Counterfactual reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing benchmarks largely target bounded settings with fixed variables or single gold outcomes, overlooking open-domain scenarios requiring causal-process evaluation. To this end, we present WhatIfBench, a diagnostic benchmark for open-domain, open-form, long-horizon counterfactual causal reasoning, containing 220 what-if questions across STEM, HSS, and Hybrid scenarios. To evaluate free-form responses, we further propose PRISM, which first converts each natural-language explanation into a Response-Derived Semantic Causal Graph of events, states, and mechanisms. On top of this graph, PRISM then jointly applies a Process Metric assessing graph-level causal validity and a Rubric Metric assessing answer-level explanatory adequacy. Evaluating six frontier LLMs with this framework, we find that WhatIfBench remains far from saturated: even the strongest model reaches only a 64.62% final score. Further analysis reveals persistent causal gaps, premise drift, and topology fragmentation, suggesting that fluent counterfactual narratives often mask fragile causal processes. The benchmark, code, and evaluation scripts are available at https://github.com/zju-gt/WhatIfBench.

Multimodal large language models show significant potential in mathematical reasoning, yet they remain constrained by a scarcity of high-quality training data for visually grounded problems. Existing approaches commonly suffer from narrow coverage, image-text inconsistency, and heavy imitation of seed problems. To address these limitations, this paper proposes AgSynth, a multi-agent collaborative reverse synthesis framework. It first constructs solution logic programmatically and renders the corresponding images, then generates problem statements backward from the visual facts, thereby embedding answer correctness and image-text consistency directly into the generation pipeline. Meanwhile, AgSynth distills the core competencies tested in seed problems and performs divergent reconstruction, enabling synthesized problems to be substantially novel in both structure and visual presentation. Based on this framework, we construct the AgSynth-10K dataset, comprising 10,477 multimodal mathematical reasoning problems that have undergone two-tier quality control, spanning four major domains: geometry, algebra, combinatorics, and number theory. Experiments demonstrate that reinforcement learning training on AgSynth-10K significantly improves model performance on reasoning-intensive benchmarks (WeMath +7.42, LogicVista+9.15), validating the effectiveness of this traceable synthesis paradigm. The framework is publicly available at https://github.com/Yaxuan7690/AgSynth.

Real-world clinical competence demands more than answering medical knowledge questions—it requires proactively gathering incomplete patient information, selecting appropriate examinations, and formulating safe, cost-conscious treatment plans through multi-turn interaction. Existing medical LLM benchmarks rely predominantly on static QA formats, failing to assess interactive information acquisition, tool use, or cost–quality tradeoffs. We propose MedCPEnv, a reproducible, cost-aware simulated hospital environment for evaluating and training medical LLM agents. Built from MIMIC-IV-NOTE discharge notes, MedCPEnv comprises 48,803 curated cases, a 1,000-case benchmark, and a 371,550-entry medical QA knowledge base, exposing four deterministic action types (ASK, EXAM, KNOWLEDGE, FINAL) to eliminate high-variance LLM role-playing artifacts. Evaluating eight frontier models, GPT-5.4 achieves the highest composite score (3.69/5) and diagnostic accuracy (60.3%), yet treatment accuracy remains only 19.4%, identifying treatment plan generation as the primary bottleneck. Substantial inter-model variation in the cost–quality tradeoff further demonstrates that clinical correctness alone is insufficient to characterize agent behavior. GRPO-based Agentic RL on Qwen3-4B shows that medical quality rewards consistently improve tool-use strategies, raising the composite score from 2.97 to 3.14 and diagnostic accuracy from 26.0% to 31.2%, establishing MedCPEnv as both a rigorous evaluation benchmark and a reproducible training environment for medical agent research.

Existing Text-to-Image (T2I) benchmarks remain anchored in foundational criteria such as image quality and text-image alignment, failing to capture the nuanced capabilities that professional creators demand in real-world workflows. Meanwhile, evaluation pipelines that rely on an untrained multimodal model as a judge diverge from human standards. We introduce Qwen-Image-Bench, a creator-centric benchmark that extends evaluation with two application-driven dimensions (Real-world Fidelity and Creative Generation), alongside the conventional pillars of Quality, Aesthetics, and Alignment. The benchmark organizes these into a three-level taxonomy (5 pillars, 23 sub-capabilities, 56 verifiable rubrics) and provides 1000 stratified bilingual prompts. We train a unified Judge Model (Q-Judger) on Qwen3.6-27B, supervised by 80 professional annotators under blind labeling and triple-review protocols, that scores every image across all 56 facets with fine-grained, rubric-grounded diagnostics. Empirically, Qwen-Image-Bench reliably distinguishes leading T2I models, achieving the greatest separation on the application-driven dimensions where existing benchmarks provide limited insight.

Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limits both interpretability and task sensitivity, especially for text-to-image generation and instruction-based image editing, where different inputs require different evaluation dimensions. We propose RubricRM, a pairwise generative reward modeling framework that first produces an input-specific rubric with evaluation dimensions, weights, and scoring criteria, and then applies the rubric to score candidate images. We train dedicated RubricRM models for text-to-image generation and image editing using a two-stage training pipeline: supervised fine-tuning teaches the model the rubric-based scoring paradigm, while GRPO further improves scoring through fine-grained dimension-level rewards. Experiments on multiple generation and editing benchmarks show that RubricRM outperforms existing specialized reward models and remains competitive with strong proprietary MLLM judges despite using smaller backbones. Our models, data, and code are available at https://github.com/zijiankan/RubricRM.