Latest / Embodied AI 101 / DataClaw0: Agentic Tailoring for Raw Multimodal Streams
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- DataClaw0: Agentic Tailoring for Raw Multimodal Streams. Modern AI systems increasingly confront vast streams of raw multimodal data – video feeds from camera sensors, user interface (GUI) event logs, embodied-agent sensors, and more. These streams brim with information but also noise and redundancy. In the words of Cong Wan et al., “massive unstructured multimodal streams suffer from high ‘data entropy,’ impeding both efficient human knowledge acquisition and high-quality AI post-training”. In practice, this means that video footage recorded on a factory floor or a home robot’s sensor logs contain huge amounts of irrelevant or low-level detail. For example, a surveillance video might capture hours of idle waiting, and a GUI usage log might record every mouse jitter or cursor movement. Left unfiltered, training a model on such raw streams wastes capacity on the unimportant parts while drowning out the core signals. Traditionally, engineers address this by manually annotating or homing in on interesting events with handcrafted rules or existing vision-language models. But these passive approaches are costly, brittle, and limited in scope. Just relying on a rule to “label every frame where the robot’s gripper moves” can miss the underlying intent and subtle context. Using a general-purpose vision-language model (VLM) to caption images can produce bland, one-size-fits-all labels. As the authors observe, existing passive annotation pipelines — heavily dependent on heuristics or generic VLMs — are “costly, monotonous, and fail to unlock the deep procedural logic embedded in raw data”. In other words, these static pipelines produce only shallow, repetitive supervision, and they cannot capture the rich, structured knowledge buried in the data. DataClaw0 aims to change this by learning how to refine data itself. Instead of treating data cleaning as a fixed pre-processing script, the authors propose turning data processing into a learnable capability. They call this an “Agentic Data Tailoring” paradigm. Under this view, the system actively “refines and structures data to align with diverse user and downstream intents”, much like an intelligent agent would tend and prune a garden of raw streams. DataClaw0 is a 9-billion-parameter model trained to be this agent. Fed raw inputs (video frames, GUI traces, etc.), it reasons about which parts are salient and how to reorganize them into a more structured, information-rich format suitable for training other models. At a high level, the approach has two main parts. First, the input streams are processed through a two-stage pipeline. In stage one, simple but reliable “factual anchors” are extracted from the raw data using deterministic methods: think classical vision algorithms or rule-based parsers that can pick out concrete facts (anchor points) with high confidence. In stage two, a generative model uses these anchors as seeds to synthesize full semantic content around them. The output is a “large-scale dataset” of high-information-density annotations covering multiple domains. Second, the DataClaw0 model itself is trained to perform this refinement task. It is supervised on the constructed dataset and then further fine-tuned via an RL-like objective (Group Relative Policy Optimization) to align even better with the ultimate goal of clarity and relevance. Finally, to verify that this tailored data is genuinely useful, the authors evaluate downstream: they train specialists on the DataClaw0 outputs (rather than generic labels) and see significantly better performance on tasks like video generation, visual question answering, and GUI navigation under limited-data regimes. In short, DataClaw0 learns to be a “data engineer” agent – cleaning and restructuring raw streams into tight, task-focused training data that boosts the efficiency of other models. Below we dig into each part of this story. We first unpack what “high data entropy” and “passive annotation” mean in modern multimodal settings, then explain the novel agentic pipeline and training procedure of DataClaw0. We describe how Cong Wan et al. build a massive multimodal dataset via factual anchoring and semantic generation, and how they train DataClaw0 with supervised fine-tuning (SFT) plus Group Relative Policy Optimization (GRPO) to master the refinement task. Finally, we look at how they measure success – the newly introduced DataClaw_0-val benchmark and downstream task results – and why this marks an important step in the data-centric AI of the future. The Data Entropy of Raw Multimodal Streams. Consider the simple act of recording a person walking around a room with a wearable camera, or interacting with a computer interface. The raw “data stream” produced is overwhelming in volume and complexity. Video footage captures millions of pixels each second, and many frames differ only imperceptibly. A GUI log might record every hover and click event, including tens of nearly identical screenshots. Even sensors on a household robot can churn out continuous gyro and depth data at high frequency. Most of this information is low value for training tasks: a frame of empty space, a mouse