arXiv subjects

Jaewon Lee

Publications and source records attributed to Jaewon Lee.

At least 37 records · Page 2Linked to original sources

arXiv2026

Forecasting Future Language: Context Design for Mention Markets

Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilistic forecasts of keyword-mention outcomes. While recent work shows that large language models (LLMs) can generate forecasts competitive with human forecasters, it remains unclear how input context should be designed to support accurate prediction. In this paper, we study this question through experiments on earnings-call mention markets, which require forecasting whether a company will mention a specified keyword during its upcoming call. We run controlled comparisons varying (i) which contextual information is provided (news and/or prior earnings-call transcripts) and (ii) how \textit{market probability}, (i.e., prediction market contract price) is used. We introduce Market-Conditioned Prompting (MCP), which explicitly treats the market-implied probability as a prior and instructs the LLM to update this prior using textual evidence, rather than re-predicting the base rate from scratch. In our experiments, we find three insights: (1) richer context consistently improves forecasting performance; (2) market-conditioned prompting (MCP), which treats the market probability as a prior and updates it using textual evidence, yields better-calibrated forecasts; and (3) a mixture of the market probability and MCP (MixMCP) outperforms the market baseline. By dampening the LLM's posterior update with the market prior, MixMCP yields more robust predictions than either the market or the LLM alone.

q-fin.GN↗

arXiv2026

On homology spheres of the trivial local equivalence class

In the homology cobordism group $Θ_\mathbb{Z}^3$, it is not known if there are non-trivial linear dependences between Seifert fibered spheres. Based on involutive Heegaard Floer theory, Hendricks, Manolescu, and Zemke introduced the local equivalence group $\mathfrak{I}$ along with the homomorphism $h:Θ_\mathbb{Z}^3 \rightarrow \mathfrak{I}$. Using the work of Dai and Stoffregen, one can find non-trivial linear dependences between the images of Seifert fibered spheres under $h$. Therefore, it is interesting to ask if such dependences in $\mathfrak{I}$ originate from $Θ_\mathbb{Z}^3$. In this paper, by employing the $r_s$-invariants from the filtered instanton Floer homology developed by Nozaki, Sato, and Taniguchi, we provide certain conditions to guarantee that such relations are not realized even in the rational homology cobordism group. We also discuss the local equivalence class of the $\operatorname{Pin(2)}$-equivariant Seiberg--Witten Floer stable homotopy type.

math.GT↗

arXiv2026

Consistency-Preserving Concept Erasure via Unsafe-Safe Pairing and Directional Fisher-weighted Adaptation

With the increasing versatility of text-to-image diffusion models, the ability to selectively erase undesirable concepts (e.g., harmful content) has become indispensable. However, existing concept erasure approaches primarily focus on removing unsafe concepts without providing guidance toward corresponding safe alternatives, which often leads to failure in preserving the structural and semantic consistency between the original and erased generations. In this paper, we propose a novel framework, PAIRed Erasing (PAIR), which reframes concept erasure from simple removal to consistency-preserving semantic realignment using unsafe-safe pairs. We first generate safe counterparts from unsafe inputs while preserving structural and semantic fidelity, forming paired unsafe-safe multimodal data. Leveraging these pairs, we introduce two key components: (1) Paired Semantic Realignment, a guided objective that uses unsafe-safe pairs to explicitly map target concepts to semantically aligned safe anchors; and (2) Fisher-weighted Initialization for DoRA, which initializes parameter-efficient low-rank adaptation matrices using unsafe-safe pairs, encouraging the generation of safe alternatives while selectively suppressing unsafe concepts. Together, these components enable fine-grained erasure that removes only the targeted concepts while maintaining overall semantic consistency. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving effective concept erasure while preserving structural integrity, semantic coherence, and generation quality.

