Knowledge–Reasoning Dissociation

• Knowledge–Reasoning Dissociation is the explicit separation between a system's stored knowledge and its reasoning processes, enhancing modular design.
• It enables modular architectures in applications like dialogue systems and visual question answering, improving interpretability and controllability.
• Empirical studies reveal that dissociating knowledge from reasoning leads to measurable gains in memorization, understanding, and exploration.

Searching arXiv for the cited papers and closely related work on knowledge–reasoning dissociation. Knowledge–Reasoning Dissociation is the explicit separation between what a system knows and how it reasons or acts with that knowledge. In the literature synthesized here, the term denotes a family of modeling choices in which knowledge is represented, acquired, filtered, or externalized as a distinct object—such as a knowledge state $K$, a knowledge sequence, a knowledge graph, a structured benchmark axis, or a dedicated generation module—while reasoning is treated as a separate process that conditions on, searches over, evaluates, or operationalizes that knowledge. Across web agents, dialogue systems, interactive reasoning frameworks, knowledge-graph reasoning, visual question answering, and analyses of large reasoning models, the recurring claim is that end-to-end mappings often conflate knowledge and reasoning, whereas explicit dissociation can improve interpretability, controllability, evaluation granularity, and, in some settings, generalization (Guo et al., 3 Aug 2025, Adolphs et al., 2021, Xiao et al., 12 Jun 2025).

1. Conceptual definition and scope

A common formulation distinguishes knowledge content from cognitive process. In "Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web Agents" (Guo et al., 3 Aug 2025), this is made explicit through a Bloom-inspired two-stage decomposition: knowledge content learning as the “what” of learning, and cognitive processes as the “how” of reasoning and acting. The associated Web-CogKnowledge Framework organizes knowledge into Factual, Conceptual, and Procedural types, which are linked to the cognitive processes Memorizing, Understanding, and Exploring, respectively. The paper states that “knowledge content learning corresponds to the agent's processes of Memorizing and Understanding,” whereas “cognitive processes correspond to Exploring,” thereby formalizing a direct knowledge–process dissociation (Guo et al., 3 Aug 2025).

A related but differently scoped decomposition appears in dialogue generation. "Reason first, then respond: Modular Generation for Knowledge-infused Dialogue" (Adolphs et al., 2021) factorizes the problem into a knowledge / reasoning step that generates an intermediate knowledge sequence $K$, and a response generation step that conditions on that sequence to produce the final response. In effect, the system replaces a monolithic $C \rightarrow R$ mapping with $C \rightarrow K \rightarrow R$, separating “what to say” from “how to say it” (Adolphs et al., 2021).

In interactive reasoning, "Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios" (Burkhardt et al., 16 Aug 2025) defines knowledge as structured content about the domain and the user—specifically a domain knowledge graph $KG_D=(E,R,T)$, user knowledge $K_U$, and LLM intrinsic knowledge $K_L$—while reasoning is the process of generating and pruning reasoning threads, i.e. ordered sequences of triples. The paper’s two-phase ReT-Eval framework uses phase 1 for knowledge extraction and enrichment, and phase 2 for reward-guided search and pruning, thereby architecturally separating knowledge selection from reasoning control (Burkhardt et al., 16 Aug 2025).

A similar distinction is stated in "Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs" (Xiao et al., 12 Jun 2025), where factual knowledge is the set of KG triples and reasoning path / logic is the organization of those triples into a coherent chain. The paper argues that “mere retrieval of these independent knowledge units remains insufficient” and that “the ability to synthesize a logically consistent and context-relevant reasoning path is essential,” making the knowledge–reasoning split the central design principle (Xiao et al., 12 Jun 2025).

2. Formalizations of dissociation

Several papers formalize dissociation by inserting an explicit knowledge variable or structured intermediate object into the computational graph. In Web-CogReasoner, the web-agent interaction is modeled as a POMDP

$P = (S, A, O, K, T, R),$

where $K$ is a dedicated knowledge component representing the agent’s internal knowledge. The policy is defined as a function of knowledge and observation history: $(h_1, a_1) = \pi_\theta(\cdot \mid K, I, Q, o_1),$

$K$0

This formalization treats knowledge as conceptually separated from the online trajectory, even if it is not implemented as a separate network (Guo et al., 3 Aug 2025).

K2R is formulated through a two-stage factorization of the joint distribution: $K$1 Although the paper does not present that exact equation, it constructs precisely this decomposition with a knowledge model $K$2 trained for $K$3 and a response model $K$4 trained for $K$5. The knowledge sequence is explicit text rather than a latent vector, which turns the intermediate representation into an inspectable object (Adolphs et al., 2021).

