Cognitive Self-Evolving Planning

• Cognitive self-evolving planning is a computational framework where agents dynamically build and refine internal models and memory to optimize complex task strategies.
• It leverages adaptive memory, metacognitive triggers, and iterative planning loops to continuously enhance reasoning and performance in varied domains.
• Experimental results demonstrate significant gains in planning success, cross-domain generalization, and error reduction through dynamic self-adaptation.

Cognitive self-evolving planning refers to a class of computational architectures, algorithms, and frameworks in which agents dynamically construct, refine, and execute plans via an interplay of memory, metacognition, adaptive reasoning, and self-improvement mechanisms. The central objective is for the system to autonomously evolve its planning competence over time—not merely by adjusting parameter weights, but by developing new forms of internal representation, memory usage, and reasoning routines that drive high-level task decomposition, strategic adaptation, and robust generalization.

1. Fundamental Principles and Theoretical Underpinnings

Cognitive self-evolving planning is anchored in the observation that both humans and advanced artificial agents benefit from the ability to flexibly adjust representations, construct and revise internal memory traces, and selectively invoke appropriate strategies as they interact with complex, changing environments. Two foundational strands are prominent:

• Dynamic Representation Control: Rather than relying on fixed, exhaustive accounts of the task or environment, agents continuously optimize the complexity and utility of their internal models. This is formalized in “value-guided construal” theory, where the agent selects implicit representations $c$ of primitive features to maximize $\mathrm{VOR}(c) = U(\pi_c) - C(c)$—the behavioral utility of its plan minus cognitive cost. The selection and modification of $c$ is a nested optimization over both policy and representation, giving rise to adaptive, self-evolving plan elaboration (Ho et al., 2021).
• Memory as an Active, Generative Process: Unlike parametric or purely retrieval-based memory (reweighting parameters or fetching from static stores), cognitive self-evolving planning employs generative latent memory: explicit mechanisms generate, insert, and adapt memory traces (planning, procedural, working) throughout reasoning, tightly interweaving memory and cognition in a reflectively controlled loop (Zhang et al., 29 Sep 2025).

Self-evolving planners are thus characterized by their ability to maintain and adapt memory, flexibly manage internal representations, and engage in ongoing reflection or critique to self-improve their reasoning procedures.

2. Architectural Design Patterns

Multiple system architectures embody cognitive self-evolving planning, with distinct but often overlapping designs:

• Trigger–Weaver Architectures: For example, MemGen attaches a lightweight metacognitive trigger (modeled via a LoRA adapter) to the frozen core of an LLM-based agent. At semantically meaningful boundaries (e.g., sentence delimiters), the trigger probabilistically invokes a generative memory weaver, which synthesizes $K$-length blocks of latent tokens. These are prepended to the context, and subsequent reasoning is thus enriched by dynamically constructed memory (Zhang et al., 29 Sep 2025).
• Hierarchical Multi-Agent Decomposition: Systems such as Mobile-Agent-E employ an explicit separation between high-level planning (Manager agent), perception (Perceptor), low-level action (Operator), error feedback (Action Reflector), and aggregation (Notetaker). The self-evolution module maintains Tips and Shortcuts—declarative and procedural memory elements—which are injected into the planning loop, modified after each episode, and linked to semantically relevant subgoals or error states (Wang et al., 20 Jan 2025).
• Multi-Stage Self-Evolution Pipelines: Plan2Evolve exemplifies a four-stage iterative loop (domain generation, planning, chain-of-thought alignment, supervised fine-tuning). The agent not only solves tasks but also generates new scalable planning domains, infers solver-validated plans, translates these into extended chain-of-thought traces, and learns from the synthesized corpus—closing the loop of skill-acquisition and symbolic grounding (Huang et al., 25 Sep 2025).
• Belief–Intent Token Co-Evolution: Rather than reconstructing full world states, frameworks such as TIWM compress observation into a minimal set of high-dimensional tokens encoding belief (current state) and intent (future goal). A causal Transformer co-evolves these tokens under a task-driven loss, enabling the emergence of robust planning dynamics without explicit reconstruction objectives (Sang, 30 Oct 2025).
• Retrieval-Reflection-Planning Loops: Agents such as Richelieu and PolicyEvol-Agent alternate between high-level plan proposal, memory retrieval, episodic reflection, and plan revision. The use of theory-of-mind prompts, empirical identification of irrationalities, and dynamic update of policy guidelines by incorporating reflective expertise patterns collectively yield improved planning accuracy and density of useful strategies (Guan et al., 2024, Yu et al., 20 Apr 2025).