cs.CV↗

arXiv2026

CuFuzz: Hardening CUDA Programs through Transformation and Fuzzing

GPUs have gained significant popularity over the past decade, extending beyond their original role in graphics rendering. This evolution has brought GPU security and reliability to the forefront of concerns. Prior research has shown that CUDA's lack of memory safety can lead to serious vulnerabilities. While fuzzing is effective for finding such bugs on CPUs, equivalent tools for GPUs are lacking due to architectural differences and lack of built-in error detection. In this paper, we propose CuFuzz, a novel compiler-runtime co-design solution to extend state-of-the-art CPU fuzzing tools to GPU programs. CuFuzz transforms GPU programs into CPU programs using compiler IR-level transformations to enable effective fuzz testing. To the best of our knowledge, CuFuzz is the first mechanism to bring fuzzing support to CUDA, addressing a critical gap in GPU security research. By leveraging CPU memory error detectors such as Address Sanitizer, CuFuzz aims to uncover memory safety bugs and related correctness vulnerabilities in CUDA code, enhancing the security and reliability of GPU-accelerated applications. To ensure high fuzzing throughput, we introduce two compiler-runtime co-optimizations tailored for GPU code: Partial Representative Execution (PREX) and Access-Index Preserving Pruning (AXIPrune), achieving average throughput improvements of 32x with PREX and an additional 33% gain with AXIPrune on top of PREX-optimized code. Together, these optimizations can yield up to a 224.31x speedup. In our fuzzing campaigns, CuFuzz uncovered 122 security vulnerabilities in widely used benchmarks.

cs.CR↗

arXiv2025

Rabi oscillations of a monolayer quantum emitter driven through its excited state

The interaction of a quantum two-level system with a resonant driving field results in the emergence of Rabi oscillations, which are the hallmark of a controlled manipulation of a quantum state on the Bloch sphere. This all-optical coherent control of solid-state two-level systems is crucial for quantum applications. In this work we study Rabi oscillations emerging in a WSe2 monolayer-based quantum dot. The emitter is driven coherently using picosecond laser pulses to a higher-energy state, while photoluminescence is probed from the ground state. The theoretical treatment based on a three-level exciton model reveals the population transfer between the exciton ground and excited states coupled by Coulomb interaction. Our calculations demonstrate that the resulting exciton ground state population can be controlled by varying driving pulse area and detuning which is evidenced by the experimental data. Our results pave the way towards the coherent control of quantum emitters in atomically thin semiconductors, a crucial ingredient for monolayer-based high-performance, on-demand single photon sources.

cond-mat.mes-hall↗

arXiv2025

Comparing LSTM-Based Sequence-to-Sequence Forecasting Strategies for 24-Hour Solar Proton Flux Profiles Using GOES Data

Solar Proton Events (SPEs) cause significant radiation hazards to satellites, astronauts, and technological systems. Accurate forecasting of their proton flux time profiles is crucial for early warnings and mitigation. This paper explores deep learning sequence-to-sequence (seq2seq) models based on Long Short-Term Memory networks to predict 24-hour proton flux profiles following SPE onsets. We used a dataset of 40 well-connected SPEs (1997-2017) observed by NOAA GOES, each associated with a >=M-class western-hemisphere solar flare and undisturbed proton flux profiles. Using 4-fold stratified cross-validation, we evaluate seq2seq model configurations (varying hidden units and embedding dimensions) under multiple forecasting scenarios: (i) proton-only input vs. combined proton+X-ray input, (ii) original flux data vs. trend-smoothed data, and (iii) autoregressive vs. one-shot forecasting. Our major results are as follows: First, one-shot forecasting consistently yields lower error than autoregressive prediction, avoiding the error accumulation seen in iterative approaches. Second, on the original data, proton-only models outperform proton+X-ray models. However, with trend-smoothed data, this gap narrows or reverses in proton+X-ray models. Third, trend-smoothing significantly enhances the performance of proton+X-ray models by mitigating fluctuations in the X-ray channel. Fourth, while models trained on trendsmoothed data perform best on average, the best-performing model was trained on original data, suggesting that architectural choices can sometimes outweigh the benefits of data preprocessing.

