Intentional Feedbuilding

• Intentional Feedbuilding is a systematic approach that designs and curates input streams to align outputs with explicit user goals and contextual constraints.
• It utilizes transparent, multi-stage pipelines—including planning, sourcing, curation, and ranking—with algorithms like reinforcement learning and contextual bandits.
• Empirical studies demonstrate enhanced personalization, improved robustness, and adaptive performance across domains, despite a higher cognitive load for intent specification.

Intentional feedbuilding is the deliberate and structured process of designing, curating, and adapting the stream of inputs—collectively termed a "feed"—presented to AI systems, robots, or human users. Rather than relying on passive, engagement-optimized, or randomly sampled input selection, intentional feedbuilding centers on transparency, expressivity, and alignment between system outputs and user goals, preferences, or contextual constraints. This paradigm manifests across reinforcement learning, recommender systems, social media platforms, conversational agents, knowledge acquisition frameworks, and adversarial robustness, each leveraging explicit intent or adaptive mechanisms to shape feed contents and dynamics.

1. Foundational Principles of Intentional Feedbuilding

Intentional feedbuilding contrasts sharply with conventional feed algorithms that maximize engagement, popularity, or exposure without accounting for purpose or explicit control (Malki et al., 13 Sep 2025). Central principles include:

• Expressivity of Intent: Users articulate goals in natural language or structured criteria, which drive subsequent feed generation (Malki et al., 13 Sep 2025).
• Procedural Transparency: Pipeline stages (planning, sourcing, curation, ranking) and selection rules are made visible and modifiable to users (Malki et al., 13 Sep 2025).
• Alignment of Input Stream: Feed construction accounts for compositional, topological, or informational structure, rather than relying solely on feedback or engagement data (Rodchenko et al., 23 Jan 2025).

In robotics and RL contexts, intentional feedbuilding also involves embedding latent intentions within generative models to drive planning, adaptation, and task-specific behaviors (Zheng et al., 10 Jun 2025, Friston et al., 2023).

2. Frameworks and Algorithms across Domains

Intentional feedbuilding is operationalized through diverse algorithmic pipelines:

Domain	Primary Mechanism	Intent Modeling Technique
Social Media	Planning → Sourcing → Curation → Ranking	Natural language intent parsing, feed scoring
Conversational AI	Language-based policy, reinforcement learning	Reward functions balancing intention with coherence
Robot-Assisted Feeding	Contextual bandit, deep feature adaptation	Contextual bandit algorithms conditioned on visual context
Knowledge Base Construction	Active learning, relationship extraction	Human-in-the-loop prioritization with uncertainty sampling
RL Pretraining	Flow-based occupancy modeling	Latent variable encoding behavioral intent
Adversarial Robustness	Modified input streams, detection networks	Explicit intent detection via embedding separation

For instance, Bonsai’s social feed system employs a four-stage pipeline where user intent, expressed in natural language, is parsed by an LM and converted into structured inclusion/exclusion rules. Candidate posts are sourced, curated by scoring alignment with user objectives, and ranked using weighted aggregation functions (e.g., weighted Borda Count: $$s(p) = w_r \cdot r_\text{relevance}(p) + w_p \cdot r_\text{popularity}(p) + w_c \cdot r_\text{recency}(p)$$) (Malki et al., 13 Sep 2025).

In RL and robotic manipulation, latent intention is modeled using a probabilistic encoder (e.g., $p_e(z | s', a')$) that conditions occupancy predictions, enabling multimodal, goal-directed plan synthesis and adaptation (Zheng et al., 10 Jun 2025, Gordon et al., 2019).

3. Expressivity, Transparency, and User Agency

Intentional feedbuilding gives users explicit agency in content selection, curation, and ongoing customization (Feng et al., 25 Jan 2024, Malki et al., 13 Sep 2025, Li et al., 14 Feb 2025). Platforms supporting intentional feedbuilding allow:

• Natural Language Specification: Users define feed objectives, exclusions, and priorities using unconstrained text, which is parsed and mapped to actionable parameters (Malki et al., 13 Sep 2025).
• Editable Configuration: Users can review, revise, and fine-tune inclusion/exclusion settings, adjusting weights and rules to calibrate the system’s response to intent (Malki et al., 13 Sep 2025, Feng et al., 25 Jan 2024).
• Transparency Mechanisms: Each feedbuilding stage is surfaced to users, with traceability back to the rules that selected each item, supporting trust and accountability (Malki et al., 13 Sep 2025).

Studies reveal users desire tighter feedback loops, with mechanisms for tracking, adjusting, and evaluating the impact of their intent-driven actions, though these affordances may increase cognitive workload compared with passive, engagement-optimized alternatives (Malki et al., 13 Sep 2025, Feng et al., 25 Jan 2024).

