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Paper Citation Record · LEDGER

Transformers Struggle to Learn to Search

As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 4 inbound Pith citation observations for arXiv:2412.04703.

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

pith.paper-citation-record.v1
2412.04703 v2

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:23:19.683563Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:58:59.001963Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T18:30:01.548468Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved55
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d1679e7f-5b94-4676-8389-92e3ff74c4e8 · outbound

This paper cites The pitfalls of next-token prediction.

Transformers Struggle to Learn to Search The pitfalls of next-token prediction

Reference 1

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source=arxiv_source observed=2026-08-11T21:23:18.034837Z digest=sha256:20ded653640c59b4659bbf43da6bb58e6063f1f596702276027396d1f8c1ade1

Observation 1d64f964-a203-4e23-8816-930b0df033f8 · outbound

This paper cites Chayes, and Oliver Riordan.

Transformers Struggle to Learn to Search Chayes, and Oliver Riordan

Reference 2

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source=arxiv_source observed=2026-08-11T21:23:18.061252Z digest=sha256:24e5193a5391ef42812746145bb19c8a8c8d709aa8cf77b4596167d8235d092b

Observation df3ea3e7-4af4-4b30-8cd9-19312a372cd0 · outbound

This paper cites Navigating the labyrinth: Evaluating and enhancing llms' ability to reason about search problems.

Transformers Struggle to Learn to Search Navigating the labyrinth: Evaluating and enhancing llms' ability to reason about search problems

Reference 3

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source=arxiv_source observed=2026-08-11T21:23:18.098761Z digest=sha256:2920919f3c5d9be2f5d546b6f139c99870480e73526cc6a482279c8dd11559f2

Observation dd610724-019f-408f-8f4d-84bb9b674d60 · outbound

This paper cites A mechanistic analysis of a transformer trained on a symbolic multi-step reasoning task.

Transformers Struggle to Learn to Search A mechanistic analysis of a transformer trained on a symbolic multi-step reasoning task

Reference 4

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source=arxiv_source observed=2026-08-11T21:23:18.107751Z digest=sha256:aa235c82a0846724bfa35fd253ef4bbe207130756dfbb28a736c2439656f3772

Observation 11274699-17dc-4186-88b2-f3586e847e94 · outbound

This paper cites Broken neural scaling laws.

Transformers Struggle to Learn to Search Broken neural scaling laws

Reference 5

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source=arxiv_source observed=2026-08-11T21:23:18.144757Z digest=sha256:35e766608bff7ff7d2d75a4e11ec494f9bd2315f333497af9467a31366ed29bf

Observation cdb70207-cb20-4f03-9c14-71c114094329 · outbound

This paper cites Selection-inference: Exploiting large language models for interpretable logical reasoning.

Transformers Struggle to Learn to Search Selection-inference: Exploiting large language models for interpretable logical reasoning

Reference 6

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source=arxiv_source observed=2026-08-11T21:23:18.176766Z digest=sha256:d9a08b8509eb51c723328707d5a42b68f814c156d5871d86ca6a79dee013c8fb

Observation 10c64ddb-cd88-4340-8f10-8fd686585049 · outbound

This paper cites MANGO : A benchmark for evaluating mapping and navigation abilities of large language models.

Transformers Struggle to Learn to Search MANGO : A benchmark for evaluating mapping and navigation abilities of large language models

Reference 7

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source=arxiv_source observed=2026-08-11T21:23:18.192561Z digest=sha256:9418b8d4946410766d592ea6f14729c060fc1f6709d4608b6e9737339f353108

Observation 1f0ea792-e8cb-4c82-9b3c-10668f5f4238 · outbound

This paper cites Understanding Emergent Abilities of Language Models from the Loss Perspective.

Transformers Struggle to Learn to Search Understanding Emergent Abilities of Language Models from the Loss Perspective

Reference 8

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source=arxiv_source observed=2026-08-11T21:23:18.236330Z digest=sha256:7285d9168fb6e4363e970355623f9abd7795e433010cc1726fee193f4b2d6cbd

Observation 73d55e71-a122-43ae-bdc1-a53d139f7400 · outbound

This paper cites On random graphs i.