moving slightly while thinking, small background motions. Yet it is stored and would be processed in a naive pipeline. When we quantify this waste, we talk about data entropy – a measure of noise or disorder in the raw streams. High entropy here means “lots of randomness and irrelevant detail”. Massive unstructured streams indeed have high entropy: the truly useful bits (the key objects, actions, intentions) are buried under a mountain of flotsam. This situation “impedes both efficient human knowledge acquisition and high-quality AI post-training”. Human annotators get overwhelmed by the sheer volume, and machine learning models trained on all that unfiltered data improve more slowly because they see too much irrelevant variation. Data-centric researchers often highlight this problem. Cutting through high-entropy data, one preferred approach is dense supervision: provide detailed labels that focus on exactly what matters. In vision, this might mean bounding boxes or pixel masks for relevant objects; in language, it could mean action scripts or question-answer pairs extracted from dialogues. Achieving dense supervision, however, has traditionally meant huge manual effort or carefully engineered pipelines. For certain domains — say, sports video or some robotics tasks — people will painstakingly annotate state changes or write domain rules that snap observations into structured records. These strategies help make data less “entropic,” but at great cost. The DataClaw0 team argues that the status quo is fundamentally passive and brittle. “Existing passive annotation paradigms, heavily reliant on heuristic rules or general VLMs, are costly, monotonous, and fail to unlock the deep procedural logic embedded in raw data”. Here passive means that the system just takes the raw input and labels it in a one-shot, automated way, without truly understanding or interacting with it. For example, one might run a generic image captioner on every frame (as a passive process). This yields captions like “person walking” or “button on screen” for each frame independently. But such an approach is costly (maybe it requires human-in-the-loop to pick good captions), monotonous (repeats similar phrases 100,000 times), and misses logic (it doesn’t realize that frames form a continuous action sequence with a common intent). What’s needed instead is something active or “agentic”: a system that doesn’t just label passively but takes actions to improve the data. The authors call their solution Agentic Data Tailoring. In their view, data processing itself becomes an adjustable policy or skill that an AI can learn. Rather than coding more heuristics by hand, you let an AI agent operate on the data, guided by objectives about quality and informativeness. The agent can ask questions like “which segments of this video truly matter?” or “how can I reorganize this GUI log into a useful task-flow?”, rather than blindly parroting a fixed labeling schema. In a sense, DataClaw0 flips the usual motif of agents and environments. Normally, an AI agent (like a household robot or game-playing bot) interacts with an environment to accomplish tasks. Here, the data is the environment, and DataClaw0 is the agent. Instead of moving its physical arm or clicking a button, this agent’s actions are to filter, compress, annotate, and restructure the data stream, its reward being the usefulness of the outcome. The key insight is that data tailoring can be learnable: the model can iteratively improve its own data-cleaning strategy by experience, rather than relying on fixed rules. Thus, the core promise: by using an agentic process to refine multimodal streams, we can turn “raw chaos” into structured, information-dense supervision. The rest of the paper details how to implement this idea. Agentic Data Tailoring: Learnable Data Processing. At its heart, DataClaw0 is about transforming data pipelines from static scripts into learned behavior. The authors frame it as a “paradigm shift” in data preparation: “We elevate data processing to a learnable capability, proposing a paradigm shift towards Agentic Data Tailoring, which actively refines and structures data to align with diverse user and downstream intents”. This is somewhat analogous to active learning or other data-centric AI ideas, but with a twist. In active learning, a model chooses which examples to label next. Here, DataClaw0 is not just selecting; it’s generating and re-formatting training examples themselves. It is more like AutoML for data, or an AI-assisted data engineer. Given raw sequences, the model’s job is to produce refined sequences that will serve as supervision. An example may clarify. Suppose we have a long video of somebody preparing a meal. A passive processor might output thousands of generic captions or simply store all frames. An agentic approach could be: extract key “facts” like “Ingredients on countertop”, “knife chopping”, “stirring in pot”, etc., possibly by running an object detector or OCR for text on packaging. Then the agent might generate a concise structured narrative: a step-by-step recipe or timeline, connecting those anchor points into a coherent description. If tuning for a cooking-assistant task, you might produce training data that a navigation agent could use to actually