Papers - 2026-07-16
吾能观之数千而面色如故
Grounding-driven Visual Reasoning#
SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding
Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc. However, real-world documents combine multiple factors such as length, layout complexity, modality, and question difficulty, which makes it difficult to attribute model failures to specific causes. We introduce SynthDocBench, a fully synthetic benchmark for long-context visual document understanding that systematically controls factors including document length, layout structure, modality composition, and question type. The benchmark is constructed using a combinatorial design, each factor is varied independently across generated documents, enabling controlled analysis of model behavior. Documents are generated end to end using an LLM pipeline across six layout archetypes, with a 40 percent random override to prevent models from exploiting spurious correlations. Additionally, SynthDocBench spans long-context documents with substantially greater length and structural diversity than existing benchmarks. Evaluating seven frontier VLMs, we uncover three failure modes that existing benchmarks cannot surface: sharp degradation with document length, a systematic positional sensitivity in which the middle third of a document is hardest for five of six models and five of six models show a negative Early-to-Late trend (steepest decline: 8.3 percentage points), and breakdown of chart comprehension in long-document settings. These results suggest that current models may be overfitting to benchmark artifacts rather than achieving robust long-context visual document understanding.
Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipulating a string or drawing a dog with five legs. These examples suggest that existing benchmarks may under-measure persistent blind spots in current systems. We introduce $\texttt{blind-spots-bench}$, a benchmark designed to expose such blind spots through tasks that appear simple for humans but remain challenging for modern AI. We collect raw questions from students in an AI course, clean and annotate them with structured reference solutions, and propose a task taxonomy tailored to the resulting dataset of 235 samples. We further develop an automated grading pipeline to evaluate a wide range of models, including open-weight and closed-source language, vision-language, and image-generation models. Our analysis on $\texttt{blind-spots-bench}$ reveals that closed-source frontier models can substantially outperform open-weight models with even $\approx10\%$ gap, even when they attain comparable performance on existing benchmarks. A more fine-grained analysis shows that no single model dominates across all task types, and that some tasks remain challenging for all evaluated models. These results highlight the value of $\texttt{blind-spots-bench}$ as a diagnostic stress test for identifying concrete weaknesses in current modern models.
Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
这篇工作提出了 ChartCynics，用于应对带有误导性结构的图表问答。它采用双路径代理框架，将诊断式视觉路径和 OCR 驱动的数据路径分开处理，再由一个 Agentic Summarizer 统一推理与校验。训练上使用 Oracle-Informed SFT 进行推理蒸馏，并用 Deception-Aware GRPO 做对抗式对齐，从而增强对视觉陷阱的鲁棒性。作者在两个基准上取得了 74.43% 和 64.55% 的准确率，相比 Qwen3-VL-8B 基座模型提升约 29 个百分点，并超过了若干最强闭源模型。
3D LLM#
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending entities, post-cutoff events, and more. This world-knowledge bottleneck is structural: generators are trained on fixed corpora, but the visual world is open-ended. We construct SearchGen-20K and SearchGen-Bench, with 20,839 prompts spanning twelve failure categories and twenty-two domains, paired with a pre-executed multimodal SearchGen-Corpus-1M to support offline, reproducible research. On SearchGen-Bench, frontier open generators score only 21 to 28 out of 100, a 40-point collapse invisible to existing benchmarks. The natural remedy is to employ search tools, enabling agentic visual generation. However, we find that naive search fails: it retrieves indiscriminately, injecting noise into prompts the generator already handles. We trace the root cause to a generator-specific, evolving knowledge boundary: the divide between what a generator can internalize through training and what must remain in external context. Although this boundary is hard to specify in advance, we show that it is discoverable through a teach-then-search co-training framework. Even a minimal version of this co-training recipe produces monotonic improvement, laying the foundation for recursive self-improvement in visual generation that can meet world-knowledge-grounded requests. We release the full dataset, co-training corpus, and search corpus as a replayable harness for tool-augmented, world-knowledge-grounded visual generation.
Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models
Coding agents must integrate external tool returns into ongoing reasoning - a capability that standard left-to-right pretraining on code exposes only in its forward direction. We observe that the action-observation-continuation loop of a coding agent is structurally isomorphic to a function call site, where a caller binds arguments, a callee returns a value computed elsewhere, and downstream code consumes that value. This conditioning structure exists at internet scale in ordinary code. We exploit it through function-aware fill-in-the-middle (FIM) mid-training: a self-supervised objective that masks functions selected via program dependency graph analysis and a complexity-inferability double criterion. We mid-train Qwen2.5-Coder-Instruct (7B/14B) and Qwen3-8B on a 2.6B-token decontaminated corpus drawn from 968 GitHub repositories, then apply existing agentic post-training pipelines. Mid-training improves SWE-Bench-Verified by +2.8/+3.0 at 7B/14B and by +3.2 on Qwen3-8B; SWE-Bench-Lite gains are +3.7/+4.0/+5.4 on the same models. The improvement holds across two post-training pipelines (R2E-Gym, SWE-Smith) and on a non-Qwen2.5 base (Qwen3-8B with SWE-Lego). Beyond in-domain gains, mid-training also mitigates the capability erosion that agentic post-training otherwise inflicts on non-agent coding (e.g., LiveCodeBench) and non-coding tool-use benchmarks (tau-bench, BFCL): although the mid-training corpus contains Python code only, the function-call inductive bias survives post-training and yields consistent gains.
Agent Training and Evaluation#
Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue Resolution
本文提出 ACQUIRE，一种 QA 驱动的仓库知识获取框架，用于提升软件 issue 修复的准确率。它将流程拆成两阶段：先由 Questioner 和 Answerer 通过自主探索仓库生成带证据的结构化问答知识，再由 Resolver 基于这些知识生成补丁。这样可以把原本隐性的知识缺口显式化，减少修复时的事实错误和上下文偏差。作者在 SWE-bench Verified 上评测，结果显示该方法相较代表性 pre-repair 方法稳定提升 Pass@1，最高提升 4.4 个百分点，且额外成本和时间开销较小。
Towards Autonomous and Auditable Medical Imaging Model Development
本文提出 AMID，一个面向医疗影像模型开发的自主多智能体框架，目标是在严格可验证的前提下自动完成建模、调试与优化。它先通过 Data-Conditioned Method Planning 将任务分析细化为可并行执行的方法路径，再用 Verification-Guided Two-Stage Optimization 在持续检查验证协议、指标计算和预测产物的基础上逐步筛选候选方案。作者在 20 个覆盖不同模态与预测类型的医疗影像挑战任务上进行了系统评测。结果表明，AMID 优于评测的通用 MLE 系统，并在若干任务上接近或达到强人类设计方案。
Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
本文对深度强化学习中的评测与设计范式做了系统分析，重点讨论了常见实验设置和结论推断中的理论问题。作者从 scaling laws 的角度出发，分析了性能、数据规模、模型容量与复杂度之间的关系，并指出渐近性能与不同数据区间下的排名并不呈单调一致。论文还通过大规模实验验证了这一点，显示在经典评测范式下，一些强化学习研究可能得出错误结论。整体上，作者给出了关于深度强化学习缩放、容量和复杂度的更原则性的分析框架。
Multimodal World Model#
Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation
In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. Instead of asking the MLLM to judge a generated image or answer decomposed verification questions, SpectraReward measures how well the original prompt can be recovered from the generated image through a single image-conditioned, teacher-forced forward pass. We use the average image-conditioned prompt log-likelihood as the reward, directly reusing the MLLM's pretrained image-text alignment ability without preference labels, reward-model fine-tuning. We further introduce Self-SpectraReward, a special case for unified multimodal models where the policy's own understanding branch serves as the reward model for its generation branch, forming a closed-loop self-improving framework without external reward models or external knowledge. Extensive experiments validate SpectraReward through a broad image-generation RL study covering two diffusion models, three RL algorithms, nine reward MLLM backbones from four MLLM families spanning 4B to 235B parameters, and five out-of-distribution text-to-image benchmarks. Results show that both SpectraReward and Self-SpectraReward significantly and consistently improve generation performance and outperform prior MLLM-derived reward training methods. Further analysis reveals that larger reward MLLMs are not always better, while Self-SpectraReward can match or surpass much larger external reward models, suggesting that reward-policy alignment is a key factor for effective image-generation RL. Project Page: https://huangrh99.github.io/SpectraReward/