Paper Citation Record · LEDGER

Large Language Models as Computable Approximations to Solomonoff Induction

As of 15 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 2 inbound Pith citation observations for arXiv:2505.15784.

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

pith.paper-citation-record.v1
2505.15784 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:16:58.216555Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:53:42.488397Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T12:25:35.666043Z

Reference resolution

77 of 77 outbound references displayed

verified exact6
verified fuzzy16
unresolved55
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 063e8730-11b7-4bb7-b2e3-a4cc6095543c · outbound

This paper cites SMS Spam Collection.

Large Language Models as Computable Approximations to Solomonoff Induction SMS Spam Collection

Reference 2

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Source-reported events for the cited work

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Observation c2303fa5-4113-4750-bb0c-ae92a9723f49 · outbound

This paper cites Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints.

Large Language Models as Computable Approximations to Solomonoff Induction Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:17:00.006070Z

Source-reported events for the cited work

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Observation 96b2a74f-4bdf-48f2-905e-8dbdb3f40190 · outbound

This paper cites Thread: A logic-based data organization paradigm for how-to question answering with retrieval augmented generation.

Large Language Models as Computable Approximations to Solomonoff Induction Thread: A logic-based data organization paradigm for how-to question answering with retrieval augmented generation

Reference 4

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Observation f965ca57-f193-44bc-a1a7-f8c78f21c757 · outbound

This paper cites Ultraif: Advancing instruction following from the wild.

Large Language Models as Computable Approximations to Solomonoff Induction Ultraif: Advancing instruction following from the wild

Reference 5

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Observation e1700061-75a5-4274-8994-3747b23cc3a0 · outbound

This paper cites Context-DPO: Aligning Language Models for Context-Faithfulness.

Large Language Models as Computable Approximations to Solomonoff Induction Context-DPO: Aligning Language Models for Context-Faithfulness

Reference 6

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Observation 7e10401d-11b9-4c8f-8d4d-0d74027c9f52 · outbound

This paper cites Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts.

Large Language Models as Computable Approximations to Solomonoff Induction Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:51.894014Z

Source-reported events for the cited work

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Observation 9f7a9e99-41f0-4520-bcdf-493f41df5f6b · outbound

This paper cites Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness.

Large Language Models as Computable Approximations to Solomonoff Induction Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:51.960526Z

Source-reported events for the cited work

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Observation 29337656-e44a-4826-bd43-932a878560c8 · outbound

This paper cites Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:52.066040Z

Source-reported events for the cited work

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Observation b565f13a-0316-46ff-9adc-beff92d250d3 · outbound

This paper cites The description length of deep learning models.

Large Language Models as Computable Approximations to Solomonoff Induction The description length of deep learning models

Reference 10

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Observation de992326-10ad-430e-8841-94dc6d12912b · outbound

This paper cites A machine-independent theory of the complexity of recursive functions.

Large Language Models as Computable Approximations to Solomonoff Induction A machine-independent theory of the complexity of recursive functions

Reference 11

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Source-reported events for the cited work

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Observation bfd1848e-beb2-43ef-8641-1cc6e1686e2a · outbound

This paper cites On the size of machines.

Large Language Models as Computable Approximations to Solomonoff Induction On the size of machines

Reference 12

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Observation de458f8b-4516-4e22-a00e-8e44a4df9466 · outbound

This paper cites Language Models are Few-Shot Learners.

Large Language Models as Computable Approximations to Solomonoff Induction Language Models are Few-Shot Learners

Reference 13

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Source-reported events for the cited work

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Observation a31ef084-821d-4167-9544-29a3849dd989 · outbound

This paper cites On the length of programs for computing finite binary sequences.

Large Language Models as Computable Approximations to Solomonoff Induction On the length of programs for computing finite binary sequences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:02.512565Z

Source-reported events for the cited work

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Observation 2f86e3ea-de33-4e34-bc26-38eed0021d0f · outbound

This paper cites Algorithmic information theory.

