AI Feeds: Adaptive Content Pipelines

Updated 9 December 2025

AI Feeds are algorithmic pipelines that select, filter, and sequence content using AI-driven personalization, multimodal integration, and real-time decision-making.
They employ modular stages—including planning, sourcing, curating, and ranking—as demonstrated by frameworks like Bonsai and XBC in diverse applications.
Applied across social media, news production, CTI, and scientific domains, AI Feeds also raise challenges in transparency, user control, and regulatory compliance.

AI Feeds are algorithmic content delivery pipelines that leverage artificial intelligence to select, filter, and sequence information streams for users in real time. Originally developing in the context of social media and recommender systems, the concept now spans domains as varied as precision agriculture, news production, threat intelligence, and molecular food design. These systems are characterized by increasingly sophisticated personalization, multimodal data integration, and the intentional or unintentional shaping of user attention, perception, or consumption patterns.

1. Architectures and Methodological Foundations

AI feed systems typically embody modular pipelines that execute stages of planning, sourcing, signal extraction, decision making, and real-time presentation. In social media, feeds are generated by ranking and filtering large pools of content—posts, videos, or articles—to maximize engagement, personalization, or user-defined intent.

For example, the Bonsai framework decomposes feed construction into Planning (goal-to-sources and filters translation via LLMs), Sourcing (API-based retrieval from candidate sources), Curating (LLM-assessed content filtering based on user-defined predicates), and Ranking (multi-objective aggregation via Weighted Borda Count). User intent is specified in natural language and validated through iterative refinement, exposing intermediate artifacts and maintaining procedural transparency (Malki et al., 13 Sep 2025).

In cyber threat intelligence (CTI), “AI Feeds” refer to pipelines that ingest, normalize, and extract structured threat events from continuous streams of unstructured or semi-structured, multilingual data. The XBC conceptual model integrates multilingual transformer embeddings (XLM-RoBERTa), bi-directional sequence modeling (BiGRU), and CRF-based structured decoding, supporting high-throughput, low-latency, and high-accuracy entity/event extraction for SIEM integration (Al-Yasiri et al., 4 Jun 2025).

AI-driven news feed production, as demonstrated in AI-Press, orchestrates multi-agent collaboration: search and retrieval, LLM-based drafting, iterative review/rewriting, and feedback simulation from a sampled user population, using both embeddings and explicit human-in-the-loop controls to optimize for factuality, style, and anticipated user sentiment (Liu et al., 10 Oct 2024).

2. Personalization, Intent, and Control Mechanisms

Personalization in AI feeds arises from algorithmic exploitation of user activity signals combined with exploration for content diversification. In TikTok’s recommendation algorithm, a “timeline” of user–item interactions is formally modeled as a sequence $R^u = \{r_1, ..., r_N\}$ , with content, user, and engagement attributes tracked for each pair $(r_i, u)$ (Vombatkere et al., 19 Mar 2024). Items are classified as resulting from “exploration” or “exploitation” using interpretable binary features (local/global), producing metrics such as the exploit fraction ( $\varepsilon_{u,i}$ ) and personalization score ( $p(r_i, u)$ ).

Conversely, Bonsai foregrounds explicit user control, parsing natural language intent into editible sets of sources and filter predicates applied via LLM-based curation and user-weighted ranking (Malki et al., 13 Sep 2025). This structurally decouples engagement optimization from user intent, enabling users to discover new content while filtering irrelevant or undesired topics. Transparency and procedural traceability are core: at each pipeline stage, Bonsai surfaces the actions taken—sources selected, filtering logic, and ranking weights.

The distinction between engagement-optimized, implicitly personalized feeds and intentionally crafted, user-directed pipelines marks a central evolution in AI feed architecture. The former often involve black-box models with low user agency, while the latter emphasize procedural transparency, explainability, and provenance of each item in the feed.

3. Applications and Case Studies

Social media feeds (TikTok, Instagram, Bluesky) rely on complex, user-adaptive recommenders that balance exploration and exploitation. Empirical studies of TikTok reveal exploitation rates of 50–70% for real users ( $\bar\varepsilon_i$ ), with exploit-labeled items showing high user-specificity (mean personalization score $p \approx 0.83$ ), and engagement signals such as “liked” or “followed” driving the highest personalization (Vombatkere et al., 19 Mar 2024).
The proliferation of low-quality, algorithmically viral AI-generated content (“AI slop”) is documented in TikTok and Instagram search results, where up to 25% (TikTok) and ~5% (Instagram) of top posts are synthetic, often photorealistic and inadequately labeled. Three categories of Agentic AI Accounts automate the mass production of such content, exploiting feed ranking for virality, with synthetic posts shared ~15% more frequently than non-synthetic ones (R ≈ 1.15) (Stanusch et al., 1 Aug 2025).
AI-Press demonstrates multi-agent, retrieval-augmented drafting, iterative LLM-based review, and public feedback simulation for news generation, enabling real-time pipeline deployment, editorial A/B testing, and what-if scenario analysis—albeit with limitations in simulation realism and retrieval breadth (Liu et al., 10 Oct 2024).

