arXiv subjects

Aditya Gupta

Publications and source records attributed to Aditya Gupta.

At least 19 recordsLinked to original sources

arXiv2026

Modular Constructions of g-Golomb Rulers

A set $\mathcal{G}$ of integers is a $g$-Golomb ruler if each positive difference appears at most $g$ times between any 2 elements of the set, and $G(g,n)$ denotes the minimum diameter of such a ruler with $n$ marks. We prove a general lemma for passing from certain modular constructions to ordinary $g$-Golomb rulers. The key point is that, in a modular $g$-Golomb ruler, no cyclic gap length can occur more than $g$ times. This gives a larger guaranteed cut than the previous average gap argument. We apply this lemma to cyclic relative difference sets, Singer sets, Ruzsa--Spence rulers, and Paley quadratic residues to provide many competing constructions for $g$-Golomb Rulers. A computation on the grid $1\le g\le500$, $n=g+b$, $2\le b\le500$, compares the four resulting construction families.

math.CO↗

arXiv2026

Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization

Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints. Existing reliability based portfolio optimization approaches primarily rely on static optimization frameworks and often fail to capture sequential decision making, tail risk, and market frictions such as transaction costs. To address these limitations, we propose a deep reinforcement learning framework for multi-objective reliability based portfolio optimization (MORP-DRL). The proposed framework jointly optimizes expected return and downside risk using three complementary risk measures: variance, Conditional Value-at-Risk (CVaR), and Entropic Value-at-Risk (EVaR). To model uncertainty and heavy-tailed market behavior, asset returns are represented using GARCH(1,1), Extreme Value Theory, and a t-copula dependence structure, while realistic scenarios are generated through quasi-Monte Carlo simulation. A Proximal Policy Optimization (PPO) based strategy is developed under practical constraints including transaction costs and portfolio bounds, and is benchmarked against NSGA-II. Experiments on ten global equity indices across pre-COVID, COVID, and post-COVID market regimes demonstrate that MORP-DRL achieves competitive risk-return performance, reduced downside risk during periods of market stress, and scalability to high-dimensional portfolio settings.

cs.LG↗

arXiv2026

Perpetual Futures for Stocks: The SpaceX Pre-IPO Market

Robert Shiller proposed perpetual futures in 1993 to create derivative markets for assets that are illiquid or whose price cannot be observed directly, such as single family homes, human capital, and the consumer price index. The crypto markets later built the instrument for a different reason and with a different funding rule. We give a single no arbitrage result that nests both designs: the perpetual price is the present value of a benchmark flow discounted at the funding rate, so the funding rule chooses both the benchmark and the discount. We give a random time change representation in which the price is the expected spot at the first event of a clock whose intensity is the funding rate, use it to show that stochastic volatility moves the basis only through the carry, so a volatility risk premium and not volatility itself can break the peg, read price discovery as the convergence of a Doob martingale driven by a stochastic approximation, and give a segmented market equilibrium that makes the pre listing premium structural rather than behavioural. The June 2026 SpaceX pre-IPO market is the first large scale realization of the idea for an equity claim, and we find that the perpetual consensus forecast the secondary clearing price more accurately than the bookbuilt offer. We close with other equity applications.

q-fin.PR↗

arXiv2026

Optimal Diameters of High Multiplicity g-Golomb Rulers

A set $\mathcal{G}$ of integers is called a $g$-Golomb ruler of length $n$ if the difference between any two distinct elements of $\mathcal{G}$ is repeated at most $g$ times. If $g=1$, these are also called $B_2$-sets, Sidon sets, and Babcock sets. We define $G(g,n)$ to represent the minimum diameter of a $g$-Golomb Ruler. In this paper, we prove that for all $b\ge 1$, if $g \ge \frac{7}{4}\left(b^{3/2} -b\right)+1,$ then $G(g,g+b)=g+2b-2$. Sharper bounds are given for $b\le 18$. The main technique is through an arithmetic property of the integers that are \emph{not} in a $g$-Golomb ruler, leading us to introduce LM rulers, a new class of rulers where every distance $d$ occurs as a difference at most $d-1$ times. We show that the minimum diameter of an $n$-element LM ruler $L(n)$ is $\sqrt{8/9} \cdot (n-1)^{3/2} \le L(n) \le \frac{7}{4}\left((n+1)^{3/2}-(n+1)\right).$

math.CO↗

arXiv2026

Transient Thermodynamic Efficiency of Adaptive Inference in Continuously Nonstationary Environments

