arXiv subjects

Eljas Linna

Publications and source records attributed to Eljas Linna.

3 recordsLinked to original sources

arXiv2026

Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling

Deep Limit Order Book forecasting models capture nonlinear market dynamics, but their ability to quantify the effects of counterfactual order book messages has not been systematically validated. We introduce a model-agnostic framework that compares a trained forecaster's predictive distributions before and after injecting mechanically valid counterfactual messages, defining short-horizon model-implied market impact. A Transformer-based forecaster recovered scenario rankings with a Spearman correlation of 0.99 and 97.2% directional agreement with realized historical outcomes among non-neutral scenarios. Observation-level analysis further showed that estimated impacts captured incremental sequence-dependent variation beyond scenario identity and the pre-event forecast. These results provide evidence that pretrained Limit Order Book forecasters can be repurposed for scenario-conditioned response modeling without retraining.

cs.LG↗

arXiv2026

LOBERT: Generative AI Foundation Model for Limit Order Book Messages

Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequency traders to visible order flow. Previous LOB models require cumbersome data representations and lack adaptability outside their original tasks, leading us to introduce LOBERT, a general-purpose encoder-only foundation model for LOB data suitable for downstream fine-tuning. LOBERT adapts the original BERT architecture for LOB data by using a novel tokenization scheme that treats complete multi-dimensional messages as single tokens while retaining continuous representations of price, volume, and time. With these methods, LOBERT achieves leading performance in tasks such as predicting mid-price movements and next messages, while reducing the required context length compared to previous methods.

cs.AI↗

arXiv2026

Challenges for Generative AI in Legal Reasoning

Large Language Models (LLMs) are being integrated into professional domains, yet their limitations in such high-stakes fields as law remain poorly understood. In response, this paper introduces examples of critical challenges to the functioning of generative and other forms of artificial intelligence (AI) as reliable reasoning tools in judicial decision-making. The study deconstructs core requirements and challenges for AI, including the ability to select the correct legal framework across jurisdictions, generate sound arguments based on the doctrine of the sources of law, distinguish ratio decidendi and obiter dicta in case law, resolve ambiguity arising from general clauses like "reasonableness", manage conflicting legal provisions, and apply the burden of proof correctly. The paper maps various AI enhancement mechanisms, such as retrieval-augmented generation (RAG), multi-agent systems and neuro-symbolic AI, to these challenges, assessing their potential to bridge the gap between the probabilistic nature of LLMs and the rigorous, choice-driven demands of legal interpretation. Furthermore, the paper sketches a path towards an evaluation framework, proposing that legal requirements be organized into normative, doctrinal, evidential, and technical categories, and subsequently operationalized into domain-specific, testable design obligations. The findings indicate that these techniques can address specific narrow challenges, but they fail to solve the more significant ones, particularly in tasks requiring discretion and transparent, justifiable reasoning. Therefore, we advocate for a staged adoption, first capturing efficiency in simple cases with technology already available today and sustaining long-term investment in new methods that handle hierarchy, temporality, and other requirements of legally sound reasoning, thus enabling expansion to complex adjudication in the future.

cs.AI↗