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EEG Foundation Models: Progresses, Benchmarking, and Open Problems
Published 25 Jan 2026 in cs.LG and cs.CV | (2601.17883v1)
Abstract: Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid progresses, there lacks fair and comprehensive comparisons of existing EEG foundation models, due to inconsistent pre-training objectives, preprocessing choices, and downstream evaluation protocols. This paper fills this gap. We first review 50 representative models and organize their design choices into a unified taxonomic framework including data standardization, model architectures, and self-supervised pre-training strategies. We then evaluate 12 open-source foundation models and competitive specialist baselines across 13 EEG datasets spanning nine BCI paradigms. Emphasizing real-world deployments, we consider both cross-subject generalization under a leave-one-subject-out protocol and rapid calibration under a within-subject few-shot setting. We further compare full-parameter fine-tuning with linear probing to assess the transferability of pre-trained representations, and examine the relationship between model scale and downstream performance. Our results indicate that: 1) linear probing is frequently insufficient; 2) specialist models trained from scratch remain competitive across many tasks; and, 3) larger foundation models do not necessarily yield better generalization performance under current data regimes and training practices.
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EEG Foundation Models: Progress, Benchmarking, and Open Problems — Explained Simply
What is this paper about?
This paper looks at a new kind of AI model for brain signals called EEG foundation models. EEG (electroencephalography) records your brain’s electrical activity using small sensors on your scalp. A brain-computer interface (BCI) uses signals like EEG to let people control computers or help doctors understand brain health. Foundation models are big “generalist” models trained on lots of data so they can learn useful patterns once and then adapt to many different tasks later with less effort.
The authors review what has been built so far, and they run fair, side-by-side tests to see what really works best. They also point out open problems that future research should solve.
What questions are the authors trying to answer?
The paper centers on three simple questions:
- Can EEG foundation models truly learn general brain-signal features that transfer well to many different tasks?
- Do these foundation models actually beat traditional or smaller, task-specific models trained from scratch?
- Is “bigger always better”? In other words, do larger models and more pretraining data reliably give better results for EEG?
How did they study this?
First, they collected and organized what the field has done:
- They reviewed 50 EEG foundation models and sorted their design choices into a clear framework: how the data is cleaned and standardized, what model shapes are used (for example, Transformers), and how the models are pre-trained without labels (called “self-supervised” learning).
Then, they built a fair, practical benchmark:
- They tested 12 open-source EEG foundation models plus strong “specialist” models on 13 datasets that cover 9 types of BCI tasks (like motor imagery, visual responses, emotion, epilepsy detection, mental workload, and imagined speech).
- They focused on two real-life situations:
- Cross-subject generalization: train on many people, then test on a new person you have never seen before (called “leave-one-subject-out”).
- Rapid calibration: adapt to a specific person using only a tiny amount of their data (few-shot learning within one subject).
- They compared two ways of adapting a pre-trained model:
- Linear probing: freeze the model and only train a small final layer (like adding a new decision head on top).
- Full fine-tuning: update the whole model so it can adjust deeply to the new task.
- They also looked at whether larger models actually perform better under today’s EEG data sizes and training methods.
To make these ideas easier to picture, think of pretraining like a giant brain-signal “puzzle game.” In self-supervised learning, the model hides or scrambles parts of the signal and practices predicting the missing pieces. Over time, it gets good at understanding common patterns across many people and tasks. Later, for a specific job (like detecting if someone is imagining moving their hand), you can add a small task-specific layer or fine-tune the whole model.
The paper also explains common EEG cleaning steps with everyday analogies:
- Resampling and filtering: like turning a high-speed video into a standard frame rate and removing buzzing noises from the audio.
- Normalization (-score): like making sure every channel’s volume is set to a similar level.
- CAR (common average reference): like subtracting the “background hum” heard by all microphones so you can hear each one more clearly.
- Alignment (e.g., Euclidean Alignment): like adjusting each person’s recordings so they line up better, reducing differences that come from the recording setup rather than the brain itself.
- Channel unification: different headsets have different sensor layouts; the models learn to handle these differences so one model can work across devices.
What did they find, and why does it matter?
The authors report three main results:
- Linear probing is often not enough. Simply adding a small layer on top of a frozen foundation model usually doesn’t give the best results. Fully fine-tuning the model tends to work better. This matters because it tells researchers and engineers not to expect plug-and-play performance from a frozen model; some deeper adaptation is usually needed.
