Tootfinder

Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Akshay Govind Srinivasan, Ryan Jacob George, Jayden Koshy Joe, Hrushikesh Kant, Harshith M R, Sachin Sundar, Sudharshan Suresh, Rahul Vimalkanth, Vijayavallabh
https://arxiv.org/abs/2509.16369

Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction
Accurate and reliable knowledge retrieval is vital for financial question-answering, where continually updated data sources and complex, high-stakes contexts demand precision. Traditional retrieval systems rely on a single database and retriever, but financial applications require more sophisticated approaches to handle intricate regulatory filings, market analyses, and extensive multi-year reports. We introduce a framework for financial Retrieval Augmented Generation (RAG) that leverages agent…

The penultimate talk at #12Clouds is Akshay M talking about Agent Name Service (ANS), a DNS-like trust layer for AI agent deployments

Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Jens Tuyls, Dylan J. Foster, Akshay Krishnamurthy, Jordan T. Ash
https://arxiv.org/abs/2510.11686

Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the discovery of novel behaviors, or simply sharpen those already present in the base model. In this paper, we investigate the value of deliberate exploration -- explicitly incentivizing the model to discover novel and diverse behaviors -- and aim to understand how the knowledge in pre-trained models can guide this search. Our main finding is that exploration wi…

Hybrid Terrain-Aware Path Planning: Integrating VD--RRT\(^{*}\) Exploration and VD--D\(^{*}\) Lite Repair
Akshay Naik, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu
https://arxiv.org/abs/2510.12169

Hybrid Terrain-Aware Path Planning: Integrating VD--RRT\(^{*}\) Exploration and VD--D\(^{*}\) Lite Repair
Autonomous ground vehicles operating off-road must plan curvature-feasible paths while accounting for spatially varying soil strength and slope hazards in real time. We present a continuous state--cost metric that combines a Bekker pressure--sinkage model with elevation-derived slope and attitude penalties. The resulting terrain cost field is analytic, bounded, and monotonic in soil modulus and slope, ensuring well-posed discretization and stable updates under sensor noise. This metric is evalu…

Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation
Vijay M. Galshetwar, Praful Hambarde, Prashant W. Patil, Akshay Dudhane, Sachin Chaudhary, Santosh Kumar Vipparathi, Subrahmanyam Murala
https://arxiv.org/abs/2510.09228

Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation
Adverse weather conditions such as haze, rain, and snow significantly degrade the quality of images and videos, posing serious challenges to intelligent transportation systems (ITS) that rely on visual input. These degradations affect critical applications including autonomous driving, traffic monitoring, and surveillance. This survey presents a comprehensive review of image and video restoration techniques developed to mitigate weather-induced visual impairments. We categorize existing approac…

Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges
Christian Bluethgen, Dave Van Veen, Daniel Truhn, Jakob Nikolas Kather, Michael Moor, Malgorzata Polacin, Akshay Chaudhari, Thomas Frauenfelder, Curtis P. Langlotz, Michael Krauthammer, Farhad Nooralahzadeh
https://arxiv.org/abs/2510.09404

Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges
Building agents, systems that perceive and act upon their environment with a degree of autonomy, has long been a focus of AI research. This pursuit has recently become vastly more practical with the emergence of large language models (LLMs) capable of using natural language to integrate information, follow instructions, and perform forms of "reasoning" and planning across a wide range of tasks. With its multimodal data streams and orchestrated workflows spanning multiple systems, radiology is u…

Crosslisted article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Hybrid Terrain-Aware Path Planning: Integrating VD--RRT\(^{*}\) Exploration and VD--D\(^{*}\) Lit...
Akshay Naik, William R. Norris, Dustin Nottage, Ahmet Soylemezoglu

Room temperature optical control of spin states in organic diradicals
Rituparno Chowdhury, Alistair Inglis, Lucy E. Walker, Petri Murto, Chiara Delpiano-Cordeiro, Colin Morrison, Naitik A. Panjwani, Yao Fu, Yan Sun, Wei Zhou, Peter J. Skabara, Akshay Rao, Alexei Chepelianskii, Hugo Bronstein, Sam L. Bayliss, Richard H. Friend
https://arxiv…

