Committee

Robbyant, Ant Group’s embodied-intelligence unit, has released LingBot-World-Infinity (LingBot-World 2.0). It is a causal video generation model that behaves as an interactive world simulator. It is how the team attacks two failure modes: long-horizon drift and interactive latency. What is LingBot-World-Infinity? An interactive world model generates video frame by frame, conditioned on a stream of user actions. Each state depends only on past frames and current input. The research team formalizes this as a causal factorization: Copy CodeCopiedUse a different Browser p_θ(x_1:T | a_1:T) = Π_t p_θ(x_t | x_<t, a_≤t) Here x_t is the visual state at time t. The action a_t combines a camera pose and a text prompt. Camera pose uses Plücker embeddings, injected through adaptive layer normalization (AdaLN). Text enters as chunk-wise prompts through cross-attention. The research team claims four upgrades over LingBot-World: An unbounded interaction horizon with consistent output quality. A distilled real-time variant sufficient to drive 720p video streams at 60 fps. A broader action space, including attacking, archery, spell-casting, and shooting. An agentic harness pairing a pilot agent with a director agent. The primary model is 14B. A lightweight 1.3B counterpart is described as deployable on a single GPU. The Architecture: MoBA and Two-Stage Training The core contribution is the Mixture of Bidirectional and Autoregressive (MoBA) Attention Mask. It explains the drift. Standard autoregressive video training uses a teacher forcing mask. Each noisy frame attends to itself and its clean context. The research team found a failure here. As context grows, the model leans on that context instead of predicting future frames. The result is overfitting and visual quality degradation. MoBA appends a bidirectional full-attention block to the teacher forcing mask. That block acts as a regularizer. It also helps the model handle flexible-length generation. The cross-attention mask mirrors the split. The autoregressive component attends to a background prompt along with chunk-wise prompts in a lower-triangular pattern. That prevents future semantics from leaking backward. The bidirectional component attends to one global prompt. Pre-training optimizes a conditional flow-matching objective with rectified-flow interpolation. Post-training then compresses the multi-step teacher into a few-step student: Consistency distillation: Latents on the same teacher probability-flow ODE (PF-ODE) trajectory must map to identical predictions. Distribution matching distillation (DMD): The generator follows the KL gradient between noised student and noised data distributions. The important detail sits in the DMD. The research team applies it over long self-rollout trajectories, not only teacher-forced states. The student is therefore optimized on the state distribution its own predictions induce. That is the stated mechanism behind anti-drift. The Agentic Harness: The Feature Worth Taking Seriously A frame predictor does not play itself. The Robbyant research team wraps the generator in a Director-Pilot Co-Simulation Framework. As described in the research paper, a Vision-Language Model is the Director. It governs macroscopic semantic rules and causal reasoning. The Diffusion Transformer video generator is the Pilot. It simulates low-level physical dynamics and renders transitions. The harness exposes two interaction modes: Mode A: Direct Semantic Interaction. The VLM reads the current frame and generates event cards. No object masks are required. Mode B: Tracking-Assisted Object Interaction. A SAM-based (Segment Anything Model) action-proposal loop tracks objects across chunks. Users select a tracked object and trigger actions. The research paper shows door-opening and ball-rotating rollouts. Users can also intervene textually. Global state shifts change time of day or weather. Local entity injection spawns creatures, and the VLM picks plausible entry points. The interface follows game conventions. WASD drives movement, IJKL controls view. Space triggers a jump; P triggers a wing glide. Keys U and O carry VLM-proposed character actions. Keys F and G carry environmental events. Numeric keys are user-registered event slots. Hands-On: What Ships and What Doesn’t Expectations need calibration