- Specialist models trained from scratch can still compete. In many tasks, smaller models carefully trained for that specific job performed as well as, or sometimes better than, large foundation models. This means you shouldn’t assume a giant general model will always win—especially when the task is narrow and the data is well-matched.
- Bigger isn’t automatically better (for now). With current EEG data sizes and training practices, simply scaling up model size does not guarantee improved generalization. This is different from trends in text or image AI, and it suggests EEG needs smarter data curation, better training objectives, or new architectures more than just raw size.
These results are important because they challenge some popular assumptions. They suggest the EEG world needs its own best practices rather than copying what works for language or vision. They also highlight the value of careful evaluation with fair, consistent rules.
What’s the bigger impact?
- For researchers: This paper gives a clear map of the field (a taxonomy of model designs) and a public benchmark so future work can be compared fairly. That helps the community grow faster and avoid confusing, apples-to-oranges comparisons.
- For practical BCIs: Knowing that full fine-tuning helps, that small specialist models can still shine, and that bigger models aren’t always better can save time and resources. It points to focusing on high-quality data, smart preprocessing, and the right kind of adaptation for each task.
- For the future: The findings suggest we need better data standards across devices, stronger self-supervised tasks designed for EEG, and training methods that truly capture brain-specific patterns. If we get these right, BCIs could become easier to deploy in clinics, schools, and daily life—even with limited labeled data.
In short, this paper organizes what we know about EEG foundation models, tests them fairly, and shows that careful fine-tuning and good data practices currently matter more than just making models bigger. It sets the stage for smarter, more reliable brain-computer interfaces.
Knowledge Gaps
Knowledge gaps, limitations, and open questions
Below is a concise list of concrete gaps and open questions that remain unresolved and can guide future EEG foundation model research.
- Standardized preprocessing pipeline: The benchmark highlights heterogeneous resampling, filtering, normalization (z-score, CAR, EA, EMA), and patching choices across models, but does not quantify their individual or joint impact. A controlled ablation suite is needed to isolate which components most improve cross-subject, cross-device transfer.
- Channel unification strategies: The paper catalogs multiple approaches (common montage, template mapping, spatial encodings, learned projections) but does not compare them head-to-head under strong cross-device and variable-channel tests. Which strategy best handles unseen montages and missing channels?
- Objective-function efficacy: Masked raw-signal reconstruction, token reconstruction, frequency-domain targets, codebook-based objectives, and autoregressive training are all used, yet there is no systematic evaluation of which objectives (or hybrids) work best for specific paradigms (e.g., MI vs ERP vs SSVEP vs sleep) and data regimes.
- Tokenization and codebooks: The effects of codebook size, training method (k-means vs learned RVQ), discrete vs continuous tokens, and cross-dataset compatibility remain unexplored. How do these design choices influence transferability and robustness?
- Scaling laws under controlled conditions: The finding that “bigger is not always better” is based on heterogeneous models/data. A controlled scaling study that jointly varies model size, data volume, and compute with matched architectures/objectives is required to establish reliable EEG scaling laws.
- Adaptation strategies beyond linear probing: The paper shows linear probing is often insufficient, but does not explore parameter-efficient fine-tuning (LoRA, adapters), prompt tuning, or test-time adaptation. Which adaptation method yields the best accuracy–efficiency–stability trade-offs?
- Zero-shot and few-shot generalization breadth: Evaluation focuses on LOSO and within-subject few-shot. How do models perform in strict zero-shot settings (new paradigms/devices with no labels), cross-lab transfers, and cross-session/day drift scenarios?
- Robustness to artifacts and low SNR: Despite extensive preprocessing, the benchmark does not quantify robustness to ocular/muscle artifacts, movement, power-line noise, or deliberate adversarial perturbations. What artifact-aware pretraining or augmentation strategies improve resilience?
- Temporal generalization and long-context modeling: The benefits of long-sequence architectures (e.g., Mamba/SSM) and context length for tasks like sleep staging or seizure detection are not analyzed. How does context length affect performance and efficiency?
- Multi-modal pretraining benefits: Several models include ECG/EMG/MEG, but cross-modal supervision and transfer gains are not systematically assessed. What is the measurable benefit of auxiliary modalities, and under which tasks or SNR conditions?
- Non-classification tasks: Current evaluations center on classification. How well do foundation models transfer to detection/segmentation (e.g., ERP peak timing), regression (e.g., workload/drowsiness), event localization, and sequence forecasting?