Room temperature optical control of spin states in organic diradicals
We report a family of luminescent alternant diradicals which, at room temperature, support a ground-state spin-triplet, near-unity photoluminescence quantum yields, and optical spin addressability. These diradicals comprise trityl groups meta-linked via pyridyl or phenyl groups, enabling optically bright triplet-to-triplet and singlet-to-singlet transitions. At room temperature, we observe optically detected magnetic resonance in these systems at zero magnetic field and a strong magneto-photolu…

IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Santhosh G S, Akshay Govind S, Gokul S Krishnan, Balaraman Ravindran, Sriraam Natarajan
https://arxiv.org/abs/2510.02742

IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context
Large Language Models (LLMs) have gained significant traction across critical domains owing to their impressive contextual understanding and generative capabilities. However, their increasing deployment in high stakes applications necessitates rigorous evaluation of embedded biases, particularly in culturally diverse contexts like India where existing embedding-based bias assessment methods often fall short in capturing nuanced stereotypes. We propose an evaluation framework based on a encoder …

EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition
Akshay Muppidi, Martin Radfar
https://arxiv.org/abs/2510.06072 https://arxiv.org/…

EmoHRNet: High-Resolution Neural Network Based Speech Emotion Recognition
Speech emotion recognition (SER) is pivotal for enhancing human-machine interactions. This paper introduces "EmoHRNet", a novel adaptation of High-Resolution Networks (HRNet) tailored for SER. The HRNet structure is designed to maintain high-resolution representations from the initial to the final layers. By transforming audio samples into spectrograms, EmoHRNet leverages the HRNet architecture to extract high-level features. EmoHRNet's unique architecture maintains high-resolution representati…

Towards Fast Option Pricing PDE Solvers Powered by PIELM
Akshay Govind Srinivasan, Anuj Jagannath Said, Sathwik Pentela, Vikas Dwivedi, Balaji Srinivasan
https://arxiv.org/abs/2510.04322

Towards Fast Option Pricing PDE Solvers Powered by PIELM
Partial differential equation (PDE) solvers underpin modern quantitative finance, governing option pricing and risk evaluation. Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving the forward and inverse problems of partial differential equations (PDEs) using deep learning. However they remain computationally expensive due to their iterative gradient descent based optimization and scale poorly with increasing model size. This paper introduces Physics-Inform…

NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
Wei-Ting Tang, Akshay Kudva, Joel A. Paulson
https://arxiv.org/abs/2510.05516

NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a local BO method that targets the Newton step by jointly learning gradient and Hessian information with Gaussian process surrogates, and selecting evaluations via a one-step lookahead bound on Newton-step error. We show that this bound (and hence the step error) contracts with batch size, so NeST-BO directly inherits inexact-Newton convergence: global progres…

Enhanced Matter Power Spectrum from Axion Kination after Big Bang Nucleosynthesis
Raymond T. Co, Nicolas Fernandez, Akshay Ghalsasi, Keisuke Harigaya, Jessie Shelton
https://arxiv.org/abs/2510.01308

Enhanced Matter Power Spectrum from Axion Kination after Big Bang Nucleosynthesis
Despite stringent constraints from Big Bang Nucleosynthesis (BBN) and cosmic microwave background (CMB) observations, it is still possible for well-motivated particle physics models to substantially alter the cosmic expansion history between BBN and recombination. In this work we consider two different axion models that can realize a period of first matter domination, then kination, in this epoch. We perform fits to both primordial element abundances as well as CMB data and determine that up to…

Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study
Rogier P. Krijnen, Akshay Joshi, Siddhant Kumar, Mathias Peirlinck
https://arxiv.org/abs/2510.09498