perform cooking steps. The agent has thus distilled the raw video into something far more useful, all in a learned way. To build DataClaw0, the authors design a two-stage pipeline that operationalizes this agentic process. Stage 1: Extract Deterministic Factual Anchors. The first stage is about identifying reliable anchors in the raw data. These anchors are facts or signals that can be extracted deterministically or with high confidence, without hallucination, which the generative stage can then use as points of truth. Examples of factual anchors might include:. In video: deteced objects, recognized actions, transcription of spoken words, or environment metadata (e.g. GPS position, timestamp). In GUI logs: event types (click, swipe, keypress), labels of UI elements (via OCR), or API call traces that indicate certain operations. In embodied sensor streams: ground-truth kinematic poses from motion capture, or a known sequence of waypoints from logged navigation data. The idea is that some aspects of the raw stream are straightforward to pin down with good accuracy. For instance, an image recognition model might reliably flag “kitchen counter” or “person knife” with high probability. Or a voice recognition engine can transcribe spoken instructions (e.g. “plywood chopped into cubes”). These serve as the deterministic scaffolding. The paper does not detail every anchor algorithm (that likely depends on the domain), but it emphasizes using “deterministic factual anchors” as the foundation. In effect, Stage 1 reshapes the raw data into a skeleton of facts. This strategy has a few advantages. By computing anchors with rule-based or pre-trained models, we avoid the risk of generative hallucination at the base. The agentic model will always have some grounding in reality. Also, anchors compress the data: instead of handling every raw video frame, we now have a sparse set of key frames or events. This reduction is itself a form of noise filtering: irrelevant bits (like a static background) leave no anchor. Most importantly, anchors can span complex domains – the same principle applies whether you are parsing code editor logs, multi-agent sensor data, or 3D environment streams. Once we have anchors, we move to the second stage:. Stage 2: Generative Semantic Synthesis. In this step, the system uses a generative model (likely a large language or vision-language model) to synthesize rich semantic content around the anchors. This is where DataClaw0’s “tailoring” happens – the previously disparate facts get woven into dense supervision. Concretely, the generative model might take as input a set of factual tokens (e.g. detected objects “pizza dough; tomato sauce; cheese”), maybe along with context like surrounding frames or scene descriptions, and then produce a structured summary or instruction. For example, the model could output a textual description, a hierarchical task list, intermediate scene reconstructions, or any format that lends itself to training an AI. If the goal is to train an assistant, the output might look like an annotated recipe script. If the goal is 3D reconstruction, the output might be a volume segmentation or annotated depth map sequence. Crucially, the generative model is grounded by the anchors. Being provided with the anchors (facts) ensures it doesn’t drift into fantasy. It has to incorporate those concrete elements. For instance, if the anchor says “detected: person using screwdriver”, the model can expand on that with how and why, without inventing an entirely unrelated scene. The anchor data serve as constraints around which the model “fills in the blanks” with coherent narrative or labeling. The authors refer to this as “grounding generative semantic synthesis in deterministic factual anchors”. The result of the two-stage pipeline is a dense, high-quality dataset. Instead of raw videos or logs, we now have semantically annotated sequences: e.g. richly captioned video clips, detailed GUI walkthroughs, or structured task traces. The idea is that this synthesized data has information density – most elements are relevant to a task, and nothing is wasted on trivial noise. An example in a different context might make this vivid: imagine we have a multi-day log of a delivery robot's trip from A to B. Stage 1 might anchor key GPS waypoints, package pick-up/delivery events, and obstacle encounters. Stage 2 (semantic synthesis) could write a step-by-step itinerary: “Departed warehouse (8:02am), turned right at POI-1 (8:10am), slowed for pedestrian crossing, delivered parcel at B (8:45am)”. That textual summary becomes a training datum, whereas the raw log is too granular. By doing this across many examples, one could accumulate a training set of “delivery scenarios” to train another navigation agent. The authors apply this pipeline across five core physical and digital domains. The paper does not explicitly list them all, but from context they likely include things like: (1) raw video streams (both natural scenes and screen-captured video), (2) GUI interaction streams, (3) embodied sensor streams from robots or simulation, (4) maybe sequential data like time-series from physics simulations, and (5) possibly text-vision hybrid environments. In any case, the key is that the pipeline was run on diverse