- Calibration cost and user burden: Few-shot “rapid calibration” is reported as reduced data volume, but the human time/effort (sessions, minutes, task difficulty) is not measured. What is the practical calibration time and how can it be minimized?
- Fairness and subgroup performance: The benchmark does not stratify results by demographic/clinical subgroups, age, or cognitive states. Are there performance disparities, and how should models be audited and mitigated?
- Uncertainty, reliability, and OOD detection: Beyond accuracy, reliability metrics (ECE, calibration curves), AUROC/PR for imbalanced tasks, and OOD detection are not reported. Can uncertainty estimation improve safety and adaptation?
- Continual learning and catastrophic forgetting: The ability of foundation models to adapt sequentially to new users/tasks without forgetting prior ones is untested. What continual learning protocols and replay/regularization strategies are effective?
- Data curation effects: While curation (subject/channel screening) is recommended, there is no quantitative analysis of its impact on pretraining or downstream performance. What curation criteria yield the most benefit, and how do they trade off data scale?
- Dataset overlap and leakage: Overlap between pretraining and downstream datasets/protocols is not audited. How does leakage affect measured transfer, and what safeguards (holdout datasets, provenance tracking) are necessary?
- Reproducibility and statistical rigor: Many models are closed-source or use different hyperparameter budgets. Standardizing seeds, multiple runs, and reporting confidence intervals is needed to ensure fair, statistically sound comparisons.
- Efficiency and deployment constraints: Inference latency, memory footprint, and energy use (especially for edge devices and real-time BCIs) are not benchmarked. What architectures/objectives provide the best accuracy–latency trade-off?
- Spatial interpretability and neurophysiological validity: There is no analysis linking learned features to known EEG phenomena (e.g., ERD/ERS, P300, frequency bands, cortical regions). Which interpretability tools validate neuroscientific plausibility?
- Handling heterogeneous sampling rates: Many pipelines resample to 200–256 Hz, but the impact of resampling artifacts and optimal target rates across paradigms is not studied. What resampling strategies minimize distortion while preserving performance?
- Class imbalance and label quality: Several datasets are imbalanced or have noisy labels. How do loss functions, reweighting, and label denoising impact foundation model transfer?
- Privacy and security: Membership inference and model inversion risks are not assessed. What privacy-preserving pretraining (DP-SGD, federated learning) and security defenses are suitable for EEG?
- Benchmark expansion and standardization: The current benchmark covers 12 open-source models and 13 datasets. A community standard task suite with unified preprocessing, curated splits, and documented protocols is needed to reduce confounders and enable durable progress.
Glossary
- Autoregressive pre-training: A training paradigm where the model predicts the next element(s) in a sequence based on past elements. "and autoregressive pre-training."
- Bandpass filtering: A signal processing technique that keeps frequencies within a specified range while attenuating lower and higher frequencies. "Bandpass filtering is commonly applied to attenuate slow drifts and high-frequency noise"
- Brain-computer interface (BCI): Systems that translate neural signals into commands for external devices. "Brain-computer interfaces (BCIs) establish a direct communication pathway between neural activities and external devices"
- Causal masking: A masking strategy that prevents each position from attending to future positions to preserve causality. "using causal masking"
- Codebook: A discrete set of learned vector representations used to quantize continuous signals into indices. "codebook-based discrete modeling"
- Common Average Reference (CAR): A re-referencing method that subtracts the average across all channels from each channel to suppress common-mode noise. "Another standard preprocessing technique is CAR"
- Common montage pre-training: Restricting training to datasets that share the same electrode set/layout. "Common montage pre-training."
- Contrastive learning: A self-supervised approach that learns representations by bringing similar pairs closer and pushing dissimilar pairs apart. "contrastive learning"
- Cross-subject generalization: The ability of a model trained on some subjects to perform well on unseen subjects. "cross-subject generalization under a leave-one-subject-out protocol"
- Electrocardiogram (ECG): Electrical recordings of heart activity, sometimes used as auxiliary signals. "electrocardiogram (ECG)"
- Electroencephalography (EEG): Non-invasive measurement of brain electrical activity via scalp electrodes. "Electroencephalography (EEG) foundation models have recently emerged"
- Electromyogram (EMG): Electrical recordings of muscle activity, sometimes used alongside EEG. "electromyogram (EMG)"
- Euclidean Alignment (EA): A whitening-based alignment that reduces covariance shifts across subjects/sessions. "Euclidean Alignment (EA)."
