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Multiple Streams of Knowledge Retrieval: Enriching and Recalling in Transformers

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
mechanistic interpretability
Abstract:

When an LLM learns a new fact during finetuning (e.g., new movie releases, updated celebrity gossip, etc.), where does this information go? Are entities enriched with relation information, or do models recall information just-in-time before a prediction? Are ``all of the above'' true with LLMs implementing multiple redundant heuristics? Existing localization approaches (e.g., activation patching) are ill-suited for this analysis because they usually replace parts of the residual stream, thus overriding previous information. To fill this gap, we propose dynamic weight grafting, a technique that selectively grafts weights from a finetuned model onto a pretrained model. Using this technique, we show two separate pathways for retrieving finetuned relation information: 1) "enriching" the residual stream with relation information while processing the tokens that correspond to an entity (e.g., "Zendaya" in "Zendaya co-starred with John David Washington") and 2) "recalling" this information at the final token position before generating a target fact. In some cases, models need information from both of these pathways to correctly generate finetuned facts while, in other cases, either the "enrichment" or "recall" pathway alone is sufficient. We localize the "recall'' pathway to model components---finding that "recall" occurs via both task-specific attention mechanisms and an entity-specific extraction step in the feedforward networks of the final layers before the target prediction. By targeting model components and parameters, as opposed to just activations, we are able to understand the mechanisms by which finetuned knowledge is retrieved during generation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces dynamic weight grafting to analyze how finetuned language models retrieve new factual knowledge, identifying two distinct pathways: entity enrichment during token processing and just-in-time recall before prediction. It resides in the Knowledge Localization and Mechanistic Analysis leaf, which contains only two papers total. This is a notably sparse research direction within the broader taxonomy of fifty papers, suggesting that mechanistic analysis of finetuned knowledge pathways remains an underexplored area compared to more crowded branches like Retrieval-Augmented Generation or Domain Adaptation.

The taxonomy reveals that most neighboring work focuses on external knowledge integration (Retrieval-Augmented Generation with twelve papers across four leaves) or parametric editing (six papers across three leaves), rather than mechanistic analysis of internal pathways. The closest sibling paper examines knowledge regions and weight distributions, but the broader field emphasizes application-level methods over interpretability. The scope note for this leaf explicitly excludes application-focused methods, positioning this work as foundational analysis rather than performance optimization. This structural isolation suggests the paper addresses a gap between knowledge editing techniques and their underlying computational mechanisms.

Among thirty candidates examined through semantic search, none clearly refuted any of the three core contributions. The dynamic weight grafting method was compared against ten candidates with no overlapping prior work identified. Similarly, the two-pathway framework and component localization findings each faced ten candidates without substantive refutation. This absence of refutable prior work within the limited search scope suggests these specific mechanistic insights—particularly the grafting technique and dual-pathway characterization—may represent novel analytical perspectives. However, the search examined top-K semantic matches rather than exhaustive coverage, leaving open the possibility of relevant work outside this candidate set.

Based on the limited literature search and sparse taxonomy position, the work appears to occupy relatively unexplored analytical territory within knowledge retrieval mechanisms. The mechanistic focus distinguishes it from the application-oriented majority of the field, though the thirty-candidate scope cannot definitively rule out related interpretability studies in adjacent communities. The dual-pathway framework and grafting methodology seem to offer fresh perspectives on how finetuned knowledge organizes within model parameters, contingent on the search boundaries examined.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: knowledge retrieval mechanisms in finetuned language models. The field encompasses diverse strategies for managing and accessing knowledge in language models, organized into several major branches. Retrieval-Augmented Generation Architectures explore external memory integration (e.g., Replug[1], In-Context Retrieval[3]), while Parametric Knowledge Editing and Updating focus on modifying stored facts without full retraining (Editing Factual Knowledge[5], Knowledge Editing Survey[8]). Knowledge Integration During Pretraining and Finetuning examines how models absorb information during training phases, and Domain Adaptation and Task Specialization address tailoring models to specific contexts (ChatDoctor[23], Domain Shift Tuning[26]). Knowledge Graph Integration (ChatKBQA[9], Plan-on-Graph[38]) and Knowledge Distillation for Small Models (Reasoning Distillation[22]) represent complementary approaches, while Comparative Evaluation Studies (Fine-Tuning vs Retrieval[12], Fine-Tuning vs RAG[29]) and General Surveys (Knowledge Enhanced Survey[6], Adaptation Survey[32]) provide cross-cutting perspectives on these methods. A central tension runs through the literature between parametric approaches that embed knowledge directly in model weights versus non-parametric methods that retrieve information at inference time, with many recent works exploring hybrid strategies. Within Knowledge Localization and Mechanistic Analysis, researchers investigate where and how knowledge resides in neural architectures. Multiple Streams Knowledge[0] sits squarely in this branch, examining the internal organization of knowledge representations. This work shares thematic connections with Knowledge Region Weight[11], which similarly probes the spatial distribution of factual information within model parameters. Compared to broader mechanistic studies like Factual Knowledge Boundary[41] that map knowledge capacity limits, Multiple Streams Knowledge[0] appears to emphasize the multiplicity and interaction of distinct knowledge pathways, offering a more granular view of how finetuned models organize retrieved information internally rather than simply locating where facts are stored.

Claimed Contributions

Dynamic weight grafting method

The authors introduce dynamic weight grafting, a method that swaps weights from a finetuned model into a pretrained model at specific layers, components, and token positions during generation. This allows causal mediation analysis of specific mechanisms without disrupting the rest of the computation, unlike activation patching which overwrites upstream information.

10 retrieved papers
Identification of two pathways for finetuned knowledge retrieval

Using dynamic weight grafting, the authors demonstrate that models retrieve finetuned relation information through two distinct pathways: an enrichment pathway that adds relation information at entity token positions, and a recall pathway that extracts information at the final token position. Either pathway can be sufficient in some cases, and both together nearly recover full finetuning performance.

10 retrieved papers
Localization of recall pathway to specific model components

The authors localize the recall pathway to specific Transformer components, showing that it relies on task-specific attention mechanisms at the first entity and final token positions, as well as relation-specific extraction in the output projection matrix and feedforward networks in the final layers before prediction.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Dynamic weight grafting method

The authors introduce dynamic weight grafting, a method that swaps weights from a finetuned model into a pretrained model at specific layers, components, and token positions during generation. This allows causal mediation analysis of specific mechanisms without disrupting the rest of the computation, unlike activation patching which overwrites upstream information.

Contribution

Identification of two pathways for finetuned knowledge retrieval

Using dynamic weight grafting, the authors demonstrate that models retrieve finetuned relation information through two distinct pathways: an enrichment pathway that adds relation information at entity token positions, and a recall pathway that extracts information at the final token position. Either pathway can be sufficient in some cases, and both together nearly recover full finetuning performance.

Contribution

Localization of recall pathway to specific model components

The authors localize the recall pathway to specific Transformer components, showing that it relies on task-specific attention mechanisms at the first entity and final token positions, as well as relation-specific extraction in the output projection matrix and feedforward networks in the final layers before prediction.

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