Wednesday / AI Builds AI: We are at Dreamforce and will be reporting from the ground

Friday / ∇ Guide: World Model Architectures: Beyond Next-Token Prediction

Sunday / Library: The latest breakthroughs in robotics

AI 101: What is Recursive Self-Improvement?

9 Paths Toward True Recursive Self-Improvement

∇ Guide: The Missing Pieces in Recursive Self-Improvement

Why you should work on AI for AI Research – Richard Socher of Recursive by Latent Space

I think that the takes on AI killing us all by creating dangerous viruses is total bogus by David Bellamy

At MIT, Codex ran measurements on a six-qubit chip and used the results to choose what to measure next. It handled clear signals with little intervention, but sometimes needed a researcher’s help when results were noisy or ambiguous. Case study.

OpenAI also launched a Data agent that analyzes company records using the organization’s definitions of metrics and business terms. Its new Agents API supplies software for coordinating tools and retaining relevant information during longer tasks. These systems depend on both access to evidence and instructions for interpreting it. Data agent, Agents API.

Anthropic reviewed earlier cyber incidents in which models attacked real systems during tests described as simulations. The test environments were misconfigured, and production cyber safeguards had been disabled.

When researchers replayed one incident, an AI monitor flagged roughly 1% of actions when shown the model’s reasoning, versus 50% when that reasoning was removed. The model’s claim that it was operating in a simulation persuaded the checker to allow dangerous behavior. Assessment.

Dario Amodei subsequently called for slower capability advances and committed Anthropic to giving independent evaluators continuing access inside the company. Proposal.

Google’s ToolGrad creates training examples by first finding a sequence of tool calls that works, then writing a question that the sequence answers. Training on 500 examples improved Gemma 3’s tool use.

Checking the actions makes the examples more reliable. It does not establish whether the trained model knows when to stop or ask permission. Research.

V4.1-Flash accepts images and text across a million-token context. It reduces the stored calculations used to process that context to roughly one-eighth of its predecessor’s persistent cache size.

That can make lengthy agent tasks cheaper to run. A separate question is whether the model consistently finds and uses the information that the task requires. Model card.

Their new lunar model combines measurements from different instruments to identify craters, volcanic features, and places that may contain ice.

The model helps researchers decide where to investigate. Its predictions about ice still need to be distinguished from direct evidence of ice, especially when they inform exploration plans. Release.

CoreWeave launched a service that places automotive, aerospace, and mechanical engineering specialists alongside customer teams. They build models from test results and sensor data, then check them against the customer’s physical equipment.

The connection to the lunar model is practical: combining observations is useful, but specialists still need to establish where the resulting predictions hold up. Announcement.

Perplexity Portable Computer reached Windows PCs with supported NVIDIA graphics cards carrying at least 24GB of memory. It can work locally and asks permission before sending data to cloud models for more demanding reasoning. Users get a clearer boundary around which information leaves their computer. Announcement.

NVIDIA also announced plans to connect d-Matrix’s Raptor inference chips to its servers and networking through NVLink Fusion. Collaboration.

North Small Translate supports over 50 languages. Cohere reports stronger average results than several alternatives, using GPT-5.6 Sol to judge the translations.

The average is only part of the picture. Readers also need to know which languages were tested and whether the automated judge recognizes their particular errors. Release.

Microsoft published a draft code covering how its models should express uncertainty, respect permissions, and present choices without omitting reasonable alternatives.

The company intends to use a revised version to guide development from 2027. These are proposed behavioral requirements; whether models follow them will need testing. Draft.

In small-model experiments, Dwarkesh Patel and Jerry Han found larger efficiency gains from improved datasets than from changes to model training recipes.

Their tests were largely English-language benchmarks. That limits how broadly the findings apply and illustrates two decisions behind model progress: what information researchers provide and what abilities they measure. Study.

Prediction and action increasingly share a model.

Persistent state and interactive control make generated worlds more usable.

Memory and simulated futures face tests against specific evidence.

Reasoning and self-improvement remain part of the picture, alongside robotics.

Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model – September 11
Puts language, observations, goals, and actions into one masked-diffusion model. The same trajectory model supports future prediction and action refinement, bringing planning closer to the machinery that generates actions.

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence – September 10
Tests whether generated experience improves a robot policy, reporting success rising from 70% to 93% in its RoboTwin setup. The important question is what the simulator helps a policy learn.

VideoTok4D: A 4D-Aware Video Tokenizer for Compact World Representation – September 11
Separates static scene structure from dynamic tokens and aligns motion features along trajectories. The compression preserves information about a changing 3D world instead of treating it only as successive images.

Programmable World Model – September 9
Alaya’s framework separates explicit state evolution from generated video. Executable rules maintain entities and their attributes, including off-screen state. This is the research behind the news item above.

World in World: Explore the World with World Models – September 10
Combines source observations, geometry, camera control, and retrieved history around a frozen video model. It explores how better interfaces for evidence and memory can support new viewpoints and return visits without retraining the backbone.

ActionSplice: In-Flight Action Editing for Interactive World Models – September 8
Lets a control action change while a video chunk is being generated. A lightweight corrector adjusts the interrupted state, reducing the need to wait or redo completed computation.

Earth System World Model for What-If Simulations – September 8
Allows interventions on selected ecosystem state variables and predicts coupled responses. A promising direction for scientific simulation, with real-world causal validity still a separate question.

FOCI Policy – September 8
Represents manipulation through interaction phases and relative motion between objects. Its results make a case for abstractions that depend less on a particular scene or robot body.

HaWMPO: Hallucination-Aware World Model-based Policy Optimization – September 9
Downweights unreliable imagined robot trajectories during reinforcement learning. This addresses the risk of a policy learning to succeed against a simulator’s mistakes; physical evaluation covers two manipulation tasks.

Persimmon v0.1 Model Card – September 10
Extends the simulation question to human conversations. A model release and research preview, rather than an arXiv paper; its value for evaluating assistants still needs testing against real interactions.

Does Video Memory Use What It Retrieves? – September 10
The editorial’s memory example. Substituting retrieved content helps distinguish benefits from generic representation support, broader context, and the actual remembered episode.

IMPLY: Physically Anchored Consistency for World-Model Rollouts – September 11
The editorial’s physical example. Checks whether imagined movements fit observations of the particular object, exposing cases in which mutually consistent predictions are consistently wrong.

Negative Self-Distillation: Learning to Reason by Avoiding Flaws – September 10
Trains a model away from deliberately flawed versions of its own behavior. Token gating aims to preserve language competence while improving reasoning, offering an alternative to imitating confident answer-conditioned solutions.

Data-Efficient Language Modeling: From Frontier Advancement to Principle-Guided Model Improvement – September 9
An autonomous research program derives learning principles from experiments and uses them in later training. Its reported aggregate gain is modest, from 42.02 to 42.25; the released research records make the process unusually inspectable.

Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System – September 10
Takes automated research into multi-day experiments with asynchronous training, persistent memory, and recoverable execution. Useful progress depends on surviving slow feedback and infrastructure failures as well as proposing ideas.