- Exponential Moving Average (EMA) normalization: Online normalization using exponentially decaying statistics to handle drift. "Exponential Moving Average (EMA) Normalization."
- Full-parameter fine-tuning: Adapting all model weights to a downstream task, as opposed to only training a classifier head. "We further compare full-parameter fine-tuning with linear probing"
- Frequency-domain reconstruction: Pre-training objective that reconstructs frequency representations (e.g., spectrograms) of EEG. "Frequency domain reconstruction"
- Independent Component Analysis (ICA): A blind source separation method used to remove artifacts from EEG. "ICA + CAR"
- Instance Norm: Normalization applied per sample (instance) to stabilize training across varying inputs. "Instance Norm"
- Interquartile range (IQR) scaling: Normalization using the IQR to reduce the influence of outliers. "IQR Scaling"
- Leave-one-subject-out (LOSO): Evaluation/fine-tuning protocol where each subject is held out in turn for testing. "leave one subject out (LOSO) scenario"
- Linear probing: Evaluating frozen representations by training only a simple classifier on top. "linear probing"
- Mamba-based designs: Sequence modeling architectures using selective state space models designed for long sequences. "Mamba-based designs that offer improved efficiency for long sequences."
- Magnetoencephalography (MEG): Measurement of magnetic fields produced by brain activity. "magnetoencephalography (MEG)"
- Masked reconstruction: Self-supervised objective where parts of the input are masked and the model learns to recover them. "Masked reconstruction of raw EEG signals"
- Montage (EEG montage): The specific configuration/layout of electrodes used in recording. "a fixed montage with a standardized channel count"
- Motor imagery (MI): A BCI paradigm where users imagine movements to modulate EEG signals. "motor imagery (MI)"
- Notch filter: A narrow band-stop filter used to suppress specific frequency interference (e.g., power-line noise). "notch filters at 50~Hz or 60~Hz"
- Power spectral density (PSD): A representation of signal power distributed over frequency. "Raw Signals + PSD"
- Resampling: Changing the sampling rate of recorded signals for standardization or efficiency. "resample signals to 200~Hz"
- Self-supervised pre-training: Learning representations from unlabeled data via auxiliary tasks. "self-supervised pre-training can leverage vast amounts of unlabeled recordings"
- Short-time Fourier transform (STFT): Time-frequency analysis method that computes Fourier transforms over short windows. "STFT + -score"
- Spatial encoding: Injecting electrode location information into the model to capture spatial structure. "Spatial encoding for channel structure."
- Spectral amplitude: The magnitude component of a frequency representation. "Spectral Amplitude"
- Spectrogram: A time-frequency representation showing how signal energy varies over time and frequency. "the spectrogram"
- Steady state visual evoked potentials (SSVEP): EEG responses elicited by periodic visual stimuli, used in BCIs. "steady state visual evoked potentials (SSVEP)"
- Template-based channel mapping: Mapping channels from diverse devices to a common template via selection or interpolation. "Template-based channel mapping."
- Tokenizer: A module that converts continuous signals into discrete indices or embeddings. "a tokenizer maps the signal to discrete codebook indices"
- Transformer-based architectures: Attention-centric neural networks adapted here for spatiotemporal EEG modeling. "Transformer-based architectures"
- Transfer learning: Reusing knowledge from pre-training to improve performance on different downstream tasks. "transfer learning"
- Volume conduction: The spread and attenuation of electrical signals through tissue, blurring spatial resolution. "volume conduction through the scalp and skull"
- Whitening: Transforming data to have identity covariance to reduce statistical shifts. "performs subject-wise or session-wise whitening"
- Within-subject few-shot adaptation: Rapidly adapting a model to a new session/subject using very few labeled samples. "within-subject few-shot adaptation scenario"
- z-score normalization: Standardizing each channel to zero mean and unit variance. "z-score normalization rescales each channel to zero-mean and unit variance"
Practical Applications
Immediate Applications
The paper’s unified taxonomy, preprocessing recipes, and cross-paradigm benchmark directly support deployable tools and workflows today:
- Model selection and procurement for BCI products (Industry, Healthcare, Academia, Robotics)
- Tools/products/workflows: Use the benchmark’s cross-subject and few-shot results to pick “specialist” models for a known paradigm (e.g., MI, SSVEP, ERP) instead of defaulting to the largest foundation model; adopt full-parameter fine-tuning rather than only linear probing.