cs.LG↗

arXiv2025

Every negative amphichiral knot is rationally slice

In 2009, Kawauchi proved that every strongly negative amphichiral knot is rationally slice. However, as shown by Hartley in 1980, there are examples of negative amphichiral knots that are not strongly negative amphichiral. In this paper, we prove that every negative amphichiral link whose amphichiral map preserves each component is rationally slice. Our proof relies on a systematic analysis of the action induced by the negative amphichiral map on the JSJ decomposition of the link exterior. Moreover, we provide sufficient conditions on such an action to deduce when a negative amphichiral knot is either isotopic to, or concordant to, a strongly negative amphichiral knot. In particular, we prove that every fibered negative amphichiral knot is strongly negative amphichiral, answering a question asked by Kim and Wu in 2016 on Miyazaki knots.

math.GT↗

arXiv2025

Quantum entanglement and extractable work for Gaussian states

The study of quantum thermodynamics aims to elucidate the role played by quantum principles in the emergent features of quantum thermodynamic processes. Specifically, it is of fundamental importance to understand how quantum correlation among different parties enables thermodynamic features distinguishable from those arising in classical thermodynamics. In this work, we investigate the relation between extractable work and quantum correlations for two-mode Gaussian states. We examine the change in local energy occurring at one party due to a Gaussian measurement performed on the other in relation to the quantum correlations of two-mode states classified as separable, entangled, and steerable states. Our analysis reveals a clear quantitative difference in the extractable work, depending on the class of states to which the two-mode state belongs.

quant-ph↗

arXiv2025

Efficient Speculative Decoding for Llama at Scale: Challenges and Solutions

Speculative decoding is a standard method for accelerating the inference speed of large language models. However, scaling it for production environments poses several engineering challenges, including efficiently implementing different operations (e.g., tree attention and multi-round speculative decoding) on GPU. In this paper, we detail the training and inference optimization techniques that we have implemented to enable EAGLE-based speculative decoding at a production scale for Llama models. With these changes, we achieve a new state-of-the-art inference latency for Llama models. For example, Llama4 Maverick decodes at a speed of about 4 ms per token (with a batch size of one) on 8 NVIDIA H100 GPUs, which is 10% faster than the previously best known method. Furthermore, for EAGLE-based speculative decoding, our optimizations enable us to achieve a speed-up for large batch sizes between 1.4x and 2.0x at production scale.

cs.CL↗

arXiv2025

3D DNA Origami-Enabled Molecularly Addressable Optical Nanocircuit

The optical nanocircuit concept provides a predictive framework analogous to an electric RLC circuit, where induced dipoles in plasmonic nanoparticle (NPs), ohmic losses in NPs, and dielectric gaps serve as inductors (L), capacitors (C), and resistors (R), respectively. This modular theory allows unprecedented design flexibility, expanding the range of achievable optical resonances in plasmonic clusters. However, existing experimental approaches, such as atomic force microscope tip-enabled nanomanipulation and electron-beam lithography, lack the critical accuracy in nanogap tuning and molecular loading required for applications like PRET. Here, we introduce a molecularly addressable optical nanocircuit enabled by DNA origami. First, we theoretically and experimentally confirmed that gold (Au) NPs and dye-loaded DNA origami can function as different circuit elements: R- and C-coupled L and R-coupled C, respectively. To assemble large Au NPs into designer optical nanocircuits, we utilized a mechanically robust 3D DNA origami design rather than conventionally used 2D origami sheet. This platform provided high reproducibility and accuracy in assembling a range of structures-from dimers to tetramers-with controlled symmetry, heterogeneity, and nanogap tunability. Together with ultrasmoothness and uniformity of Au NPs, we achieved the highest Q-factor for magnetic resonance of a nanoparticle-based optical nanocircuit (~19.2). Also, selective molecular cargo loading onto designated 3D DNA origami sites within plasmonic clusters enabled deterministic, predictive light-molecule coupling in optical nanocircuits. This resulted in 100-fold stronger PRET signal in dimeric clusters compared to monomeric NPs. Our approach opens promising directions in designing custom optical resonances for use in molecular sensing, nonlinear optics, and quantum photonics.

