1 citations · 1 across the 12 of their papers we have counts for
13 papers
Small Agents with Semantic Search: Efficient Multilingual Code Localization
Maxence Lasbordes, Aarush Sinha, Raphael Sourty +2
Locating relevant files from natural-language requests is a core subtask for agents operating over code repositories. We investigate whether this task can be delegated to compact,…
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
Avijit Ghosh, Anka Reuel, Jenny Chim +45
AI evaluation results are produced at scale but reported inconsistently across leaderboards, model cards, benchmark papers, and company blogs. The cost is interpretive: readers can…
Mechanistic Analysis of Alignment Algorithms in Language Models
Aarush Sinha, Ishan Garg, Veeraraju Elluru +2
Post-training alignment algorithms are predominantly evaluated as black boxes, obscuring how they reshape language models' internal computations. We present a systematic mechanisti…
CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation
Aarush Sinha, Arion Das, Soumyadeep Nag +7
As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environments has become an important ali…
What Tracks Hard-Negative Transfer? A Study of Lexical and Semantic Signals
Aarush Sinha, Rahul Seetharaman, Aman Bansal
Which properties of hard-negative supervision track dense-retriever transfer before training? We compare lexical noncoverage, frozenencoder similarity statistics, hardness proxies,…
BiCA: Effective Biomedical Dense Retrieval with Citation-Aware Hard Negatives
Aarush Sinha, Pavan Kumar S, Roshan Balaji +1
Hard negatives are essential for training effective retrieval models. Hard-negative mining typically relies on ranking documents using cross-encoders or static embedding models bas…
Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval
Aarush Sinha
Training effective dense retrieval models typically relies on hard negative (HN) examples mined from large document corpora using methods such as BM25 or cross-encoders, which requ…
GMLM: Bridging Graph Neural Networks and Language Models for Heterophilic Node Classification
Aarush Sinha
Integrating Pre-trained Language Models (PLMs) with Graph Neural Networks (GNNs) remains a central challenge in text-rich heterophilic graph learning. We propose a novel integratio…
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
Jaskaran Singh Walia, Aarush Sinha, Naman Saraswat +2
Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framew…
ArxEval: Evaluating Retrieval and Generation in Language Models for Scientific Literature
Aarush Sinha, Viraj Virk, Dipshikha Chakraborty +1
Language Models [LMs] are now playing an increasingly large role in information generation and synthesis; the representation of scientific knowledge in these systems needs to be hi…
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
Vipula Rawte, Sarthak Jain, Aarush Sinha +9
Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textu…
A Multi-Branched Radial Basis Network Approach to Predicting Complex Chaotic Behaviours
Aarush Sinha
In this study, we propose a multi branched network approach to predict the dynamics of a physics attractor characterized by intricate and chaotic behavior. We introduce a unique ne…
Generation Of Colors using Bidirectional Long Short Term Memory Networks
A. Sinha
Human vision can distinguish between a vast spectrum of colours, estimated to be between 2 to 7 million discernible shades. However, this impressive range does not inherently imply…