Adaptive physical and biological systems continually process fluctuating information from their environments. When the environment is nonstationary, inference itself becomes a nonequilibrium process with thermodynamic cost. We analyse a minimal stochastic model which is an overdamped particle in an adaptive double well potential whose control parameter tracks a drifting Ornstein Uhlenbeck signal. Using stochastic energetics, we derive explicit expressions for entropy production, mutual information rate, and a time dependent learning efficiency. High precision Langevin simulations reveal transient peaks in learning efficiency during rapid environmental shifts, absent in steady state averages. These results identify transient adaptive regimes as moments of maximal information to energy conversion, highlighting that maximal thermodynamic learning performance arises transiently rather than in steady state. Throughout this work, the environment is treated as an externally driven stochastic signal rather than a thermodynamic subsystem under control, and its intrinsic entropy production is therefore excluded from the thermodynamic accounting.

cond-mat.stat-mech↗

arXiv2026

PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation

Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden users with clarifying questions or rely on context-based extrapolation, often leading to unnecessary or mistimed interventions. Such systems lack explicit mechanisms to model users' knowledge gaps, resulting in incomplete or suboptimal task outcomes. To address this, we propose PROPER, a framework that explicitly models user-specific knowledge gaps in a controlled manner. Central to our approach is the notion of dimensions: structured, task-relevant factors that define the considerations required for effective task completion. Given a user query, the DGA (Dimension Generating Agent) identifies explicit dimensions (from the user's query) and generates a set of candidate implicit dimensions capturing unarticulated aspects of the task. The RGA (Response Generating Agent) integrates both explicit and implicit dimensions selectively to produce personalized, context-aware, and proactively informative responses. We evaluate PROPER across multiple domains using a structured, gap-aware rubric that measures coverage, initiative appropriateness, and intent alignment. PROPER improves on quality scores and win rates across all domains, achieving up to 84% gains in single-turn evaluation and consistent dominance in multi-turn interactions. All code for PROPER is available at: https://github.com/i-kiran/ProPer-Agent.

cs.LG↗

arXiv2025

NordFKB: a fine-grained benchmark dataset for geospatial AI in Norway

We present NordFKB, a fine-grained benchmark dataset for geospatial AI in Norway, derived from the authoritative, highly accurate, national Felles KartdataBase (FKB). The dataset contains high-resolution orthophotos paired with detailed annotations for 36 semantic classes, including both per-class binary segmentation masks in GeoTIFF format and COCO-style bounding box annotations. Data is collected from seven geographically diverse areas, ensuring variation in climate, topography, and urbanization. Only tiles containing at least one annotated object are included, and training/validation splits are created through random sampling across areas to ensure representative class and context distributions. Human expert review and quality control ensures high annotation accuracy. Alongside the dataset, we release a benchmarking repository with standardized evaluation protocols and tools for semantic segmentation and object detection, enabling reproducible and comparable research. NordFKB provides a robust foundation for advancing AI methods in mapping, land administration, and spatial planning, and paves the way for future expansions in coverage, temporal scope, and data modalities.