Transformers Struggle to Learn to Search On random graphs i

Reference 9

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source=arxiv_source observed=2026-08-11T21:23:18.264180Z digest=sha256:6ca7573a59e6b24c153ba3d409b66f1df7b1a8c76e97b24302c9399b89b3f80e

Observation 9b985b0a-d8b0-4dff-b00d-c400a6f802b5 · outbound

This paper cites Nphardeval: Dynamic benchmark on reasoning ability of large language models via complexity classes.

Transformers Struggle to Learn to Search Nphardeval: Dynamic benchmark on reasoning ability of large language models via complexity classes

Reference 10

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source=arxiv_source observed=2026-08-11T21:23:18.290090Z digest=sha256:2d3db3891b37150cff32f4904fa09256fbaa718fb939b64140be336c74712261

Observation 71cd4a9e-414f-4992-b289-3f1ac3ee3f6a · outbound

This paper cites IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations.

Transformers Struggle to Learn to Search IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations

Reference 11

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source=arxiv_source observed=2026-08-11T21:23:18.311622Z digest=sha256:0c41aec5876294096bb6cd91fb5f41d5b13622fd6b43d8fc62d0d4137c64c64f

Observation 104e3407-c852-48ec-8abb-ae75d4c1cd3f · outbound

This paper cites Stream of Search (SoS): Learning to Search in Language.

Transformers Struggle to Learn to Search Stream of Search (SoS): Learning to Search in Language

Reference 12

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source=arxiv_source observed=2026-08-11T21:23:18.345312Z digest=sha256:393e87fa7b5d70e92a5297b58139f606cda6fb2dbdc93127853b91c834a76aa3

Observation 4f143306-0a22-45ae-9dae-81d64194d569 · outbound

This paper cites Causal abstractions of neural networks.

Transformers Struggle to Learn to Search Causal abstractions of neural networks

Reference 13

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source=arxiv_source observed=2026-08-11T21:23:18.375908Z digest=sha256:0d2a7d2b2f696911981d94e51db0670c43b9a8065c34461d6d0b05eed31f5dbe

Observation 26b39704-4a58-4d26-9a4d-de74fb655623 · outbound

This paper cites Reasoning with language model is planning with world model.

Transformers Struggle to Learn to Search Reasoning with language model is planning with world model

Reference 14

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source=arxiv_source observed=2026-08-11T21:23:18.405868Z digest=sha256:44ee9be710e61b98a9b60e5be59f60865728855bc5b160de4fe7f7ff30ffce8d

Observation f5f8c87c-d2d6-433d-8be8-6b5842bfcfcb · outbound

This paper cites How to use and interpret activation patching.

Transformers Struggle to Learn to Search How to use and interpret activation patching

Reference 15

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source=arxiv_source observed=2026-08-11T21:23:18.443944Z digest=sha256:81ead0427c016260ca67cc836302ec6fdeeb2f5d02db4a2ca2bd48839854d98c

Observation 51da9c90-7a30-46a2-b557-aaaa1d566fad · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Transformers Struggle to Learn to Search Scaling Laws for Autoregressive Generative Modeling

Reference 16

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source=arxiv_source observed=2026-08-11T21:23:18.485404Z digest=sha256:88c8e9710473da3cfcaf72f63d354500eeb06acf57eacdcf48c5ca4906cc0e20

Observation 9864039c-8677-4f0e-9005-0799b019d8f9 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Transformers Struggle to Learn to Search Training Compute-Optimal Large Language Models

Reference 17

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source=arxiv_source observed=2026-08-11T21:23:18.505370Z digest=sha256:78cbf702e8593da2ce91e563717d0df20b3d3ec9098f1036cf923b95bdce4d8b

Observation 657f5358-2040-4dc4-a379-10e34192d1e0 · outbound

This paper cites Towards a mechanistic interpretation of multi-step reasoning capabilities of language models.

Transformers Struggle to Learn to Search Towards a mechanistic interpretation of multi-step reasoning capabilities of language models

Reference 18

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source=arxiv_source observed=2026-08-11T21:23:18.534764Z digest=sha256:e31780f52d8537a6aed60ae2e2d7e1b2dbbc6f907fcbf50e68ad7438cc199db0

Observation 5aa628f1-20cb-447e-880d-b8409832fafc · outbound

This paper cites Ivanitskiy, Alex F.