source data, producing a unified dataset of examples. This large, multi-domain dataset is intended to span the kinds of tasks the model will need to tailor data for. We should emphasize: the dataset produced is tailored. It’s not just any random labeling. It’s specifically designed to train an agent to refine and tailor data. So the labels include both “what is the refined output” (the structured annotations) and information about the “intent” of the refinement. Essentially, in training the model on this data, we teach it not only to map raw input to an output, but also what quality we seek in the output (concise, focused, aligned). Training DataClaw0 with SFT + GRPO. Once this massive synthesized dataset is in hand, the authors proceed to train the DataClaw0 model. Its purpose is to learn the agentic tailoring behavior. They do this in two phases:. Supervised Fine-Tuning (SFT) on the constructed dataset. Group Relative Policy Optimization (GRPO) as a second-stage fine-tuning to refine performance on complex intents. Let’s unpack these. (1) Supervised Fine-Tuning (SFT): In this phase, DataClaw0 (a 9B-parameter multimodal model) is trained on examples where the input is a raw multimodal stream and the target is the human-designed “refined” output from the two-stage pipeline. Essentially, it’s fed the raw video/GUI/sensor data (or a representation thereof) and learned to produce the high-level annotations that were generated. This teaches it the basic mapping: how to identify anchors and weave them into structured content. Because the dataset spans five domains, SFT exposes the model to a wide variety of scenarios (e.g. different visual scenes, interface layouts, embodied tasks). After SFT, DataClaw0 has a decent ability to perform data refinement by imitation. However, SFT alone may not fully capture the “intent” behind tailoring. The model might learn surface patterns ("add this annotation after that anchor"), but it might not always know why an output is considered high quality. Sometimes there are multiple ways to annotate, or subtle trade-offs (e.g., include details vs brevity). To push the model towards outputs that truly serve downstream goals, the authors add a reinforcement learning step. (2) Group Relative Policy Optimization (GRPO): This is a variant of policy gradient (akin to PPO) adapted for fine-tuning language or multimodal models. The key idea is to treat the model’s refinement of data as a policy that can be rewarded for good outcomes. In practice, they define a reward signal that measures how well the tailored data meets the desired criteria (which likely involves downstream utility or information density). Then DataClaw0’s parameters are updated to maximize this reward, adjusting its predictions beyond what supervised examples alone would do. Specifically, “DataClaw_0-9B model synergizes Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO), achieving robust alignment with complex refinement and tailoring intents”. This use of GRPO is about stability and group-wise normalization of rewards – it’s a recent RL technique shown to work well for LMs. The “group” part essentially normalizes rewards over a batch to reduce variance. The details aside, the result is that DataClaw0 learns not just from static examples but also from feedback on how good its refined data is. It can fine-tune to prefer, say, more concise annotations if that gave higher scores, or to focus on certain aspects more. We don’t have the exact reward function, but one can imagine it incorporates metrics like the informativeness of the output and consistency with anchors. For instance, if DataClaw0 chooses to drop a fact that was crucial, it might get a penalty; if it adds useful bridging text, it might get rewarded. By optimizing such a reward, GRPO helps the model handle “complex refinement and tailoring intents” that SFT alone may miss. In RL terms, the model is learning to be an agent that consistently produces the best quality data tailoring it can, given the raw input. To summarize the training: first, the model imitates the two-stage pipeline on a large dataset. Then it self-improves with policy optimization. The combination is reminiscent of how large language models are often trained: imitate human text with SFT, then refine with RLHF. Here the “human text” is the refined data, and the “human feedback” is an automatic reward reflecting task success. The DataClaw_0-val Benchmark. Building a model like DataClaw0 raises the question: how do we measure its performance? Traditional metrics like accuracy or F1 don’t directly apply to “how well you refine data.” Instead, the authors introduce a specialized evaluation suite: DataClaw_0-val. This is described as “the first benchmark dedicated to data refinement”. DataClaw_0-val likely consists of tasks where the model is given raw multimodal inputs and must produce outputs that can be scored. For example, one could include test videos with gold-standard refined annotations, and measure how close DataClaw0’s output comes. Or one could measure the utility: feed DataClaw0’s output into a simulated downstream training pipeline (like a video captioner or a navigation policy) and measure final performance. The paper abstract hints that the ultimate test is “downstream post-training