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TOPIC · ENTITY #16864

iterative refinement

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3

RESEARCH · 1 SOURCE · arXiv cs.AI

Seq@K: an evaluation protocol for sequential feedback-driven solving by LMs

This paper introduces Seq@K, a protocol that measures whether a language model can solve a problem within K sequential attempts when given feedback after each failed attempt. Evaluated on 7 benchmarks and 16 frontier models, Seq@K often yields different model rankings than Pass@K, changes Pareto frontiers for model/configuration trade-offs, and reveals behaviors in iterative refinement and sensitivity to feedback design.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv paper evaluates LLMs as numerical optimizers (arXiv:2610.37932v1)

This new arXiv paper (arXiv:2610.37932v1) develops an evaluation framework to study large language models' ability to produce feasible, near‑optimal solutions for mathematical optimization problems (convex optimization, fractional programming, constrained projection). It finds LLMs can capture useful structure and sometimes generate near‑optimal outputs, but reliability falls with problem dimension and numerical/formatting errors are common; iterative refinement improves validity but persistent numerical and structural failures limit use as standalone optimizers.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

LLM-driven pipeline converts hardware datasheets into faithful hierarchical knowledge graphs (arXiv:2610.12762v1)

The paper introduces an automated, staged pipeline that uses LLMs for chunk-based extraction, hierarchical routing, and iterative refinement to build source-grounded hierarchical hardware knowledge graphs from datasheets. Evaluated on 20 datasheets, the method reports 98.8% property faithfulness versus 76.1% for a one-shot baseline and higher inter-graph completeness (it fully covers 83.6% of faithful baseline properties while the baseline covers only 10.5% of this method's properties).

6.0