arXiv subjects

Jyoutir Raj

Publications and source records attributed to Jyoutir Raj.

4 recordsLinked to original sources

arXiv2026

When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

Artificial intelligence benchmarks are an important mechanism to measure model progress and guide deployment decisions. However, benchmarks quickly "saturate", making it difficult to differentiate models and diminishing their long-term value. In this study, we define benchmark saturation and analyze it across 60 language model benchmarks using 14 properties that relate to saturation. We find that nearly half of our benchmarks exhibit saturation, with rates increasing with age. Further, we find that resilience to saturation is impacted by expert-curation, not by public test data. Our results suggest that design choices can extend benchmark longevity and inform more durable evaluation approaches.

cs.AI↗

arXiv2026

BlasBench: An Open Benchmark for Irish Speech Recognition

Existing multilingual benchmarks include Irish among dozens of languages but apply no Irish-aware text normalisation, leaving reliable and reproducible ASR comparison impossible. We introduce BlasBench, an open evaluation harness that provides a standalone Irish-aware normaliser preserving fadas, lenition, and eclipsis; a reproducible scoring harness and per-utterance predictions released for all evaluated runs. We pilot this by benchmarking 12 systems across four architecture families on Common Voice ga-IE and FLEURS ga-IE. All Whisper variants exceed 100% WER through insertion-driven hallucination. Microsoft Azure reaches 22.2% WER on Common Voice and 57.5% on FLEURS; the best open model, Omnilingual ASR 7B, reaches 30.65% and 39.09% respectively. Models fine-tuned on Common Voice degrade 33-43 points moving to FLEURS, while massively multilingual models degrade only 7-10 - a generalisation gap that single-dataset evaluation misses.

cs.CL↗

arXiv2026

ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense

Large language models (LLMs) are increasingly being deployed as software engineering agents that autonomously contribute to repositories. A major benefit these agents present is their ability to find and patch security vulnerabilities in the codebases they oversee. To estimate the capability of agents in this domain, we introduce ZeroDayBench, a benchmark where LLM agents find and patch 22 novel critical vulnerabilities in open-source codebases. We focus our efforts on three popular frontier agentic LLMs: GPT-5.2, Claude Sonnet 4.5, and Grok 4.1. We find that frontier LLMs are not yet capable of autonomously solving our tasks and observe some behavioral patterns that suggest how these models can be improved in the domain of proactive cyberdefense.

cs.CR↗

arXiv2025

Time-Dependent Queuing Model for Traffic Congestion Using Mt/D/1/K: Simulation and Policy Insights

This study proposes a generalised macroscopic traffic simulation using a Mt/D/1/K queue to model congestion, using the Enniskillen to Belfast route as a case study. Empirical traffic data from Google's Directions API is used to calibrate the model, thus explaining peak commute times which we model using queue length. Simulations of staggered institutional start times showed significant reductions in queue lengths, suggesting time based interventions to improve rural to urban traffic flow.

physics.soc-ph↗