3. Emergent Memory Functions and Planning Specializations

Empirical investigations into cognitive self-evolving frameworks unveil spontaneously organized latent memory structures with distinct roles:

Memory Type	Functionality	Impact of Ablation
Planning Memory	High-level task decomposition, sequencing of subgoals	Increases “Planning Failure” rates
Procedural Memory	Encodes operational skills, API formats, templates	Causes “Tool Parsing”/“Formatting” failures
Working Memory	Maintains local context and coherence	Leads to “Think–Act Inconsistency”, “Demand Misunderstanding”

All these types coexist as clusters in a shared embedding space produced by mechanisms such as MemGen’s weaver. Their invocation patterns and influence dynamically adapt as the system interacts with new environments and tasks (Zhang et al., 29 Sep 2025).

Additionally, empirical ablations demonstrate that ablating specific memory types predictably degrades performance on the corresponding reasoning or compositional planning submodules in benchmarks such as ALFWorld and code-generation tasks.

4. Core Algorithms and Mathematical Formalisms

Cognitive self-evolving planning employs a diversity of algorithmic mechanisms and formal models, unified by their meta-cognitive orientation:

• Meta-MDP and Meta-RL Optimization: Discovery of new planning strategies may be framed as a meta-level MDP, wherein meta-states summarize the agent’s planning beliefs or current internal operations. Actions correspond to metacognitive operations (expand node, stop planning, construct new features). Learning is performed via policy-gradient or REINFORCE updates based on composite or pseudo-reward signals:

$$w \leftarrow w + \alpha \cdot \sum_{t=1}^O \gamma^{t-1} r_t \cdot \nabla_w \log \pi_w(c_t|b_t)$$

with $r_t$ augmented by intrinsic pseudo-rewards and subjective effort costs (He et al., 29 May 2025, He et al., 2024).

• Nested (Inner–Outer) Planning Loops: For representational adaptation, agents select a planning construal $c$ from among subsets of primitive features, optimize an inner-loop policy for $c$, and maximize their overall value-of-representation utility:

$\mathrm{VOR}(c)=U(\pi_c)-|c|$

and $c$ is stochastically selected via softmax (Ho et al., 2021).

• Latent Memory Weaving: Generative modules synthesize context-sensitive blocks of latent tokens from current state representations:

$$M_t = W_{\text{weaver}}(H_{t,<j}) \in \mathbb{R}^{K\times d}$$

which are then inserted into the model’s context for the next decoding step (Zhang et al., 29 Sep 2025).

• Self-Reflective Planning: In distributed robotic frameworks, explicit pre- and post-condition checks (via VLMs or LLMs) govern the loop: failed checks trigger accumulation of explanations and local plan revision based on updated “reflection buffers,” supporting continual adaption (Yuan et al., 28 Mar 2025).
• Self-Evolving Entailment Graphs: PathWise represents its evolving planning memory as a compact entailment graph, in which each node encodes a heuristic, its derivation rationale, and performance metadata. Planning proceeds as a sequence of (parent-select, rationale-explain, simulate, critique, update) cycles, each expanding the graph and refining future planning actions (Gungordu et al., 28 Jan 2026).