as the ultimate validation touchstone”. In other words, they assess DataClaw0 by seeing how useful the data it creates is for actual end tasks. The construction of DataClaw_0-val shows that the authors take evaluation seriously. It’s not enough to have a lofty idea — we must quantify “data refinement ability.” DataClaw_0-val allows direct benchmarks of whether DataClaw0 can consistently compress and annotate input streams effectively. Even though we don’t have the benchmark’s specifics, its mention implies tasks like:. Video clip refinement: compressing a short video into a high-level textual script. GUI sequence condensation: given a UI interaction trace, output a distilled list of key actions and outcomes. Sensor log summarization: condense embodied sensor sequences into actionable events. Possibly each domain has its own validation set. Importantly, since they “adopt downstream post-training as the ultimate validation”, improving on DataClaw_0-val should correlate with actual gains on downstream tasks. This means the benchmark is designed such that better scores do indicate better data. It's a pragmatic approach: ultimately what matters is if the data helps other models learn better. Empirical Evaluation: Post-Training on Downstream Tasks. Once DataClaw0 is trained, how do we know if it really produces better training data? The authors’ answer is to run experiments on target tasks using the tailored data and observe the improvement. They pick several representative tasks and train models with and without the DataClaw0 augmentation. Concretely, they set up scenarios in at least three domains:. Video generation: Possibly tasks like “generate a video given a caption” or forecasting future frames. They may train a generative model on static or dynamic visual data. Real-world VQA (Visual Question Answering): A task where a model answers questions about images from the real world. This obviously benefits from well-annotated or richly described images. GUI navigation: Perhaps tasks where an agent must interact with a user interface to accomplish goals (e.g. clicking through menus). There are emerging benchmarks for GUI agents, and quality of training data makes a big difference. For each of these, the idea is: take a base model (or train one from scratch) using only the original limited data, and then train an equivalent model using the DataClaw0-refined data on top of it. If DataClaw0’s data is truly denser and more informative, the model trained on it should achieve higher accuracy or better quality, especially when the amount of original data is small. In their evaluations, the authors report that DataClaw0 “delivers high-information-density tailored data, facilitating efficient model adaptation to new tasks under limited training data regimes”. In plainer terms, models that learned from DataClaw0-processed data needed fewer examples to achieve the same performance. The high signal-to-noise ratio of the tailored data meant that even small training sets were highly effective. Although we don’t have exact numbers, the paper claims statistically significant gains across the domains tested. The phrase “under limited training data” suggests a few-shot or low-data context: what DataClaw0 offers is a form of intelligent data augmentation. One might imagine, for example, a GUI navigation task where normally a model struggles on novel interfaces with only 100 hand-annotated examples. If DataClaw0 takes longer GUI tutorial videos or raw clickstreams and outputs structured training examples, it might boost performance enough that only 50 examples plus DataClaw0 output matches the 100 example baseline. Similarly, in VQA, textual “visual quips” generated by DataClaw0 to describe images could help a VQA model generalize better. And in video generation, structured “scene decomposition” from DataClaw0 could help a generative video model key on important dynamics. In sum, the downstream experiments show the practical payoff: DataClaw0’s tailored data is not merely well-formed, it is useful. This closes the loop: it turns the abstract notion of “agentic data tailoring” into a concrete gain on vision and interface tasks. We can cite directly: evaluations on varied tasks “confirm that DataClaw_0 delivers high-information-density tailored data”. Discussion and Related Work. DataClaw0 enters a growing landscape of data-centric AI and agentic systems. The notion of using language or vision models to generate or clean training data has precedents. For instance, recent work has used LLMs to produce synthetic question-answer pairs or program snippets for fine-tuning (e.g. self-instruct methods). In the vision domain, researchers have tried to synthesize image-text pairs with models to bootstrap vision-language training. DataClaw0’s distinctive shift is positioning a multimodal model as an active agent in data preparation, using RL-style feedback. It also resonates with other agentic pipelines in related areas. For example, Xiong et al. (2026) explore Video2GUI, automatically synthesizing GUI interaction trajectories from video for agent pretraining. They convert annotated video demonstration into sequences of interface actions. Similarly, Chen et al. (2026) created GUI-World, a 12,000-video dataset specifically to benchmark multimodal