a many-body related bandwidth change. In TDBG, on the other hand, we find a complex behaviour of the low-energy bands, combining non-monotonous bandwidth changes and tuneable gap openings. Our work establishes the extent of electric field tunability of the low energy electronic states in twisted graphene superlattices and can serve to underpin the theoretical understanding of the resulting phenomena.

cond-mat.mes-hall↗

arXiv2024

B-TMS: Bayesian Traversable Terrain Modeling and Segmentation Across 3D LiDAR Scans and Maps for Enhanced Off-Road Navigation

Recognizing traversable terrain from 3D point cloud data is critical, as it directly impacts the performance of autonomous navigation in off-road environments. However, existing segmentation algorithms often struggle with challenges related to changes in data distribution, environmental specificity, and sensor variations. Moreover, when encountering sunken areas, their performance is frequently compromised, and they may even fail to recognize them. To address these challenges, we introduce B-TMS, a novel approach that performs map-wise terrain modeling and segmentation by utilizing Bayesian generalized kernel (BGK) within the graph structure known as the tri-grid field (TGF). Our experiments encompass various data distributions, ranging from single scans to partial maps, utilizing both public datasets representing urban scenes and off-road environments, and our own dataset acquired from extremely bumpy terrains. Our results demonstrate notable contributions, particularly in terms of robustness to data distribution variations, adaptability to diverse environmental conditions, and resilience against the challenges associated with parameter changes.

cs.RO↗

arXiv2024

Moiré flat bands and antiferroelectric domains in lattice relaxed twisted bilayer hexagonal boron nitride under perpendicular electric fields

Local interlayer charge polarization of twisted bilayer hexagonal boron nitride (t2BN) is calculated and parametrized as a function of twist angle and perpendicular electric fields through tight-binding calculations on lattice relaxed geometries Lattice relaxations tend to increase the bandwidth of the nearly flat bands, where widths smaller than 1 meV are expected for angle less than 1.08 degree for parallel BN/BN alignment, and for angle less than 1.5 degree for the antiparallel BN/NB alignment. Local interlayer charge polarization maxima of 2.6 pC/m corresponding are expected at the AB and BA stacking sites of BN/BN aligned t2BN in the long moire period limit for angle less than 1 degree, and evolves non-monotonically with a maximum of 3.5 pC/m at angle equal to 1.6 degree before reaching 2 pC/m for angle equal to 6 degree. The electrostatic potential maxima due to the t2BN are overall enhanced by 20 percentage with respect to the rigid system assuming potential modulation depths of up to 300 mV near its surface. In BN/BN aligned bilayers the relative areas of the AB or BA local stacking regions can be expanded or reduced through a vertical electric field depending on its sign.

cond-mat.mes-hall↗

arXiv2024

Optimal Qubit Mapping Search for Encoding Classical Data into Matrix Product State Representation with Minimal Loss

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing.

quant-ph↗

arXiv2024

MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources

Leveraging external knowledge is crucial for achieving high performance in knowledge-intensive tasks, such as question answering. The retrieve-and-read approach is widely adopted for integrating external knowledge into a language model. However, this approach suffers from increased computational cost and latency due to the long context length, which grows proportionally with the number of retrieved knowledge. Furthermore, existing retrieval-augmented models typically retrieve information from a single type of knowledge source, limiting their scalability to diverse knowledge sources with varying structures. In this work, we introduce an efficient memory-augmented transformer called MATTER, designed to retrieve relevant knowledge from multiple heterogeneous knowledge sources. Specifically, our model retrieves and reads from both unstructured sources (paragraphs) and semi-structured sources (QA pairs) in the form of fixed-length neural memories. We demonstrate that our model outperforms existing efficient retrieval-augmented models on popular QA benchmarks in terms of both accuracy and speed. Furthermore, MATTER achieves competitive results compared to conventional read-and-retrieve models while having 100x throughput during inference.

cs.CL↗

arXiv2023

Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, a community's seismic safety and resilience are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by discovering an implicit connection between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.

stat.AP↗