Paper Citation Record · LEDGER

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

As of 7 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 3 inbound Pith citation observations for arXiv:2506.10378.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.10378 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:39:09.218815Z

measured 82 of 82 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:58:13.488639Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T13:03:26.399621Z

Reference resolution

79 of 79 outbound references displayed

verified exact3
verified fuzzy41
unresolved35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b5eb337-be98-45fc-8e67-20706459c945 · outbound

This paper cites GPT-4 Technical Report.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning GPT-4 Technical Report

Reference 1

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Observation 2f156c47-68dc-435c-bd82-16a2dde1290e · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2

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Observation bc2b8b17-0cce-49d4-94c1-d35c8a2e4659 · outbound

This paper cites An integrated theory of the mind.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning An integrated theory of the mind

Reference 3

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Observation 65b8ab84-64af-4b6d-8b25-5c2d766e2cc4 · outbound

This paper cites Invariant Risk Minimization.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Invariant Risk Minimization

Reference 4

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Observation 323f5800-c654-42bf-bf47-a1cd174d365e · outbound

This paper cites A Theory for Emergence of Complex Skills in Language Models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning A Theory for Emergence of Complex Skills in Language Models

Reference 5

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This paper cites Act: A simple theory of complex cognition.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Act: A simple theory of complex cognition

Reference 6

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Observation ca7e7632-217f-4820-802b-4c8cee2d224f · outbound

This paper cites Claude 3.5 sonnet.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Claude 3.5 sonnet

Reference 7

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Observation 97128679-f3c6-4143-a62b-96ebb4500eac · outbound

This paper cites Invariant risk minimization games.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Invariant risk minimization games

Reference 8

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Observation 2fd55be9-42e3-4bcf-b189-a167d0608724 · outbound

This paper cites Sample complexity of interventional causal representation learning.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Sample complexity of interventional causal representation learning

Reference 9

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Observation fd4b8802-aceb-47e8-92be-dcf3980c068b · outbound

This paper cites An Empirical Study of Scaling Laws for Transfer.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning An Empirical Study of Scaling Laws for Transfer

Reference 10

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Observation 40d4409f-9bed-4740-8485-c6a929d68c88 · outbound

This paper cites Sparks of artificial general intelligence: Early experiments with gpt-4, 2023.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Sparks of artificial general intelligence: Early experiments with gpt-4, 2023

Reference 11

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Observation c3209fdd-7f3b-4df6-92c4-d79458060d6e · outbound

This paper cites Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex

Reference 12

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Observation 28e5998b-8552-4b06-98a8-47f9b8422bde · outbound

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Unresolved cited work

Reference 13

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Observation de90e3fb-28f1-4a21-98d1-5b775466815d · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 14

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Observation d1bc4a66-c46d-4d88-9e53-8b750f4aa34b · outbound

This paper cites Language models are few-shot learners.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Language models are few-shot learners

Reference 15

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Observation 7649992c-1cff-4468-8717-a0b1067f98db · outbound

This paper cites Doubly robust estimation in missing data and causal inference models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Doubly robust estimation in missing data and causal inference models

Reference 16

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Observation 857afd46-23ef-43f6-9372-24649b25d46b · outbound

This paper cites Learning linear causal representations from interventions under general nonlinear mixing.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Learning linear causal representations from interventions under general nonlinear mixing

Reference 17

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Observation 3bb65a48-2ff1-4618-8223-bc7b379979e2 · outbound

This paper cites Rethink reporting of evaluation results in ai.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Rethink reporting of evaluation results in ai

Reference 18

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Observation 84b957a0-8a8e-41cb-bb57-2f13895a89bc · outbound

This paper cites Human cognitive abilities: A survey of factor-analytic studies.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Human cognitive abilities: A survey of factor-analytic studies

Reference 19

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Observation bf2150c2-40a9-474f-b6d2-6d27f54b0205 · outbound

This paper cites Structured matrix completion with applications to genomic data integration.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Structured matrix completion with applications to genomic data integration

Reference 20

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Observation 6620d5e0-d391-495b-981b-11bed8aa9c9d · outbound

This paper cites Scaling instruction-finetuned language models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Scaling instruction-finetuned language models

Reference 21

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Observation d8a950a4-6679-4d8a-92ba-1c55ccafa577 · outbound

This paper cites Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models

Reference 22

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning The rising costs of training frontier ai models, 2024

