Granular Feed Curator

• Granular feed curators are systems that provide precise, user-driven control over content feeds by mapping explicit control inputs to output mixtures.
• They integrate interactive models and meta-learned methodologies to tailor feed composition through rule-based filtering, ranking, and feedback loops.
• These systems are applied across social media, machine learning data curation, and physical mixing to improve transparency, efficiency, and operational scalability.

A granular feed curator is a computational or physical system designed to provide highly fine-grained, user-driven control over the composition, ranking, and transparency of content feeds—whether in social, informational, or physical (granular materials) contexts. Across domains, such curators operate by exposing explicit control parameters or meta-learned models that directly map user intent or physical settings to the final output mixture, enabling precise regulation of heterogeneity, diversity, topicality, or physical homogeneity. State-of-the-art granular feed curation architectures include interactive social media pipelines, supervised and meta-learned data curation networks, as well as continuum models for physical granular mixing. Recent advances stress direct manipulability, interpretability, feedback at multiple resolutions, and robust scalability.

1. Foundations and Motivation

Granular feed curation emerges to address the limitations of both algorithmic black-boxes (which can obscure user agency and nuanced objectives) and manual curation (which lacks scalability and responsiveness). In social media, the drive is to maximize user control, transparency, and intentionality—enabling users or communities to specify explicit inclusion/exclusion criteria, weights, and feedback signals that shape the feed construction process (Feng et al., 25 Jan 2024, Malki et al., 13 Sep 2025). In machine learning, data curation at granular resolution is essential for optimizing the quality and efficiency of model training, often requiring automated selection mechanisms capable of operating over massive, heterogeneous datasets (Calian et al., 23 May 2025). In granular physics, a "feed curator" denotes the set of design and operation principles for devices that mix or segregate physical particles, governed by explicit, dimensionless parameters controlling regime transitions (Fan et al., 2014, Xiao et al., 2022).

The unifying feature of all granular feed curators is the direct, interpretable mapping from a set of control inputs—manual, rule-based, meta-learned, or physical process variables—to the resulting feed output, often exposing the interventions as part of a closed feedback loop.

2. Taxonomies of Control and Feedback

Granular feed curators are distinguished by how they operationalize control and interpret feedback at scale. In the digital/social context, control axes include:

• Account-based signals: Features such as author identity, network engagement, and source reputation are made explicit and subject to filtering, hard constraints, or weight adjustment. Features like "friend affinity" or "preferred engagement band" become slider-exposed parameters (Feng et al., 25 Jan 2024).
• Content-based signals: Mutable parameters include post topic, media format, emotional tone, language style, recency, quality, and explicit advertisement markers. Each is translated into vectorized features for filtering or ranking.
• Structured and unstructured feedback: Users engage in direct facet-based teaching (marking +/– per feature), curriculum/folder-level teaching (seeded multi-feed organizers), and episodic or stack-based natural language feedback, all of which update internal ranking models or rule sets.

In physical systems, controls are realized through physical or process variables:

• Feed rate ($q$ or $Q(t)$): Governs advection and mixing regime.
• Layer geometry (flowing layer thickness $\delta$, device length $L$): Directly shifts segregation and diffusion parameters.
• Particle size distribution ($d_s$, $d_l$): Modifies percolation and collisional diffusion scales.

Feedback in all systems is formalized by concrete segregation or homogeneity metrics such as Danckwerts’ intensity ($I_d$) in physical systems or engagement/satisfaction discovery metrics in digital systems.

3. Formal Models and Ranking Functions

The backend of granular feed curators is specified by formal, often parametric, models enabling predictable, manipulable output.

A. Social and Informational Feeds

• Preference Model: Feature vectors $F(p)$ are constructed per item, weighted by $w \in \mathbb{R}^k$ and combined with rule-based adjustments $R$. The ranking function takes the form:

$$\text{Score}(p) = w \cdot F(p) + \sum_{r \in R} \delta_r(p)$$

where $\delta_r(p)$ can represent hard filtering ($-∞$), soft adjustments ($\pm\alpha$), or no adjustment (0).

• Intent Specification and Multi-Objective Curation: The user’s free-text intent is parsed into include/exclude rule sets $(I^+, I^-)$, each with discrete strength (e.g., "strong," "never"). The selection and ranking of items $p \in P$ is then formalized as a constrained maximization:

$$\max_{p \in P} U_I(p) \quad \text{s.t.} \quad p \notin \text{ExcludeTags}(I^-)$$

with $U_I(p)$ derived from rule-alignment or LLM-based scoring (Malki et al., 13 Sep 2025).

• Weighted Borda Aggregation (Bonsai): Relevance, recency, and popularity are ranked and merged:

$$\mathrm{Score}(p) = w_r\frac{|P|+1 - \mathrm{rank}_{\mathrm{rel}}(p)}{|P|} + w_c\frac{|P|+1 - \mathrm{rank}_{\mathrm{rec}}(p)}{|P|} + w_p\frac{|P|+1 - \mathrm{rank}_{\mathrm{pop}}(p)}{|P|}$$

• Rule-Based Interleaving (Braids): Feeds are merged with queue interleaving where the selection probability per feed $k$ is:

$$P(\text{choose queue } k) = \frac{w_k}{\sum_{i} w_i}$$

B. Physical Granular Systems

• Continuum Transport Equation (Fan et al.):

$$\frac{\partial c}{\partial t} + \nabla \cdot (\vec{u} c) + \nabla \cdot (w_p c) - \nabla \cdot (D \nabla c) = 0$$

with species percolation flux $w_p = \pm S \dot{\gamma} (1 - c)$, advection by flow field $\vec{u}$, and diffusion $D$.