Transformers Struggle to Learn to Search Ivanitskiy, Alex F

Reference 19

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source=arxiv_source observed=2026-08-11T21:23:18.574748Z digest=sha256:5f67a363c2da1d7a25baa1f08e2dedd40ca38ef221187d2a0950c48e164b8c1b

Observation ecd14e13-ffdd-4850-a129-91145b991296 · outbound

This paper cites Evidence of Learned Look-Ahead in a Chess-Playing Neural Network.

Transformers Struggle to Learn to Search Evidence of Learned Look-Ahead in a Chess-Playing Neural Network

Reference 20

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source=arxiv_source observed=2026-08-11T21:23:18.624747Z digest=sha256:23fedd25e849acb363c0c07e7256c84a040d1633c68a915b5eb5f93af0c65593

Observation 493c13b4-9c45-4bf9-b3c3-8955a2392c41 · outbound

This paper cites LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks.

Transformers Struggle to Learn to Search LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

Reference 21

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source=arxiv_source observed=2026-08-11T21:23:18.674748Z digest=sha256:e06eb3ceee6b4a83b2e2abb94b13f2176d21a54a48cc6b3d8af61099a71fdced

Observation 55fd31fc-6907-405a-8990-85bea024bd9f · outbound

This paper cites Scaling Laws for Neural Language Models.

Transformers Struggle to Learn to Search Scaling Laws for Neural Language Models

Reference 22

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source=arxiv_source observed=2026-08-11T21:23:18.744739Z digest=sha256:2cd0455b1f141f7adc5d2abaca5f47438e97d122acd0d75196d21c595f81d2a3

Observation c2025373-42ef-46bc-822e-77ccc89a5eda · outbound

This paper cites LAMBADA : Backward chaining for automated reasoning in natural language.

Transformers Struggle to Learn to Search LAMBADA : Backward chaining for automated reasoning in natural language

Reference 23

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source=arxiv_source observed=2026-08-11T21:23:18.784758Z digest=sha256:3c2f5240fc4f195e44ed1d01159086342e656789f5a39a4bf5afe61d49cb7708

Observation f3e3bbd2-6a0c-432e-bf77-f4b9ee2b1347 · outbound

This paper cites Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference.

Transformers Struggle to Learn to Search Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference

Reference 24

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source=arxiv_source observed=2026-08-11T21:23:18.824751Z digest=sha256:06572382596e83c94553bcc912500fd2f1dd8f825c1949e2812a78450026c880

Observation e562d411-b2a5-4453-8c72-c03e739c8379 · outbound

This paper cites COGS: A compositional generalization challenge based on semantic interpretation.

Transformers Struggle to Learn to Search COGS: A compositional generalization challenge based on semantic interpretation

Reference 25

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source=arxiv_source observed=2026-08-11T21:23:18.854842Z digest=sha256:102a0c8bf7ccb2441dc3b3cb2478b251f451ef5c8129b340314ca106e1363aa4

Observation b5afb4e6-5ce5-41df-bcd5-f3b7493ce5fd · outbound

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

Transformers Struggle to Learn to Search Large language models are zero-shot reasoners

Reference 26

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source=arxiv_source observed=2026-08-11T21:23:18.917084Z digest=sha256:15860d0060da79385713de38dc24064db75b34b0c053c839f9bfaeddff948fab

Observation 2ce00579-0d70-4bca-836a-c0bed74f2036 · outbound

This paper cites Sophia: A scalable stochastic second-order optimizer for language model pre-training.

Transformers Struggle to Learn to Search Sophia: A scalable stochastic second-order optimizer for language model pre-training

Reference 27

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source=arxiv_source observed=2026-08-11T21:23:18.961375Z digest=sha256:cbc5fcc37e362f37028e6f04ffa0d8fa9c240060d2a56ceb835baf85231d75f9

Observation 52108f53-b0c5-4b1f-adba-be64a0cf6e71 · outbound

This paper cites The expressive power of transformers with chain of thought.

Transformers Struggle to Learn to Search The expressive power of transformers with chain of thought

Reference 28

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source=arxiv_source observed=2026-08-11T21:23:18.994755Z digest=sha256:04348bd36225fa2e7097e1a1e5991a9648e02ec442d4b6eb9e3a60865e4c0b8d

Observation 5c00560c-6586-4218-8b35-dbc7b4633c79 · outbound

This paper cites Show Your Work: Scratchpads for Intermediate Computation with Language Models.