ReT-Eval formalizes the knowledge side through a domain graph $K$6, a user-conditioned pruned graph $K$7, and an enriched graph $K$8. The pruning step is defined by

$K$9

with $C \rightarrow R$0, and reasoning threads are sequences

$C \rightarrow R$1

Thread quality is evaluated by a composite reward

$C \rightarrow R$2

which dissociates the knowledge structure itself from the control logic that searches over it (Burkhardt et al., 16 Aug 2025).

RRP separates knowledge graphs from reasoning paths through two levels of representation. The KG is

$C \rightarrow R$3

while a reasoning path is

$C \rightarrow R$4

The semantic module minimizes a KL divergence between a KG-defined posterior over valid shortest paths and an LLM prior over paths, while the structural module uses bidirectional distribution learning with a Jensen–Shannon term to enforce path consistency (Xiao et al., 12 Jun 2025). This suggests a layered formalization in which facts and paths are treated as different representational objects.

3. Architectural patterns

Architecturally, the most direct pattern is the separation of knowledge modules from reasoning or generation modules. Web-CogReasoner divides its architecture into knowledge-related components—Web-CogKnowledge and Web-CogDataset—and reasoning-related components, especially a knowledge-driven Chain-of-Thought (CoT) reasoning module. The CoT itself is scaffolded into a Factual knowledge segment, a Conceptual knowledge segment, and a Procedural knowledge segment, corresponding to “What is on the page?”, “Why is this here? What is its purpose?”, and “How can I achieve this goal using what I know?” (Guo et al., 3 Aug 2025). The paper emphasizes that this CoT “is not a monolithic or opaque text block,” but a template grounded in distinct knowledge types.

K2R implements a modular dialogue architecture with a knowledge model and a response model. Variants include a standard two-model K2R, a shared-parameter K2R, and a confidence-score–conditioned K2R. In all cases, the system exposes a visible intermediate knowledge artifact that can be inspected, swapped, or manually injected, and the response model is tightly conditioned on it (Adolphs et al., 2021).

ReT-Eval uses a two-phase architecture. Phase 1 uses user knowledge extraction, ontology matching, KG pruning, LLM enrichment, and a GNN to produce candidate knowledge threads $C \rightarrow R$5. Phase 2 treats these threads as a search space and applies reward-guided MCTS to produce a selected reasoning thread $C \rightarrow R$6 (Burkhardt et al., 16 Aug 2025). The architectural boundary between graph construction and thread evaluation is explicit.

RRP similarly uses three modules: an LLM-based semantic reasoning path generator, a structural reasoning path generator based on relation embeddings and bidirectional distributions, and a rethinking module that scores and filters candidate paths before they are passed to the final LLM (Xiao et al., 12 Jun 2025). This is a strong form of dissociation because path extraction and path evaluation occur outside the final answering model.

In KBVQA, "Knowledge Condensation and Reasoning for Knowledge-based VQA" (Hao et al., 2024) separates a Knowledge Condensation model from a Knowledge Reasoning model. The condensation stage uses a VLM and an LLM to reduce lengthy retrieved passages into short knowledge concepts and knowledge essence, which are then consumed by a separate T5-based reasoning model. The paper explicitly argues that reasoning should operate over compact, relevant representations rather than raw retrieved passages (Hao et al., 2024).

HinD extends this modular trend by separating a CoT Generator, a Knowledge Generator, and an Answer Generator, each with its own objective. The knowledge module is further optimized with Knowledge Encouragement Preference Optimization (KEPO), whereas reasoning trajectories are distilled through Hindsight Distilled Reasoning (Zhao et al., 14 Nov 2025). This suggests a finer-grained dissociation in which knowledge generation and reasoning generation are themselves distinct from answer production.

4. Training regimes and optimization strategies

A recurring training pattern is stage-wise optimization aligned with the knowledge–reasoning split. Web-CogReasoner uses multi-stage Imitation Learning. Stage 1 focuses on Factual and Conceptual knowledge and trains Memorizing and Understanding through tasks such as Element Attribute Recognition, Page Change Prediction, Element Understanding, and WebPage Understanding. Stage 2 focuses on Procedural knowledge and trains Exploring through User Intention Prediction, Popup Close, Single-Step Web Task, and Noisy Multi-Step Web Task (Guo et al., 3 Aug 2025). This curriculum enforces the idea that agents should first acquire knowledge content and only then learn reasoning and action.

