Papers with recursive complexity
SLR: Automated Synthesis for Scalable Logical Reasoning (2026.acl-long)
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Lukas Helff, Ahmad Omar, Felix Friedrich, Antonia Wüst, Hikaru Shindo, Rupert Mitchell, Tim Woydt, Patrick Schramowski, Wolfgang Stammer, Kristian Kersting
| Challenge: | Existing benchmarks intended to evaluate reasoning capabilities emphasize deductive reasoning, where conclusions necessarily follow from given premises. |
| Approach: | They propose an end-to-end framework for systematic evaluation and training of Large Language Models via Scalable Logical Reasoning. |
| Outcome: | The proposed framework doubles Llama-3-8B accuracy on SLR-Bench, achieving parity with Gemini-Flash-Thinking at a fraction of computational cost. |
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction (D18-1)
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| Challenge: | Recent attempts to improve grammar induction systems by bounding complexity of the model have not been compared against unbounded induction models. |
| Approach: | They propose to use a Bayesian induction inducer to limit the search space of the model and then sample trees with or without bounding. |
| Outcome: | The proposed model produces trees more accurately than or competitively with state-of-the-art constituency grammar induction models. |