here. The codebase is built on Wan2.2. Only lingbot-world-v2-14b-causal-fast is downloadable today. The causal-pretrained 14B, the bidirectional 14B, and both 1.3B variants are marked TODO. Copy CodeCopiedUse a different Browser git clone https://github.com/robbyant/lingbot-world-v2.git cd lingbot-world-v2 pip install -r requirements.txt # torch >= 2.4.0 pip install flash-attn –no-build-isolation huggingface-cli download robbyant/lingbot-world-v2-14b-causal-fast –local-dir ./lingbot-world-v2-14b-causal-fast The provided generate.py runs causal inference with KV caching. It processes frames chunk-by-chunk rather than all at once. The reference command is eight-GPU and 480P: Copy CodeCopiedUse a different Browser torchrun –nproc_per_node=8 generate.py –task i2v-A14B –size 480*832 –ckpt_dir lingbot-world-v2-14b-causal-fast –image examples/03/image.jpg –action_path examples/03 –dit_fsdp –t5_fsdp –ulysses_size 8 –frame_num 361 –local_attn_size 18 –sink_size 6 –prompt “A serene lakeside scene with a lone tree standing in calm water…” The released reference script is 480×832 across eight GPUs. The 60 fps figure describes the deployed stream, which passes a spatio-temporal refiner. That refiner upsamples decoded frames, then synthesizes intermediate frames for a higher frame rate. Both stages compile into TensorRT engines. A Diffusers checkpoint also exists: Copy CodeCopiedUse a different Browser import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video pipe = DiffusionPipeline.from_pretrained( “robbyant/lingbot-world-v2-14b-causal-fast”, dtype=torch.bfloat16, device_map=”cuda”) frames = pipe(image=load_image(“seed.png”), prompt=”…”).frames[0] export_to_video(frames, “output.mp4″) For a hosted path, Reactor serves the model as reactor/lingbot-world-2. Its docs list 48 fps at 1664×960. Sessions are command-driven and stateful: Copy CodeCopiedUse a different Browser from reactor_sdk import Reactor, ReactorStatus reactor = Reactor(model_name=”reactor/lingbot-world-2”, api_key=KEY) @reactor.on_status(ReactorStatus.READY) async def on_ready(status): ref = await reactor.upload_file(“seed.jpg”) await reactor.send_command(“set_image”, {“image”: ref}) await reactor.send_command(“set_prompt”, {“prompt”: “A misty alpine valley.”}) await reactor.send_command(“start”, {}) Movement is persistent state, not a pulse. set_move_longitudinal: “forward” drives until you send “idle”. Commands land at the next chunk boundary.  Comparison The research paper’s comparison is qualitative. Every superiority claim rests on side-by-side frame grids. Property M-G 3.0 D-W LingBot-World HappyOyster Genie 3 LingBot-World-Infinity Generation duration Minutes Minutes Minutes Minutes Minutes Hours (Infinite) Semantic interaction None None None Few Few Infinite Domain Game General General General General General Dynamic degree Medium Medium High Medium Medium High Real-time Yes Yes Yes Yes Yes Yes Open-source Yes Yes Yes No No Yes Use Cases Game and level prototyping: Seed an image of a canal town. Hot-swap the prompt to summon a snowstorm. Iterate on mood before any asset pipeline exists. Embodied simulation: Generate first-person rollouts under scripted camera poses. Feed frames to

arXiv:2607.08010v1 Announce Type: new Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment. The tool-maker grounds synthesis in the live environment as it collects execution traces, observes backend schemas and values, generates candidate tools, and repairs them against labeled cases. At runtime, the production agent calls these tools directly and falls back to code generation only when needed. We deploy the approach in a Fulfillment Center alarm-triage system, where an agent diagnoses alarms against a 44-node SOP over heterogeneous metric backends. In production, tool calls reduce p50 latency by 42%. On 1,500 historical alarms, they reduce end-to-end error rate by up to 53% by suppressing run-to-run variance in repeated steps. Because tools return compact structured verdicts, they also enable a simpler direct-call architecture, reducing p50 latency by a further 62% in a controlled ablation. Versioned tools also improve auditability and expose specification gaps and upstream data drift. Our results show that self-evolving agents can make industrial LLM systems faster, more reliable, and easier to operate.