Large Language Models as Computable Approximations to Solomonoff Induction Algorithmic information theory

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:02.344520Z

Source-reported events for the cited work

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Observation 10afb4f7-b91e-43cd-b3bd-42aae8f80c23 · outbound

This paper cites Kolmogorov's contributions to information theory and algorithmic complexity.

Large Language Models as Computable Approximations to Solomonoff Induction Kolmogorov's contributions to information theory and algorithmic complexity

Reference 16

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Source-reported events for the cited work

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Observation 1a7301b0-73a8-4dc7-8ecd-26b186dd2ff0 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Large Language Models as Computable Approximations to Solomonoff Induction DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 17

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Observation 3805c81f-5a5e-4f6c-8704-b754bd75d894 · outbound

This paper cites DeepSeek-V3 Technical Report.

Large Language Models as Computable Approximations to Solomonoff Induction DeepSeek-V3 Technical Report

Reference 18

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Source-reported events for the cited work

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Observation 9b17efdb-994a-4f66-acc9-99adddee4590 · outbound

This paper cites Language Modeling Is Compression.

Large Language Models as Computable Approximations to Solomonoff Induction Language Modeling Is Compression

Reference 20

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Source-reported events for the cited work

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Observation 3a9d30c0-8e9c-4076-b85d-7e259281f449 · outbound

This paper cites LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating.

Large Language Models as Computable Approximations to Solomonoff Induction LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:53.305435Z

Source-reported events for the cited work

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Observation 39b5fd5a-8065-435b-9419-6ca2a0953f54 · outbound

This paper cites A Survey on In-context Learning.

Large Language Models as Computable Approximations to Solomonoff Induction A Survey on In-context Learning

Reference 22

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Observation da6596a0-67e9-4cf5-bb0c-59202bf5a026 · outbound

This paper cites Algorithmic randomness and complexity.

Large Language Models as Computable Approximations to Solomonoff Induction Algorithmic randomness and complexity

Reference 23

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Observation ef2d8872-f4f3-43c6-afce-f5f230f2d151 · outbound

This paper cites Universal artificial intelligence: Practical agents and fundamental challenges.

Large Language Models as Computable Approximations to Solomonoff Induction Universal artificial intelligence: Practical agents and fundamental challenges

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:01.886478Z

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Observation f4d3b6f9-ea66-41ec-84a0-5f8bad2452f3 · outbound

This paper cites Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking.

Large Language Models as Computable Approximations to Solomonoff Induction Innate Reasoning is Not Enough: In-Context Learning Enhances Reasoning Large Language Models with Less Overthinking

Reference 25

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Observation 81aaf466-036e-4103-8824-ae3b8996ae75 · outbound

This paper cites Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:53.836079Z

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Observation 11072295-2885-46d5-98ab-1d28c599932b · outbound

This paper cites The Llama 3 Herd of Models.

Large Language Models as Computable Approximations to Solomonoff Induction The Llama 3 Herd of Models

Reference 27

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Observation 81c8af4e-bd3d-4810-85cf-e5d9d5c1edc5 · outbound

This paper cites Learning Universal Predictors.

Large Language Models as Computable Approximations to Solomonoff Induction Learning Universal Predictors

Reference 28

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Observation b3bd4cd0-35fc-449d-af00-30a7c793c9d9 · outbound

This paper cites Skywork open reasoner series.

Large Language Models as Computable Approximations to Solomonoff Induction Skywork open reasoner series

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:01.744607Z

Source-reported events for the cited work

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Observation 00a5545e-0df4-4ca0-a72b-6cd2a7724462 · outbound

This paper cites Execoder: Empowering large language models with executability representation for code translation.

Large Language Models as Computable Approximations to Solomonoff Induction Execoder: Empowering large language models with executability representation for code translation

Reference 30

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Observation 7021705c-4ef4-42d1-b236-c125abfa1979 · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

Large Language Models as Computable Approximations to Solomonoff Induction MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 31

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Observation 34251197-bd0b-4a06-b84f-34d70b086294 · outbound

This paper cites Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents.