Reference 23

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This paper cites Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Fundamental Safety-Capability Trade-offs in Fine-tuning Large Language Models

Reference 24

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Dorner, and Moritz Hardt

Reference 25

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Observation 807a6921-792e-43e7-875f-c09fd906eb46 · outbound

This paper cites Identifiability, separability, and uniqueness of linear ica models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Identifiability, separability, and uniqueness of linear ica models

Reference 26

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Observation 2c12e803-a29a-4dde-af3c-70755a429496 · outbound

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Principal stratification in causal inference

Reference 27

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Observation c20bbc66-5403-4642-90ff-540b56ac89df · outbound

This paper cites Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Reference 28

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning The Llama 3 Herd of Models

Reference 29

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This paper cites A Closer Look at the Limitations of Instruction Tuning.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning A Closer Look at the Limitations of Instruction Tuning

Reference 30

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Time Travel in LLMs: Tracing Data Contamination in Large Language Models

Reference 31

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning The False Promise of Imitating Proprietary LLMs

Reference 32

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 33

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Measuring Massive Multitask Language Understanding

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Training Compute-Optimal Large Language Models

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning A sober look at progress in language model reasoning: Pitfalls and paths to reproducibility

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Does RLHF Scale? Exploring the Impacts From Data, Model, and Method

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning a rinen, Jarmo Hurri, Patrik O Hoyer, Aapo Hyv \

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Causal discovery from heterogeneous/nonstationary data

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Learning linear causal representations from general environments: Identifiability and intrinsic ambiguity

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Mistral 7B

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning The construct of creativity: Structural model for self-reported creativity ratings

Reference 42

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Scaling Laws for Neural Language Models

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning The architecture of cognitive control in the human prefrontal cortex

Reference 44

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Solving quantitative reasoning problems with language models

Reference 45

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Holistic evaluation of language models

Reference 46

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This paper cites Not-just-scaling laws: Towards a better understanding of the downstream impact of language model design decisions.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Not-just-scaling laws: Towards a better understanding of the downstream impact of language model design decisions

Reference 47

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning a tsch, Bernhard Sch \

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Unresolved cited work

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Unresolved cited work

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This paper cites Training language models to follow instructions with human feedback.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Training language models to follow instructions with human feedback

Reference 51

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Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Causal diagrams for empirical research

Reference 52

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This paper cites On the identifiability of bayesian factor analytic models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning On the identifiability of bayesian factor analytic models

Reference 53

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Observation 083c9f47-7b1d-403f-86ab-81f1d9818fe8 · outbound

This paper cites Sloth: scaling laws for llm skills to predict multi-benchmark performance across families.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Sloth: scaling laws for llm skills to predict multi-benchmark performance across families

Reference 54

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:39:09.635730Z

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Observation 1db11138-c297-4adc-a336-ddc03eb2eccf · outbound

This paper cites Evolm: In search of lost language model training dynamics, 2025.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Evolm: In search of lost language model training dynamics, 2025

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:14.927579Z

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Observation ff26cae3-b612-4087-9aec-7b5096df579f · outbound

This paper cites Safetywashing: Do ai safety benchmarks actually measure safety progress? Advances in Neural Information Processing Systems , 37:68559--68594, 2025.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Safetywashing: Do ai safety benchmarks actually measure safety progress? Advances in Neural Information Processing Systems , 37:68559--68594, 2025

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:14.638098Z

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Observation a1ca435d-8a21-45ea-ab4b-468a3923f8da · outbound

This paper cites A simpler approach to matrix completion.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning A simpler approach to matrix completion

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:14.398228Z

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Observation 020ec77d-927a-44b5-bd45-76219c69c749 · outbound

This paper cites Maddison, and Tatsunori Hashimoto.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Maddison, and Tatsunori Hashimoto

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:14.153380Z

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Observation 2ce67ae2-8170-467a-9f1a-493f5c3cf32a · outbound

This paper cites The architecture of complexity.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning The architecture of complexity

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:13.814004Z

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Observation cbcacfde-0564-4ca1-b2d9-e9efccb98d3f · outbound

This paper cites Toward causal representation learning.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Toward causal representation learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:13.607870Z

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Observation d5ef6c67-2fbf-4fc2-b28c-4f51f55ad966 · outbound

This paper cites Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems , 36:55565--55581, 2023.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems , 36:55565--55581, 2023

