Papers by Roman Vainshtein
MAPS: A Multilingual Benchmark for Agent Performance and Security (2026.findings-eacl)
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Omer Hofman, Jonathan Brokman, Oren Rachmil, Shamik Bose, Vikas Pahuja, Toshiya Shimizu, Trisha Starostina, Kelly Marchisio, Seraphina Goldfarb-Tarrant, Roman Vainshtein
| Challenge: | Existing benchmarks do not provide a comprehensive, multi-domain, security-aware evaluation of multilingual agentic AI systems. |
| Approach: | They propose a multilingual benchmark suite to evaluate agentic AI systems across languages and tasks. |
| Outcome: | The proposed framework evaluates agentic AI systems across languages and tasks. |
CAIR: Counterfactual-based Agent Influence Ranker for Agentic AI Workflows (2025.emnlp-main)
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Amit Giloni, Chiara Picardi, Roy Betser, Shamik Bose, Aishvariya Priya Rathina Sabapathy, Roman Vainshtein
| Challenge: | Existing methods to assess the influence of each agent on the AAW’s output perform only static structural analysis, which is unsuitable for inference time execution. |
| Approach: | They propose to use an LLM-based agent influence Ranker to assess the influence level of each agent on the AAW's output and determine which agents are the most influential. |
| Outcome: | The proposed method outperforms baseline methods and produces consistent rankings and relevancy of downstream tasks. |
TFDP: Token-Efficient Disparity Audits for Autoregressive LLMs via Single-Token Masked Evaluation (2025.emnlp-main)
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| Challenge: | Existing methods for auditing autoregressive Large Language Models for disparities are limited and expensive. |
| Approach: | They propose a method to detect disparities in autoregressive Large Language Models by token querying . they propose 'token-focused disparity probing' to measure disparities between sentence pairs . |
| Outcome: | The proposed method detects disparities with 42 times fewer output tokens than previous methods. |