Large Language Models as Computable Approximations to Solomonoff Induction Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Reference 32

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Observation 9ee812aa-c167-4ff8-84d8-9d6af7c3d67f · outbound

This paper cites Universal artificial intelligence: Sequential decisions based on algorithmic probability.

Large Language Models as Computable Approximations to Solomonoff Induction Universal artificial intelligence: Sequential decisions based on algorithmic probability

Reference 33

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Observation 4880b93c-558a-4201-a765-86a8924081b3 · outbound

This paper cites Large language models are zero-shot reasoners.

Large Language Models as Computable Approximations to Solomonoff Induction Large language models are zero-shot reasoners

Reference 34

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Observation 30eb98ae-58b9-420e-9a6c-25879fcbfdcc · outbound

This paper cites Three approaches to the quantitative definition ofinformation’.

Large Language Models as Computable Approximations to Solomonoff Induction Three approaches to the quantitative definition ofinformation’

Reference 35

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Observation 0347d50a-547c-49e4-b624-e663eee1dca0 · outbound

This paper cites An introduction to Kolmogorov complexity and its applications, volume 3.

Large Language Models as Computable Approximations to Solomonoff Induction An introduction to Kolmogorov complexity and its applications, volume 3

Reference 36

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Observation 0fb635be-79ec-4198-adf7-c64a290b74e0 · outbound

This paper cites LANS: A Layout-Aware Neural Solver for Plane Geometry Problem.

Large Language Models as Computable Approximations to Solomonoff Induction LANS: A Layout-Aware Neural Solver for Plane Geometry Problem

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:16:59.354860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a2d1bc4-7646-444a-aad8-f7820aa139d3 · outbound

This paper cites CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models.

Large Language Models as Computable Approximations to Solomonoff Induction CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models

Reference 38

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Observation b9d9f04a-19ea-4bca-8a46-886a32ab02dd · outbound

This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 40

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This paper cites AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents.

Large Language Models as Computable Approximations to Solomonoff Induction AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents

Reference 41

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Observation 1c9a0532-3550-4e71-ac41-4885455bbf60 · outbound

This paper cites Transformer-based Image Compression.

Large Language Models as Computable Approximations to Solomonoff Induction Transformer-based Image Compression

Reference 42

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Observation 42bf7bef-ce1e-4a87-b93f-7a4aa653eac6 · outbound

This paper cites From Understanding to Utilization: A Survey on Explainability for Large Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction From Understanding to Utilization: A Survey on Explainability for Large Language Models

Reference 43

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Observation ce65875d-9c8b-4131-be96-ce13e587cbcc · outbound

This paper cites SLANG: New Concept Comprehension of Large Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction SLANG: New Concept Comprehension of Large Language Models

Reference 44

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Observation 2c302736-5bdd-4d74-b443-f6b53293b9b8 · outbound

This paper cites "Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak.

Large Language Models as Computable Approximations to Solomonoff Induction "Not Aligned" is Not "Malicious": Being Careful about Hallucinations of Large Language Models' Jailbreak

Reference 45

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Observation 73e0e226-e525-44f9-ba6f-ce2ed602f289 · outbound

This paper cites HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router.

Large Language Models as Computable Approximations to Solomonoff Induction HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router

Reference 46

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Observation 1cfa35e8-1059-4f10-be37-eeac3ae781cc · outbound

This paper cites a1: Steep Test-time Scaling Law via Environment Augmented Generation.

Large Language Models as Computable Approximations to Solomonoff Induction a1: Steep Test-time Scaling Law via Environment Augmented Generation

Reference 47

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Observation 0d744c41-e6d3-4e06-9767-a979718b2ee8 · outbound

This paper cites Locating and Editing Factual Associations in GPT.

Large Language Models as Computable Approximations to Solomonoff Induction Locating and Editing Factual Associations in GPT

Reference 48

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Observation 4fb1d51c-f772-44c4-a2cf-d3ebad3455ff · outbound

This paper cites Transformerlens.