Transformers Struggle to Learn to Search Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 29

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source=arxiv_source observed=2026-08-11T21:23:19.054746Z digest=sha256:a50ce210aa9d4a7a7462c3a23b44ccd86763fe4e0cf579c72a39b3b0ec74e481

Observation 777ea6ec-d165-47f2-8604-fa065da21f42 · outbound

This paper cites Language models are unsupervised multitask learners.

Transformers Struggle to Learn to Search Language models are unsupervised multitask learners

Reference 30

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source=arxiv_source observed=2026-08-11T21:23:19.104066Z digest=sha256:0594b2a07084f11542692d8eaf5547e31791143f0c1c19628ebc50525a59fedb

Observation a28cfea9-b106-4e3c-a79a-13f68bf97052 · outbound

This paper cites Amortized Planning with Large-Scale Transformers: A Case Study on Chess.

Transformers Struggle to Learn to Search Amortized Planning with Large-Scale Transformers: A Case Study on Chess

Reference 31

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source=arxiv_source observed=2026-08-11T21:23:19.110027Z digest=sha256:72293ad6ce549625922af0fe16ee5429d4b44876c4ebbcb6b5bdfe4e7a21bb27

Observation 5a115100-4814-4650-b092-38713c5a8973 · outbound

This paper cites Understanding Transformer Reasoning Capabilities via Graph Algorithms.

Transformers Struggle to Learn to Search Understanding Transformer Reasoning Capabilities via Graph Algorithms

Reference 32

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source=arxiv_source observed=2026-08-11T21:23:19.134743Z digest=sha256:883e85c346075b34f1732c270ca7d361888025de5ab64d819ed4fb0f38e4c785

Observation 02ec3486-2925-4105-a811-a5146056454f · outbound

This paper cites Transformers, parallel computation, and logarithmic depth.

Transformers Struggle to Learn to Search Transformers, parallel computation, and logarithmic depth

Reference 33

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source=arxiv_source observed=2026-08-11T21:23:19.159899Z digest=sha256:5c5ed146d40529a22237335bc5d766552a654388ffb272bfff270fb114450d0e

Observation 087589f8-3887-4c0f-9f11-47a265fb73b2 · outbound

This paper cites Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought.

Transformers Struggle to Learn to Search Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought

Reference 34

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source=arxiv_source observed=2026-08-11T21:23:19.169820Z digest=sha256:4fdc36dd96a2bf1556e42f05720254f3be674595ee9375223272e8061e3660a4

Observation 8c99d95a-d46b-4483-8d07-1686e85dd71e · outbound

This paper cites an unresolved cited work.

Transformers Struggle to Learn to Search Unresolved cited work

Reference 35

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source=arxiv_source observed=2026-08-11T21:23:19.178280Z digest=sha256:1aace848e9d1c8fe590f8b825120ef74837e8fb3c5d19b4ac7709b70dd8169d3

Observation 934fcccc-6664-403d-b2a8-50bdbf46c7c3 · outbound

This paper cites Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles.

Transformers Struggle to Learn to Search Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles

Reference 36

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source=arxiv_source observed=2026-08-11T21:23:19.190653Z digest=sha256:654d1a8302ec5d449799a0cf581f3d08f05873e37a83c20d8b7566ee6d768eea

Observation ef64644b-6a8d-4e49-b72d-f87027294819 · outbound

This paper cites Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning.

Transformers Struggle to Learn to Search Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning

Reference 37

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source=arxiv_source observed=2026-08-11T21:23:19.196272Z digest=sha256:86e08d6d00247868eb16690fb6823c2060db43672b2e20e6fd812e73a011d382

Observation e47e32e1-24b8-4d4e-ad8c-4ff3b2fab67d · outbound

This paper cites A mechanistic interpretation of arithmetic reasoning in language models using causal mediation analysis.

Transformers Struggle to Learn to Search A mechanistic interpretation of arithmetic reasoning in language models using causal mediation analysis

Reference 38

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source=arxiv_source observed=2026-08-11T21:23:19.208020Z digest=sha256:8dc137f0cb69247061f2b60f32931126df63e78d24b7580d4e672b39ca0e5bda

Observation 1e8f52fa-ae47-46de-a58b-88a1bd386e61 · outbound

This paper cites an unresolved cited work.

