arXiv subjects

Sachit Mahajan

Publications and source records attributed to Sachit Mahajan.

6 recordsLinked to original sources

arXiv2026

Participatory provenance as representational auditing for AI-mediated public consultation

AI-assisted consultation can speed large-scale public engagement, but concise summaries may reflect some submissions more closely than others. This paper introduces participatory provenance, a framework for auditing how semantic coverage is distributed from submissions to summary sentences. Applied to two topics in Canada's 2025 AI Strategy consultation (5,253 records; 2,861 participants), official summaries had higher observed mean coverage than exact-length random text, although statistical significance depended on the embedding model. Low coverage concentrated in semantic regions, especially those centered on criticism of educational technology and distrust of technology and oversight, whereas few or no records crossed the operational threshold in several better-covered regions. Same-budget, cross-fitted extractive benchmarks improved mean and lower-tail coverage on held-out submissions, showing that better semantic coverage was feasible without longer summaries. Consultation summaries should be evaluated not only for coherence and factual support, but also for how coverage is distributed across the range of submitted views.

cs.AI↗

arXiv2026

Beyond the Townhall: Spatial Anchoring and LLM Agents for Scalable Participatory Urban Planning

Participatory urban planning is central to sustainable city-making, yet the technically demanding nature of such interventions often limits meaningful involvement by diverse publics. We introduce a scalable digital participation platform that embeds sustainability projects within a navigable digital twin. Citizens experience a guided virtual walkthrough with audio narration employing the method of loci and spatial anchoring to support mnemonic encoding and recall. This immersive interface is augmented by two purpose-built LLM assistants: one delivers source-grounded factual clarifications, while the other facilitates reflective discussion. We evaluated this system in a randomized controlled online experiment (N = 195) against conventional industry practices (static visualizations and text-based consultations). Results show that spatially anchored immersive presentation significantly improved information recall, which substantially shifted participants' attention from individual inconveniences to collective, community-oriented sustainability benefits. Consequently, participants provided significantly more constructive, solution-focused feedback to the (simulated) municipality. These findings establish a practical tool for cities and policymakers to foster inclusive, democratic participation in sustainability transitions.

cs.HC↗

arXiv2025

POp-GS: Next Best View in 3D-Gaussian Splatting with P-Optimality

In this paper, we present a novel algorithm for quantifying uncertainty and information gained within 3D Gaussian Splatting (3D-GS) through P-Optimality. While 3D-GS has proven to be a useful world model with high-quality rasterizations, it does not natively quantify uncertainty or information, posing a challenge for real-world applications such as 3D-GS SLAM. We propose to quantify information gain in 3D-GS by reformulating the problem through the lens of optimal experimental design, which is a classical solution widely used in literature. By restructuring information quantification of 3D-GS through optimal experimental design, we arrive at multiple solutions, of which T-Optimality and D-Optimality perform the best quantitatively and qualitatively as measured on two popular datasets. Additionally, we propose a block diagonal covariance approximation which provides a measure of correlation at the expense of a greater computation cost.

cs.CV↗

arXiv2025

Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making

We present an ethical decision-making framework that refines a pre-trained reinforcement learning (RL) model using a task-agnostic ethical layer. Following initial training, the RL model undergoes ethical fine-tuning, where human feedback is replaced by feedback generated from a large language model (LLM). The LLM embodies consequentialist, deontological, virtue, social justice, and care ethics as moral principles to assign belief values to recommended actions during ethical decision-making. An ethical layer aggregates belief scores from multiple LLM-derived moral perspectives using Belief Jensen-Shannon Divergence and Dempster-Shafer Theory into probability scores that also serve as the shaping reward, steering the agent toward choices that align with a balanced ethical framework. This integrated learning framework helps the RL agent navigate moral uncertainty in complex environments and enables it to make morally sound decisions across diverse tasks. Our approach, tested across different LLM variants and compared with other belief aggregation techniques, demonstrates improved consistency, adaptability, and reduced reliance on handcrafted ethical rewards. This method is especially effective in dynamic scenarios where ethical challenges arise unexpectedly, making it well-suited for real-world applications.

cs.CY↗

arXiv2024

Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting

In this paper, we present a novel algorithm for probabilistically updating and rasterizing semantic maps within 3D Gaussian Splatting (3D-GS). Although previous methods have introduced algorithms which learn to rasterize features in 3D-GS for enhanced scene understanding, 3D-GS can fail without warning which presents a challenge for safety-critical robotic applications. To address this gap, we propose a method which advances the literature of continuous semantic mapping from voxels to ellipsoids, combining the precise structure of 3D-GS with the ability to quantify uncertainty of probabilistic robotic maps. Given a set of images, our algorithm performs a probabilistic semantic update directly on the 3D ellipsoids to obtain an expectation and variance through the use of conjugate priors. We also propose a probabilistic rasterization which returns per-pixel segmentation predictions with quantifiable uncertainty. We compare our method with similar probabilistic voxel-based methods to verify our extension to 3D ellipsoids, and perform ablation studies on uncertainty quantification and temporal smoothing.

cs.RO↗

arXiv2024

DePIN: A Framework for Token-Incentivized Participatory Sensing

There is always demand for integrating data into microeconomic decision making. Participatory sensing deals with how real-world data may be extracted with stakeholder participation and resolves a problem of Big Data, which is concerned with monetizing data extracted from individuals without their participation. We present how Decentralized Physical Infrastructure Networks (DePINs) extend participatory sensing. We discuss the threat models of these networks and how DePIN cryptoeconomics can advance participatory sensing.

cs.GT↗