agents on GUI tasks. GUI-World is essentially a giant “data tailoring” effort – human-collected examples of interface navigation, which DataClaw0 could potentially have streamlined automatically. These efforts illustrate that the community sees value in generating structured training data for agent tasks. DataClaw0 contributes to this trend by providing an end-to-end learned solution instead of hand-build pipelines. Another parallel is in the robotics and embodied AI space. RoboAgent (Xu et al., 2026) and similar work chain together skills to solve tasks; DataClaw0 could, in principle, take long robotic demonstrations and break them into labeled sub-tasks. Likewise, in video understanding, tools like VideoWebArena (Jang et al., 2025) create evaluations from web videos—DataClaw0’s generative refinement might one day help populate those benchmarks. On the RL fine-tuning side, the use of Group Relative Policy Optimization (GRPO) aligns with contemporary advances. GRPO is a recent variant of PPO designed to stabilize language model alignment (see Mroueh et al., 2025). Its usage here shows how techniques from RLHF (reinforcement learning from human feedback) are being repurposed for “RL from performance feedback”. The group-normalization aspect presumably helps when rewards (like a downstream model’s accuracy) are noisy. It may be worth noting that evaluating an entire downstream training cycle for every possible data refinement is expensive, so in practice the reward function might rely on proxies. Still, Cong Wan et al. demonstrate the feasibility of this approach by showing clear gains. From a broader perspective, DataClaw0 exemplifies the data-centric paradigm that Andrew Ng and others have advocated: focusing on improving the data can yield better returns than scaling models forever. By making data refinement itself learnable, the system can adapt to diverse user intents. For example, if I want data tailored for autonomous driving, I could train DataClaw0 on driving logs anchored by road signs and trajectories; if I want data for game-playing, I could feed it game screen recordings and have it output key game-state summaries. The same underlying model handles both, because it has learned the general skill of “tailoring multimodal data to intent”. There are challenges and open questions. The dependence on deterministic anchors is a design choice to prevent hallucination, but it means DataClaw0’s performance is tied to the quality of those anchors. If the anchor extraction fails (missed object, mis-OCR, etc.), the synthesized output might be flawed. Scalability is also a concern: the paper uses a 9B model, which is large but not gargantuan; how well could this pipeline work at 100B+ scale or with real-time continuous data? And how domain-specific are the skill sets; e.g. does DataClaw0 trained on five domains generalize to an entirely new domain of raw streams? These are questions for further research. Ethical issues are fewer than with some AI work, but not zero. The process creates new synthetic data – one must ensure it does not inadvertently generate biased or unsafe content. If training involves human interaction logs, privacy needs attention (though anchors like PII are presumably stripped). Since DataClaw0 is meant to assist in generating training data, any biases in the initial anchor mechanisms or the generative model could propagate into the new dataset. It underlines that even “automated” data curation has human oversight needs. Finally, it’s worth noting how the name DataClaw (and “MakeAnyData” on the project page) suggests “clawing” or grabbing data out of noise. There is a separate open-source “DataClaw” tool for exporting AI conversation data (by Peter O’Mallet) – despite the similar name, it’s unrelated (it focuses on dialogue export to JSON). DataClaw0 is a research project, not a product launch; but the fact that it has a project page (czjdsg.github.io/MakeAnyData) indicates the authors intend to share code or demos. If released, it could provide a valuable toolkit: plug in your raw dataset and get back a polished training set. Conclusion. DataClaw0 takes a bold step in making data wrangling itself an agentic AI task. By combining the strengths of rule-based anchoring and generative modeling, and by training a 9B-parameter model with both supervised and policy-optimization objectives, the team has shown how to convert messy multimodal streams into concentrated supervision. Their results suggest this can truly improve downstream learning under scarce data conditions. For roboticists and ML practitioners, DataClaw0 offers a new way to think about the data pipeline. Instead of being a passive recipient of annotated data, your model can become part of the loop that actively cleanses and structures its own input. In the era of foundation models and agents, maybe it makes sense that we build agents to help build data. DataClaw0 is an early but promising example of this vision: an AI that downgrades data entropy and upgrades the training signal. References: As summarized above, the core ideas are drawn from Cong Wan et al., DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams (June 2026). Additional context on related efforts like GUI-World for UI datasets was noted from relevant sources.