Text-to-SQL deployment is moving from one-shot query generation to interactive repair of executable but semantically incorrect SQL. In this setting, a model must revise an erroneous query under public tool feedback while preserving correct input structures. Existing reinforcement-learning signals are ill matched: outcome rewards reveal only whether the final SQL passes, whereas unconstrained process supervision may reward steps not grounded in SQL semantics. We propose SCOPE-SQL, a reinforcement-learning framework that treats SQL structure as an auditable credit space for interactive repair. It maps candidate queries and public feedback into F0–F6 modules—schema/runtime, table/join, projection, predicate, aggregation/grouping, order/limit, and nesting—and assigns action-level credit for repairing target modules, preserving stable modules, gathering evidence, and submitting when ready. Persistent low-credit regions then drive semantic refresh, generating validator-filtered frontier repair tasks. On BIRD-CRITIC-SQLite, SCOPE-SQL with semantic refresh reaches 48.8% success using a Qwen2.5-Coder-7B backbone, improving over current sota RL baselines by 4.4 and 4.2 points. Ablations attribute 4.4 and 1.8 points to process credit and semantic refresh. Trajectory analysis further shows better repair behavior: Repair+Preserve paths rise from 38.8% to 77.5%, while damage-and-recover paths fall from 43.8% to 1.2%. These results suggest that SQL-structured credit is an interpretable training signal for both policy learning and data expansion in interactive SQL repair.

Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. Recent large language model (LLM)-based approaches have demonstrated immense potential for code translation. Training-based methods are important because LLMs currently do not effectively adapt to domain-specific settings that suffer from a lack of knowledge without targeted training. However, existing training-based approaches rely on a pairwise transpilation paradigm, making it impractical to support a diverse range of PLs. Furthermore, these methods shape the reinforcement learning (RL) reward linearly in the test-case pass rate, which underweights the earliest partial progress. To address these limitations, we propose CodePivot, a training framework that leverages Python as an intermediate representation (IR), augmented by a novel RL reward mechanism, Aggressive-Partial-Functional reward, to bootstrap the model’s multilingual transpilation ability with reduced reliance on parallel corpora. Experiments involving 10 PLs show that the resulting 7B model, trained on Python-to-Others tasks, consistently improves performance across both general and low-resource PL-related transpilation tasks. It outperforms substantially larger mainstream models with hundreds of billions more parameters, such as DeepSeek-R1 and Qwen3-235B-A22B-Instruct-2507, on Python-to-Others tasks and Others-to-All tasks, respectively, and surpasses its counterpart trained directly on Any-to-Any tasks on general transpilation tasks. Code and data: https://github.com/lishangyu-hkust/CodePivot.

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.

While Large Language Models (LLMs) have revolutionized code generation, standard “System 1” approaches that generate solutions in a single forward pass often hit a performance ceiling on complex algorithmic tasks. Existing iterative refinement strategies attempt to bridge this gap at inference time, yet they predominantly rely on external oracles, execution feedback, or computationally expensive prompt-response cycles. In this work, we propose ReflexiCoder, a novel reinforcement learning (RL) framework that internalizes the structured reasoning trajectory, encompassing initial generation, bug and optimization aware reflection, and self-correction, directly into the model’s weights. Unlike prior methods, ReflexiCoder shifts the paradigm from external-dependent refinement to an intrinsic, fully autonomous self-reflection and self-correction capabilities at inference time. We utilize an RL-only training paradigm with granular reward functions to optimize the entire reflection-correction trajectory, teaching the model how to debug without reliance on ground-truth feedback or execution engines at inference time. Extensive experiments across seven benchmarks demonstrate that our ReflexiCoder-8B establishes a new state-of-the-art (SOTA) among leading open-source models in the 1.5B-14B range, achieving 94.51% (87.20%) on HumanEval (Plus), 81.80% (78.57%) on MBPP (Plus), 35.00% on BigCodeBench, 52.21% on LiveCodeBench, and 37.34% on CodeForces in a single-attempt setting, rivaling or surpassing proprietary models like GPT-5.1. Notably, our framework is significantly more token-efficient than base models, reducing inference-time compute overhead by approximately 40% through disciplined, efficient reasoning and reflection patterns. The source code and data are available at https://github.com/juyongjiang/ReflexiCoder.

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.

Transformers have shown great success in learning representations for language modelling. However, an open challenge still remains on how to systematically aggregate semantic information (word embedding) with positional (or temporal) information (word orders). In this work, we propose a new architecture to aggregate the two sources of information using cascaded semantic and positional self-attention network (CSPAN) in the context of document classification. The CSPAN uses a semantic self-attention layer cascaded with Bi-LSTM to process the semantic and positional information in a sequential manner, and then adaptively combine them together through a residue connection. Compared with commonly used positional encoding schemes, CSPAN can exploit the interaction between semantics and word positions in a more interpretable and adaptive manner, and the classification performance can be notably improved while simultaneously preserving a compact model size and high convergence rate. We evaluate the CSPAN model on several benchmark data sets for document classification with careful ablation studies, and demonstrate the encouraging results compared with state of the art.