Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-16T17:30:49.929420Z
Paper Citation Record · LEDGER
Are NLP Models really able to Solve Simple Math Word Problems?
As of 15 July 2026, this Paper Citation Record lists 12 of 12 outbound references and 57 inbound Pith citation observations for arXiv:2103.07191.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-15T06:30:58.975436+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-13T04:08:39.594367Z
A source-named dated measurement, never combined with another source.
Source: doi_reference, observed 2026-07-10T13:57:06.790782Z
Reference resolution
12 of 12 outbound references displayed
- verified exact0
- verified fuzzy12
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
148
doi_reference, observed 2026-07-10T13:57:06.790782Z
Outbound references
Observation 1e1fe462-611d-421b-8be5-ff3ba0290c46 · outbound
This paper cites Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A.
Are NLP Models really able to Solve Simple Math Word Problems? Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.941376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 2a94fb90-a2b4-4df7-beb9-169556f2d251 · outbound
This paper cites In Proceed- ings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 975–984, On- line.
Are NLP Models really able to Solve Simple Math Word Problems? In Proceed- ings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 975–984, On- line
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.945060Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 96873b23-cd5e-4ad4-b59c-4dad717f0438 · outbound
This paper cites In Proceed- ings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 3702–3710, Online.
Are NLP Models really able to Solve Simple Math Word Problems? In Proceed- ings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 3702–3710, Online
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.948200Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation e1c2c8c2-fd6b-4f07-94ae-8b140104821a · outbound
This paper cites IEEE Transac- tions on Pattern Analysis and Machine Intelligence , 42(9):2287–2305.
Are NLP Models really able to Solve Simple Math Word Problems? IEEE Transac- tions on Pattern Analysis and Machine Intelligence , 42(9):2287–2305
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.951137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 08c1859f-d76f-465b-8b42-9624c5f7e4ef · outbound
This paper cites B Implementation Details We use 8 NVIDIA Tesla P100 GPUs each with 16 GB memory to run our experiments.
Are NLP Models really able to Solve Simple Math Word Problems? B Implementation Details We use 8 NVIDIA Tesla P100 GPUs each with 16 GB memory to run our experiments
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.953928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation d3f8cb83-8675-44f6-bcd3-c4de72b10174 · outbound
This paper cites The best hyperparameters are highlighted in bold.
Are NLP Models really able to Solve Simple Math Word Problems? The best hyperparameters are highlighted in bold
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.956820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation b8386915-32ff-40c2-b476-e20766320102 · outbound
This paper cites an unresolved cited work.
Are NLP Models really able to Solve Simple Math Word Problems? Unresolved cited work
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.959649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 9dabf924-5ea3-4a0a-a107-e14d69848098 · outbound
This paper cites an unresolved cited work.
Are NLP Models really able to Solve Simple Math Word Problems? Unresolved cited work
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.962328Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation f5a3f326-1b9e-4536-bb50-ad98ff91cb07 · outbound
This paper cites Then considering these variations as Base Examples, apply the Ques- tion Sensitivity variations.
Are NLP Models really able to Solve Simple Math Word Problems? Then considering these variations as Base Examples, apply the Ques- tion Sensitivity variations
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.965063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 73584e04-4de4-4549-8bad-53fbfaf3b72f · outbound
This paper cites an unresolved cited work.
Are NLP Models really able to Solve Simple Math Word Problems? Unresolved cited work
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.967589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 5eb6947f-cf96-40ac-bc38-ba620ba0fa84 · outbound
This paper cites an unresolved cited work.
Are NLP Models really able to Solve Simple Math Word Problems? Unresolved cited work
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.970116Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation e2dd4840-cb2a-4f73-a52a-0b232a7cbb6e · outbound
This paper cites Table 25 provides some variations for the exam- ple in Table 24.
Are NLP Models really able to Solve Simple Math Word Problems? Table 25 provides some variations for the exam- ple in Table 24
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-16T17:30:49.972699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Pith citing papers
Observation 27338a8e-abdc-4b10-bdbf-76809b1e215f · inbound
PaLM: Scaling Language Modeling with Pathways cites this paper.
PaLM: Scaling Language Modeling with Pathways Are NLP Models really able to Solve Simple Math Word Problems?