Ties van der Meer doesn’t know how many siblings he has. The 47-year-old was conceived at a private fertility clinic in the Netherlands using sperm provided by an anonymous donor. After the Netherlands banned anonymous donation in 2004, the doctor who ran the clinic destroyed records that might have identified those donors, he says. He describes the situation as “problematic.” Children have a right to know their biological parents, he says. While he did ultimately track down one sibling, who helped him identify his father along with other genetic relatives, he may have others he’ll never find. Other donor-conceived people who have been able to track down siblings have found they have tens or even hundreds of them. One donor-conceived woman who found 25 half-siblings over the course of seven years told the Guardian, “It does make you feel a bit mass-produced.” We need international limits on the number of children a single donor can contribute to, a European fertility organization argued yesterday. At a conference in London, members laid out plans to start with a Europe-wide limit. Today many countries, including the UK, have banned anonymous egg and sperm donation. But anonymity can’t be guaranteed even in places where it is technically allowed. Genetic tests offered by companies like Ancestry and 23andMe, along with genetic registries, have made it much easier for donor-conceived people to find parents and siblings who share their genes. And because sperm can be frozen and stored for years before it is eventually used, the current set-up can result in situations where donor-conceived people discover the identity of a genetic parent only after the person’s death. They might also find that they have siblings of very different ages, all around the world. Some people are finding hundreds of siblings. Sperm from Jonathan Meijer, a Dutch man who began donating in 2007, was used to conceive between 550 and 600 children. (Stichting Donorkind, a foundation and advocacy group for donor-conceived people that’s chaired by van der Meer, took him to court, and he was ordered to stop donating in 2023.) Stories like these can be distressing for donor-conceived people. And there are other reasons why limits are considered important. The offspring of a prolific donor might be at risk of unknowingly forming romantic or sexual relationships, for instance. And some people are concerned that a donor with a harmful genetic mutation might pass that down to many children. This is unlikely, given the level of screening that most donors undergo. But it has happened. A man who donated his sperm to a sperm bank in Denmark was found to have a genetic mutation that significantly increased the risk of multiple cancers. But his sperm had already been used to conceive at least 197 children across Europe. Some of those children developed cancer. Some died. Many countries already have legal limits for donors. In Malta and Cyprus, for example, both egg and sperm donors are allowed to contribute to the birth of just a single child, according to data presented at the European Society of Human Reproduction and Embryology (ESHRE) meeting in London on July 8. Other countries set limits based on the number of families a single donor can contribute to, allowing recipients to have children who share a genetic link. In the UK, that limit is set at 10 families per donor. But these limits are difficult to enforce, partly because donated gametes don’t necessarily stay in their original country. In Denmark, the national limit is set at 12 families. But the country is a major exporter of sperm. In the UK, for example, more than half of sperm donations in 2020 were imported—with most of those coming from either Denmark or the US. “The only thing that really makes sense is a transnational limit,” Jackson Kirkman-Brown, a professor of reproductive biology at the University of Birmingham, said at the meeting. Kirkman-Brown and his colleagues have spent months putting together a document that represents ESHRE’s position on these limits. After consulting with fertility specialists, clinics, sperm and egg banks, donors, and donor-conceived people, the team has developed a plan to start with a Europe-wide limit on sperm and egg donations. ESHRE is calling on sperm and egg banks, as well as fertility clinics, to respect an initial limit of 50 families per donor. That’s still very high, according to a handful of people I spoke to at the meeting. But at least it’s a start. Europe should move toward setting limits at 15 families per donor, Kirkman-Brown said. “We may find that 15 is also too high,” says Vasanti Jadva, who studies the psychological well-being of people conceived using donated eggs, sperm, and embryos at City St George’s in London. “We still don’t know what the right number is.” It will be difficult to enforce these limits, too. And if they end up limiting the supply of donor sperm, there’s a chance that some people will turn to unregulated sperm donations from people who do not undergo health screening. Unregulated donations can lead to other problems for prospective parents, including the possibility that donors will seek parental rights over the children conceived using their sperm. And it will be even harder to establish international limits. When I asked the American Society of Reproductive Medicine for its thoughts on ESHRE’s proposed limits, a representative directed me to a guidance document saying “it has been suggested” that for a population of 800,000, single donors should be limited to “no more than 25 births” in order to avoid the risk that relatives will have children together. (Considering the US has a population of over 340 million, the total figure could be pretty high, but many sperm banks opt to limit the number of families contributed to by a single donor at around 25.) Van der Meer thinks that even a limit of five families from a single donor would be high. International donation makes it even harder for donor-conceived people to connect with genetic relatives, so the limit for international contributions should be set at two

