The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2610.03695v1 Announce Type: new Abstract: Modern chess engines are silent experts: they play at a superhuman level, but do not offer explanations for their play. On the other hand, language mod...
arXiv:2603. 20510v2 Announce Type: replace Abstract: Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel.
arXiv:2607. 16097v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it.
arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in ma...
arXiv:2608. 12626v1 Announce Type: cross Abstract: Strategic reasoning in Large Language Models (LLMs) within long-horizon environments is often limited by inconsistent subgoals.
SpireBench evaluates long‑horizon strategic play by having large language models play the full commercial deck‑builder Slay the Spire, a game where early choices determine long‑term success. Across 20 LLMs at the highest difficulty, frontier models beat heuristic baselines and match average human performance but lag far behind experts, especially as runs lengthen. The benchmark reveals that strong models excel in early fights but misjudge risk over a run, and that their performance depends partly on prior game knowledge rather than on memories written during play.