Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper’s method, protocol and artifacts. This is difficult because the specification is split: explicit paper content such as algorithms, metrics and artifacts is often lost across long agent trajectories, while implicit details such as framework defaults and conventions inherited from related work are absent from the paper. We introduce ReproAgent a four-stage Prepare–Plan–Generate–Repair pipeline built around a persistent implementation contract with two channels: an implementation-requirement channel that turns paper snippets into code obligations, and a reference-evidence channel that retrieves content and structure evidence from related repositories. Both are bound to work packages, projected into file-level contracts, and consumed across generation and repair. On PaperBench Code-Dev, ReproAgent reaches the highest mean score among same-backbone scaffolds under both Claude-Sonnet-4.5 and Gemini-3-Flash. End-to-end channel ablations and per-paper cases support the contribution of both channels.Code and experimental artifacts are publicly available at https://anonymous.4open.science/r/ReproAgent-E760.

LLM agents can generate paper reproduction code, yet often produce scientifically unfaithful implementations. We define this failure mode as semantic drift, where generated code silently diverges from the paper’s specifications. We introduce SemanticAlign-Bench(SA-Bench), a diagnostic benchmark covering 30 papers from ICLR, ICML and NeurIPS 2025. For each paper, we decompose its specifications into atomic and verifiable implementation claims, which we call Semantic Alignment Units (SAUs) and evaluate repositories along four diagnostic dimensions spanning numerical, methodological, protocol and ordering drift. In total, we construct 1,491 SAUs across five ML domains and evaluate 12 generator configurations (4 models 3 scaffolds). Even the strongest configuration (Claude + PaperCoder) achieves a mean SAU score of only 0.301 out of 1.0, with an overall mean of 0.221 across 360 evaluations. A failure taxonomy reveals that agents attempt most requirements but implement them incorrectly, with implementation mismatch and stubs accounting for the majority of zero-scored claims. Our analysis further indicates that scaffolds optimized for executability provide limited leverage for scientific reproduction; narrowing the gap requires scaffolds that prioritize semantic specification verification. The benchmark, annotations and evaluation pipeline are publicly available.

Code:https://anonymous.4open.science/r/SemanticAlign-Bench-0676

Data:https://anonymous-hf.up.railway.app/a/rrgn430zpfui/

Real-world analysis often requires combining directly queryable structured sources (e.g., SQL and CSV) with evidence preserved only in visual documents, such as scanned reports, invoices, and dashboards. Current analytics agents still focus primarily on structured and textual inputs, leaving this cross-modal setting underexplored. We introduce DataCross, a benchmark and agent framework for heterogeneous, insight-driven data analysis. DataCrossBench contains 200 end-to-end tasks across six fine-grained categories: Fund Analysis, FinTech, Internet Development, Internet Firms, Short-form Drama, and AI Healthcare, grouped into four macro sectors and mixing SQL/CSV/JSON/TXT sources with image-based evidence. The benchmark is built with a human-in-the-loop reverse-synthesis pipeline to preserve cross-source dependency while keeping the target insights verifiable. We further propose DataCrossAgent, which coordinates source-specialized agents through intra-source exploration, source prioritization, cross-source pollination, and Reasoning ReAct (reReAct)-based executable verification. Experiments show that DataCrossBench reveals a difficulty gap for most baselines, especially on vision-dependent Hard tasks, and that DataCrossAgent improves average factuality over vanilla GPT-4o from 0.3291 to 0.4270 (+29.7%) while showing a smaller Easy-to-Hard degradation than several baselines. These results position DataCrossBench as a practical testbed for cross-modal heterogeneous data analysis.

We introduce LongInsightBench, the first benchmark designed to assess models’ ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements, while integrating visual, audio, and text modalities. Our benchmark excels in three key areas: a) Long-Duration, Human-Centric Videos: We carefully selected approximately 1,000 videos from open-source datasets FineVideo based on duration limit and multi-modal information density, focusing on content like lectures, interviews, and vlogs, which contain rich human-centric semantic and contextual attributes. b) Diverse and Challenging Task Scenarios: We have designed six challenging task scenarios, including both Intra-Event and Inter-Event Tasks. c) Rigorous and Comprehensive Quality Assurance Pipelines: We have developed a three-step, semi-automated data quality assurance pipeline to ensure the difficulty and validity of the synthesized questions and answer options. Based on LongInsightBench, we designed a series of experiments. which shows that Omni-modal models(OLMs) still face challenge in tasks requiring precise temporal localization (T-Loc) and long-range causal inference (CE-Caus). Surprisingly, extended experiments reveal the information loss in modal fusion of OLMs, which we called the Fusion Deficit Paradox.