I was really looking forward to July 4, and not just because I love a poolside barbecue. This year the American holiday also marked a big symbolic deadline for US nuclear power. Last year the Trump administration set a goal to see three new microreactors achieve criticality, a technical milestone establishing that a reactor can sustain a chain reaction, by the nation’s 250th birthday. And just in time, four reactors did so. It was a lofty goal, and seeing not just three but four companies meet it is certainly a positive sign for emerging nuclear technologies at a time when the world is facing increased need to increase electricity supply and address climate change with emissions-free technologies. But achieving criticality doesn’t mean a reactor is ready to provide electricity for the grid (or at all, for that matter). Let’s untangle what this program’s success could mean for nuclear power in the US, and where these companies might go from here. The Reactor Pilot Program essentially opened a special door for prototype reactors to fast-track development. In August, the US Department of Energy selected 11 reactor projects for the program and offered them land and support from the national labs system. These are all microreactors; the large light-water reactors that dominate the grid today are tens or even hundreds of times their size.  Antares Nuclear was the first to achieve criticality, reaching the milestone in June in its Mark-0 test reactor. Reactors from Valar Atomics, Deployable Energy, and Aalo Atomics followed. (Aalo hit the mark in the early hours of July 4—an inspiring example of just barely meeting a deadline.) The speed with which these companies hit this milestone is impressive, especially in an industry known for massive projects that frequently blow past deadlines and stated budgets. (Valar, Antares, and Aalo were all founded in 2023, and Deployable started in 2025.) But reaching criticality and running a reactor that can produce electricity are two totally different things. All these reactors reached what’s called zero-power criticality. Basically, it’s a test of whether you can start a nuclear chain reaction, with no meaningful power coming from the reactor. “A zero-power-criticality test can be achieved without making real engineering progress on fuel or design,” Kathryn Huff, a former assistant secretary for nuclear energy and chair of the Department of Nuclear Engineering and Engineering Physics of the University of Wisconsin–Madison, said on an episode of the Catalyst podcast earlier this year. Now, with the completion of this program, the companies will need to continue their work to make power, which could involve some big technical challenges. In some cases they’ll need to add significant equipment, like the cooling systems to transfer the heat out of the reactor core. The companies are projecting aggressive timelines moving forward. Aalo says it’s already begun work on the second reactor and plans to produce 10 megawatts of electricity to power an on-site data center in 2027. Deployable Energy says it plans to deploy commercial reactors by 2028.  I tend to take timelines from startups, especially in nuclear, with a grain of salt. Not only are these remarkably complex technical machines, but companies often run into problems outside their own control, like regulatory challenges—which these new projects could soon face.  The Nuclear Regulatory Commission is in charge of civilian and commercial nuclear use in the US, and historically, the process to get nuclear reactors approved has been quite slow. The agency did propose a new framework for microreactor approvals earlier this year, which is designed to speed up the process—but it’s yet to be seen how quickly things will move. (And it’s worth noting here that some nuclear experts have questioned whether the agency under the Trump administration is loosening nuclear rules too much.) Some nuclear supporters aren’t applauding the microreactor milestone. Federal focus on the program is an “unhelpful diversion” from goals to meaningfully increase nuclear capacity, according to one analysis by Third Way, a public policy think tank. “Artificially accelerating project timelines is a short-term solution, not a long-term fix,” the memo reads.  Criticality is a big first step, but a lot will still have to happen for any of these microreactors to come online, much less for these small reactors to be a significant source of electricity for the grid.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Datalab’s Lift is a focused document extraction tool with a specific promise: give it a PDF or image plus a JSON Schema, and it returns schema-shaped JSON directly. Instead of converting a document to Markdown first and then asking another model to extract fields, Lift reads rendered page images and attempts to emit the final structured object in a single pass. According to Datalab, Lift is a 9B vision model for structured JSON extraction from PDFs and images, supports schema-constrained decoding, and returns JSON that matches the user’s schema. That positioning matters because Lift is not mainly an OCR engine, not mainly a PDF-to-Markdown converter, and not a full enterprise document review platform. It is best understood as a schema-first document extractor: a model for turning visually complex documents into application-ready fields. First, the distinction that organizes everything: parsing vs. extraction Most document AI tools solve one of two different problems: Parsers turn documents into faithful intermediate representations: Markdown, HTML, JSON blocks, layout trees, tables, headings, reading order, and chunks for retrieval. Tools such as Docling, MinerU, Marker, Unstructured, PyMuPDF, OCRmyPDF, and Surya