cs.CV↗

arXiv2025

QLENS: Towards A Quantum Perspective of Language Transformers

In natural language processing, current methods for understanding Transformers are successful at identifying intermediate predictions during a model's inference. However, these approaches function as limited diagnostic checkpoints, lacking a mathematical framework for mechanistically modeling how each layer facilitates transitions between these evolving states. This interpretability gap and past successes of interdisciplinary outlooks inspire us to turn to physics in search of a descriptive mathematical framework for Transformers. We observe that language models are intrinsically probabilistic, an attribute that is echoed in the core postulates of quantum mechanics. This parallel inspires us to translate insights from this discipline to that of natural language processing. Towards this objective, we propose QLENS a novel attempt to develop a physics-based perspective on the Transformer generation process. Under QLENS, a Transformer is studied by converting its latent activations into a state vector in a Hilbert space derived from the model's output units. This state subsequently evolves through hidden layers - reformulated as unitary operators and analogously defined Hamiltonians - during inference. The model's final probability distribution is obtained by applying the Born rule to the end state using a specific measurement operator. To demonstrate QLENS's potential, we conduct a proof-of-concept by probing a toy Transformer to investigate the influence of individual layers in a model's prediction trajectory. We present our work as a foundation for cross-domain insights to be leveraged towards a broader understanding of Transformers.

cs.LG↗

arXiv2025

Novel View Synthesis using DDIM Inversion

Synthesizing novel views from a single input image is a challenging task. It requires extrapolating the 3D structure of a scene while inferring details in occluded regions, and maintaining geometric consistency across viewpoints. Many existing methods must fine-tune large diffusion backbones using multiple views or train a diffusion model from scratch, which is extremely expensive. Additionally, they suffer from blurry reconstruction and poor generalization. This gap presents the opportunity to explore an explicit lightweight view translation framework that can directly utilize the high-fidelity generative capabilities of a pretrained diffusion model while reconstructing a scene from a novel view. Given the DDIM-inverted latent of a single input image, we employ a camera pose-conditioned translation U-Net, TUNet, to predict the inverted latent corresponding to the desired target view. However, the image sampled using the predicted latent may result in a blurry reconstruction. To this end, we propose a novel fusion strategy that exploits the inherent noise correlation structure observed in DDIM inversion. The proposed fusion strategy helps preserve the texture and fine-grained details. To synthesize the novel view, we use the fused latent as the initial condition for DDIM sampling, leveraging the generative prior of the pretrained diffusion model. Extensive experiments on MVImgNet demonstrate that our method outperforms existing methods.

cs.CV↗

arXiv2025

Advancing Digital Accessibility In Digital Pharmacy, Healthcare, And Wearable Devices: Inclusive Solutions for Enhanced Patient Engagement

Modern healthcare facilities demand digital accessibility to guarantee equal access to telemedicine platforms, online pharmacy services, and health monitoring devices that can be worn or are handy. With the rising call for the implementation of robust digital healthcare solutions, people with disabilities encounter impediments in their endeavor of managing and getting accustomed to these modern technologies owing to insufficient accessibility features. The paper highlights the role of comprehensive solutions for enhanced patient engagement and usability, particularly, in digital pharmacy, healthcare, and wearable devices. Besides, it elucidates the key obstructions faced by users experiencing auditory, visual, cognitive, and motor impairments. Through a kind consideration of present accessibility guidelines, practices, and emerging technologies, the paper provides a holistic overview by offering innovative solutions, accentuating the vitality of compliance with Web Content Accessibility Guidelines (WCAG), Americans with Disabilities Act (ADA), and other regulatory structures to foster easy access to digital healthcare services. Moreover, there is due focus on using AI-driven tools, speech-activated interfaces, and tactile feedback in wearable health devices to assist persons with disabilities. The outcome of the research explicates the necessity of prioritizing accessibility for individuals with disabilities and cultivating a culture where healthcare providers, policymakers, and officials build a patient-centered digital healthcare ecosystem that is all-encompassing in nature.

cs.HC↗

arXiv2025

Advancing Digital Accessibility: Integrating AR/VR and Health Tech for Inclusive Healthcare Solutions

Modern healthcare domain incorporates a feature of digital accessibility to ensure seamless flow of online services for the patients. However, this feature of digital accessibility poses a challenge particularly for patients with disabilities. To eradicate this issue and provide immersive and user-friendly experiences, evolving technologies like Augmented Reality (AR) and Virtual Reality (VR) are integrated in medical applications to enhance accessibility. The present research paper aims to study inclusivity and accessibility features of AR/VR in revolutionizing healthcare practices especially in domains like telemedicine, patient education, assistive tools, and rehabilitation for persons with disabilities. The current trends of advancements and case studies are also analyzed to measure the efficacy of AR/VR in healthcare. Moreover, the paper entails a detailed analysis of the challenges of its adoption particularly technical limitations, implementation costs, and regulatory aspects. Finally, the paper concludes with recommendations for integrating AR/VR to foster a more equitable and inclusive healthcare system and provide individuals with auditory, visual, and motor impairments with digital healthcare solutions.

cs.HC↗

arXiv2025

Bridging the Gap: Enhancing Digital Accessibility for Medicaid Populations in Telehealth Adoption

The swift evolution of telehealth has revolutionized how medical professionals deliver healthcare services and boost convenience and accessibility. Yet, the Medicaid population encounters several impediments in utilizing facilities especially owing to poor internet connectivity, less awareness about digital platforms, and a shortage of assistive technologies. The paper aims to explicate key factors behind digital accessibility for Medicaid populations and expounds robust solutions to eradicate these challenges. Through inclusive design ideas, AI-assisted technologies, and all-encompassing policies by the concerned authorities, healthcare professionals can enhance usability and efficacy and thus better serve the needy. This revolution not only enhances convenience but also expands access, mainly for underserved groups such as rural populations or those with mobility issues, thereby ensuring inclusivity and flexibility in the healthcare domain. Besides, the paper highlights the vitality of collaboration between healthcare professionals, policymakers, and tech developers in unveiling the accessibility and usability impediments. What else helps in minimizing healthcare differences and enhancing patient outcomes is guaranteeing equitable access to telehealth for Medicaid beneficiaries. The paper systematically offers major recommendations to increase digital accessibility in telehealth, thereby creating a patient-oriented and all-encompassing healthcare system.

cs.CY↗

arXiv2025

A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking

Document chunking fundamentally impacts Retrieval-Augmented Generation (RAG) by determining how source materials are segmented before indexing. Despite evidence that Large Language Models (LLMs) are sensitive to the layout and structure of retrieved data, there is currently no framework to analyze the impact of different chunking methods. In this paper, we introduce a novel methodology that defines essential characteristics of the chunking process at three levels: intrinsic passage properties, extrinsic passage properties, and passages-document coherence. We propose HOPE (Holistic Passage Evaluation), a domain-agnostic, automatic evaluation metric that quantifies and aggregates these characteristics. Our empirical evaluations across seven domains demonstrate that the HOPE metric correlates significantly (p > 0.13) with various RAG performance indicators, revealing contrasts between the importance of extrinsic and intrinsic properties of passages. Semantic independence between passages proves essential for system performance with a performance gain of up to 56.2% in factual correctness and 21.1% in answer correctness. On the contrary, traditional assumptions about maintaining concept unity within passages show minimal impact. These findings provide actionable insights for optimizing chunking strategies, thus improving RAG system design to produce more factually correct responses.

cs.CL↗

arXiv2025

XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants

AI coding assistants are widely used for tasks like code generation. These tools now require large and complex contexts, automatically sourced from various origins$\unicode{x2014}$across files, projects, and contributors$\unicode{x2014}$forming part of the prompt fed to underlying LLMs. This automatic context-gathering introduces new vulnerabilities, allowing attackers to subtly poison input to compromise the assistant's outputs, potentially generating vulnerable code or introducing critical errors. We propose a novel attack, Cross-Origin Context Poisoning (XOXO), that is challenging to detect as it relies on adversarial code modifications that are semantically equivalent. Traditional program analysis techniques struggle to identify these perturbations since the semantics of the code remains correct, making it appear legitimate. This allows attackers to manipulate coding assistants into producing incorrect outputs, while shifting the blame to the victim developer. We introduce a novel, task-agnostic, black-box attack algorithm GCGS that systematically searches the transformation space using a Cayley Graph, achieving a 75.72% attack success rate on average across five tasks and eleven models, including GPT 4.1 and Claude 3.5 Sonnet v2 used by popular AI coding assistants. Furthermore, defenses like adversarial fine-tuning are ineffective against our attack, underscoring the need for new security measures in LLM-powered coding tools.