Unsupervised full-field Bayesian inference of orthotropic hyperelasticity from a single biaxial test: a myocardial case study
Fully capturing this behavior in traditional homogenized tissue testing requires the excitation of multiple deformation modes, i.e. combined triaxial shear tests and biaxial stretch tests. Inherently, such multimodal experimental protocols necessitate multiple tissue samples and extensive sample manipulations. Intrinsic inter-sample variability and manipulation-induced tissue damage might have an adverse effect on the inversely identified tissue behavior. In this work, we aim to overcome this g…

cuHPX: GPU-Accelerated Differentiable Spherical Harmonic Transforms on HEALPix Grids
Xiaopo Cheng, Akshay Subramaniam, Shixun Wu, Noah Brenowitz
https://arxiv.org/abs/2510.01785

cuHPX: GPU-Accelerated Differentiable Spherical Harmonic Transforms on HEALPix Grids
HEALPix (Hierarchical Equal Area isoLatitude Pixelization) is a widely adopted spherical grid system in astrophysics, cosmology, and Earth sciences. Its equal-area, iso-latitude structure makes it particularly well-suited for large-scale data analysis on the sphere. However, implementing high-performance spherical harmonic transforms (SHTs) on HEALPix grids remains challenging due to irregular pixel geometry, latitude-dependent alignments, and the demands for high-resolution transforms at scale…

Teaching Machines to Speak Using Articulatory Control
Akshay Anand, Chenxu Guo, Cheol Jun Cho, Jiachen Lian, Gopala Anumanchipalli
https://arxiv.org/abs/2510.05619 https://

Teaching Machines to Speak Using Articulatory Control
Current speech production systems predominantly rely on large transformer models that operate as black boxes, providing little interpretability or grounding in the physical mechanisms of human speech. We address this limitation by proposing a new framework: speech generation through explicit articulatory control. This reframes speech as a motor control task similar to robotic manipulation. Our approach uses reinforcement learning to train a policy that directly controls the movements of vocal t…

Technique-agnostic exoplanet demography for the Roman era - I. Testing a retrieval framework using simulated Kepler-like transit datasets
Akshay Priyadarshi, Eamonn Kerins
https://arxiv.org/abs/2509.24401

Technique-agnostic exoplanet demography for the Roman era - I. Testing a retrieval framework using simulated Kepler-like transit datasets
The Nancy Grace Roman Space Telescope (Roman) will unveil for the first time the full architecture of planetary systems across Galactic distances through the discovery of up to 200,000 cool and hot exoplanets using microlensing and transit detection methods. Roman's huge exoplanet haul, and Galactic reach, will require new methods to leverage the full exoplanet demographic content of the combined microlensing and transit samples, given the different sensitivity bias of the techniques to planet …

Crosslisted article(s) found for cs.MS. https://arxiv.org/list/cs.MS/new
[1/1]:
- cuHPX: GPU-Accelerated Differentiable Spherical Harmonic Transforms on HEALPix Grids
Xiaopo Cheng, Akshay Subramaniam, Shixun Wu, Noah Brenowitz

Editing Physiological Signals in Videos Using Latent Representations
Tianwen Zhou, Akshay Paruchuri, Josef Spjut, Kaan Ak\c{s}it
https://arxiv.org/abs/2509.25348 https://…

Editing Physiological Signals in Videos Using Latent Representations
Camera-based physiological signal estimation provides a non-contact and convenient means to monitor Heart Rate (HR). However, the presence of vital signals in facial videos raises significant privacy concerns, as they can reveal sensitive personal information related to the health and emotional states of an individual. To address this, we propose a learned framework that edits physiological signals in videos while preserving visual fidelity. First, we encode an input video into a latent space v…

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
Seokju Chung, Jack Cleeve, Akshay Malige, Georgia Karagiorgi, Lino Gerlach, Adrian A. Pol, Isobel Ojalvo
https://arxiv.org/abs/2509.21817

