1. DeepMind Crowdsources the Definition of AGI Google DeepMind this week released a cognitive framework for measuring progress toward artificial general intelligence. That alone would have been notable.

2. Developers Call AI Coding a Gamble as Code-Model Investment Grows Three signals this week trace the same fault line in AI-assisted programming, and they point in opposite directions.

3. OpenAI Ships Teen Safety Controls in Japan While xAI Faces Child Exploitation Lawsuit OpenAI Japan published a Teen Safety Blueprint this week, rolling out age verification, parental controls, and well-being safeguards for teenage users of its generative AI products.

In Brief

• Mistral AI Launches Forge for Enterprise Custom Model Training Forge lets organizations pre-train, fine-tune, and run reinforcement learning on proprietary data across dense and mixture-of-experts architectures. The platform automates hyperparameter tuning, generates synthetic training data, and runs evaluation against internal benchmarks before production deployment. ASML, the European Space Agency, Ericsson, and DSO National Laboratories Singapore are among early partners.

• Researchers Propose Attention Residuals to Replace Fixed Residual Connections in Transformers Attention Residuals (AttnRes) swaps the standard fixed-weight residual connections in LLMs for softmax attention over preceding layer outputs. Each layer selectively aggregates earlier representations using learned, input-dependent weights. Standard residual connections cause uncontrolled hidden-state growth at depth, diluting individual layer contributions.

• Qianfan-OCR Unifies Document Parsing, Layout Analysis, and QA in a Single 4B-Parameter Model Qianfan-OCR performs direct image-to-Markdown conversion and supports table extraction, chart understanding, document QA, and key information extraction through prompt-driven tasks. The model introduces Layout-as-Thought, an optional reasoning phase that preserves explicit layout analysis within end-to-end OCR.

• Online Experiential Learning Framework Lets LLMs Improve from Live Deployment OEL extracts transferable knowledge from real-world interaction traces and feeds it back into model updates during deployment. The approach targets a gap in current training: models discard all experience accumulated while serving users.

• TRUST-SQL Tackles Text-to-SQL for Enterprise Databases with Unknown Schemas TRUST-SQL uses multi-turn reinforcement learning to let agents discover relevant schema subsets in databases with hundreds of tables and noisy metadata. The method drops the standard assumption that full schema is available upfront, matching real enterprise conditions.

• Entropy-Aware Decoding Targets Hallucinations at Transition Words in Multimodal Models Researchers found that transition words like "because" and "however" correlate with high-entropy states and hallucinations in multimodal reasoning models. Their decoding method extracts contextual reasoning signals from token probability distributions to suppress hallucinated outputs.

• Study Shows Video Diffusion Models Reason Along Denoising Steps, Not Across Frames New analysis overturns the assumption that video models reason sequentially across frames via a Chain-of-Frames mechanism. Reasoning instead emerges along diffusion denoising steps, a finding with direct implications for how video model architectures are designed.

• MiroThinker-H1 Adds Verification to Research Agents for Multi-Step Problem Solving MiroThinker-1.7 improves agent reliability through structured planning and tool interaction during an agentic mid-training stage. The H1 extension layers on verification capabilities for longer-horizon reasoning tasks.

• FinToolBench Benchmarks LLM Agents on Real-World Financial Tool Use FinToolBench evaluates LLM agents on multi-step financial tasks requiring real-time data retrieval and compliance-aware reasoning. Existing financial AI benchmarks test static text analysis; this one measures dynamic tool interaction under domain-specific constraints.

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