cs.CR↗

arXiv2024

On a probabilistic global optimizer derived from the Walker slice sampling

This article presents a zeroth order probabilistic global optimization algorithm -- SwiftNav -- for (not necessarily convex) functions over a compact domain. A discretization procedure is deployed on the compact domain, starting with a small step-size $h > 0$ and subsequently adaptively refining it in the course of a simulated annealing routine utilizing the Walker slice and the Gibbs sampler, in order to identify a set of global optimizers up to good precision. SwiftNav is parallelizable, which helps with scalability as the dimension of decision variables increases. Several numerical experiments are included here to demonstrate the effectiveness and accuracy of SwiftNav in high-dimensional benchmark optimization problems.

math.OC↗

arXiv2023

AutoMix: Automatically Mixing Language Models

Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present Automix, an approach that strategically routes queries to larger LMs, based on the approximate correctness of outputs from a smaller LM. Central to Automix are two key technical contributions. First, it has a few-shot self-verification mechanism, which estimates the reliability of its own outputs without requiring extensive training. Second, given that self-verification can be noisy, it employs a POMDP based router that can effectively select an appropriately sized model, based on answer confidence. Experiments across five language models and five challenging datasets show that Automix consistently surpasses strong baselines, reducing computational cost by over 50% for comparable performance.

cs.CL↗

arXiv2023

Agri-GNN: A Novel Genotypic-Topological Graph Neural Network Framework Built on GraphSAGE for Optimized Yield Prediction

Agriculture, as the cornerstone of human civilization, constantly seeks to integrate technology for enhanced productivity and sustainability. This paper introduces $\textit{Agri-GNN}$, a novel Genotypic-Topological Graph Neural Network Framework tailored to capture the intricate spatial and genotypic interactions of crops, paving the way for optimized predictions of harvest yields. $\textit{Agri-GNN}$ constructs a Graph $\mathcal{G}$ that considers farming plots as nodes, and then methodically constructs edges between nodes based on spatial and genotypic similarity, allowing for the aggregation of node information through a genotypic-topological filter. Graph Neural Networks (GNN), by design, consider the relationships between data points, enabling them to efficiently model the interconnected agricultural ecosystem. By harnessing the power of GNNs, $\textit{Agri-GNN}$ encapsulates both local and global information from plants, considering their inherent connections based on spatial proximity and shared genotypes, allowing stronger predictions to be made than traditional Machine Learning architectures. $\textit{Agri-GNN}$ is built from the GraphSAGE architecture, because of its optimal calibration with large graphs, like those of farming plots and breeding experiments. $\textit{Agri-GNN}$ experiments, conducted on a comprehensive dataset of vegetation indices, time, genotype information, and location data, demonstrate that $\textit{Agri-GNN}$ achieves an $R^2 = .876$ in yield predictions for farming fields in Iowa. The results show significant improvement over the baselines and other work in the field. $\textit{Agri-GNN}$ represents a blueprint for using advanced graph-based neural architectures to predict crop yield, providing significant improvements over baselines in the field.

cs.LG↗

arXiv2023

How FaR Are Large Language Models From Agents with Theory-of-Mind?

"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those inferences. Existing question answering benchmarks such as ToMi ask models questions to make inferences about beliefs of characters in a story, but do not test whether models can then use these inferences to guide their actions. We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios. Experiments on T4D demonstrate that LLMs such as GPT-4 and PaLM 2 seemingly excel at tracking characters' beliefs in stories, but they struggle to translate this capability into strategic action. Our analysis reveals the core challenge for LLMs lies in identifying the implicit inferences about mental states without being explicitly asked about as in ToMi, that lead to choosing the correct action in T4D. To bridge this gap, we introduce a zero-shot prompting framework, Foresee and Reflect (FaR), which provides a reasoning structure that encourages LLMs to anticipate future challenges and reason about potential actions. FaR boosts GPT-4's performance from 50% to 71% on T4D, outperforming other prompting methods such as Chain-of-Thought and Self-Ask. Moreover, FaR generalizes to diverse out-of-distribution story structures and scenarios that also require ToM inferences to choose an action, consistently outperforming other methods including few-shot in-context learning.

cs.CL↗