Ancient Chinese artifact recognition is fundamental to heritage digitization, and benchmarks for ancient texts are essential for evaluating current model capabilities. However, existing benchmarks suffer from “fragmentation”, manifested in limited temporal coverage, limited medium diversity, and incomplete script types. Therefore, we present Ancient-Bench, a comprehensive benchmark of 2,700 images for ancient Chinese artifact text recognition, featuring three dimensions: Multi-millennial (spanning 3,000 years of character evolution), Multi-medium (covering nine artifact categories), and Multi-script (encompassing seven historical script forms). To enable consistent and fair evaluation across heterogeneous media, we further define three annotation standards tailored to the medium-specific characteristics of ancient texts: symbol standardization, character standardization, and parsing standardization. Extensive experiments on Ancient-Bench covering general Vision-Language Models (VLMs) and OCR-specialist models reveal that ancient Chinese recognition remains fundamentally unsolved, with persistent challenges in variant characters, specialized symbols, and hallucination. The data and evaluation code will be publicly available.

While RLVR is effective for deterministically verifiable tasks, many vision-language tasks are only *partially verifiable*, demanding multi-criterion supervision. To bridge this gap, we propose *Reinforcement Learning with Robust Rubric Rewards (RLR³), extending RL with criterion-level rewards. To ensure faithful scoring, RLR³ routes each criterion to one of two execution paths: an LLM-as-an-extractor paired with a verifier, or an LLM-as-a-Judge for fuzzy criteria. We also introduce a minimal exposure strategy that masks verifier targets from extractors and source images from the GenRM. Furthermore, RLR³ employs decoupled normalization to mitigate score saturation and hierarchical aggregation to prioritize essential criteria. Evaluated on Qwen3-VL-30B-A3B across 15 benchmarks, RLR³ consistently outperforms RLVR, yielding a 4.7-point improvement over the base model and exceeding the official instruct-to-thinking model gap. Controlled audits show that deterministic verifier and minimal exposure strategy reduce exploitable false positives.

The effectiveness of Direct Preference Optimization (DPO) depends on data pairs that capture task-relevant quality differences. Existing data pipelines often rely on *off-policy* perturbations or *coarse* outcome-based signals, which are not well suited to fine-grained visual reasoning. We propose *rDPO*, a rubric-based data pipeline for DPO-based visual preference optimization. For each image-instruction pair, we create a checklist-style rubric of essential and additional criteria to score responses from any possible policies. The instruction-rubric pool is built *offline* and reused during the construction of *on-policy* preference data. On public reward modeling benchmarks, rubric-based prompting massively improves a 30B-A3B judge and brings it close to GPT-5.4. On public downstream benchmarks, rubric-based filtering raises the macro average to 82.69, whereas outcome-based filtering drops it to 75.82 from 81.14. In scalability evaluation on a comprehensive benchmark, rDPO achieves 61.01, markedly outperforming the style-constrained baseline (52.36) and surpassing the 59.48 base model. Together, these results show that visual preference optimization benefits from combining on-policy data construction with instance-specific criterion-level feedback.

Large language model (LLM) leads to a surge of autonomous GUI agents for smartphone, which completes a task triggered by natural language through predicting a sequence of actions of API. Even though the task highly relies on past actions and visual observations, existing studies typically consider little semantic information carried out by intermediate screenshots and screen operations. To address this, this work presents Chain-of-Action-Thought (dubbed CoAT), which takes the description of the previous actions, the current screen, and more importantly the action thinking of what actions should be performed and the outcomes led by the chosen action. We demonstrate that, in a zero-shot setting upon three off-the-shelf LMMs, CoAT significantly improves the action prediction compared to previous proposed context modeling. To further facilitate the research in this line, we construct a dataset Android-In-The-Zoo (AitZ), which contains 18,643 screen-action pairs together with chain-of-action-thought annotations. Experiments show that fine-tuning a 1B model (i.e. AUTO-UI-base) on our AitZ dataset achieves on-par performance with CogAgent-Chat-18B.