- Assumptions/dependencies: Target task resembles benchmarked paradigms; access to the paper’s code and comparable fine-tuning data volumes.
- Few-shot calibration onboarding for new users (Industry, Healthcare, Education, Robotics)
- Tools/products/workflows: Integrate the within-subject few-shot protocol (1/20–1/100 standard LOSO data) into product “calibration wizards” to enable rapid personalization; prioritize full-parameter fine-tuning in the onboarding flow.
- Assumptions/dependencies: Short per-user recording session is feasible; on-device or cloud compute supports quick fine-tuning; appropriate consent and IRB where needed.
- Cross-device preprocessing SDKs (Software, Device OEMs)
- Tools/products/workflows: Package the paper’s preprocessing operators G(X)—z-score, CAR, Euclidean Alignment (EA), EMA, resampling/filters—into reusable libraries; add channel unification modules (template-based mapping, spatial encodings, projection layers) to handle heterogeneous montages.
- Assumptions/dependencies: Device channel locations available; consistent metadata; integration with device drivers.
- Quality-controlled data curation pipelines (Academia, Industry Consortia, Hospitals)
- Tools/products/workflows: Adopt subject- and channel-level screening to filter unreliable recordings prior to pre-training or clinical model development; maintain audit trails for exclusions.
- Assumptions/dependencies: Access to large raw EEG corpora; standardized artifact criteria; privacy-compliant storage.
- Reproducible evaluation harness for product QA (Industry, OEMs)
- Tools/products/workflows: Use the benchmark’s LOSO and few-shot protocols to validate generalization claims across the nine paradigms; maintain model cards that report preprocessing, fine-tuning regime, and dataset splits.
- Assumptions/dependencies: Availability of representative test datasets; adherence to the benchmark’s split policies.
- Energy- and cost-aware model deployment (Energy/Green AI, Industry IT/Cloud)
- Tools/products/workflows: Favor smaller or specialist models when the benchmark shows no consistent benefit from larger models; apply quantization/pruning with regression tests against benchmark tasks.
- Assumptions/dependencies: Performance targets permit smaller-capacity models; hardware supports optimized inference.
- Streamed BCI normalization for online use (Industry, Healthcare, Neuroergonomics)
- Tools/products/workflows: Implement EMA-based normalization for nonstationary, long-form sessions (e.g., workload monitoring, home neurofeedback) to stabilize drift in real time.
- Assumptions/dependencies: Real-time compute budget; robust handling of missing data and sensor disconnects.
- Curriculum and lab modules for EEG ML (Academia, Education)
- Tools/products/workflows: Teach the paper’s taxonomic framework (architectures, masking, codebooks, autoregression), preprocessing, and benchmark protocols in coursework; scaffold reproducible comparisons.
- Assumptions/dependencies: Access to subsets of public datasets; compute for small-scale pre-training/fine-tuning.
- AutoML-style pipelines for EEG (Software, MLOps)
- Tools/products/workflows: Build “EEG model hubs” that automatically test a shortlist of specialist models and compact FMs under both linear probing and full fine-tuning, reporting the best operational trade-off.
- Assumptions/dependencies: Dataset privacy constraints permit automated evaluation; standardized input adapters.
- Clinical trial protocol templates for EEG devices (Healthcare, Policy/Regulation)
- Tools/products/workflows: Embed LOSO generalization and few-shot adaptation as standard reporting requirements; mandate explicit disclosure of preprocessing choices (CAR/EA/filters) and fine-tuning scope.
- Assumptions/dependencies: Regulator buy-in; accessible reference datasets to contextualize performance.
- BCI teleoperation and assistive control with fast setup (Robotics, Assistive Tech)
- Tools/products/workflows: Deploy MI-focused specialist models (e.g., trained for motor imagery) with the few-shot calibration workflow to minimize setup time for exoskeletons/robotic arms.
- Assumptions/dependencies: Stable MI paradigm execution; safety validation for closed-loop control.
- Cross-headset consumer neurotech apps (Industry, Daily Life)
- Tools/products/workflows: Use channel mapping + spatial encodings for multi-headset compatibility in wellness/meditation and basic attention-tracking apps; include quick recalibration across sessions.
- Assumptions/dependencies: Access to electrode templates per headset; conservative user-facing claims and privacy safeguards.