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:13.298759Z

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Observation f1ba9a89-d3de-498a-bbe6-5fc7d8e95d76 · outbound

This paper cites Linear causal disentanglement via interventions.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Linear causal disentanglement via interventions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:13.047722Z

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Observation 438ce11a-8062-4d6d-be7d-3c818cee1042 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 63

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Observation 4892c736-dcf1-442d-9bff-2c472a3fbf90 · outbound

This paper cites Matching methods for causal inference: A review and a look forward.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Matching methods for causal inference: A review and a look forward

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:12.811869Z

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Observation a36f9775-fad5-4d4c-8e26-65fa47f22c1d · outbound

This paper cites Multitask prompted training enables zero-shot task generalization.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Multitask prompted training enables zero-shot task generalization

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:12.582736Z

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Observation 15e875bb-d667-44a3-bec2-c9254e3f50d1 · outbound

This paper cites How to grow a mind: Statistics, structure, and abstraction.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning How to grow a mind: Statistics, structure, and abstraction

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:12.236470Z

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Observation 66a6f830-b240-4aa7-9e91-150b1b1525c0 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Gemma 2: Improving Open Language Models at a Practical Size

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:07.609846Z

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Observation 452ada49-09c1-4ce3-b06b-6cce064e17b0 · outbound

This paper cites Reliable and Efficient Amortized Model-based Evaluation.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Reliable and Efficient Amortized Model-based Evaluation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:07.776135Z

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Observation 41453dd2-3a78-490f-9438-c4781d8369bc · outbound

This paper cites u gelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Keki \'c , Elias Bareinboim, David Blei, and Bernhard Sch \.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning u gelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Keki \'c , Elias Bareinboim, David Blei, and Bernhard Sch \

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:11.977760Z

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Observation ba62fe5e-c8d8-42d4-a336-c7fd2d071bec · outbound

This paper cites an unresolved cited work.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:39:11.736778Z

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Observation 5f73c6e8-86c4-4284-861f-1fc363db16fd · outbound

This paper cites Emergent Abilities of Large Language Models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Emergent Abilities of Large Language Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:08.295838Z

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Observation 2ca6bb1b-f90a-44ac-a0a6-4acc444e8649 · outbound

This paper cites Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models

Reference 72

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Observation 4f700dc1-78d5-40f7-88eb-c72c490540eb · outbound

This paper cites Qwen2.5 Technical Report.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Qwen2.5 Technical Report

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:08.522540Z

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Observation 3b99727a-a679-4969-a46c-9ba35f4e0e31 · outbound

This paper cites Unveiling the impact of coding data instruction fine-tuning on large language models reasoning.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Unveiling the impact of coding data instruction fine-tuning on large language models reasoning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:11.451018Z

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Observation a238c3eb-9b06-4095-973b-b8ec36056841 · outbound

This paper cites Identifiability guarantees for causal disentanglement from soft interventions.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Identifiability guarantees for causal disentanglement from soft interventions

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:11.181960Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8d350f2-3699-4bd4-bfc3-42f09638ad17 · outbound

This paper cites When scaling meets llm finetuning: The effect of data, model and finetuning method.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning When scaling meets llm finetuning: The effect of data, model and finetuning method

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:10.917752Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 77f34f5e-32a2-486f-a497-2323514f9ea2 · outbound

This paper cites Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:08.980643Z

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Observation 1eb0872c-5acd-485a-87f7-9bb5fa1505fd · outbound

This paper cites Investigating the Catastrophic Forgetting in Multimodal Large Language Models.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Investigating the Catastrophic Forgetting in Multimodal Large Language Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T04:39:09.135335Z

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Observation d0606cbd-59fc-4474-a7f6-d801b108c258 · outbound

This paper cites Causal representation learning from multiple distributions: A general setting.

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning Causal representation learning from multiple distributions: A general setting

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:10.609813Z

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Pith citing papers

Observation b0f1d4cb-6768-449a-b1fc-34542ff1c298 · inbound

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities cites this paper.

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T22:58:13.488639Z

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Observation 65d6faf7-e7b7-4b4d-abb7-fedcd5281eae · inbound

SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling cites this paper.

SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:03:26.401147Z

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Observation b300a453-5410-4831-a99e-864932b4d533 · inbound

Domain-Aware Scaling Laws Uncover Data Synergy cites this paper.

Domain-Aware Scaling Laws Uncover Data Synergy Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T07:24:27.255815Z

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Unavailable: canonical work link unavailable.