Large Language Models as Computable Approximations to Solomonoff Induction Transformerlens

Reference 49

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Observation 312c8e3a-6d7b-426e-b813-ba0421a1bc6c · outbound

This paper cites GPT-4 Technical Report.

Large Language Models as Computable Approximations to Solomonoff Induction GPT-4 Technical Report

Reference 50

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Large Language Models as Computable Approximations to Solomonoff Induction Introducing openai o1-preview

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:01.301367Z

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Observation 9cc79de1-34e4-403b-8ef2-b44a6b44a7b7 · outbound

This paper cites Instruction Tuning with GPT-4.

Large Language Models as Computable Approximations to Solomonoff Induction Instruction Tuning with GPT-4

Reference 52

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This paper cites A practical review of mechanistic interpretability for transformer-based language models, 2025.

Large Language Models as Computable Approximations to Solomonoff Induction A practical review of mechanistic interpretability for transformer-based language models, 2025

Reference 54

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Observation f492f16e-e45c-4ce0-982a-86eac8499a15 · outbound

This paper cites CARER : Contextualized affect representations for emotion recognition.

Large Language Models as Computable Approximations to Solomonoff Induction CARER : Contextualized affect representations for emotion recognition

Reference 55

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Observation 89fc6a48-9d8d-48a9-b0af-02d9e19479ff · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 57

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Observation a188d018-802a-479f-a1b1-0ff7f46b14c2 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Large Language Models as Computable Approximations to Solomonoff Induction Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:56.587332Z

Source-reported events for the cited work

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Observation 210ed9f3-4430-4385-9d6a-6b6258149046 · outbound

This paper cites A preliminary report on a general theory of inductive inference.

Large Language Models as Computable Approximations to Solomonoff Induction A preliminary report on a general theory of inductive inference

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:01.104483Z

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Observation 814e54eb-b853-4673-9f1f-e33ffba982b7 · outbound

This paper cites A formal theory of inductive inference.

Large Language Models as Computable Approximations to Solomonoff Induction A formal theory of inductive inference

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:00.935166Z

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Observation 62648050-39f1-4b38-9eb1-3cb905f02c25 · outbound

This paper cites A formal theory of inductive inference.

Large Language Models as Computable Approximations to Solomonoff Induction A formal theory of inductive inference

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:00.785520Z

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Observation b9f17ff1-1d57-42ea-9394-d713f93832ba · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

Large Language Models as Computable Approximations to Solomonoff Induction Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 62

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Observation cfb34b46-7e04-432c-b965-e8246a76d14e · outbound

This paper cites Qwq: Reflect deeply on the boundaries of the unknown, November 2024.

Large Language Models as Computable Approximations to Solomonoff Induction Qwq: Reflect deeply on the boundaries of the unknown, November 2024

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.012634Z

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This paper cites On computable numbers, with an application to the entscheidungsproblem.

Large Language Models as Computable Approximations to Solomonoff Induction On computable numbers, with an application to the entscheidungsproblem

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:00.596753Z

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This paper cites Solomonoff induction: A solution to the problem of the priors? 2012.

Large Language Models as Computable Approximations to Solomonoff Induction Solomonoff induction: A solution to the problem of the priors? 2012

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:00.465245Z

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This paper cites Unifying Two Types of Scaling Laws from the Perspective of Conditional Kolmogorov Complexity.

Large Language Models as Computable Approximations to Solomonoff Induction Unifying Two Types of Scaling Laws from the Perspective of Conditional Kolmogorov Complexity

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:16:58.849955Z

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Observation 1ce54816-f4f9-4630-aa7b-8d46f994fbe2 · outbound

This paper cites Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning.

Large Language Models as Computable Approximations to Solomonoff Induction Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.387765Z

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Observation 2692954f-9d70-4812-bab0-80dbe18c3b38 · outbound

This paper cites Large Action Models: From Inception to Implementation.

