Large language models (LLMs) have made rapid progress in code generation, yet their ability to verify whether a program is correct has not necessarily improved at the same pace. We identify a generation-verification gap in code LLMs, where scaling substantially improves test-passing code generation, while execution-free correctness reasoning improves much more slowly. This gap challenges existing self-rewarding approaches, which often implicitly assume that a stronger generator also serves as a better judge. To bridge this gap, we propose CoVera, an execution-grounded co-evolution framework that jointly trains a code LLM to generate code and to verify code correctness. In generator mode, the model produces candidate programs and receives execution-based rewards from available tests. In verifier mode, the same model reads a problem-program pair, produces a reasoning trace, and predicts a correctness judgment, which is rewarded according to agreement with execution-derived labels. The calibrated verifier is then used as an auxiliary self-verification signal for generation, while the evolving generator continuously supplies increasingly challenging candidates for verifier training. This co-evolution enables an on-policy curriculum for correctness reasoning and grounds self-verification in execution feedback, without requiring exhaustive test suites or golden reference solutions. Extensive experiments across multiple benchmarks and model scales show that CoVera improves both code generation and intrinsic correctness verification, outperforming representative baselines from diverse code-learning paradigms.

Large language models and coding agents can produce web applications that appear functional, yet surface plausibility is an unreliable proxy for behavioral conformance under interaction. We introduce ConformWeb, a contract-grounded protocol and benchmark that operationalizes public behavioral contracts to construct hidden interaction scenarios, evaluating whether agent-generated applications preserve strict conformance under dynamic execution. By decoupling hidden interaction sequences from the public behavioral scope, the protocol guarantees a fair held-out workflow evaluation without relying on undisclosed reference implementations. Evaluated across 24 validated instances scaling to 756 evaluation scenarios and 6,433 semantic steps, only 252 of the 2,160 total execution runs achieve full conformance, yielding a strict pass rate of 11.7%. While scenario-level conformance reaches 36.7% and step-level progress averages 49.4%, this quantitative gap demonstrates that generative systems frequently achieve substantial partial progress but fail to maintain the critical behavioral invariants required for end-to-end conformance. All code and data are released at https://github.com/deep-diver/ConformWeb.

The widespread adoption of cloud-based proprietary large language models (LLMs) has introduced significant challenges, including operational dependencies, privacy concerns, and the necessity of continuous internet connectivity. In this work, we introduce an LLMOps pipeline, “LlamaDuo”, for the seamless migration of knowledge and abilities from service-oriented LLMs to smaller, locally manageable models. This pipeline is crucial for ensuring service continuity in the presence of operational failures, strict privacy policies, or offline requirements. Our LlamaDuo involves fine-tuning a small language model against the service LLM using a synthetic dataset generated by the latter. If the performance of the fine-tuned model falls short of expectations, it is automatically improved through additional fine-tuning using extra similar data generated by the service LLM. This multi-turn process guarantees that the smaller model can eventually match or even surpass the service LLM’s capabilities in specific downstream tasks, offering a practical and scalable solution for managing AI deployments in constrained environments. Extensive experiments with leading-edge LLMs are conducted to demonstrate the effectiveness, adaptability, and affordability of LlamaDuo across various downstream tasks. Our pipeline implementation is available at https://github.com/deep-diver/llamaduo.