Paper Citation Record · LEDGER

LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2409.13373.

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

pith.paper-citation-record.v1
2409.13373 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:42:49.263920Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:46:14.059699Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b76cbf24-7d04-465b-ad59-d167cca69c75 · inbound

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach cites this paper.

Efficient Self-Improvement in Multimodal Large Language Models: A Model-Level Judge-Free Approach LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T12:42:49.263920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be6321c4-1366-420e-add6-5cdf7abb7dc5 · inbound

Online Knowledge Integration for 3D Semantic Mapping: A Survey cites this paper.

Online Knowledge Integration for 3D Semantic Mapping: A Survey LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 159

Resolution: unresolved; no resolver link, observed 2026-08-12T11:31:14.690983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35243b79-c6c3-4935-b2ed-79fec76bb65d · inbound

Graph Learning for Planning: The Story Thus Far and Open Challenges cites this paper.

Graph Learning for Planning: The Story Thus Far and Open Challenges LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T23:51:53.839149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 493b5894-f399-42a2-823c-143da3e17195 · inbound

AI Planning: A Primer and Survey (Preliminary Report) cites this paper.

AI Planning: A Primer and Survey (Preliminary Report) LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T20:43:34.529366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfa1a89b-06f9-408f-95b3-0bb9fe725984 · inbound

The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of Instruction Tuning and In-Context Learning Capabilities cites this paper.

The Inherent Limits of Pretrained LLMs: The Unexpected Convergence of Instruction Tuning and In-Context Learning Capabilities LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:00.944897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ef2c66e-f8bd-463a-af41-24e46fee40ee · inbound

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models cites this paper.

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 146

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T21:20:59.312729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8db6e529-9719-4a96-84a7-908ef14ccbbb · inbound

A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions cites this paper.

A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 157

Resolution: verified exact; arxiv_id, observed 2026-05-23T05:02:35.955850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d1ec938e-c08e-4c54-8447-311a147ecf4c · inbound

Generating Symbolic World Models via Test-time Scaling of Large Language Models cites this paper.

Generating Symbolic World Models via Test-time Scaling of Large Language Models LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T21:45:45.438262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2581515-9656-4d94-8da8-fefae582a480 · inbound

InSTA: Towards Internet-Scale Training For Agents cites this paper.

InSTA: Towards Internet-Scale Training For Agents LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T14:24:50.439003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a252b1d1-2e84-4f6c-997d-bb3ee9f24002 · inbound

Evaluating the Systematic Reasoning Abilities of Large Language Models through Graph Coloring cites this paper.

Evaluating the Systematic Reasoning Abilities of Large Language Models through Graph Coloring LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T13:53:58.434511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22eb4399-b9e0-4c8c-bcac-f6660bdcc979 · inbound

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models cites this paper.

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T12:17:11.933468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16a779d1-6d61-4191-9907-fea88806053e · inbound

LogiPlan: A Structured Benchmark for Logical Planning and Relational Reasoning in LLMs cites this paper.

LogiPlan: A Structured Benchmark for Logical Planning and Relational Reasoning in LLMs LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:46.939636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eea2786-3587-46b6-8b5f-be4c58072569 · inbound

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories cites this paper.

Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T21:13:13.017038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcc2a004-06f6-47e3-a5c9-d9af1404e752 · inbound

Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study cites this paper.

Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T10:45:59.558754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db498e8f-a55a-414b-8b03-363333ea7d8e · inbound

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling cites this paper.

Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:51:50.864328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ca01ad81-91f9-4a27-a92f-8287cd1823b2 · inbound

CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs cites this paper.

CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T13:05:33.736041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a34e7a8-7f36-43df-ac75-2086d5ec205c · inbound

OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling cites this paper.

OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:18:05.361987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2f2f336b-d722-4d80-9679-9e5b1989b44a · inbound

LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs? cites this paper.

LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs? LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T22:26:38.525975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 271c43f6-fbee-40df-ac67-99d40c02221d · inbound

SYMBOLIZER: Symbolic Model-free Task Planning with VLMs cites this paper.

SYMBOLIZER: Symbolic Model-free Task Planning with VLMs LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T09:43:49.597853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d62170c4-c495-43ea-99d1-c0f787ed6409 · inbound

Robust Asynchronous Planning via Auto-Formalization cites this paper.

Robust Asynchronous Planning via Auto-Formalization LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T20:46:14.061633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2ae09f3e-bb85-4f3c-8a70-a6d10b6d9e6b · inbound

Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planning cites this paper.

Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planning LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T10:13:55.891370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1378f71-7fa8-4b2a-a07d-ac12e0427fd7 · inbound

SymStep: Symbolic Step Verification for Logical Reasoning cites this paper.

SymStep: Symbolic Step Verification for Logical Reasoning LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-01T03:48:35.008851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d53ffc1-8162-43c9-9076-006a108fe105 · inbound

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation cites this paper.

Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T23:41:20.670945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.