• Dimensionless Parameters Control Regime:
  • Péclet number $Pe = \frac{2 q\, \delta}{D L}$
  • Segregation parameter $\Lambda = \frac{S L}{\delta^2}$

These parameters partition operational regimes: advection-dominated (preserves inlet pattern), segregation-dominated (strong separation), and diffusion-dominated (remixing, homogeneity).

• Stratification Control: Time-varying feed rates $Q(t)$ define stratified layers with penetration scales and uniformity predicted by explicit geometric and temporal relations (Xiao et al., 2022).

4. Learning-Based and Meta-Learned Curation

Recent approaches to granular feed curation leverage meta-learning to automate data valuation and selection, particularly within large-scale ML training pipelines (Calian et al., 23 May 2025).

• Meta-Learned DataRater Model ($\phi_\eta$): Assigns per-sample scores within each minibatch, normalized by softmax, informing a weighted training loss. The bi-level optimization operates over:
  • Inner loop: Base model $f_\theta$ updated by weighted gradients
  • Outer loop: Meta-gradient with respect to DataRater parameters $\eta$ derived by differentiating through $T$ inner updates.
• Practical Filtering: After meta-training, low-value samples are discarded using batch-level or online acceptance schemes:
  1. Batch Top-K: Retain top (1–$\rho$)$\times$N samples with highest $\phi_\eta(x)$.
  2. Probabilistic Acceptance per CDF rank.

• Cross-Scale Transfer: Meta-learned curators trained on models of one scale generalize their selection policies across model sizes and datasets, as empirically validated across >70 cross-domain settings.

• Efficiency Outcome: DataRater filtering yields compute savings up to 46.6% with no accuracy tradeoff for LLM training.

5. Implementation Architectures and Scalability

Feed curation systems must combine low-latency inference, efficient update cycles, and transparent UIs.

• Social/Editorial Systems: Systems like Cura (He et al., 2023) use BERT-mini backbones with curator, post, and vote features as unified sequence input; per-curator or persona-based predictions aggregate in a configurable, thresholded manner for inclusion in "frontstage" or "backstage" feeds.
• Rule-Driven UI Engineering: Design guidelines prioritize continuous, seamless, in-feed feedback, with interactive sliders, grading per source/feature, and switchable feed tabs or curriculum folders (Feng et al., 25 Jan 2024, Liu et al., 26 Apr 2025).
• Physical Systems: Device geometries (flowing layer depth, chute length, particle input) are iteratively tuned for control parameters $(Pe, \Lambda)$; mixing aids or modifiers (vibration, baffles, wall roughness) are directly inserted to target operational points.

A table below summarizes key implementation axes for major contexts:

Domain	Control Parameters/Signals	Main Backend Model/Algorithm
Social	Author, topic, source, weights, rules	Weighted scoring, Borda, faceted UI
ML Data	DataRater scores, batch acceptance	Meta-learned weighting, outer loss
Physical	$q,\,L,\,d_s,\,d_l,\,\delta,\,D$	Advection-diffusion-segregation PDE

6. Operational Regimes, Evaluation, and Outcomes

For each application area, regime analysis and empirical validation support the operational deployment of granular feed curation:

• Mixing/Segregation Regimes (Physical): Regimes predicted by $Pe$ and $\Lambda$ guide device design—diffusion-dominated ($Pe \ll 1$) yields homogeneity; segregation-dominated ($\Lambda \gg 1$) produces sharp separation. Direct, quantitative evaluation of $I_d$ selects for required performance envelopes (Fan et al., 2014).
• Social/Ecosystem-Specific Metrics: Engagement, topical diversity, user-specified satisfaction, explicit recognition tests (84.7% human-identifiability of persona-curated feeds) serve as operational metrics.
• Reduction of Undesired Content: In community models, explicit curation reduces anti-social content by more than 50% without additional moderation (He et al., 2023), with configurable thresholds for further tuning.
• Compute/Mixing Efficiency: Meta-learned curators achieve up to 46.6% reduction in resource-to-performance ratio (Calian et al., 23 May 2025).

7. Challenges, Open Problems, and Future Directions

Granular feed curation research faces challenges in (i) scaling transparent control interfaces without cognitive overload, (ii) collecting user or system feedback in privacy-preserving and interpretable formats, and (iii) achieving cross-platform compatibility for ecosystem-wide plugin and protocol standards (Feng et al., 25 Jan 2024, Liu et al., 26 Apr 2025, Malki et al., 13 Sep 2025).

Emerging hybrid algorithms combine explicit user weighting with data-driven re-ranking. Efforts to formalize feedback aggregation (multi-timescale, structured/unstructured), extend to new data modalities, and standardize interaction APIs are active areas of development. In physical domains, refining continuum models for more complex particle types or unsteady flows remains open. In all instances, the goal is direct, repeatable, and robust mapping between user or operator input and system output, maintaining interpretability and performance guarantees at scale.