Research
LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning
Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algorithmic puzzles, we demonstrate that while decomposit
Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algorithmic puzzles, we demonstrate that while decomposition is essential for stability, extreme decomposition creates a “no-recovery bottleneck”. We show that this bottleneck becomes critical due to highly non-uniform error distribution, where consistent errors on a few “hard” steps become irreversible. To address this, we propose Lookahead-Enhanced Atomic Decomposition (LEAD). By incorporating short-horizon future validation and aggregating…
Related
- How Well Do LLMs Perform on the Simplest Long-Chain Reasoning Tasks: An Empirical Study on the Equivalence Class Problem
- PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
- On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
- Message Passing Enables Efficient Reasoning
Source: Apple ML Research | 2026-07-24