5. Experimental Results and Empirical Validation

Extensive empirical studies demonstrate that cognitive self-evolving planning architectures outperform static or parametric baselines across reasoning, robotics, and combinatorial optimization domains:

• MemGen achieves up to $+38.22\%$ over leading external memory systems and generalizes cross-domain without explicit retraining. Sequential learning maintains balanced accuracy across tasks, mitigating catastrophic forgetting (Zhang et al., 29 Sep 2025).
• Plan2Evolve models trained via self-generated domain–plan–CoT traces improve planning success up to $+17\%$ over SFT and outpace larger baseline LLMs in cross-domain and unseen real-world tasks (Huang et al., 25 Sep 2025).
• Mobile-Agent-E demonstrates a $+22\%$ satisfaction score improvement (human-judged success) attributable to Tips/Shortcuts memory, and further $+6.5\%$ with self-evolution enabled, as well as marked reductions in action error and termination failure rates (Wang et al., 20 Jan 2025).
• PathWise converges to superior heuristics for TSP and CVRP with fewer evaluations and larger relative gap improvements as instance sizes grow. Removing critics or prompt diversity mechanisms degrades performance by $>20\%$ (Gungordu et al., 28 Jan 2026).
• REMAC boosts average multi-robot manipulation task success rates by $+40\%$, with plan reflection and self-evolvement directly increasing robustness and efficiency (Yuan et al., 28 Mar 2025).
• TIWM demonstrates that tokenized belief–intent co-evolution suffices for robust autonomous driving prediction, yielding a $21.6\%$ performance improvement (ADE) without explicit reconstruction or future scene modeling (Sang, 30 Oct 2025).

6. Relation to Biological and Cognitive Plausibility

Several works explicitly draw comparisons between cognitive self-evolving planning and neurocognitive architectures in humans:

• Complementary Memory Systems: The trigger–weaver separation mirrors prefrontal (metacognitive decision) and hippocampal (generative memory reconstruction) functions, echoing the complementary learning systems hypothesis (Zhang et al., 29 Sep 2025).
• Reflection and Expertise Patterns: Episodic memory retrieval and guided reflection steps instantiate forms of theory-of-mind and self-evaluation, contributing to the continuous calibration of internal models and mitigation of cognitive bias (Yu et al., 20 Apr 2025).
• Dynamic Representation Control: The value-guided construal framework analytically models the cognitive cost–benefit trade-off in maintaining, elaborating, and simplifying task representations, paralleling observations from human planning experiments (Ho et al., 2021).

7. Open Questions and Limitations

Despite substantial progress, cognitive self-evolving planning faces unresolved challenges:

• Representation Growth and Management: In systems maintaining open-ended memory stores (e.g., Tips/Shortcuts, entailment graphs), scaling, pruning, and retrieval policies remain an open area for large-scale, lifelong deployment (Wang et al., 20 Jan 2025, Gungordu et al., 28 Jan 2026).
• Strategy Discovery Gaps: Even with intrinsic pseudo-rewards and subjective effort modeling, state-of-the-art metacognitive RL models do not fully match human rates of strategy discovery, suggesting that higher-level inductive biases or abstraction mechanisms are missing (He et al., 29 May 2025, He et al., 2024).
• Integration with Deep Generative Models: Hybrid designs—combining dynamic latent memory, neurosymbolic models, and deep perception modules—are only beginning to address the synthesis of fast reflexive control and long-horizon strategic reasoning (Priorelli et al., 2024).
• Automated Validation and Repair: For procedural memory and shortcut extraction, automated testing or revision to ensure robustness under generalization is required (Wang et al., 20 Jan 2025).
• Cognitive Consistency and Temporal Credit Assignment: The emergence and maintenance of stable planning dynamics under prolonged self-evolution is only partially understood, especially in ambiguous, noisy, or adversarial environments (Sang, 30 Oct 2025).

Ongoing research focuses on convergence analysis, continual learning stability, explainability, and transferability to domains with adversarial dynamics, stochasticity, or partial observability.