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Mixing Mechanisms: How Language Models Retrieve Bound Entities In-Context

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 8.0Download Report PDF
InterpretabilityEntity BindingCausal AbstractionMechanistic Interpretability
Abstract:

A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval. For example, an LM might represent Ann loves pie by binding Ann to pie, allowing it to later retrieve Ann when asked Who loves pie? Prior research on short lists of bound entities found strong evidence that LMs implement such retrieval via a positional mechanism, where Ann is retrieved based on its position in context. In this work, we find that this mechanism generalizes poorly to more complex settings; as the number of bound entities in context increases, the positional mechanism becomes noisy and unreliable in middle positions. To compensate for this, we find that LMs supplement the positional mechanism with a lexical mechanism (retrieving Ann using its bound counterpart pie) and a reflexive mechanism (retrieving Ann through a direct pointer). Through extensive experiments on nine models and ten binding tasks, we uncover a consistent pattern in how LMs mix these mechanisms to drive model behavior. We leverage these insights to develop a causal model combining all three mechanisms that estimates next token distributions with 95% agreement. Finally, we show that our model generalizes to substantially longer inputs of open-ended text interleaved with entity groups, further demonstrating the robustness of our findings in more natural settings. Overall, our study establishes a more complete picture of how LMs bind and retrieve entities in-context.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper investigates how language models retrieve bound entities in context, identifying three distinct mechanisms: positional (retrieving based on context position), lexical (using bound counterparts), and reflexive (direct pointers). It resides in the 'In-Context Entity Binding and Tracking' leaf, which contains only two papers total, indicating a relatively sparse research direction. This leaf focuses specifically on internal binding mechanisms during inference, distinguishing it from the broader 'Entity Knowledge Representation in Pretrained Models' branch (five papers) that examines parametric entity storage rather than dynamic in-context tracking.

The taxonomy reveals that entity binding research divides into several neighboring areas: entity linking systems (four leaves, ~16 papers) focus on mapping mentions to external knowledge bases, while retrieval-augmented approaches (four leaves, ~13 papers) integrate external knowledge sources. The paper's leaf explicitly excludes these external-knowledge methods, positioning the work within a narrower investigation of purely internal mechanisms. The 'In-Context Learning and Entity Reasoning' branch (two papers) addresses related phenomena but emphasizes task performance over mechanistic analysis, whereas this work dissects the underlying retrieval strategies.

Among 20 candidates examined across three contributions, no clearly refuting prior work was identified. The 'Discovery of three mechanisms' contribution examined one candidate with no refutation; the 'Causal model combining mechanisms' examined nine candidates, none refuting; and the 'Counterfactual intervention methodology' examined ten candidates, also without refutation. This suggests that within the limited search scope, the specific combination of positional, lexical, and reflexive mechanisms—and their integration into a unified causal model—appears not to have direct precedent in the examined literature.

The analysis reflects a constrained literature search (top-20 semantic matches), not an exhaustive survey. The sparse population of the target leaf (two papers) and absence of refuting candidates among examined works suggest the mechanistic decomposition may be novel within this scope. However, the limited search scale means potentially relevant work in adjacent areas—such as attention mechanism studies or broader interpretability research—may not have been captured, leaving open questions about the contribution's novelty relative to the full field.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: entity binding and retrieval in language models. This field examines how models associate linguistic mentions with structured entity representations and retrieve relevant entity information during inference. The taxonomy organizes research into several major branches: Entity Binding Mechanisms and Representations explores how models internally encode and track entities, including in-context binding strategies; Entity Linking Systems focuses on mapping text spans to knowledge base entries, often through dense retrieval methods like Dense Entity Retrieval[9] or neural architectures surveyed in Neural Entity Linking Survey[8]; Named Entity Recognition addresses the identification and classification of entity mentions, with approaches ranging from traditional methods to large language model adaptations such as GPT NER[1] and UniversalNER[5]; Retrieval-Augmented Entity Processing integrates external knowledge sources to enhance entity understanding, exemplified by Retrieve Anything[2] and Retrieval Augmented Pretraining[12]; In-Context Learning and Entity Reasoning investigates how models leverage contextual examples to perform entity-related tasks, as seen in In Context Learning[16]; and Specialized Entity Applications targets domain-specific challenges in areas like biomedicine, with works such as Greek Clinical Entity[13] and BioLinkerAI[36]. Across these branches, a central tension emerges between end-to-end neural approaches that learn entity representations implicitly and modular systems that explicitly retrieve and bind entities to external knowledge. Many studies explore how retrieval mechanisms can be tightly integrated with language model architectures, balancing computational efficiency with representational richness. Mixing Mechanisms[0] sits within the Entity Binding Mechanisms branch, specifically addressing in-context entity binding and tracking—a line of work concerned with how models maintain entity references across discourse without explicit symbolic grounding. This emphasis aligns closely with Binding Representational Analysis[4], which examines the internal representational structures that support entity tracking, and contrasts with retrieval-heavy approaches like Dense Entity Retrieval[9] that rely on external knowledge bases. While Grounding Visual Entity[3] extends binding to multimodal settings, Mixing Mechanisms[0] focuses on the linguistic mechanisms that enable coherent entity reference resolution within textual contexts, highlighting ongoing questions about the sufficiency of implicit versus explicit entity representations.

Claimed Contributions

Discovery of three mechanisms for entity retrieval

The authors identify that language models use not just a positional mechanism but also lexical and reflexive mechanisms to retrieve bound entities in context. The lexical mechanism retrieves entities using their bound counterparts, while the reflexive mechanism uses direct self-referential pointers.

1 retrieved paper
Causal model combining three mechanisms

The authors develop a formal causal model that combines positional, lexical, and reflexive mechanisms as a position-weighted mixture to predict next token distributions. This model achieves 95% agreement with actual language model behavior.

9 retrieved papers
Counterfactual intervention methodology

The authors design a novel counterfactual dataset construction method where interchange interventions on paired inputs cause each of the three proposed mechanisms to predict different entities, enabling systematic separation and validation of the mechanisms.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Discovery of three mechanisms for entity retrieval

The authors identify that language models use not just a positional mechanism but also lexical and reflexive mechanisms to retrieve bound entities in context. The lexical mechanism retrieves entities using their bound counterparts, while the reflexive mechanism uses direct self-referential pointers.

Contribution

Causal model combining three mechanisms

The authors develop a formal causal model that combines positional, lexical, and reflexive mechanisms as a position-weighted mixture to predict next token distributions. This model achieves 95% agreement with actual language model behavior.

Contribution

Counterfactual intervention methodology

The authors design a novel counterfactual dataset construction method where interchange interventions on paired inputs cause each of the three proposed mechanisms to predict different entities, enabling systematic separation and validation of the mechanisms.

Table of Contents