EdgeFlow: Edge-Map Augmented VLM-Based Flowchart Processing for Industrial Requirements Engineering
EdgeFlow augments Vision Language Models with deterministically extracted Canny edge maps as structural priors to improve flowchart-to-Mermaid conversion in industrial requirements engineering, requiring no annotated training data or fine-tuning. Evaluated on IndusReqFlow, a real-world industrial dataset, it achieves +17.39 pp node-level F1 and +16.94 pp edge-level F1 over off-the-shelf VLMs. Cross-dataset evaluation on a synthetic benchmark shows no significant gains, highlighting the gap between synthetic and industrial benchmarks for VLM-based RE tools.
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Self-Ensembling Vision-Language Models for Chart Data Extraction
This paper proposes a self-ensembling method for chart-to-table extraction using vision-language models (VLMs), where multiple tabular outputs are sampled from the same VLM for a given chart image and aggregated via per-cell median over numerical values. The approach includes convergence detection and uncertainty estimation based on sample dispersion. The authors also introduce WB-ChartExtract, a new benchmark built from World Bank data featuring charts with ~7x more datapoints than ChartQA. The method achieves up to 23% relative improvement on WB-ChartExtract over single-pass VLM baselines.
DIRECT: Adaptive test-time compute routing for embodied VLM planners
Researchers introduce DIRECT, a routing framework that dynamically allocates test-time compute for Vision-Language Models acting as embodied planners, using multimodal scene context to decide per-prompt how much compute to spend. Experiments on VLABench and RoboMME benchmarks show that different scaling axes (chain-of-thought depth, model size, memory history) yield qualitatively distinct gains, and that naive uniform scaling is wasteful. On a physical Franka arm, DIRECT matches or exceeds a stronger model's success rate at up to 65% lower average latency, improving the success-cost Pareto frontier.
Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models
Chartographer is a framework for generating counterfactual chart variants to rigorously evaluate visual reasoning in vision-language models (VLMs), addressing the problem of shortcut-taking and prior knowledge exploitation in chart QA benchmarks. The system reverse-engineers charts into executable code, generates seed-controlled variants, and derives new ground-truth answers via executable QA logic. Evaluation of proprietary and open-source VLMs reveals that models frequently fail to generalize to counterfactual charts even after correctly answering the original, with failures most common when novel visual reasoning pathways are required.
VeriTrace: Cognitive-Graph Framework with Explicit Regulatory Loops for Deep Research Agents
VeriTrace introduces a cognitive-graph framework for deep research agents that replaces implicit LLM reasoning over intermediate representations with three explicit regulatory loops: interpretive update, deviation feedback, and schema revision. The system addresses contamination and error propagation in evolving mental models during complex multi-step research tasks. Using Qwen3.5-27B backbones, VeriTrace improves over the strongest matched baseline by 4.22 pp on DeepResearch Bench Insight and 5.9 pp Overall win rate on DeepConsult. With Config-DeepSeek, it achieves the strongest reproducible open-source result on DeepResearch Bench.
Vision Language Models (Better, faster, stronger)
A Hugging Face blog post surveys the state of vision-language models (VLMs) in 2025, covering advances in architecture, training, efficiency, and deployment. The post reviews progress across major open and closed VLMs, highlighting trends in multimodal capability, speed improvements, and practical deployment patterns. As a tier-2 commentary piece, it synthesizes the current landscape rather than announcing new research.
ETCHR: Decoupled Image Editing for Visual Chain-of-Thought Reasoning in MLLMs
ETCHR introduces a question-conditioned, reasoning-aware image editing model that decouples visual transformation from downstream understanding in multimodal LLMs. It addresses two identified gaps—language-side (mapping abstract questions to visual edits) and generation-side (edit quality degrading with reasoning depth)—via a two-stage training recipe combining supervised fine-tuning on edit trajectories and VLM-derived reward signals. Because the editor is decoupled, it plugs into arbitrary MLLMs without retraining, yielding Pass@1 gains of roughly +4.6 to +5.5 points across five task families when paired with Qwen3-VL-8B, Gemini-3.1-Flash-Lite, and Kimi K2.5. The work advances the 'think with images' paradigm beyond fixed toolkits and unified multimodal approaches.
Vision Language Model Alignment in TRL
Hugging Face's TRL library has added support for aligning Vision Language Models (VLMs), extending existing RLHF and preference optimization tooling to multimodal settings. The blog post covers the new capabilities for training VLMs with alignment techniques such as DPO and related methods. This expands the open-source ecosystem for multimodal model fine-tuning and alignment.
Reroute: Training-free recoverable visual token routing for vision-language models
A new arXiv preprint proposes Reroute, a training-free plug-in that replaces the standard rank-and-remove visual token pruning paradigm in VLMs with a recoverable routing mechanism. Instead of permanently discarding low-ranked tokens, Reroute defers them to re-enter the candidate pool at later decoder stages, addressing the problem that token importance shifts across decoder depth. Evaluated on LLaVA-1.5 and Qwen backbones augmented with FastV, PDrop, and Nüwa pruning methods, Reroute improves grounding performance under aggressive token reduction without sacrificing general VQA accuracy. The approach preserves the theoretical compute and KV-cache budget of the underlying pruning method.