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distil…

Testing the Constancy of Type Ia Supernova Luminosities with Gaussian Process
Akshay Rana
https://arxiv.org/abs/2509.19494 https://arxiv.org/pdf/2509.19494…

Testing the Constancy of Type Ia Supernova Luminosities with Gaussian Process
Type Ia supernovae (SNe~Ia) are central to studies of cosmic expansion, under the assumption that their absolute magnitude $M_B$ does not evolve with redshift. Even small drifts in brightness can bias cosmological parameters such as $H_0$ and $w$. Here we test this assumption using a non-parametric Gaussian Process (GP) reconstruction of the expansion history from cosmic chronometer $H(z)$ data, which provides a model-independent baseline distance modulus, $μ_{\rm GP}(z)$. To propagate uncerta…

Crosslisted article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- Editing Physiological Signals in Videos Using Latent Representations
Tianwen Zhou, Akshay Paruchuri, Josef Spjut, Kaan Ak\c{s}it

Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
Akshay Govind Srinivasan, Vikas Dwivedi, Balaji Srinivasan
https://arxiv.org/abs/2510.04490

Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
Partial differential equation (PDE) solvers are fundamental to engineering simulation. Classical mesh-based approaches (finite difference/volume/element) are fast and accurate on high-quality meshes but struggle with higher-order operators and complex, hard-to-mesh geometries. Recently developed physics-informed neural networks (PINNs) and their variants are mesh-free and flexible, yet compute-intensive and often less accurate. This paper systematically benchmarks RBF-PIELM, a rapid PINN varian…

Self-driving cars: Are we there yet?
Merve Atasever, Zhuochen Liu, Qingpei Li, Akshay Hitendra Shah, Hans Walker, Jyotirmoy V. Deshmukh, Rahul Jain
https://arxiv.org/abs/2509.22754

Self-driving cars: Are we there yet?
Autonomous driving remains a highly active research domain that seeks to enable vehicles to perceive dynamic environments, predict the future trajectories of traffic agents such as vehicles, pedestrians, and cyclists and plan safe and efficient future motions. To advance the field, several competitive platforms and benchmarks have been established to provide standardized datasets and evaluation protocols. Among these, leaderboards by the CARLA organization and nuPlan and the Waymo Open Dataset …

Replaced article(s) found for physics.geo-ph. https://arxiv.org/list/physics.geo-ph/new
[1/1]:
- Weighted Lagrange Multiplier Method for Robust Source-Independent Waveform Inversion
Ali Gholami, Kamal Aghazade, Akshay Vishwakarma

Trajectory Planning Using Safe Ellipsoidal Corridors as Projections of Orthogonal Trust Regions
Akshay Jaitly, Jon Arrizabalaga, Guanrui Li
https://arxiv.org/abs/2509.19734 http…

Trajectory Planning Using Safe Ellipsoidal Corridors as Projections of Orthogonal Trust Regions
Planning collision free trajectories in complex environments remains a core challenge in robotics. Existing corridor based planners which rely on decomposition of the free space into collision free subsets scale poorly with environmental complexity and require explicit allocations of time windows to trajectory segments. We introduce a new trajectory parameterization that represents trajectories in a nonconvex collision free corridor as being in a convex cartesian product of balls. This paramete…

Avalanche-like lithium intercalation and intraparticle correlations in graphite
Jiho Han, George S. Phillips, Alice J. Merryweather, Juhwan Lim, Christoph Schnedermann, Robert L. Jack, Clare P. Grey, Akshay Rao
https://arxiv.org/abs/2509.21047

Avalanche-like lithium intercalation and intraparticle correlations in graphite
Graphite is the most widely used anode material in lithium-ion batteries with over 98% market share. However, despite its first application over 30 years ago, the lithium insertion processes and associated dynamics in graphite remain poorly understood, especially for the dilute stages. A fundamental understanding of how the symmetry-breaking phase transitions occur pseudo-continuously under operating conditions is still lacking. Here, we provide a unified picture of ion intercalation dynamics d…