REVIEW 5 cited by
MetaScale: Test-Time Scaling with Evolving Meta-Thoughts
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
One critical challenge for large language models (LLMs) for making complex reasoning is their reliance on matching reasoning patterns from training data, instead of proactively selecting the most appropriate cognitive strategy to solve a given task. Existing approaches impose fixed cognitive structures that enhance performance in specific tasks but lack adaptability across diverse scenarios. To address this limitation, we introduce METASCALE, a test-time scaling framework based on meta-thoughts -- adaptive thinking strategies tailored to each task. METASCALE initializes a pool of candidate meta-thoughts, then iteratively selects and evaluates them using a multi-armed bandit algorithm with upper confidence bound selection, guided by a reward model. To further enhance adaptability, a genetic algorithm evolves high-reward meta-thoughts, refining and extending the strategy pool over time. By dynamically proposing and optimizing meta-thoughts at inference time, METASCALE improves both accuracy and generalization across a wide range of tasks. Experimental results demonstrate that MetaScale consistently outperforms standard inference approaches, achieving an 11% performance gain in win rate on Arena-Hard for GPT-4o, surpassing o1-mini by 0.9% under style control. Notably, METASCALE scales more effectively with increasing sampling budgets and produces more structured, expert-level responses.
Forward citations
Cited by 5 Pith papers
-
Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models
A training-free meta-reasoning loop that tracks remaining cognitive demand and steers an LLM's next action improves average accuracy by about 9 percent over chain-of-thought across three models and six benchmarks.
-
Building Agent Harnesses for Scientific Curation from Multimodal Sources
Beaver agent harness achieves 81.0 GRAS on multimodal scientific curation, outperforming frontier agents by over 23 points through scaffolding and evidence tooling.
-
DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling
DynScaling improves verifier-free inference-time scaling by merging parallel and sequential sampling and allocating budget across queries with a UCB-based uncertainty rule.
-
AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up
The framework trains small models on synthetic AI-generated data to produce PFD/PID text, then validates two examples by manual DWSIM setup, leaving the industrial-viability claim unproven.
-
Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models
A probabilistic saturation model for test-time scaling is proposed and fitted to reasoning benchmarks, but the plateau 'prediction' is computed from the same per-problem data used to measure it.
Discussion (0). Continue with ORCID to comment.