Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct trajectories. Existing dense reward alternatives, from surface heuristics to process reward models, either ignore the expert solutions already present in training corpora or require expensive offline annotation. We propose Gradient-Aligned Reward (GAR), which operates in the policy’s own gradient space: truncated backpropagation through the output projection layer extracts a compact gradient vector for each rollout, and cosine similarity with an expert-anchor gradient yields a dense, reasoning-aware reward with less than 9% wall-clock overhead. We prove that this cosine admits a multiplicative decomposition into prediction-error and activation-pattern factors, providing a concrete characterization of what the alignment signal measures. On Qwen3-4B and Qwen3-8B, GAR consistently improves over GRPO and other baselines on competition-level math benchmarks and transfers to GPQA Diamond and MMLU-Pro without domain-specific data. Code and data are available at https://github.com/LQgdwind/GAR .

The application of autonomous agents to scientific literature review is often constrained by the reliability of online search infrastructures and linear text generation. To address these limitations, we introduce SciLENS (Scientific Localized Evidence Navigation and Synthesis), an autonomous agent framework operating entirely upon a localized dual-tier data infrastructure for deterministic access to massive academic corpora. To mitigate online dependency, SciLENS utilizes a comprehensive offline toolbox featuring topological graph traversal and structural visualization. This empowers the agent to navigate deep citation networks and organize macro-level trends into structured visual representations, mitigating context exhaustion. To train this system without human annotation, we develop an automated multi-hop data synthesis pipeline validated by cross-model consensus, followed by rubric-based reinforcement learning to align the logical planning of the agent. Evaluations across standard tasks and newly constructed complex synthesis benchmarks demonstrate that SciLENS achieves robust factual precision, outperforming existing open-source baselines and achieving performance comparable to frontier proprietary models. Our source code and data are released at https://anonymous.4open.science/r/SciLENS-48DB .

While large language models have revolutionized Text-to-SQL tasks, translating natural language into Graph Query Languages (Text2GQL) remains underexplored due to the topological heterogeneity and syntactic diversity of graph query languages (e.g., Cypher, Gremlin, SPARQL). Existing approaches often struggle with structural hallucinations and lack adaptability in cold-start scenarios. In this paper, we present a unified, training-free Text2GQL framework. First, Structural Twig Linking elevates schema grounding to the identification of semantic substructures (“twigs”), providing robust topological priors. Second, addressing data scarcity, Evolutionary In-Context Learning operates in a Tabula Rasa setting to implicitly construct a self-growing repository of verified examples driven by syntactic utility. Finally, our Adversarial Execution-Guided Correction agent enforces fidelity through synergistic static critique and dynamic verification. Experiments demonstrate significant improvements over baselines in both accuracy and executability across diverse GQLs. The code is available at https://github.com/nf202/Text2Graph.