Transformers Struggle to Learn to Search Unresolved cited work

Reference 39

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source=arxiv_source observed=2026-08-11T21:23:19.235159Z digest=sha256:c7f18f36c3cb485f3f65d10600fbdfe93d668106e0e44571af439a3c6fcf7327

Observation 3de13916-13a9-4696-9126-bf425ab08576 · outbound

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

Transformers Struggle to Learn to Search Large language models still can't plan (a benchmark for LLM s on planning and reasoning about change)

Reference 40

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Observation 5398d32d-0858-41de-aaa2-ead31066c5ce · outbound

This paper cites an unresolved cited work.

Transformers Struggle to Learn to Search Unresolved cited work

Reference 41

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source=arxiv_source observed=2026-08-11T21:23:19.256053Z digest=sha256:52e37e5346f63e194e051868df09f414abe39d368badee29b78448b7a307c4e8

Observation 3be3e374-19ec-4da8-b54c-517a536f6a6b · outbound

This paper cites Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias.

Transformers Struggle to Learn to Search Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

Reference 42

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source=arxiv_source observed=2026-08-11T21:23:19.294795Z digest=sha256:d7650df36b8f3deb4af2088b1c197af451a6004bf6b168371c55fa655b1d45bf

Observation 90c1f23c-d696-4e64-8ff5-908393abd3bc · outbound

This paper cites Can language models solve graph problems in natural language? In Thirty-seventh Conference on Neural Information Processing Systems, 2023.

Transformers Struggle to Learn to Search Can language models solve graph problems in natural language? In Thirty-seventh Conference on Neural Information Processing Systems, 2023

Reference 43

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source=arxiv_source observed=2026-08-11T21:23:19.327818Z digest=sha256:5ffbcdce21e01d255366730a23044fec59753ddd09f7ec4a39161067bc85b4fa

Observation cbb33be4-010e-4c34-98c5-ee753dcca058 · outbound

This paper cites Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

Transformers Struggle to Learn to Search Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus

Reference 44

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source=arxiv_source observed=2026-08-11T21:23:19.337469Z digest=sha256:1ae77e931f6127de7bc1565cf4de7237a729ad52a930600f876912b9c25c3644

Observation 9f99029e-9c2e-476c-8ab5-0400bdcf1f33 · outbound

This paper cites Chi, Quoc V Le, and Denny Zhou.

Transformers Struggle to Learn to Search Chi, Quoc V Le, and Denny Zhou

Reference 45

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source=arxiv_source observed=2026-08-11T21:23:19.355709Z digest=sha256:f4c29c932f62871830651755c89954986f3c23dd6a7e9ff94a8d47a59d242f77

Observation 4e07f63b-1334-449b-94a8-e7d062c3810d · outbound

This paper cites Do large language models latently perform multi-hop reasoning? In Annual Meeting of the Association for Computational Linguistics, 2024.

Transformers Struggle to Learn to Search Do large language models latently perform multi-hop reasoning? In Annual Meeting of the Association for Computational Linguistics, 2024

Reference 46

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source=arxiv_source observed=2026-08-11T21:23:19.368564Z digest=sha256:fcc55af020adfc63c10261aa81e61ec2e4fb4909bb7f8197bf0be7aa7165bcfe

Observation 218b0302-467a-4fbe-bc87-14cdd170672a · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Transformers Struggle to Learn to Search Tree of thoughts: Deliberate problem solving with large language models

Reference 47

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source=arxiv_source observed=2026-08-11T21:23:19.386912Z digest=sha256:1886f39957323ae11ad62845e25639b071656227aabba091b66ad3840007324b

Observation 305afb5f-b017-472a-bd74-cf02cb095d33 · outbound

This paper cites Towards best practices of activation patching in language models: Metrics and methods.

Transformers Struggle to Learn to Search Towards best practices of activation patching in language models: Metrics and methods

Reference 48

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source=arxiv_source observed=2026-08-11T21:23:19.424031Z digest=sha256:c5d1ced63fb8b4fc611806313788e5fa17b43fe78061ae99038404162b529a49

Observation a58bb081-ad87-4fb9-ae56-6ab526a07d2b · outbound

This paper cites On the paradox of learning to reason from data.