K2R trains the knowledge and response modules separately with standard seq2seq cross-entropy. In the supervised setting, the knowledge model predicts the gold knowledge sentence and the response model predicts the gold response conditioned on gold knowledge. The confidence-score conditioned variant deliberately corrupts knowledge during training to teach calibrated reliance on the intermediate knowledge sequence (Adolphs et al., 2021). This is not only a modular architecture but a modular learning signal.

ReT-Eval separates the objectives across phases. Phase 1 optimizes graph- and similarity-based knowledge extraction, while phase 2 uses a reward-guided MCTS with a composite reward that includes semantic coherence, user alignment, and domain-layer progression. The reward is further decomposed into seven implementation-level components with weights $C \rightarrow R$7, $C \rightarrow R$8, $C \rightarrow R$9, $C \rightarrow K \rightarrow R$0, $C \rightarrow K \rightarrow R$1, $C \rightarrow K \rightarrow R$2, and $C \rightarrow K \rightarrow R$3 (Burkhardt et al., 16 Aug 2025). The knowledge phase and the reasoning-control phase therefore receive different optimization signals.

RRP trains a semantic module with a KL objective over valid shortest paths, a structural module with bidirectional distribution learning, and a rethinking module that combines semantic and structural similarity: $C \rightarrow K \rightarrow R$4 This layered optimization treats path mining, structural validation, and final answer guidance as separate problems (Xiao et al., 12 Jun 2025).

HinD is especially explicit. CoT HDFT and Knowledge HDFT are separate cross-entropy objectives, while KEPO is a DPO-style preference loss applied only to the Knowledge Generator. The answer module is then trained with its own negative log-likelihood over $C \rightarrow K \rightarrow R$5 given the generated CoT and knowledge context (Zhao et al., 14 Nov 2025). This suggests a modular training stack in which reasoning quality and knowledge usefulness can be improved without collapsing them into a single objective.

5. Empirical evidence and benchmark patterns

The most direct empirical evidence for dissociation comes from experiments where changing the knowledge side selectively changes the reasoning side. In Web-CogReasoner’s ablation study, progressively adding Factual, Conceptual, and Procedural knowledge changes distinct cognitive dimensions:

Model	Memorizing	Understanding	Exploring
Base Qwen2.5-VL-7B	67.6	61.0	77.9
+ Factual (S1)	85.5	64.2	60.1
+ Conceptual (S2)	88.1	75.5	65.8
+ Procedural (S3)	90.8	74.1	85.0

Factual training mostly improves Memorizing, Conceptual training yields the largest gain in Understanding, and Procedural training dramatically improves Exploring, which the authors interpret as evidence that different knowledge types selectively support different cognitive abilities (Guo et al., 3 Aug 2025).

K2R provides a comparable empirical dissociation in dialogue. On Wizard of Wikipedia, Knowledge F1 rises from 26.1 for BART RAG DPR to 29.2 for K2R, and human evaluation reports hallucination rates of 16% for RAG DPR versus 7% for K2R, while maintaining comparable knowledgeable judgments (Adolphs et al., 2021). The modular knowledge sequence therefore improves factual alignment without simply scaling a monolithic generator.

ReT-Eval isolates the effect of reasoning-thread control by comparing the full model to a GNN baseline that uses the same knowledge threads but no MCTS optimization, and to an RM baseline that uses a large reasoning model without KG integration. On the overall effectiveness metric,

$C \rightarrow K \rightarrow R$6

ReT-Eval scores 0.627 versus 0.611 for GNN and 0.600 for RM (Burkhardt et al., 16 Aug 2025). This suggests that reasoning-thread pruning contributes beyond knowledge structuring alone.

RRP’s ablation study likewise shows that semantic path generation alone and structural path generation alone are each weaker than their combination with rethinking. On WebQSP, Hits@1 and F1 improve from 84.8/68.6 for the semantic module and 74.2/67.3 for the structural module to 90.0/72.5 for full RRP (Xiao et al., 12 Jun 2025). This is evidence that structuring and evaluating reasoning paths matters beyond retrieving facts.

In KBVQA, the condensation/reasoning split yields a similarly diagnostic pattern. On OK-VQA, a T5-XL reasoner with raw knowledge passages scores 61.28, while replacing raw passages with knowledge essence + concepts + implicit knowledge raises performance to 65.10; adding the raw passages back slightly reduces it to 64.90 (Hao et al., 2024). This supports the claim that cleaner knowledge representations improve the downstream reasoning process.