Reference 109
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation d6d8fdcb-e6b2-46ec-b286-b5501d8f33a3 · inbound
Automatic Chain of Thought Prompting in Large Language Models cites this paper.
Automatic Chain of Thought Prompting in Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 1f314e76-e304-4c09-958e-e13dcd314a24 · inbound
PAL: Program-aided Language Models cites this paper.
PAL: Program-aided Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation a99aeccb-229b-453c-a608-6b727f0e2107 · inbound
ART: Automatic multi-step reasoning and tool-use for large language models cites this paper.
ART: Automatic multi-step reasoning and tool-use for large language models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 22
- Resolution
- verified exact
- doi, observed 2026-05-16T19:03:05.880969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 467dbdae-d821-433e-80a2-d5e03093ab01 · inbound
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes cites this paper.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Are NLP Models really able to Solve Simple Math Word Problems?
Reference 92
- Resolution
- verified exact
- doi, observed 2026-05-21T20:50:09.328550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 94ae675c-161d-4d22-90f6-9cc819142086 · inbound
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models cites this paper.
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 87
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation aed7a452-a460-4c63-8a80-9847d232737f · inbound
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning cites this paper.
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 34
- Resolution
- verified exact
- doi, observed 2026-05-17T23:46:39.413665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 1d750edb-c303-4074-8174-7731b76c8006 · inbound
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving cites this paper.
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving Are NLP Models really able to Solve Simple Math Word Problems?
Reference 32
- Resolution
- verified exact
- doi, observed 2026-05-19T09:19:36.024102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 8cf9a4a9-4cf3-4be6-9886-a6de484ab015 · inbound
GAIA: a benchmark for General AI Assistants cites this paper.
GAIA: a benchmark for General AI Assistants Are NLP Models really able to Solve Simple Math Word Problems?
Reference 131
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 96244216-2bf6-411f-a33d-cf4d28d7fb62 · inbound
Automated Design of Agentic Systems cites this paper.
Automated Design of Agentic Systems Are NLP Models really able to Solve Simple Math Word Problems?
Reference 193
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 862bb119-7efa-44a7-b9c1-656fa8f90b60 · inbound
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning cites this paper.
AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T18:48:20.459026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 90ad721a-8762-4134-8025-27803607ce07 · inbound
Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models cites this paper.
Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 24
- Resolution
- verified exact
- doi, observed 2026-05-23T18:05:44.694475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation a14a300b-385e-4072-b019-21149910831f · inbound
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model cites this paper.
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model Are NLP Models really able to Solve Simple Math Word Problems?
Reference 260
- Resolution
- verified exact
- doi, observed 2026-05-19T08:02:23.216293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation b69d2867-67f0-44e1-9979-2534d30bde2d · inbound
CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation cites this paper.
CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation Are NLP Models really able to Solve Simple Math Word Problems?
Reference 118
- Resolution
- verified exact
- doi, observed 2026-05-17T23:14:57.619147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 83ebc89a-a09e-4972-b617-4b7db27aa7d4 · inbound
HyperAdapt: Simple High-Rank Adaptation cites this paper.
HyperAdapt: Simple High-Rank Adaptation Are NLP Models really able to Solve Simple Math Word Problems?
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:46:25.951294Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 5784062e-c269-4c75-834c-a802dc190fe6 · inbound
EDUMATH: Generating Standards-aligned Educational Math Word Problems cites this paper.
EDUMATH: Generating Standards-aligned Educational Math Word Problems Are NLP Models really able to Solve Simple Math Word Problems?
Reference 5
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-18T09:41:11.947579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 0c700f00-a1ed-40b8-a734-32baa3bb838f · inbound
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? cites this paper.
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning? Are NLP Models really able to Solve Simple Math Word Problems?
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T13:01:34.066334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 3f347d34-066a-42a5-ac1e-a09e5a9610e2 · inbound
Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective cites this paper.
Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective Are NLP Models really able to Solve Simple Math Word Problems?
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T00:55:33.487602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 5a203e69-7478-4f26-9987-fc24d1688a5a · inbound
Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization cites this paper.
Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization Are NLP Models really able to Solve Simple Math Word Problems?
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T20:30:11.547280Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 413a9423-a1d7-472e-92cd-26eaa3b4b88b · inbound
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark cites this paper.