primarily fall into this category. Their output is document-shaped. Extractors turn documents into the fields an application actually needs. You define a schema — for example, invoice_number, vendor_name, total, due_date, or line_items[] — and the system tries to return those values directly. Lift, NuExtract3, LlamaExtract, Reducto Extract, Extend, Azure Content Understanding, and other cloud extraction APIs belong closer to this category. Their output is schema-shaped. That distinction matters because many production systems still follow a parse-then-extract pattern: convert a PDF to Markdown or structured text, then send that representation to an LLM with a schema. Lift’s bet is to collapse that workflow into a single visual extraction pass. That can reduce pipeline complexity, but only when the real goal is field extraction rather than faithful document reconstruction. The competitive map Lift sits at the intersection of several overlapping categories: Open-weight extraction VLMs such as NuExtract3 Frontier multimodal LLMs with structured-output modes Cloud document AI systems such as Azure, Google, and AWS Commercial extraction platforms such as Reducto, Extend, LlamaExtract, and Datalab’s own API Open-source document parsers such as Docling, MinerU, Marker, and Unstructured Structured-generation libraries such as XGrammar, Outlines, Instructor, BAML, and related JSON-output systems The important point is that not all of these tools are direct competitors. Some compete with Lift directly. Others are adjacent infrastructure. A parser like Docling is not trying to solve the same problem as Lift. A constrained-decoding library is not a document model at all. A commercial extraction platform may include extraction models, citations, review workflows, and compliance infrastructure. Lift is narrower: it is the raw schema-first extractor. Lift vs. NuExtract3: the closest open-weight comparison NuExtract3 is probably Lift’s closest open-weight competitor. NuMind describes NuExtract3 as a unified 4B vision-language reasoning model for document understanding, combining structured information extraction with image-to-Markdown conversion for documents such as scans, receipts, forms, invoices, contracts, and tables. Its Hugging Face model card lists it under an Apache-2.0 license. The contrast is straightforward. Lift is larger at 9B and, in Datalab’s own benchmark, reports stronger field accuracy than NuExtract3: 90.2% versus 81.5%. NuExtract3 is smaller, more permissively licensed, and also positioned as a Markdown-conversion model. So the practical decision is not only accuracy. If the priorities are permissive licensing, smaller local deployment, and a single model that can also convert documents to Markdown, NuExtract3 is attractive. If the priority is schema-first field extraction with Datalab’s reported speed-accuracy trade-off, Lift becomes more compelling. Lift vs. frontier multimodal LLMs A common alternative is to send the document to a frontier multimodal LLM and ask for structured output. In Datalab’s benchmark, Gemini Flash 3.5 slightly outperforms Lift on field accuracy and full-document accuracy, while Lift is much faster in the reported setup: 9.5 seconds median latency for Lift versus 28.1 seconds for Gemini Flash 3.5. That does not mean Lift is always better. Frontier models remain attractive when volume is modest, setup time matters more than infrastructure control, and cloud processing is acceptable. Lift’s advantage appears when latency, data residency, repeatable self-hosting, and large-volume cost control matter. Lift vs. cloud document AI platforms Azure AI Document Intelligence, Azure Content Understanding, Google Document AI, and AWS Textract are managed cloud services rather than just models. They provide enterprise infrastructure for document processing, including deployment controls, service reliability, monitoring, procurement processes, and integration with broader cloud ecosystems. Microsoft describes Azure Content Understanding as a way to transform unstructured data into structured, machine-readable information while preserving structural relationships. In Datalab’s benchmark, Azure Content Understanding reports lower field accuracy and higher latency than Lift, but it includes citations, which Lift’s open weights do not. Datalab’s own hosted API also adds per-field verification, citations, and confidence scores beyond the open model. This is the cloud tradeoff. Cloud platforms are usually easier to adopt within companies already standardized on Azure, Google Cloud, or AWS. They may also be stronger choices when enterprise governance matters more than raw speed. Lift’s counterargument is portability: teams can run the extraction model locally or through their own vLLM deployment rather than sending every document to a hosted API. For handwriting-heavy, low-quality scans, clinical forms, annotation-heavy documents, or regulated workflows requiring traceability, the cloud and managed platforms should be benchmarked directly against Lift rather than assumed inferior. Lift vs. commercial extraction platforms Reducto, Extend, LlamaExtract, Mindee, and Datalab’s own hosted API occupy a different layer of the market. They are not only extraction models; they are extraction systems. Their value is not limited to field accuracy. They add provenance, review workflows, schema management, confidence scoring, citations, deployment controls, and enterprise compliance. Reducto’s Extract product is positioned around schema-typed JSON extraction with optional citations, while its Parse product emphasizes typed blocks, bounding boxes, and confidence scores. LlamaExtract similarly advertises custom-schema extraction with granular citations and confidence scores. This is where Lift’s open model is intentionally thinner. The open weights prioritize fast, schema-first extraction. Datalab’s hosted API adds the production features