4. Methodologies for Intent Capture and Adaptive Feed Structuring

Intentional feedbuilding is underpinned by algorithmic methodologies to capture, operationalize, and adapt intent:

• Contextual Bandit and Online Learning: In robot manipulation, contextual features from visual deep networks (e.g., SPANet) are mapped to success estimates for discrete actions; linear regression or bandit algorithms adapt strategy selection based on online feedback and prior experience (Gordon et al., 2019).
• IMT (Interactive Machine Teaching): Users interactively decompose post features, assign signals, and "teach" systems structured judgments that extend beyond simple likes or engagement metrics (Feng et al., 25 Jan 2024).
• Active Learning for Knowledge Bases: Pool-based uncertainty sampling prioritizes ambiguous or underrepresented relations for human labeling, accelerating discovery and improving extraction quality (Youn et al., 2023).
• Flow Occupancy Modeling in RL: Latent intention variables condition flows over occupancy measures, generating long-term predictions consistent with diverse task objectives and generalized policy improvement (Zheng et al., 10 Jun 2025).

In adversarial settings, intentional feedbuilding may involve augmenting training feeds with adversarially perturbed inputs or detection modules capable of discriminating both intent and source of noise, using clustering and embedding separation loss functions (Jain et al., 29 Sep 2024, Sayyed et al., 31 Jul 2024).

5. Empirical Findings, Impact, and Trade-offs

Empirical evaluations of intentional feedbuilding frameworks report:

• Rapid Adaptation: Contextual bandit methods (e.g., LinUCB) reliably converge to optimal food manipulation strategies within ~10 failures per new item, even with strong covariate shift (Gordon et al., 2019).
• Improved Personalization and Filtering: Bonsai users successfully created feeds that surfaced novel, relevant content while filtering out toxicity and irrelevance, but experienced greater effort compared to engagement-centric systems (Malki et al., 13 Sep 2025).
• Scalability and Active Discovery: Active learning strategies improved rate of positive relation discovery by 21% compared to random sampling (Youn et al., 2023).
• Robustness across RL Tasks: Intention-conditioned flow models yielded $1.8\times$ improvement in median returns and 36% increase in success rates over competing RL pre-training strategies (Zheng et al., 10 Jun 2025).
• Security via Intent Detection: Vision Transformer-based detectors classified intentional vs. unintentional perturbations with near-perfect accuracy across multiple datasets (Jain et al., 29 Sep 2024).

Trade-offs noted include increased cognitive load for users when specifying intent, and potential challenges in operationalizing diverse, multi-dimensional preferences, especially when moving beyond engagement maximization to more nuanced feed objectives (Malki et al., 13 Sep 2025).

6. Applications and Extensions

Intentional feedbuilding is applicable across domains:

• Social Media Personalization: Supporting granular user control over feed content, mitigating echo chambers, and ensuring curation aligns with stated values or interests (Malki et al., 13 Sep 2025, Feng et al., 25 Jan 2024).
• Conversational Interfaces: Enabling bots to influence human interlocutors by training with explicit intention rewards (e.g., sentence length, emotional valence, lexical content) (Su et al., 2021).
• Robotic Automation: Rapidly adapt manipulation strategies for unseen objects in non-stationary environments (Gordon et al., 2019).
• Knowledge Base and Data Acquisition: Efficient construction of food composition or other domain-specific databases using active, intent-prioritized sampling (Youn et al., 2023, Rodchenko et al., 23 Jan 2025).
• Adversarial Robustness: Integrating adversarial examples or intent-aware detectors in input feeds to enhance DNN security and reliability (Jain et al., 29 Sep 2024, Sayyed et al., 31 Jul 2024).
• RL and Agent Training: Long-horizon policy adaptation with latent intention conditioning for sample efficiency and behavioral diversity (Zheng et al., 10 Jun 2025, Friston et al., 2023).

Hybrid approaches that blend explicit intent with behavior signals, feedback loops, and collaborative agency (e.g., multi-modal, conversational adjustments) are cited as promising future directions (Malki et al., 13 Sep 2025).

7. Technical and Design Challenges

Challenges inherent to intentional feedbuilding include:

• Cognitive and Specification Burden: Articulating nuanced intent requires sustained user effort and may suffer from ambiguity, especially when natural language alone is insufficient to capture complex objectives (Malki et al., 13 Sep 2025).
• Operationalizing Negative Implicit Signals: Systems must distinguish deliberate negative feedback (e.g., rapid swipe as rejection) from casual behaviors, requiring refined modeling and interface cues (Li et al., 14 Feb 2025).
• Algorithmic Harmonization: Maintaining robustness against adversarial attacks or changing user goals often necessitates continuous monitoring, recalibration, and avoidance of defense overfitting (Sayyed et al., 31 Jul 2024).
• Data and Structural Prioritization: Devising selection weights reflecting both intrinsic data quality and structural topological features for efficient scaling remains a key research direction (Rodchenko et al., 23 Jan 2025).
• Transparency versus Usability Trade-off: Exposed procedural steps empower agency, but may overwhelm users lacking domain knowledge or familiarity with feedbuilding systems (Malki et al., 13 Sep 2025).

Intentional feedbuilding represents a principled shift in feed design approach—privileging transparency, expressivity, and alignment with user, agent, or domain objectives over maximized engagement or passive content exposure. Its success in diverse applications underscores both its technical significance and the complexities of realizing intent-centered interaction in practical, scalable systems.