Papers by Mateusz Bystroński
Factual State Discovery Benchmark: Evaluating Fact Elicitation in Polish Tax Law (2026.acl-srw)
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Mateusz Bystroński, Kamil Tagowski, Denis Janiak, Julia Farganus, Lukasz Augustyniak, Monika Kajdanowicz, Tomasz Jan Kajdanowicz
| Challenge: | FSDBench is a benchmark for eliciting all relevant facts through dialogue . missing facts may lead the authority to apply the wrong provision or issue a ruling that is inapplicable to the actual situation. |
| Approach: | They propose a method to systematically elicit facts through dialogue from a simulated taxpayer . they use 500 narratives from official Polish tax interpretations to test their models . |
| Outcome: | The proposed model recovers only 77% of facts on easy and hard samples and under 49% on hard samples after 50 turns. |
When Will the Tokens End? Graph-Based Forecasting for LLMs Output Length (2025.acl-srw)
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Grzegorz Piotrowski, Mateusz Bystroński, Mikołaj Hołysz, Jakub Binkowski, Grzegorz Chodak, Tomasz Jan Kajdanowicz
| Challenge: | Large Language Models (LLMs) are typically trained to predict the next token in a sequence. However, their internal representations encode signals that go beyond immediate next-token prediction. |
| Approach: | They propose an aggregation-based model that combines hidden states from multiple transformer layers l 8, dots, 15 using element-wise operations such as mean or sum. |
| Outcome: | The proposed model reduces NMAE by over 50% on the Alpaca dataset. |
Continuous Context Sampling Allows Extending Diversity Boundaries of Large Language Models (2026.acl-srw)
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| Challenge: | Large language models exhibit a persistent limitation: repeated generations from the same prompt tend to be semantically similar. |
| Approach: | They propose to construct a conditioning distribution from a small set of diverse anchor generations and use it to condition an LLM's generation distribution. |
| Outcome: | The proposed framework significantly expands the model's reachable semantic range by constructing a conditioning distribution from a small set of diverse anchor generations. |
Beyond Discrete Search: Divergent Thinking as Intention Optimization in Latent Space (2026.acl-srw)
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| Challenge: | Despite rapid progress in LLM-based code generation, a persistent gap remains between what models can solve and what they solve on a given attempt. |
| Approach: | They propose a framework that recasts coding as optimization overconditioning contexts that influence the generation of natural-languagesolution intentions. |
| Outcome: | The proposed framework raises resolution rate of weak, quantized 24B open-weight model to parity with frontier models +25 its size. |