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark Are NLP Models really able to Solve Simple Math Word Problems?
Reference 41
- Resolution
- verified exact
- doi, observed 2026-05-17T05:09:03.329512Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 3822e27f-b4d5-46f2-97a7-e935349bf920 · inbound
PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models cites this paper.
PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 54
- Resolution
- verified exact
- doi, observed 2026-05-17T02:38:53.299455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation e55c1a01-0aaf-49c8-bd30-330f00663751 · inbound
CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning cites this paper.
CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 17
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-16T20:28:23.788903Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation cf45d3f7-004e-40dd-a808-1a2abd77a35e · inbound
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs cites this paper.
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs Are NLP Models really able to Solve Simple Math Word Problems?
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 070ed5eb-78ef-43df-9003-e6bbc6eb6773 · inbound
From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs cites this paper.
From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs Are NLP Models really able to Solve Simple Math Word Problems?
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 3d32e135-78a6-48b3-a6f6-c1721f3e6ec6 · inbound
Factored Causal Representation Learning for Robust Reward Modeling in RLHF cites this paper.
Factored Causal Representation Learning for Robust Reward Modeling in RLHF Are NLP Models really able to Solve Simple Math Word Problems?
Reference 21
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T14:20:13.645297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 9eaf150e-7c0b-4cb5-a301-60bceed6db7f · inbound
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression cites this paper.
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression Are NLP Models really able to Solve Simple Math Word Problems?
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T13:44:11.407646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation ffd52cb5-e273-40b7-9c24-badde521482e · inbound
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization cites this paper.
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization Are NLP Models really able to Solve Simple Math Word Problems?
Reference 8
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 17bf2c73-9ba8-4dc8-b55c-d231deaf5f3a · inbound
The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs? cites this paper.
The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs? Are NLP Models really able to Solve Simple Math Word Problems?
Reference 19
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation b6ab5f39-9e51-49af-9d3e-5c18794a4c93 · inbound
The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment cites this paper.
The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment Are NLP Models really able to Solve Simple Math Word Problems?
Reference 50
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation fe3c6642-c348-4eef-bb21-3958dcfa3acd · inbound
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning cites this paper.
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 21
- Resolution
- metadata mismatch
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 348582c1-df5b-44d6-90a7-233af4f3c30f · inbound
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning cites this paper.
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 23
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 190a6e5c-5678-488f-9148-8d531eaf72b6 · inbound
ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation cites this paper.
ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation Are NLP Models really able to Solve Simple Math Word Problems?
Reference 54
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 6f9e93c6-f243-4cb2-a898-e17d8eb42c80 · inbound
From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity cites this paper.
From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity Are NLP Models really able to Solve Simple Math Word Problems?
Reference 5
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation ada3d749-f4ff-4216-ab29-49ea4daa2a51 · inbound
From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity cites this paper.
From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity Are NLP Models really able to Solve Simple Math Word Problems?
Reference 5
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 72369d59-3fea-4e52-861e-3e3ebcdeef64 · inbound
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards cites this paper.
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards Are NLP Models really able to Solve Simple Math Word Problems?
Reference 21
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation b8ff5f61-e722-411b-939a-b154eca117a9 · inbound
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards cites this paper.
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards Are NLP Models really able to Solve Simple Math Word Problems?
Reference 21
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 9fd6f625-fde2-4713-b858-fff81f26c404 · inbound
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards cites this paper.
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards Are NLP Models really able to Solve Simple Math Word Problems?
Reference 21
- Resolution
- verified exact
- doi, observed 2026-07-01T00:05:08.797598Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 556a63f4-a48d-4ce6-8a60-63de113914ce · inbound
Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models cites this paper.
Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 25
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation ebaea8ab-ed0a-4d0c-a195-4a38e14a0903 · inbound
Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models cites this paper.
Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 25
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 30edc594-5385-4c86-8698-74275ba3e033 · inbound
BOOKMARKS: Efficient Active Storyline Memory for Role-playing cites this paper.
BOOKMARKS: Efficient Active Storyline Memory for Role-playing Are NLP Models really able to Solve Simple Math Word Problems?
Reference 44
- Resolution
- verified exact
- doi, observed 2026-05-16T17:30:49.973393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 13e7ec11-c8e2-41aa-85eb-8d53f4149d3e · inbound
Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models cites this paper.
Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 103
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:53:42.288479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation e03d17a3-630e-42a5-b4b8-2e8176f2b5f8 · inbound
Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models cites this paper.
Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T19:45:00.803610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 52437002-5cb2-46f4-a0a1-dbe44222ae1b · inbound
Multi-Agent Coordination Adaptation via Structure-Guided Orchestration cites this paper.
Multi-Agent Coordination Adaptation via Structure-Guided Orchestration Are NLP Models really able to Solve Simple Math Word Problems?
Reference 5
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T19:43:54.919601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation b576ba66-4c81-4c0a-b586-e7ef96a50aab · inbound
ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy cites this paper.
ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy Are NLP Models really able to Solve Simple Math Word Problems?
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T20:03:56.309638Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation ec3131e9-5296-4738-9cb7-56d9b1304d4d · inbound
Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models cites this paper.
Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-07-01T16:55:50.829024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation a8661db5-5a2d-4543-a887-f3748669e253 · inbound
Learning to Adapt SFT Data for Better Reasoning Generalization cites this paper.
Learning to Adapt SFT Data for Better Reasoning Generalization Are NLP Models really able to Solve Simple Math Word Problems?
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T18:23:51.098391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 9938bc78-f396-4c86-ba3c-95c9521743dc · inbound
eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion cites this paper.
eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion Are NLP Models really able to Solve Simple Math Word Problems?
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-07-01T23:36:22.164413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 548ac182-552d-45a9-b490-e93ea1130d75 · inbound
Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation cites this paper.
Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation Are NLP Models really able to Solve Simple Math Word Problems?
Reference 28
- Resolution
- verified exact
- doi, observed 2026-06-28T03:11:30.084461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 02c66fa5-6af0-4d17-8af4-9dcf0c23615b · inbound
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution cites this paper.
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution Are NLP Models really able to Solve Simple Math Word Problems?
Reference 17
- Resolution
- verified exact
- doi, observed 2026-06-28T23:42:49.133490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation f4abc168-09b5-4266-b92e-5f1960fa87bb · inbound
DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination cites this paper.
DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination Are NLP Models really able to Solve Simple Math Word Problems?
Reference 180
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T20:57:23.849373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 97c74649-b1d5-462b-bed1-eb1cd7b45403 · inbound
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery cites this paper.
Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery Are NLP Models really able to Solve Simple Math Word Problems?
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T22:47:25.958251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 8860cb57-0dd4-41ab-998e-c28d512aa49f · inbound
Dropout-GRPO: Variational Stochasticity for Continuous Latent Reasoning cites this paper.
Dropout-GRPO: Variational Stochasticity for Continuous Latent Reasoning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 21
- Resolution
- verified exact
- doi, observed 2026-06-27T17:21:06.576676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 0ae09ba8-f692-4017-b392-9e04e8b57858 · inbound
Enhancing Multilingual Reasoning via Steerable Model Merging cites this paper.
Enhancing Multilingual Reasoning via Steerable Model Merging Are NLP Models really able to Solve Simple Math Word Problems?
Reference 11
- Resolution
- verified exact
- doi, observed 2026-06-26T20:39:56.002024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 221fd158-fad9-429d-b880-ac2eb065dde0 · inbound
SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Design cites this paper.
SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Design Are NLP Models really able to Solve Simple Math Word Problems?
Reference 53
- Resolution
- verified exact
- doi, observed 2026-06-26T15:29:33.828305Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation ab091341-2e56-4125-b74b-dbf5b3537254 · inbound
Future Confidence Distillation in Large Language Models cites this paper.
Future Confidence Distillation in Large Language Models Are NLP Models really able to Solve Simple Math Word Problems?
Reference 16
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-09T04:55:58.838106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation e4e82535-22de-42b2-b6ee-016d90725c88 · inbound
From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs cites this paper.
From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs Are NLP Models really able to Solve Simple Math Word Problems?
Reference 133
- Resolution
- verified exact
- doi, observed 2026-07-10T13:57:06.792532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.
Observation 0f88c719-42cd-43ac-9e97-214c7f146209 · inbound
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning cites this paper.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Are NLP Models really able to Solve Simple Math Word Problems?
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.