Interactive Continual Learning Framework

Updated 3 December 2025

Interactive continual learning frameworks are systems that enable machine learning agents to adapt continuously via real-time perception, memory management, and human interaction.
They integrate modular components such as short- and long-term memory, dynamic buffer management, and on-the-fly algorithmic updates to balance stability and plasticity.
These frameworks are applied in robotics, image segmentation, and automation, achieving measurable improvements in adaptation through interactive updates and consolidated learning.

Interactive continual learning frameworks are architectural and algorithmic systems designed to enable machine learning agents to adapt to non-stationary, open-ended environments via ongoing interaction—whether with human users, autonomous agents, or dynamic data streams. Distinct from static batch learning, these frameworks integrate real-time perception, memory management, adaptive consolidation, and explicit support for interaction (including feedback, correction, and context). Their applications span robotics, embodied agents, scientific automation, image segmentation, and knowledge management.

1. Architectural Foundations and Modalities

Interactive continual learning (ICL) architectures typically feature modular subsystems for perception, memory, adaptation, and interaction. Representative realizations include neuro-inspired split-memory systems, cluster-based learners, multi-agent automation, and collaborative fast/slow model pairings.

For example, home service robots utilize an ICL architecture composed of:

Perception Layer: RGB-D sensors + CNN feature extractors (e.g., YOLO + ResNet18)
Interaction Layer: Human GUI for direct teaching and context assignment
Memory/Continual Learning: Short- and long-term memory (STM/LTM), clustering-based concept formation, and time-based fading/consolidation
Reasoning & Planning: Semantic context reasoning, navigation, and manipulation planners Such systems rely on the continuous acquisition of semantic knowledge by interacting directly with humans through real-world feedback and teaching events (Ayub et al., 6 Mar 2024).

Other contemporary frameworks leverage multiagent system architectures, as exemplified by freephdlabor (Li et al., 17 Oct 2025), where agents such as ManagerAgent, IdeationAgent, ExperimentationAgent, and WriteupAgent interact in a star topology, maintain persistent memory, and dynamically route workflows in response to feedback and intermediate findings.

Interactive continual learning frameworks for embodied agents introduce stream-based decision policies parameterized by $\theta$ , processing $(v_t, l)$ tuples (visual observation + language directive), and updating behavior- or environment-specific capacities on a task-free, streaming basis (Kim et al., 12 Mar 2024). These modalities generalize across supervised, reinforcement, and hybrid learning regimes.

2. Memory Management and Consolidation Strategies

Memory representation and management is central to ICL frameworks, directly determining the agent’s ability to retain, recall, and selectively forget information over time. Architectures utilize:

Episodic and semantic memory splits (STM and LTM), with distinct decay rates and consolidation criteria
Cluster-based learners (akin to SUSTAIN): cluster centroids with receptive-field weights are updated or recruited based on activation thresholds, supporting rapid, semi-supervised adaptation to new objects or contexts (Ayub et al., 6 Mar 2024)
Semi-parametric, biomimetic memory modules (BrainCL): binary-coded non-parametric cues $\{v_k\}$ represent exemplars, which, when replayed through pattern-completion networks $N_C^t$ , reconstruct proxy inputs for classifier retraining. Wake-sleep consolidation cycles alternate between rapid adaptation (plastic learning on current task data) and slow cross-task replay (stability) (Liu et al., 20 Apr 2025)
Dynamic buffer management: fixed replay buffers regulated by reservoir sampling ensure representative sampling of prior data, critical for robust knowledge retention and accurate unlearning (Chatterjee et al., 21 Aug 2024)

Long-term memory fading is controlled via predictive-performance equations, and selective consolidation from STM to LTM is triggered by encounter frequencies exceeding specific thresholds (Ayub et al., 6 Mar 2024).

3. Algorithms for Continual Adaptation and Interactive Update

Core algorithmic advances in ICL frameworks combine online, streaming updates with selective, interactive interventions. Notable algorithmic methodologies include:

Knowledge aggregation via expert-weighted predictions: in lifelong online learning, the framework combines predictions from accumulated classifiers $K_T = \{h_1, \ldots, h_T\}$ and a current task-specific learner $h_{T+1}$ , adjusting the mixing coefficient $\alpha_t$ dynamically as new data arrive (Shui et al., 2018)
Confidence-Aware Moving Average (CAMA): episodic memory stores logits updated via dynamic, class-wise moving average coefficients $\gamma$ derived from short-term confidence windows, enabling adaptive retention of past behaviors or environments without explicit task boundaries (Kim et al., 12 Mar 2024)
Knowledge distillation from multi-teacher architectures: UniCLUN merges continual learning (stability) and targeted unlearning (plasticity) by balancing logit distillation losses from student, CL teacher $T$ , and unlearning teacher $B$ , with momentum-based updates and contrastive distillation for buffer and new samples (Chatterjee et al., 21 Aug 2024)
Reinforced interactive agents: both model-confusion thresholding and RL-based policy learning determine when to seek user intervention (“ask” action), with reward shaping terms that penalize unnecessary questions and encourage learning only as needed (Chi et al., 2019)
Preference-based optimization and contrastive learning under noisy labels: RiCL employs a temporal consistency-aware purifier to filter noisy feedback, a preference-driven policy to ensure alignment with human intent, and contrastive learning on noisy data for robust representation (Yang et al., 15 May 2025)

These techniques support both task-free and boundary-aware continual adaptation across supervised, reinforcement, and interactive modalities. Memory updates, confidence estimation, and task similarity weighting are enacted in real time, leveraging user feedback, active querying, and agent–human collaboration.

