Reasoning over Boundaries: Enhancing Specification Alignment via
  Test-time Delibration
Authors: name: Haoran Zhang, name: Yafu Li, name: Xuyang Hu, name: Dongrui Liu, name: Zhilin Wang, name: Bo Li, name: Yu Cheng

Large language models (LLMs) are increasingly applied in diverse real-world
scenarios, each governed by bespoke behavioral and safety specifications (spec)
custom-tailored by users or organizations. These spec, categorized into
safety-spec and behavioral-spec, vary across scenarios and evolve with changing
preferences and requirements. We formalize this challenge as specification
alignment, focusing on LLMs' ability to follow dynamic, scenario-specific spec
from both behavioral and safety perspectives. To address this challenge, we
propose Align3, a lightweight method that employs Test-Time Deliberation (TTD)
with hierarchical reflection and revision to reason over the specification
boundaries. We further present SpecBench, a unified benchmark for measuring
specification alignment, covering 5 scenarios, 103 spec, and 1,500 prompts.
Experiments on 15 reasoning and 18 instruct models with several TTD methods,
including Self-Refine, TPO, and MoreThink, yield three key findings: (i)
test-time deliberation enhances specification alignment; (ii) Align3 advances
the safety-helpfulness trade-off frontier with minimal overhead; (iii)
SpecBench effectively reveals alignment gaps. These results highlight the
potential of test-time deliberation as an effective strategy for reasoning over
the real-world specification boundaries.