arXiv subjects

Arindam Sharma

Publications and source records attributed to Arindam Sharma.

6 recordsLinked to original sources

arXiv2026

Shape of U: Measuring the Curvature of the Universe with Gravitational Waves

Gravitational waves (GWs) from compact binary mergers are standard sirens that can measure distances across the Universe without external calibrators. When an electromagnetic counterpart enables an independent redshift measurement, such "bright sirens" can be used to probe the expansion history of the Universe and constrain cosmological models. In this work, we investigate the ability of future GW observatories to measure the spatial curvature parameter, $\Omega_{\rm k}$, in a non-flat $\Lambda$CDM cosmology. We focus on intermediate-mass binary black hole mergers (with masses similar to GW231123) as bright siren sources, motivated by their detectability to high redshifts with next-generation ground-based detectors and by the possibility that mergers in active galactic nucleus disks may produce electromagnetic counterparts. Using Fisher matrix forecasts, we find that a network consisting of two Cosmic Explorer detectors and Einstein Telescope can constrain $\Omega_{\rm k}$ to a $1\sigma$ uncertainty of $0.029$ with these bright sirens. We further show that multiband observations with LISA or the Lunar Gravitational Wave Antenna do not significantly improve these cosmological constraints, because the additional signal-to-noise ratios accumulated in their bands are modest. Further, a population of binary neutron stars as bright sirens provides substantially broader constraints on $\Omega_{\rm k}$, with $1\sigma$ error of $0.055$. Our results show that bright intermediate-mass binary black hole and binary neutron star mergers observed with next-generation GW detectors together can provide an independent and informative probe of spatial curvature, with systematics distinct from those of other cosmological observations.

gr-qc↗

arXiv2026

Using Semantic Distance to Estimate Uncertainty in LLM-Based Code Generation

LLMs show strong performance in code generation, but their outputs lack correctness guarantees. Sample-based uncertainty estimators address this by generating multiple candidate programs and measuring their disagreement. However, existing estimators make different design choices about how behaviours are identified, aggregated, referenced and compared, making them difficult to assess. We therefore first introduce a taxonomy that disentangles these choices and reveals a missing design point: semantic distance-aware uncertainty estimation, which measures not only whether sampled programs disagree, but how severely their execution behaviours differ. Across LiveCodeBench, MBPP, HumanEval-X and BigCodeBench, spanning Python, Java and C++, our metrics provide strong proxies for correctness, and consistently outperform state-of-the-art sample-based baselines across both closed-source models (GPT-3.5-Turbo, GPT-4o-mini, Gemini-2.5-Flash-Lite, Claude Opus 4.5) and an open-source model (DeepSeek-Coder-V2). The method is practical: it requires neither model internals nor LLM-as-judge calls, remains robust across models, languages, sampling temperatures and fuzzing settings, and reduces runtime by approximately 48-79% relative to existing baselines.

cs.SE↗

arXiv2026

Validated Code Translation for Projects with External Libraries

Large Language Models (LLMs) have shown promise for program translation, particularly for migrating systems code to memory-safe languages such as Rust. However, existing approaches struggle when source programs depend on external libraries: LLMs frequently hallucinate non-existent target APIs and fail to generate call-enabling imports; moreover, validating semantic equivalence is challenging when the code manipulates opaque, library-defined types. We present a translation and validation framework for translating Go projects with external dependencies to Rust. Our approach combines (i) a retrieval mechanism that maps Go library APIs to Rust APIs, and (ii) a cross-language validation pipeline that establishes language interoperability in the presence of opaque library types by synthesising adapters exclusively from public library APIs, prior to validating I/O equivalence. We evaluate our system on six real-world Go repositories with non-trivial external dependencies. Our approach significantly increases both the compilation and equivalence success rate (up to 100% in the most dependency-heavy case; approx. 2x on average) by enabling validated translation that manipulate opaque, library-defined types.

cs.SE↗

arXiv2025

TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation

Large language models (LLMs) have shown remarkable ability to generate code, yet their outputs often violate syntactic or semantic constraints when guided only through natural language prompts. We introduce TreeCoder, the most general and flexible framework to date for exploring decoding strategies, constraints, and hyperparameters in LLMs, and use it in code generation to enforce correctness and structure during decoding rather than relying on prompt engineering. TreeCoder represents decoding as a tree search over candidate programs, where both decoding strategies and constraint functions - such as style, syntax, execution - are treated as first-class, optimisable components. This design enables systematic exploration and automatic tuning of decoding configurations using standard optimisation techniques. Experiments on the MBPP (Python) and SQL-Spider benchmarks show that TreeCoder consistently improves accuracy across open-source models such as CodeLlama, Mistral and DeepSeek, often outperforming their unconstrained baselines by considerable margins.

cs.LG↗

arXiv2025

Efficient Search for Detection Candidates Using a Peak Finder Strategy for All-Sky-All-Frequency Gravitational Wave Radiometer

The first all-sky-all-frequency (ASAF) radiometer search was conducted using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. The significance of this search lies in its fast and unmodeled approach, leveraging a cross-correlation technique to identify common signals across the detector network. As a result, this method serves as an excellent alternative to search for unknown or poorly modeled continuous wave sources and narrowband components of the gravitational wave (GW) background. For continuous wave sources whose waveform can be modeled, this method can serve as the first stage of a hierarchical scheme by identifying sub-threshold candidates to be followed up with more optimal but computationally expensive searches. The ASAF search, however, presently suffers from beam smearing, where multiple candidates may arise due to the same noise fluctuations, detector artifact, or a GW source. This can reduce the detection probability in follow-up analyzes, especially with limited computing resources. To mitigate this issue and reduce the number of correlated and unnecessary candidates, we introduce a novel Peak Finder algorithm. This algorithm helps identifying the most representative candidates while preserving detection sensitivity, thereby allowing follow up of a much larger number of independent candidates. The reduction in correlated samples leads to a significant reduction in False Dismissal Rate (FDR) using the Peak Finder method compared to the Full-sky method. For instance, following up 2 Peak Finder candidates at 30 Hz reduces FDR by a factor of 3.

gr-qc↗

arXiv2025

Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation

In this work, we explore uncertainty estimation as a proxy for correctness in LLM-generated code. To this end, we adapt two state-of-the-art techniques from natural language generation -- one based on entropy and another on mutual information -- to the domain of code generation. Given the distinct semantic properties of code, we introduce modifications, including a semantic equivalence check based on symbolic execution. Our findings indicate a strong correlation between the uncertainty computed through these techniques and correctness, highlighting the potential of uncertainty estimation for quality assessment. Additionally, we propose a simplified version of the entropy-based method that assumes a uniform distribution over the LLM's responses, demonstrating comparable effectiveness. Using these techniques, we develop an abstention policy that prevents the model from making predictions when uncertainty is high, reducing incorrect outputs to near zero. Our evaluation on the LiveCodeBench shows that our approach significantly outperforms a baseline relying solely on LLM-reported log-probabilities.

cs.SE↗