4. Evaluation Methodologies and Empirical Results

Evaluation of interactive continual learning frameworks spans object recognition, instruction following, navigation, and scientific automation:

Home robotics: 16 interactive “increments” over 2 months produce object and task execution accuracy within 6% of joint-training baselines; real-time adaptation to moved/removed items demonstrated via context cluster updating (Ayub et al., 6 Mar 2024)
Embodied agents: CAMA yields last-task success rates superior to ER, DER++, and regularization methods, with ablation confirming the necessity of dynamic coefficients for retaining prior behaviors (Kim et al., 12 Mar 2024)
Lifelong online learning: AKLO-Sum and AKLO-Sample algorithms achieve cumulative error rates substantially below standard baselines, robust to non-i.i.d. task arrivals (Shui et al., 2018)
Unlearning and retention: UniCLUN achieves >90% accuracy on retained classes after multiple learn/unlearn sequences and can drive accuracy on forgotten classes to near zero, outperforming retrain and DER baselines (Chatterjee et al., 21 Aug 2024)
Reinforced interactive learning: “Ask” actions boost navigation success by ≥15% absolute, maintaining data-efficiency and resilience to noisy oracle intervention (Chi et al., 2019)
Semi-parametric consolidation (BrainCL): achieves average ImageNet-100 accuracy of 78.7% vs DER’s 71.3%, with robustness to distributional shifts and explicit avoidance of feature collapse in replayed exemplars (Liu et al., 20 Apr 2025)

Table: Example empirical results from diverse frameworks

Framework	Domain	Key Metric / Result
CAMA (Kim et al., 12 Mar 2024)	Instruction following	Unseen SR_avg: DER++ 10.6%, CAMA 14.2%
BrainCL (Liu et al., 20 Apr 2025)	ImageNet-100 class-IL	Joint: 84.2%, DER: 71.3%, BrainCL: 78.7%
UniCLUN (Chatterjee et al., 21 Aug 2024)	CIFAR-10 unlearning	Retained-cls acc: 93.9% (vs DER 83.8%)
Home robot ICL (Ayub et al., 6 Mar 2024)	Object fetch	Obj. acc. 73.3% post 10 increments

5. Interaction, Human-in-the-Loop, and Multiagent Support

ICL frameworks are characterized by explicit interfaces for human or agent interaction:

GUI-based incremental teaching: users can present novel objects or contexts, label semantic regions, and issue fetch requests, all of which are processed in real time by perception and memory modules (Ayub et al., 6 Mar 2024)
Multiagent perpetual research: subagents maintain autonomous yet coordinated exploration, with non-blocking human intervention mechanisms allowing external corrections or directives to be asynchronously injected into any agent’s memory and reasoning loop (Li et al., 17 Oct 2025)
Dynamic workflow and adaptive memory: agents in frameworks like freephdlabor manipulate persistent workspace files rather than sequential text streams, ensuring robust and lossless communication across long horizons
Interactive unlearning: batch-API design allows systems to accept “unlearn” flags, removing data from active buffers and invoking on-demand retraining (Chatterjee et al., 21 Aug 2024)
Real-time noisy supervision: temporal consistency modules and preference-based updates allow agents to adapt to and even correct noisy human feedback, safeguarding both retention and robustness (Yang et al., 15 May 2025)
Fast-and-slow collaborative reasoning: continual learners (System 1: ViT + buffer) consult multimodal LLMs (System 2) selectively when hard/outlier cases are detected, merging rapid inference with high-level reasoning for challenging samples (Qi et al., 5 Mar 2024)

These interactive mechanisms underpin adaptive, personalized, and resilient continual learning.

6. Extensibility, Generalization, and Open Challenges

Sequoia provides a taxonomy and software platform for analyzing and benchmarking CL algorithms across various interactive and non-interactive modalities, leveraging inheritance among settings (e.g., incremental learning, task-agnostic, RL, SL) and enabling reusable code, metrics, and plugins (Normandin et al., 2021). This approach facilitates the systematic evaluation of methods and settings, including memory budgets, non-stationarity, and the transfer/forgetting matrix over tasks.

Current limitations of ICL frameworks include:

Computational/memory overhead: increasing buffer size improves retention but can diminish purity of targeted unlearning (Chatterjee et al., 21 Aug 2024), and pattern-separation/consolidation cycles require additional compute (Liu et al., 20 Apr 2025)
Granularity of interaction: most support only single-turn feedback and lack fine control over hierarchical or multi-turn dialog corrections (Yang et al., 15 May 2025)
Representation compression: methods for pruning, merging, or compressing knowledge bases are under development, with memory usage scaling with the number of tasks (Shui et al., 2018)
Sensitivity to hyperparameter choices (decay rates, consolidation frequency, buffer size) and their effects on forgetting/plasticity remain to be systematically optimized Potential extensions include reward-driven updates, multimodal capability (vision, language), similarity-weighted memory, and adaptive scheduling for consolidation or human intervention.

7. Broader Implications and Future Directions

Interactive continual learning frameworks are fundamental for personalizing agents in open-world environments, science automation, lifelong adaptation, and dynamic task management. Their ability to blend rapid online learning with robust retention, handle noisy and dynamic feedback, and support modular expansion and human-in-the-loop supervision positions them as foundational infrastructure for next-generation autonomous systems.

Research continues on:

Integrating more biologically plausible mechanisms (local credit assignment, dopaminergic reward)
Scaling interactive CL to heterogeneous, Internet-scale embodied agents (Kim et al., 12 Mar 2024)
Expanding context-aware, multiagent systems for perpetual and automated discovery (Li et al., 17 Oct 2025)
Formalizing evaluation protocols and benchmarks for interactive continual learning across modalities (Normandin et al., 2021)

The convergence of real-time interaction, memory consolidation, preference-based optimization, and modular architecture characterizes state-of-the-art interactive continual learning frameworks in both academic research and emerging practical deployments.