Large Language Models as Computable Approximations to Solomonoff Induction Large Action Models: From Inception to Implementation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.441509Z

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Observation 6313a9a0-bfa3-4c41-9dc2-90a4c2c4056c · outbound

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Large Language Models as Computable Approximations to Solomonoff Induction MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.541637Z

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Observation 0bd0ce87-526f-45a9-a168-a6756be01759 · outbound

This paper cites Emergent Abilities of Large Language Models.

Large Language Models as Computable Approximations to Solomonoff Induction Emergent Abilities of Large Language Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.602694Z

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Observation 5ab0b59c-3cf5-4dd5-82e3-e4859904b8d9 · outbound

This paper cites DocTER: Evaluating Document-based Knowledge Editing.

Large Language Models as Computable Approximations to Solomonoff Induction DocTER: Evaluating Document-based Knowledge Editing

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.652743Z

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This paper cites Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach.

Large Language Models as Computable Approximations to Solomonoff Induction Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:16:58.661373Z

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Observation 745326d5-d3df-44cf-8a7b-fc14584ec5d4 · outbound

This paper cites RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?.

Large Language Models as Computable Approximations to Solomonoff Induction RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.734912Z

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This paper cites Qwen2.5 Technical Report.

Large Language Models as Computable Approximations to Solomonoff Induction Qwen2.5 Technical Report

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.828522Z

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Observation a74f186e-0304-43c7-93f0-d1c0e90b6b20 · outbound

This paper cites Make pixels dance: High-dynamic video generation.

Large Language Models as Computable Approximations to Solomonoff Induction Make pixels dance: High-dynamic video generation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:00.325172Z

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Observation d9db8339-f999-4bc2-9144-494bcc9d353b · outbound

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Large Language Models as Computable Approximations to Solomonoff Induction Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:17:00.202992Z

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Observation 2b655570-8226-4053-8528-253118b0354a · outbound

This paper cites GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving.

Large Language Models as Computable Approximations to Solomonoff Induction GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:57.983725Z

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Observation 9831e4d2-ae48-47a0-9902-72a05f445a34 · outbound

This paper cites Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram.

Large Language Models as Computable Approximations to Solomonoff Induction Fuse, Reason and Verify: Geometry Problem Solving with Parsed Clauses from Diagram

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:58.023434Z

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Observation 58260eb3-a5aa-48e6-ae5c-17988f85a9be · outbound

This paper cites Character-level convolutional networks for text classification.

Large Language Models as Computable Approximations to Solomonoff Induction Character-level convolutional networks for text classification

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:58.083588Z

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Observation 8de304f0-333c-429c-8dc2-d762c8d67e0c · outbound

This paper cites Distributed rule vectors is a key mechanism in large language models' in-context learning, 2024.

Large Language Models as Computable Approximations to Solomonoff Induction Distributed rule vectors is a key mechanism in large language models' in-context learning, 2024

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:58.115211Z

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Observation 1372aead-09ce-45b9-a4b8-73246fe1fc1a · outbound

This paper cites VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model.

Large Language Models as Computable Approximations to Solomonoff Induction VEM: Environment-Free Exploration for Training GUI Agent with Value Environment Model

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:58.158791Z

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Observation bb2a710f-6021-4e02-a6d1-55ff4db90a19 · outbound

This paper cites TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation.

Large Language Models as Computable Approximations to Solomonoff Induction TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T15:16:58.216555Z

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Pith citing papers

Observation 3942ecaa-2bba-460f-bf79-188dfc651ab7 · inbound

Truth as a Compression Artifact in Language Model Training cites this paper.

Truth as a Compression Artifact in Language Model Training Large Language Models as Computable Approximations to Solomonoff Induction

Reference 10

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Observation 0576a033-cee0-4769-80a0-a9d0efacd47c · inbound

Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model cites this paper.

Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model Large Language Models as Computable Approximations to Solomonoff Induction

Reference 6

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