physics.optics↗

arXiv2025

DNA Nanotechnology for Superradiance

Superradiance, first proposed by Dicke in 1954, is a highly efficient quantum light source that differs from conventional spontaneous emission. Unlike typical spontaneous emission, where intensity scales linearly with the number of electric dipoles, superradiance exhibits an intensity that scales quadratically with the number of electric dipoles. Similarly, the decay rate also increases proportionally to the dipole numbers. This collective emission is especially powerful when it manifests as superfluorescence, a physical regime of superradiance where spontaneously emerging coherence is achieved by arranging excited dipoles in the same orientation within a volume much smaller than their emission wavelength. Numerous experimental strategies have been employed to generate superradiance, with one common approach being the use of stochastically formed aggregates of quantum dots and organic dyes. However, the inherent randomness in such systems prevents precise control over the number, spatial distribution, and relative orientation of the emitters. This often leads to non-uniform coupling strengths and parasitic dephasing effects, which make it difficult to predict the resulting quantum emission and limit its use in engineered devices. A deterministic platform that provides precise control over these parameters is therefore essential for realizing the full potential of superradiant systems. Here, we (i) specifically outline the advantages of DNA nanotechnology in tackling this challenge, (ii) discuss the reasons why superradiance has not yet been realized even with the state-of-the art DNA nanotechnology, and (iii) propose potential solutions for overcoming the current limitations.

quant-ph↗

arXiv2025

Extraction of coherence times of biexciton and exciton photons emitted by a single resonantly excited quantum dot under controlled dephasing

The visibility of two-photon interference is limited by the indistinguishability of the photons. In the cascaded emission of a three-level system, such as a single quantum dot, the indistinguishability of each photon in the pair is primarily affected by two main factors: the temporal correlation between paired photons and dephasing. Investigating the individual effects of these factors on photon indistinguishability is challenging, as both factors affect it simultaneously. In this study, we investigate the temperature-dependent two-photon interference visibility of the biexciton and exciton photons emitted from a single quantum dot under two-photon resonant excitation, while keeping temporal correlation between the paired photons intact. Finally, we simultaneously extract the coherence times of the biexciton and exciton photons as a function of temperature.

quant-ph↗

arXiv2025

Rational concordance of double twist knots

Double twist knots $K_{m, n}$ are known to be rationally slice if $mn = 0$, $n = -m\pm 1$, or $n = -m$. In this paper, we prove the converse. It is done by showing that infinitely many prime power-fold cyclic branched covers of the other cases do not bound a rational ball. Our rational ball obstruction is based on Donaldson's diagonalization theorem.

math.GT↗

arXiv2025

Fast Global Localization on Neural Radiance Field

Neural Radiance Fields (NeRF) presented a novel way to represent scenes, allowing for high-quality 3D reconstruction from 2D images. Following its remarkable achievements, global localization within NeRF maps is an essential task for enabling a wide range of applications. Recently, Loc-NeRF demonstrated a localization approach that combines traditional Monte Carlo Localization with NeRF, showing promising results for using NeRF as an environment map. However, despite its advancements, Loc-NeRF encounters the challenge of a time-intensive ray rendering process, which can be a significant limitation in practical applications. To address this issue, we introduce Fast Loc-NeRF, which leverages a coarse-to-fine approach to enable more efficient and accurate NeRF map-based global localization. Specifically, Fast Loc-NeRF matches rendered pixels and observed images on a multi-resolution from low to high resolution. As a result, it speeds up the costly particle update process while maintaining precise localization results. Additionally, to reject the abnormal particles, we propose particle rejection weighting, which estimates the uncertainty of particles by exploiting NeRF's characteristics and considers them in the particle weighting process. Our Fast Loc-NeRF sets new state-of-the-art localization performances on several benchmarks, convincing its accuracy and efficiency.