Extending large language models (LLMs) to low-resource languages often incurs an “align- ment tax”: improvements in the target lan- guage come at the cost of catastrophic forget- ting in general capabilities. We argue that this trade-off arises from the rigidity of supervised fine-tuning (SFT), which enforces token-level surface imitation on narrow and biased data distributions. To address this limitation, we propose a semantic-space alignment paradigm powered by Group Relative Policy Optimiza- tion (GRPO), where the model is optimized us- ing embedding-level semantic rewards rather than likelihood maximization. This objective encourages meaning preservation through flex- ible realizations, enabling controlled updates that reduce destructive interference with pre- trained knowledge. We evaluate our approach on Tibetan–Chinese machine translation and Ti- betan headline generation. Experiments show that our method acquires low-resource capa- bilities while markedly mitigating alignment tax, preserving general competence more effec- tively than SFT. Despite producing less rigid surface overlap, semantic RL yields higher se- mantic quality and preference in open-ended generation, and few-shot transfer results indi- cate that it learns more transferable and ro- bust representations under limited supervision. Overall, our study demonstrates that reinforce- ment learning with semantic rewards provides a safer and more reliable pathway for inclusive low-resource language expansion.

Low-resource target-language generation is often limited by scarce parallel data, while high-resource source-language monolingual data is abundant but difficult to use with standard supervised fine-tuning. We propose Source-Grounded Semantic Reinforcement Learning (SG-SRL), a resource-utilization framework that converts source-language monolingual data into cross-lingual semantic supervision. SG-SRL performs reference-free reinforcement learning (RL) on source-language data with a cross-lingual semantic reward, followed by lightweight supervised recovery. Although direct reward optimization induces verbosity-based reward hacking, recovery restores fluency, conciseness, and task format without erasing the learned semantic alignment. On Chinese-to-Thai generation, SG-SRL outperforms cold-start SFT, source-only continued pretraining, and synthetic-data SFT; human preference and training-independent metrics corroborate the gains. Recovery ablations further show that semantic retention and form restoration are jointly controlled by recovery data and intensity. An in-domain Chinese-to-Tibetan experiment shows that the reward interface can use a contrastively trained encoder when a capable multilingual reranker is unavailable, while also delimiting the framework’s dependence on reward-model discrimination.

Scientific diagrams convey explicit structural information, yet modern text-to-image models often produce visually plausible but structurally incorrect results. Existing benchmarks either rely on image-centric or subjective metrics insensitive to structure, or evaluate intermediate symbolic representations rather than final rendered images, leaving pixel-based diagram generation underexplored. We introduce SciFlow-Bench, a structure-first benchmark for evaluating scientific diagram generation directly from pixel-level outputs. Built from real scientific PDFs, SciFlow-Bench pairs each source framework figure with a canonical ground-truth graph and evaluates models as black-box image generators under a closed-loop, round-trip protocol that inverse-parses generated diagram images back into structured graphs for comparison. This design enforces evaluation by structural recoverability rather than visual similarity alone, and is enabled by a hierarchical multi-agent system that coordinates planning, perception, and structural reasoning. Experiments show that preserving structural correctness remains a fundamental challenge, particularly for diagrams with complex topology, underscoring the need for structure-aware evaluation.

In real-world business environments, data is stored in a variety of sources, including structured relational databases, semi-structured databases, and unstructured files. The ability to extract reasonable insights across these diverse sources is integral to data-driven decision-making. Existing benchmarks, however, are limited in assessing agents’ capabilities across these diverse data types. To address this gap, we introduce UniDataBench, a multi-source benchmark designed to evaluate the performance of data analytics agents in handling diverse data sources. Specifically, UniDataBench is constructed based on real-life industry analysis reports, employing a pipeline to synthesize data that aligns with authentic analytical trends. It encompasses diverse datasets spanning relational databases, CSV files, and NoSQL stores to reflect real-world business settings, and provides a unified framework for evaluating how effectively agents can explore multiple data formats, extract insights, and generate meaningful summaries and recommendations. Based on UniDataBench, we propose a novel LLM-based agent named ReActInsight, an autonomous agent that performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights. Our benchmark and agent together provide a framework for facilitating the development of data analytics agents in real-world applications.