Scientific and Operational Domains

In medical imaging, real-time AI feeds process surgical endoscopic video, employing deep convolutional encoders/decoders (e.g., U-Net++) for object segmentation with >90% Dice coefficient at >30 FPS, enabling live clinical feedback and dataset expansion (Stoebner et al., 2022).
Sustainable food design frameworks use hierarchical AI feeds that map molecular composition through GNNs, simulate mechanical and sensory properties, and close experimental loops via self-driving laboratories and “deep reasoning” LLM agents. These pipelines accelerate ingredient and process innovation, optimizing for measurable end traits under sustainability and health-driven constraints (Datta et al., 25 Sep 2025).
Precision livestock management pipelines integrate >16M longitudinal feed intake records with high-frequency meteorological data, engineer hybrid behavioral–environmental indices (EASI-Index), and optimize prediction with XGBoost models, yielding daily pen-level RMSEs as low as 0.14 kg/(day·animal) (Maia et al., 20 Nov 2025).

4. Metrics, Evaluation, and Transparency

AI feeds employ both domain-specific and generalizable metrics to assess quality, accuracy, and user alignment.

Personalization Metrics: Exploit fractions ( $\varepsilon_{u,i}$ ), personalization scores ( $p(r_i,u)$ ), and ablation analyses quantify the degree and drivers of personalization in content feeds (Vombatkere et al., 19 Mar 2024).
Synthetic Content Prevalence: Prevalence of synthetic content ( $PS = S/(S+N)$ ), labeling compliance ( $PL = Labeled_{AI}/Total_{AI}$ ), and virality ratios ( $R$ ) establish the impact of AI-generated content on feed integrity (Stanusch et al., 1 Aug 2025).
Feed Ranking: Weighted Borda Count aggregates relevance (LLM score), recency, and popularity with tunable weights, allowing users to pivot between discovery, trending, or focused configurations in intentional feed systems (Malki et al., 13 Sep 2025).
Model Performance: In CTI, token-level and event-level F1 metrics, throughput (docs/sec), and latency benchmarks distinguish baseline and advanced extraction pipelines, with XBC models achieving multilingual average F1 gains of +5.6 over best baselines at ~400 docs/sec (Al-Yasiri et al., 4 Jun 2025).
AI-Press Evaluation: News quality scores (by genre) and simulated-vs-real comment similarity ( $\mathrm{sim}_{\mathrm{real},\mathrm{sim}}=0.82$ ) demonstrate measurable improvement and high feedback realism (Liu et al., 10 Oct 2024).

Transparency, interpretability, and user-side procedural traceability are increasingly prioritized in modern AI feed design. Systems such as Bonsai surface intermediate outputs, map user prompts to filter logic, and offer immediate, in-situ adjustment—addressing agency and trust concerns observed in contemporary feed usage studies (Malki et al., 13 Sep 2025).

5. Risks, Limitations, and Regulatory Considerations

AI feeds expose novel attack and failure surfaces, including adversarial manipulation via synthetic content (AI slop), the risk of filter bubbles through overexploitation of user interests, and opaque or untrustworthy personalization.

Regulatory and platform responses recommended in the literature encompass:

Standardized and automated AI-content labeling, visible across platforms and content formats (Stanusch et al., 1 Aug 2025).
Robust detection mechanisms (watermarking, content credentials), auditing pipelines for compliance with rules such as the EU Digital Services Act (Stanusch et al., 1 Aug 2025).
Auditable, semi-automatic explanation frameworks decomposing feed items into exploration/exploitation and exposing user-interest triggers (Vombatkere et al., 19 Mar 2024).
Privacy and security considerations for CTI feeds, ensuring responsible data handling and minimizing risk of adversarial reverse engineering (Al-Yasiri et al., 4 Jun 2025).
Human-in-the-loop and transparency mechanisms to counter hallucination, mislabeling, and overreliance on simulation in news feed construction (Liu et al., 10 Oct 2024).

6. Future Directions and Open Challenges

Open lines of inquiry raised across the corpus include:

Hybridization of emergent engagement signals and explicit user intent: Bootstrapping feeds with NL-driven predicates and refining with behavioral telemetry (Malki et al., 13 Sep 2025).
Quantification of long-term echo-chamber formation and exposure diversity under intentional feed paradigms.
Transferability of agricultural, clinical, or threat-optimal pipelines across local and global contexts, including data standardization, cross-cultural design, and explainable regulatory compliance (Datta et al., 25 Sep 2025, Maia et al., 20 Nov 2025).
Expansion of simulation pools and realism in feedback mechanisms, to bridge the gap between automated audience simulation and the emergent, chaotic dynamics of real-world user engagement (Liu et al., 10 Oct 2024).
Democratization of feed-building logic through lightweight, locally deployable models, and federated approaches to feed curation and control (Malki et al., 13 Sep 2025).

AI feeds, as a unifying paradigm, now constitute a core infrastructure for the intelligent, adaptive selection and sequencing of digital content across science, industry, and society. Their continued evolution depends on advances in user-aligned modeling, multilingual and multimodal learning frameworks, scalable feedback and transparency mechanisms, and robust socio-technical governance.