5 citations · 10 across the 14 of their papers we have counts for
16 papers
Trade-Adaptive Aggregation of Probabilistic Financial Forecasts
Yankai Chen, Rassul Magauin, Bowei He +5
Financial NLP systems produce probabilistic forecasts from news, reports, and filings. Prediction markets can aggregate these forecasts sequentially, but their fees must reward inf…
How Robust Are Automated Fact-Checking Systems? A Cross-Benchmark Evaluation
Aida Usmanova, Zangir Iklassov, Markus Leippold +1
Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a single benchmark and cannot b…
SymStep: Symbolic Step Verification for Logical Reasoning
Aida Usmanova, Rui Gao, Dilshod Azizov +2
Chain-of-thought (CoT) prompting can fail severely on constraint-dense logical reasoning tasks, where unverified errors accumulate silently across steps. We introduce SymStep: an L…
Einstein World Models
Munachiso Samuel Nwadike, Zangir Iklassov, Ali Mekky +2
Does intelligence require the ability to reason about phenomena beyond direct experience? It is natural to suspect that some complex thought cannot be captured through language alo…
Measuring AI Reasoning: A Guide for Researchers
Munachiso Samuel Nwadike, Zangir Iklassov, Kareem Ali +2
In this paper, we offer a guide for researchers on evaluating reasoning in language models, building the case that reasoning should be assessed through evidence of adaptive, multi-…
The AI Data Scientist
Farkhad Akimov, Munachiso Samuel Nwadike, Zangir Iklassov +1
Imagine decision-makers uploading data and, within minutes, receiving clear, actionable insights delivered straight to their fingertips. That is the promise of the AI Data Scientis…
SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem
Ahmed Heakl, Yahia Salaheldin Shaaban, Martin Takac +2
Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to captur…
LLM-BabyBench: Can Language Models Plan in Worlds They Can Simulate?
Omar Choukrani, Idriss Malek, Daniil Orel +5
Interactive benchmarks for LLMs usually report a single metric, even though succeeding in them requires models to show several different skills. While those benchmarks are useful f…
RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles
Munachiso Nwadike, Zangir Iklassov, Toluwani Aremu +6
We introduce the concept of the self-referencing causal cycle (abbreviated RECALL) - a mechanism that enables large language models (LLMs) to bypass the limitations of unidirection…
A Decade of Deep Learning: A Survey on The Magnificent Seven
Dilshod Azizov, Muhammad Arslan Manzoor, Velibor Bojkovic +9
Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade, enabling remarkable achievements across diverse domains. At the heart of the…
Self-Guiding Exploration for Combinatorial Problems
Zangir Iklassov, Yali Du, Farkhad Akimov +1
Large Language Models (LLMs) have become pivotal in addressing reasoning tasks across diverse domains, including arithmetic, commonsense, and symbolic reasoning. They utilize promp…
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal +1
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods deli…
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal +1
This study addresses a gap in the utilization of Reinforcement Learning (RL) and Machine Learning (ML) techniques in solving the Stochastic Vehicle Routing Problem (SVRP) that invo…
Learning to generalize Dispatching rules on the Job Shop Scheduling
Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal +1
This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not…
Robust Reinforcement Learning on Graphs for Logistics optimization
Zangir Iklassov, Dmitrii Medvedev
Logistics optimization nowadays is becoming one of the hottest areas in the AI community. In the past year, significant advancements in the domain were achieved by representing the…
AI for Porosity and Permeability Prediction from Geologic Core X-Ray Micro-Tomography
Zangir Iklassov, Dmitrii Medvedev, Otabek Nazarov +1
Geologic cores are rock samples that are extracted from deep under the ground during the well drilling process. They are used for petroleum reservoirs' performance characterization…