HinD provides unusually direct evidence. On OK-VQA, HinD-Know reaches PRR@K 94.3 and VQA Score 68.3, while removing KEPO drops these to 85.0 and 63.5, and removing both KEPO and HDFT yields 80.6 and 62.9 (Zhao et al., 14 Nov 2025). On the same dataset, the end-to-end CoT-Know HDFT ablation performs much worse than the modular design, with PRR@K 80.7 versus 95.3 and VQA Score 63.1 versus 67.5 (Zhao et al., 14 Nov 2025). This is strong evidence that dissociating knowledge from reasoning is not only conceptually cleaner but empirically beneficial.

6. Broader interpretations, limits, and controversies

Knowledge–Reasoning Dissociation also appears in analyses that are not primarily about building modular systems. "LLMs are Limited in Out-of-Context Knowledge Reasoning" (Hu et al., 2024) argues that LLMs can store and recall training facts and can reason nearly perfectly when those facts are provided in-context, yet fail to internally retrieve and combine the same facts in out-of-context knowledge reasoning. The paper concludes that the bottleneck is primarily knowledge retrieval, especially for relational knowledge, rather than the reasoning mechanism itself. This suggests a dissociation between stored knowledge and the ability to access it for reasoning (Hu et al., 2024).

"Dissociation of Faithful and Unfaithful Reasoning in LLMs" (Yee et al., 2024) reports another form of split: models can produce correct answers after an injected CoT error either by faithful recovery, where the reasoning text visibly repairs the error, or by unfaithful recovery, where the final answer is correct without coherent textual repair. The divergent effects of error obviousness, context noise, and prompting on these two modes are interpreted as evidence for distinct mechanisms. A plausible implication is that explicit reasoning text and latent answer computation can be dissociated even when the final answer is correct (Yee et al., 2024).

"Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation" (Wang, 25 Jun 2026) pushes the dissociation into metareasoning. It separates difficulty registration from deliberation allocation and shows that humans and large reasoning models align on cross-item difficulty registration but diverge within items: LRMs spend more tokens on failures, while humans spend less time on failures. The paper interprets this as a separation between a shared difficulty signal and different stopping policies (Wang, 25 Jun 2026). This suggests that knowledge of difficulty and control of reasoning effort are distinct.

Other work complicates the picture by arguing against sharp separability. "RECKONING: Reasoning through Dynamic Knowledge Encoding" (Chen et al., 2023) shows that encoding contextual knowledge into parameters before reasoning improves robustness to distractors and generalization to longer chains. The paper effectively inserts a knowledge-encoding phase before reasoning, but it also blurs the boundary between parametric and contextual knowledge because both are ultimately handled through the same parameter space (Chen et al., 2023). This suggests that dissociation can be engineered as a pipeline even when the final representational substrate is unified.

A stronger critique of strict separation appears in "Five dimensions of reasoning in the wild" (Perlis, 2016), which argues that reasoning “does not work well when done in isolation from its significance” and that perception, knowledge, action, and simulation should cohabit richer dynamic structures rather than a static knowledge base plus a separate reasoning engine. This suggests that, while knowledge–reasoning dissociation is a useful design principle, it may be insufficient for fully embodied or situated intelligence (Perlis, 2016).

Finally, "On Representational Dissociation of Language and Arithmetic in LLMs" (Kisako et al., 17 Feb 2025) offers a representational rather than architectural perspective. It finds that simple arithmetic equations and general language inputs occupy completely separated regions in LLM representation space across layers, while combined tasks such as GSM8K and LangNumEq form their own distinct clusters (Kisako et al., 17 Feb 2025). A plausible implication is that dissociation can arise geometrically inside a monolithic model, not only through explicit modular design.

Taken together, these works show that Knowledge–Reasoning Dissociation is not a single doctrine but a spectrum of claims. In some settings, it denotes explicit modular factorization of knowledge retrieval, knowledge representation, and reasoning control. In others, it denotes empirical divergence between stored knowledge, visible reasoning, and latent decision processes. The common thread is the rejection of a purely monolithic view in which knowledge and reasoning are inseparably fused. The open question is not whether the two can ever be separated, but which separations are useful, faithful, and stable across tasks, domains, and forms of intelligence (Guo et al., 3 Aug 2025, Adolphs et al., 2021, Burkhardt et al., 16 Aug 2025, Xiao et al., 12 Jun 2025).