Paper Citation Record · LEDGER

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning

As of 12 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2501.02825.

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

pith.paper-citation-record.v1
2501.02825 v6

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:10:33.987988Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact3
verified fuzzy4
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a9b79030-59fd-413d-b2d7-6fe505652f35 · outbound

This paper cites MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.854506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75fde983-afdc-4e11-8214-834b28a28461 · outbound

This paper cites Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.859283Z

Source-reported events for the cited work

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Observation afdcaab2-ade6-46f9-8b63-4e4181efaac3 · outbound

This paper cites Leveraging Code to Improve In-context Learning for Semantic Parsing.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Leveraging Code to Improve In-context Learning for Semantic Parsing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.863014Z

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Unavailable: canonical work link unavailable.

Observation a45e671c-63d7-4195-bdc0-8ce0d543e64f · outbound

This paper cites What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.867385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbd20f3a-290b-4af2-aa46-90bb51ae1cdb · outbound

This paper cites A Survey on In-context Learning.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning A Survey on In-context Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.879293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e9c1e5d-b7d2-4a3b-9162-c3baea682e27 · outbound

This paper cites NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.883159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3fc47b-4691-41ec-b8eb-a04fd73a5396 · outbound

This paper cites Can Large Language Models Reason? A Characterization via 3-SAT.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Can Large Language Models Reason? A Characterization via 3-SAT

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.886879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 190905dd-c76f-4f02-abe8-52281f50b206 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Measuring Mathematical Problem Solving With the MATH Dataset

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.890379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9282bb9-3db8-4b77-babb-775e9a84a7c4 · outbound

This paper cites Towards Reasoning in Large Language Models: A Survey.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Towards Reasoning in Large Language Models: A Survey

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.898396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 046560cf-a17f-4c32-81e7-6f6251c3f414 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Qwen2.5-Coder Technical Report

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.902713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08bd2e69-ada9-465b-8168-02c3ef9d2d0a · outbound

This paper cites J., and Fard, F.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning J., and Fard, F

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.907157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 767b303b-4695-43c6-ac93-e115be0c6200 · outbound

This paper cites Decomposed Prompting: A Modular Approach for Solving Complex Tasks.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Decomposed Prompting: A Modular Approach for Solving Complex Tasks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.911801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37847121-1ec9-410a-9616-f60a4c014c16 · outbound

This paper cites What Makes Good In-Context Examples for GPT-$3$?.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning What Makes Good In-Context Examples for GPT-$3$?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.924731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50cdb32e-dcee-4130-8ab9-e74528d60954 · outbound

This paper cites Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.928345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd7332f-eede-4e82-9984-9f5805b82e18 · outbound

This paper cites GPT-4o System Card.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning GPT-4o System Card

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.935761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4abc7723-f7c3-4645-a4d9-ab2def554173 · outbound

This paper cites Impact of Pretraining Term Frequencies on Few-Shot Reasoning.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.942902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87f4b708-9a58-41d4-8a33-28b6c120a126 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.946740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06894e49-10b6-43be-86ba-55e10b525c71 · outbound

This paper cites Transformers Can Represent $n$-gram Language Models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Transformers Can Represent $n$-gram Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.950664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96a2e78f-bf73-46a0-bc5e-f58173959aa3 · outbound

This paper cites Qwen2 Technical Report.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Qwen2 Technical Report

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.954084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7e1efa2-0623-4c87-add4-713970728a32 · outbound

This paper cites Benchmarking Compositionality with Formal Languages.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Benchmarking Compositionality with Formal Languages

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:10:34.099232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d3ca8049-e46d-4c87-990e-8655c9e0a668 · outbound

This paper cites Emergent Abilities of Large Language Models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Emergent Abilities of Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.965076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b15bd1c-ea64-40f1-adeb-f7380993ee0f · outbound

This paper cites Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.968811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 735652dd-48cc-481a-937b-17a30184b793 · outbound

This paper cites Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:34.546337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2574572b-c654-4050-a5e1-7b8a9a1bb965 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.976038Z

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Unavailable: canonical work link unavailable.

Observation 152d9614-42de-48bb-b9b0-2dfafb172f4a · outbound

This paper cites Structural generalization is hard for sequence-to-sequence models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Structural generalization is hard for sequence-to-sequence models

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:10:34.040811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d0dc217f-a9cf-49ff-9b88-87ae3d9a2dc4 · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.984099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4cd3895-f4cb-471d-86d0-2600ce995865 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:34.533994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 46b60efc-9470-4dee-911a-93fb7fdfe304 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Training Verifiers to Solve Math Word Problems

Reference 1959

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.875844Z

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Observation 9e20f6ef-51d7-41f2-83c0-3f1eea70831a · outbound

This paper cites Language Models as Models of Language.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Language Models as Models of Language

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.932079Z

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Observation 0cc1b4e3-5563-47d1-ba0f-aff7fcab2bc3 · outbound

This paper cites Are NLP Models really able to Solve Simple Math Word Problems?.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Are NLP Models really able to Solve Simple Math Word Problems?

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.939429Z

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Unavailable: canonical work link unavailable.

Observation 22fbca40-f542-4007-bfa0-8653d4cfba24 · outbound

This paper cites CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.915800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5675f72-1a54-4ec1-9cfa-5a4df56e7230 · outbound

This paper cites Few-Shot Adaptation for Parsing Contextual Utterances with LLMs.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Few-Shot Adaptation for Parsing Contextual Utterances with LLMs

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:10:34.234188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21e36506-4524-4e58-a2c1-adb0de38e4b5 · outbound

This paper cites and Zhang, Y.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning and Zhang, Y

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:34.571693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 767515cf-4674-4504-87be-58f2d322ae1e · outbound

This paper cites In-Context Language Learning: Architectures and Algorithms.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning In-Context Language Learning: Architectures and Algorithms

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.850271Z

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Unavailable: canonical work link unavailable.

Observation e0bc7ab2-f6fd-42f2-837d-d40eeb619d11 · outbound

This paper cites What learning algorithm is in-context learning? Investigations with linear models.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning What learning algorithm is in-context learning? Investigations with linear models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.779874Z

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Unavailable: canonical work link unavailable.

Observation a30c4d5a-8266-4f55-b462-0a6360d36fc7 · outbound

This paper cites Training Neural Networks as Recognizers of Formal Languages.

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Training Neural Networks as Recognizers of Formal Languages

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:33.871513Z

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Unavailable: canonical work link unavailable.

Observation de0aa6ba-a0c2-4710-8395-787912159874 · outbound

This paper cites Large language models still can’t plan (a benchmark for llms on planning and reasoning about change).

Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:10:34.559482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.