cs.RO↗

arXiv2025

Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function

We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on reach-avoid problems. These problems require computing the reach-avoid set, which ensures that all its elements can safely reach a target set despite disturbances within pre-specified bounds. Our framework has two main parts: offline learning of a newly designed reachavoid value function, and post-learning certification. Compared to prior work, our new value function is Lipschitz continuous and its associated Bellman operator is a contraction mapping, both of which improve the learning performance. To ensure deterministic guarantees of our learned reach-avoid set, we introduce two efficient post-learning certification methods. Both methods can be used online for real-time local certification or offline for comprehensive certification. We validate our framework in a 12-dimensional crazyflie drone racing hardware experiment and a simulated 10-dimensional highway take-over example.

eess.SY↗

arXiv2025

RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines

We present RPGBench, the first benchmark designed to evaluate large language models (LLMs) as text-based role-playing game (RPG) engines. RPGBench comprises two core tasks: Game Creation (GC) and Game Simulation (GS). In GC, an LLM must craft a valid and playable RPG world using a structured event-state representation, ensuring logical coherence and proper termination conditions. In GS, the LLM simulates interactive gameplay across multiple rounds while consistently updating states and enforcing game rules. To comprehensively assess performance, RPGBench integrates objective and subjective evaluation methodologies. Objective measures verify adherence to event mechanics and check variable updates without requiring human intervention. Subjective measures, such as content interestingness, action quality, and role-playing capability, are evaluated via an LLM-as-a-judge framework, where a strong LLM grades each candidate's outputs. Empirical results demonstrate that state-of-the-art LLMs can produce engaging stories but often struggle to implement consistent, verifiable game mechanics, particularly in long or complex scenarios. By combining structured, rule-based assessments with LLM-based judgments, RPGBench provides a new standard for evaluating how well LLMs can balance creativity, coherence, and complexity in text-based RPGs, opening avenues for more immersive and controllable interactive storytelling.

cs.CL↗

arXiv2025

GECKO Follow-up Observation of the Binary Neutron Star-Black Hole Merger Candidate S230518h

The gravitational wave (GW) event S230518h is a potential binary neutron star-black hole merger (NSBH) event that was detected during engineering run 15 (ER15), which served as the commissioning period before the LIGO-Virgo-KAGRA (LVK) O4a observing run. Despite its low probability of producing detectable electromagnetic emissions, we performed extensive follow-up observations of this event using the GECKO telescopes in the southern hemisphere. Our observation covered 61.7\% of the 90\% credible region, a $\rm 284\:deg^2$ area accessible from the southern hemisphere, reaching a median limiting magnitude of $R=21.6$ mag. In these images, we conducted a systematic search for an optical counterpart of this event by combining a CNN-based classifier and human verification. We identified 128 transient candidates, but no significant optical counterpart was found that could have caused the GW signal. Furthermore, we provide feasible KN properties that are consistent with the upper limits of observation. Although no optical counterpart was found, our result demonstrates both GECKO's efficient wide-field follow-up capabilities and usefulness for constraining properties of kilonovae from NSBH mergers at distances of $\sim 200$ Mpc.

astro-ph.HE↗

arXiv2025

GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization

Mapping and localization are crucial problems in robotics and autonomous driving. Recent advances in 3D Gaussian Splatting (3DGS) have enabled precise 3D mapping and scene understanding by rendering photo-realistic images. However, existing 3DGS methods often struggle to accurately reconstruct a 3D map that reflects the actual scale and geometry of the real world, which degrades localization performance. To address these limitations, we propose a novel 3DGS method called Geometry-Aware Gaussian Splatting (GeomGS). This method fully integrates LiDAR data into 3D Gaussian primitives via a probabilistic approach, as opposed to approaches that only use LiDAR as initial points or introduce simple constraints for Gaussian points. To this end, we introduce a Geometric Confidence Score (GCS), which identifies the structural reliability of each Gaussian point. The GCS is optimized simultaneously with Gaussians under probabilistic distance constraints to construct a precise structure. Furthermore, we propose a novel localization method that fully utilizes both the geometric and photometric properties of GeomGS. Our GeomGS demonstrates state-of-the-art geometric and localization performance across several benchmarks, while also improving photometric performance.

cs.RO↗