Transformers Struggle to Learn to Search On the paradox of learning to reason from data

Reference 49

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source=arxiv_source observed=2026-08-11T21:23:19.473617Z digest=sha256:febc3a6adb9800e1ab9c3c3b9e15a2cb6e5c26865f9781d2ca9b3a14725a04a8

Observation 0230a035-c28a-4762-9624-869fb46ef87f · outbound

This paper cites Can LLM Graph Reasoning Generalize beyond Pattern Memorization?.

Transformers Struggle to Learn to Search Can LLM Graph Reasoning Generalize beyond Pattern Memorization?

Reference 50

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source=arxiv_source observed=2026-08-11T21:23:19.512369Z digest=sha256:f74933640f2eb14a693e528c7d9118a42004b56fd892c9c9666444ddefd90d11

Observation e60dc3b5-80b9-4b17-aa45-d811891e0c9f · outbound

This paper cites Transformers Can Achieve Length Generalization But Not Robustly.

Transformers Struggle to Learn to Search Transformers Can Achieve Length Generalization But Not Robustly

Reference 51

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source=arxiv_source observed=2026-08-11T21:23:19.546715Z digest=sha256:05527a6ba998712532aae7e5d0b3e6b3439f3577dcc4cdb0b3c4d799e473c30f

Observation 32f652f6-3e09-4452-9d40-62d422537df7 · outbound

This paper cites write newline.

Transformers Struggle to Learn to Search write newline

Reference 52

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source=arxiv_source observed=2026-08-11T21:23:19.574757Z digest=sha256:38043e7d54f94afcfd108e1b1a5e3b1c35e34ff3bb0f83da169cd22da825c3f0

Observation 2b860569-a968-4e93-87cb-8fc35f612c36 · outbound

This paper cites @esa (Ref.

Transformers Struggle to Learn to Search @esa (Ref

Reference 53

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source=arxiv_source observed=2026-08-11T21:23:19.634762Z digest=sha256:bc9e90e4b898e0925a3bb58f0a022355434314023341856b40b5372ca898b247

Observation e769fb05-7b93-4f7c-8bc3-339ee432b330 · outbound

This paper cites an unresolved cited work.

Transformers Struggle to Learn to Search Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-11T21:23:19.676295Z digest=sha256:3e92faf1e70c72a096fc533e5c2a4a55ce877d7a4cd4338a839d95c17c309625

Observation fc4baed3-7ea8-419d-ab76-cb9d33ba0d55 · outbound

This paper cites an unresolved cited work.

Transformers Struggle to Learn to Search Unresolved cited work

Reference 55

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source=arxiv_source observed=2026-08-11T21:23:19.683563Z digest=sha256:613ba056d002d6d874caa733f6696a55bf4b650a0724237a7c230a7483dffa6c

Pith citing papers

Observation d4f6cb72-df3d-437e-b23b-b124e62a1508 · inbound

Learning Model Successors cites this paper.

Learning Model Successors Transformers Struggle to Learn to Search

Reference 140

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source=pdf_text observed=2026-08-09T19:58:59.001963Z digest=sha256:80159d0ea16dfb325ee5909567fb562c4254e66fee69e0c28b1f6fde7a16daf6

Observation 7ec384c1-1725-47de-93dc-1d0d5b419856 · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Transformers Struggle to Learn to Search

Reference 156

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verified exact
arxiv_id, observed 2026-05-21T20:40:36.248925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-21T20:38:18.005002Z digest=sha256:79989b9118c40363dfa68ca18cb3147f8456b4cacbcc6ca3d7d69cf28fc99f39

Observation d7c55bfe-74ec-4d1e-81ca-04faee1eca52 · inbound

Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR cites this paper.

Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR Transformers Struggle to Learn to Search

Reference 49

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arxiv_id, observed 2026-07-04T18:30:01.550549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-25T22:47:09.330723Z digest=sha256:bc3cfef4d81e37b2391a2732e7a429f37643a30c347b665378009855d52996f0

Observation a3fe979f-3292-456c-9016-47fbe5b3efea · inbound

Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR cites this paper.

Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR Transformers Struggle to Learn to Search

Reference 49

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arxiv_id, observed 2026-06-30T12:54:40.469233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-30T09:55:19.804589Z digest=sha256:31b58e2c62b851c5bfea7d4a2acea6b9c1354b516f7693b5b0ed6a0e51cc54ad