Long-Term Applications
Building on the paper’s insights and open problems, several ambitious directions require more data, scaling, or validation:
- Universal, plug-and-play EEG foundation models across paradigms (Industry, Healthcare)
- Tools/products/workflows: General-purpose encoders that reliably transfer from heterogeneous pre-training to many downstream tasks without heavy fine-tuning; standardized channel-space projection across devices.
- Assumptions/dependencies: Larger, better-curated, demographically diverse corpora; community standards for channel templates and spatial encodings.
- Multimodal neuro foundation models (EEG + MEG/ECG/EMG) for robust decoding (Healthcare, Neuroergonomics)
- Tools/products/workflows: Joint encoders that fuse EEG with correlated biosignals to enhance SNR and task robustness; plug-in adapters for each modality.
- Assumptions/dependencies: Synchronized multimodal datasets at scale; cross-institution consent and governance.
- Federated/self-supervised pre-training across hospitals and OEMs (Healthcare, Policy)
- Tools/products/workflows: Privacy-preserving training (federated, DP, secure aggregation) to aggregate large unlabeled EEG without sharing raw data; standardized reporting of privacy budgets.
- Assumptions/dependencies: Legal agreements and secure infrastructure; robust aggregation under site heterogeneity.
- On-device tiny foundation models for wearables (Industry, Energy/Edge AI)
- Tools/products/workflows: Architectures optimized for long sequences (e.g., Mamba-style) plus distillation to microcontrollers; battery-aware streaming normalization.
- Assumptions/dependencies: Mature toolchains for embedded deployment; validated performance vs. cloud models.
- Clinical-grade digital biomarkers and monitoring (Healthcare)
- Tools/products/workflows: FM-derived biomarkers for epilepsy risk, cognitive decline, depression, or sleep staging, validated across sites and devices; integration into EHRs and remote monitoring.
- Assumptions/dependencies: Prospective studies; regulatory evidence; harmonized preprocessing across clinical centers.
- Auto-annotation and data cleaning assistants (Academia, Industry)
- Tools/products/workflows: Use foundation encoders for semi-automatic labeling (e.g., event boundaries, artifact flags), boosting dataset creation throughput.
- Assumptions/dependencies: High-precision heuristics and human-in-the-loop verification to avoid error propagation.
- Interoperability standards for EEG device ecosystems (Policy, Industry Consortia)
- Tools/products/workflows: Formalize channel templates, spatial encoding schemas, and reporting checklists for preprocessing and fine-tuning; certification via benchmark suites.
- Assumptions/dependencies: Multi-stakeholder agreement; mechanism for versioning standards as devices evolve.
- Adaptive, closed-loop neurofeedback and BCI rehabilitation (Healthcare, Assistive Tech)
- Tools/products/workflows: Personalized controllers that re-tune via few-shot adaptation across sessions and contexts, delivering stable performance for home-based rehab or neurofeedback.
- Assumptions/dependencies: Safe adaptation policies; clinician oversight; robust handling of nonstationarity.
- Fairness and generalizability frameworks (Policy, Academia)
- Tools/products/workflows: Benchmark extensions to assess performance across demographics, pathologies, and devices; bias audits incorporated into reporting standards.
- Assumptions/dependencies: Access to balanced datasets; agreed-upon fairness metrics for neurodata.
- Marketplaces for EEG models, datasets, and benchmarks (Software, Industry)
- Tools/products/workflows: Exchanges that host vetted models with standardized evaluations and model cards; reproducible leaderboards for paradigms and device classes.
- Assumptions/dependencies: IP/licensing clarity; governance for dataset usage and consent.
- Neuro-robotics teleoperation at scale (Robotics, Industrial Automation)
- Tools/products/workflows: Generalist FMs that reduce per-operator training time and provide robust control signals across tasks and robots, with safety interlocks.
- Assumptions/dependencies: Real-world robustness to noise and fatigue; regulatory and safety certification.
- Evidence-based guidance for “right-sized” models (Energy/Green AI, Finance/IT Budgeting)
- Tools/products/workflows: Decision frameworks tying model size to expected gains on specific tasks, reducing CAPEX/OPEX for cloud inference and training.
- Assumptions/dependencies: Expanded scaling-law studies with more datasets; standardized cost–performance metrics.
Each long-term direction depends on stronger data governance, broader consortium efforts, and sustained validation. The paper’s central findings—limited utility of linear probing, competitiveness of specialist models, and non-monotonic gains from scaling—provide immediate guidance for current deployments and a roadmap for future standards and research investments.
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