arXiv AI
EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering
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arXiv AI
Jun 2
BenchEvolver: Frontier Task Synthesis via Solution-Centric Evolution
arXiv:2606. 01286v1 Announce Type: cross Abstract: The rapid progress of frontier large language models has led to widespread benchmark saturation, limiting the ability of existing datasets to differentiate model capabilities or provide useful training signal.
arXiv AI
3d ago
CodeTS: Verifiable Text-to-Time Series Generation via Executable Code
CodeTS introduces a verifiable framework for generating time series from natural language by translating textual temporal descriptions into executable code, which then produces the desired series. The approach constructs aligned Text‑Code‑TS triplets for supervised initialization and employs multi‑stage execution‑based rewards to ensure code validity and time‑series quality. Experiments on eight benchmarks show that CodeTS outperforms both LLM‑based and supervised generative baselines, offering a strong zero‑shot solution for Text‑to‑TS generation.
arXiv AI
Jun 11
ClawEnvKit: Automatic Environment Generation for Claw-Like Agents
arXiv:2604. 18543v4 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale.
arXiv AI
Jun 11
TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
arXiv:2606. 12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult.
arXiv AI
Aug 12
ELMER: Evolutionary Language Model that Explores and Refines
arXiv:2608. 10196v1 Announce Type: cross Abstract: Program evolution can measure whether a mutation helped, but it rarely controls how far the mutation moves in behavior space.
arXiv AI
Jul 24
EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL
arXiv:2607. 20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction.