Paper Citation Record · LEDGER

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

As of 24 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 100 inbound Pith citation observations for arXiv:2210.09261.

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

pith.paper-citation-record.v1
2210.09261 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T07:15:23.725397Z

measured 142 of 142 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 100 of 283 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:06:23.497019Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

42 of 42 outbound references displayed

verified exact20
verified fuzzy15
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch6

External citation measurements

43
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 139f30fc-d28b-4343-9148-bd909e57aa28 · outbound

This paper cites Program Synthesis with Large Language Models.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Program Synthesis with Large Language Models

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:24.027742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation acddba65-daf0-40f7-9100-e30585c1b417 · outbound

This paper cites Language models are few-shot learners.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Language models are few-shot learners

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.120171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6dccc3fc-f32c-40ab-ab2f-3159e18ec2cb · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Evaluating Large Language Models Trained on Code

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:23.837037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 79ccd860-217a-4c3a-8d1b-d8ef05da769c · outbound

This paper cites Binding Language Models in Symbolic Languages.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Binding Language Models in Symbolic Languages

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:23.845653Z

Source-reported events for the cited work

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Observation c0c0b945-7a89-40f8-ba73-fb912374962d · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them PaLM: Scaling Language Modeling with Pathways

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:23.813765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f005f24f-786a-4632-8139-017886228b40 · outbound

This paper cites Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:23.821018Z

Source-reported events for the cited work

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Observation a760d8ff-71d1-4099-a915-cc4a0e1b9a81 · outbound

This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.066731Z

Source-reported events for the cited work

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Observation 9606bdc8-4492-48c0-8535-90d7666127eb · outbound

This paper cites BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: metadata mismatch; doi, observed 2026-05-11T07:15:23.807295Z

Source-reported events for the cited work

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Observation 8c667d39-a06e-458a-9e3e-ff1fa6290db1 · outbound

This paper cites Predictability and surprise in large generative models.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Predictability and surprise in large generative models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.061553Z

Source-reported events for the cited work

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Observation ec7c3819-619a-4d5f-b441-51e8cf0f0afd · outbound

This paper cites doi: 10.18653/v1/2022.lnls-1.4.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them doi: 10.18653/v1/2022.lnls-1.4

Reference 10

Resolution: verified exact; doi, observed 2026-05-11T07:15:23.794252Z

Source-reported events for the cited work

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Observation db24b1b4-0fcd-41f4-a370-2b0ba4560c7e · outbound

This paper cites Training Compute-Optimal Large Language Models.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Training Compute-Optimal Large Language Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:23.829606Z

Source-reported events for the cited work

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Observation 51b424ed-a4fa-4891-997c-a44498ae50b7 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Language Models (Mostly) Know What They Know

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:24.038913Z

Source-reported events for the cited work

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Observation 8eb2db11-1a53-478a-8d23-97021e0b0107 · outbound

This paper cites Large Language Models are Zero-Shot Reasoners.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Large Language Models are Zero-Shot Reasoners

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T19:04:13.904485Z

Source-reported events for the cited work

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Observation 305a21fa-7344-4800-9742-eb55c66f988b · outbound

This paper cites Can language models learn from explanations in context?.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Can language models learn from explanations in context?

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:24.053343Z

Source-reported events for the cited work

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Observation 93c3de3b-3f6b-41a5-861f-4c7873294fff · outbound

This paper cites The power of scale for parameter-efﬁcient prompt tuning.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them The power of scale for parameter-efﬁcient prompt tuning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.091649Z

Source-reported events for the cited work

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Observation 4b1ba2fe-294d-417d-89ea-e4648efad14b · outbound

This paper cites Making Large Language Models Better Reasoners with Step-Aware Verifier.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Making Large Language Models Better Reasoners with Step-Aware Verifier

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:23.855070Z

Source-reported events for the cited work

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Observation 6fbdbbb5-6289-4254-b8bc-7fd1f4492dbb · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T09:51:47.316034Z

Source-reported events for the cited work

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Observation d5a89317-0e3c-4829-95fd-1395f931bd28 · outbound

This paper cites AmbiPun: Generating Humorous Puns with Ambiguous Context.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them AmbiPun: Generating Humorous Puns with Ambiguous Context

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:23.877092Z

Source-reported events for the cited work

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Observation bd7ee3ad-eaaa-4649-9e01-5e711d4c4ba8 · outbound

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

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Show Your Work: Scratchpads for Intermediate Computation with Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-13T00:31:41.641039Z

Source-reported events for the cited work

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Observation f7d7a848-228f-4eed-9d67-9df47b46083c · outbound

This paper cites Training language models to follow instructions with human feedback.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Training language models to follow instructions with human feedback

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:23.895169Z

Source-reported events for the cited work

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Observation f1cacc23-6704-4283-9fb3-2605388a6c9e · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:13:40.454049Z

Source-reported events for the cited work

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Observation 2beb95dc-4a3d-4c72-9b2e-a45488c0319c · outbound

This paper cites Language Models are Multilingual Chain-of-Thought Reasoners.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Language Models are Multilingual Chain-of-Thought Reasoners

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:06:10.854128Z

Source-reported events for the cited work

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Observation ca686e16-f7fd-4a9b-9aa8-209b6866ec99 · outbound

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

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:23.956384Z

Source-reported events for the cited work

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Observation 44f3be94-b7ff-4f14-bad4-572625b01e0e · outbound

This paper cites Supervising Model Attention with Human Explanations for Robust Natural Language Inference.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Supervising Model Attention with Human Explanations for Robust Natural Language Inference

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:23.966839Z

Source-reported events for the cited work

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Observation 0761b1ec-59cb-4d05-a049-06bc526ccb92 · outbound

This paper cites Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:23.975130Z

Source-reported events for the cited work

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Observation 03afd620-e358-4239-917f-2c503614cdc8 · outbound

This paper cites On the machine learning of ethical judgments from natural language.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them On the machine learning of ethical judgments from natural language

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.082993Z

Source-reported events for the cited work

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Observation 531a4e0d-1024-4c0e-9a46-b30274c20eab · outbound

This paper cites Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:23.984689Z

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Observation b8965c91-0608-4935-b298-9c5ca332b766 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:15:23.995615Z

Source-reported events for the cited work

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Observation 468806fe-57e1-4417-9b95-ec668a8d4753 · outbound

This paper cites Do Prompt-Based Models Really Understand the Meaning of their Prompts?.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Do Prompt-Based Models Really Understand the Meaning of their Prompts?

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:24.008493Z

Source-reported events for the cited work

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Observation 5c90b0d1-3d6a-495e-bcc8-4df51c5342b9 · outbound

This paper cites Finetuned language models are zero-shot learners.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Finetuned language models are zero-shot learners

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.109528Z

Source-reported events for the cited work

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Observation 480e90e9-1442-463f-8a75-140e69f51cf3 · outbound

This paper cites Emergent abilities of large language models.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Emergent abilities of large language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.116398Z

Source-reported events for the cited work

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Observation d9c1d26c-5fa7-4781-868c-27109588a307 · outbound

This paper cites In: Zong, C., Xia, F., Li, W., Navigli, R.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them In: Zong, C., Xia, F., Li, W., Navigli, R

Reference 32

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Observation 9c32e6b1-ad75-46d8-8758-c7cbc61597ac · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:22:08.467971Z

Source-reported events for the cited work

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Observation 8149b2b4-0d23-40a7-a5b1-41952995e9f6 · outbound

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

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:44:37.129111Z

Source-reported events for the cited work

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Observation c31f716e-99c9-4921-9a0c-e2f375813af6 · outbound

This paper cites The concert was scheduled to be on 06/01/1943, but was delayed by one day to today. What is the date yesterday in MM/DD/YYYY?.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them The concert was scheduled to be on 06/01/1943, but was delayed by one day to today. What is the date yesterday in MM/DD/YYYY?

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.131928Z

Source-reported events for the cited work

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Observation 11a821fa-b545-45ed-b203-483b607a5982 · outbound

This paper cites If today is Christmas Eve of 1937, then today's date is December 24.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them If today is Christmas Eve of 1937, then today's date is December 24

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.071693Z

Source-reported events for the cited work

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Observation b4c151a8-9de5-422f-8989-f97b03f14ef9 · outbound

This paper cites So the answer is (D).

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them So the answer is (D)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.078440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2286532c-d893-4374-bff4-4bdadecce391 · outbound

This paper cites What is the date tomorrow in MM/DD/YYYY? Options: (A) 01/11/1961 (B) 01/03/1963 (C) 01/18/1961 (D) 10/14/1960 (E) 01/03/1982 (F) 12/03/1960 A: Let's think step by step.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them What is the date tomorrow in MM/DD/YYYY? Options: (A) 01/11/1961 (B) 01/03/1963 (C) 01/18/1961 (D) 10/14/1960 (E) 01/03/1982 (F) 12/03/1960 A: Let's think step by step

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.087626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c0534341-9d86-413c-b147-6a4f60eef4a8 · outbound

This paper cites [ { [". We will need to pop out.

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them [ { [". We will need to pop out

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.097936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2c00ffa0-59d2-42f5-bc6f-31b70274fa5f · outbound

This paper cites So the answer is (C).

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them So the answer is (C)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.102188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 348249cf-404c-401a-ac50-e7d37b76fc31 · outbound

This paper cites Amongst all the options, the only movie similar to these ones seems to be Forrest Gump (comedy, drama, romance; 1994).

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them Amongst all the options, the only movie similar to these ones seems to be Forrest Gump (comedy, drama, romance; 1994)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.124001Z

Source-reported events for the cited work

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Observation e5f91c67-0523-489a-9ca1-6afff9eb5583 · outbound

This paper cites So the answer is (D).

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them So the answer is (D)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T07:15:24.127300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation f0333b25-5c63-40e7-8461-841883c1209d · inbound

Emergent Abilities of Large Language Models cites this paper.

Emergent Abilities of Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 81

Resolution: verified exact; local_arxiv, observed 2026-05-11T07:38:38.197917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b7da897-ea02-44ca-960c-b833576979fc · inbound

Large Language Models Are Human-Level Prompt Engineers cites this paper.

Large Language Models Are Human-Level Prompt Engineers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-24T09:43:26.350456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b0cca8d-9e7c-4ed4-bb13-95e217394241 · inbound

Galactica: A Large Language Model for Science cites this paper.

Galactica: A Large Language Model for Science Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 91

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:53:22.040938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bddf11af-ec0a-4edc-b1b5-be632b0b97dc · inbound

Galactica: A Large Language Model for Science cites this paper.

Galactica: A Large Language Model for Science Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 239

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:53:22.264120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b6d7bc77-56a8-441a-870b-61d95b38c45f · inbound

PAL: Program-aided Language Models cites this paper.

PAL: Program-aided Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 37

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T05:02:50.153759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3df3f9de-a657-4dc7-bb3f-ff7ba0676cc3 · inbound

Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks cites this paper.

Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-12T16:48:27.967874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2676db0a-d9b8-45b7-ba3b-7aca5b8e5b5f · inbound

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning cites this paper.

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 55

Resolution: metadata mismatch; local_arxiv, observed 2026-05-24T09:14:16.498887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c73acf03-6702-4408-bbb9-ace0c7aded1d · 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 Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 139

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T19:03:06.201861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 68cb8867-7fa5-4d80-b8cf-4384ca190bef · inbound

BloombergGPT: A Large Language Model for Finance cites this paper.

BloombergGPT: A Large Language Model for Finance Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 111

Resolution: verified exact; local_arxiv, observed 2026-05-13T23:19:46.371352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c91e47a0-7d56-4fc0-a359-af894c635074 · inbound

Teaching Large Language Models to Self-Debug cites this paper.

Teaching Large Language Models to Self-Debug Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 126

Resolution: verified exact; local_arxiv, observed 2026-05-12T06:24:24.878306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e1bbe217-6aa7-4138-a51f-bbc01a072d8a · inbound

PaLM 2 Technical Report cites this paper.

PaLM 2 Technical Report Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 141

Resolution: metadata mismatch; local_arxiv, observed 2026-05-12T11:59:27.410500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f6b9d02f-a752-4d8e-907f-78e4dad68e3e · inbound

Orca: Progressive Learning from Complex Explanation Traces of GPT-4 cites this paper.

Orca: Progressive Learning from Complex Explanation Traces of GPT-4 Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-15T09:42:05.301378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a15a149-943a-447b-8420-a36dedb74cb0 · inbound

Simple synthetic data reduces sycophancy in large language models cites this paper.

Simple synthetic data reduces sycophancy in large language models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-16T14:48:08.691124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b085600b-ffa1-4f92-9e5a-ddb97706d2fa · inbound

Large Language Models as Optimizers cites this paper.

Large Language Models as Optimizers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-15T00:04:31.361212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2a8951ca-0d3b-4519-92c1-079ad2a6ba44 · inbound

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning cites this paper.

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-17T23:46:39.456545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2452ba05-f82e-449f-849f-ab857b1dfd5b · inbound

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers cites this paper.

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 174

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T06:11:49.617688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 58747a67-a160-4111-b430-6ef360966dbd · inbound

Baichuan 2: Open Large-scale Language Models cites this paper.

Baichuan 2: Open Large-scale Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-05-24T06:54:03.539145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6c697736-8f0d-4ef0-ad6e-9d587565a606 · inbound

Mistral 7B cites this paper.

Mistral 7B Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-24T06:14:00.034133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c92b47f-a279-4e35-9317-1968abfce620 · inbound

mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration cites this paper.

mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-05-18T03:18:51.753187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4ff6870-4368-4756-89e3-0147b47cd79b · inbound

Gemini: A Family of Highly Capable Multimodal Models cites this paper.

Gemini: A Family of Highly Capable Multimodal Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 100

Resolution: verified exact; local_arxiv, observed 2026-05-24T05:03:55.523806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation faedab8f-a203-430e-adf0-ff7ba29349b1 · inbound

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism cites this paper.

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 110

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e9d23976-1bd5-4960-8377-340f7f31e2bf · inbound

Mixtral of Experts cites this paper.

Mixtral of Experts Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-24T04:13:53.877887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 37cde923-24da-4194-b6d7-50ef693b4a29 · inbound

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models cites this paper.

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 96

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T22:50:13.705634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cf57d103-882b-41a8-b1b5-8d95b2eea44f · inbound

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads cites this paper.

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 105

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T10:36:18.171383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation adba7707-e189-428a-8f4c-33132d408ad4 · inbound

DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence cites this paper.

DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a434e9a6-928c-4296-aba4-a533e097827c · inbound

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

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-24T03:23:49.665779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8072bf01-20ef-4a54-8a5f-ed70093276c1 · inbound

Yi: Open Foundation Models by 01.AI cites this paper.

Yi: Open Foundation Models by 01.AI Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-05-13T05:47:27.911955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0b330107-2b0b-4a25-9099-5b686843acc6 · inbound

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 125

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 63d22337-1b03-41f1-8275-a38837e26b52 · inbound

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model cites this paper.

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 100

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 049ef5fc-793f-4d5f-b474-063a1f4ffff8 · inbound

Lessons from the Trenches on Reproducible Evaluation of Language Models cites this paper.

Lessons from the Trenches on Reproducible Evaluation of Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 122

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T18:44:49.770643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 825355f2-d67c-41ec-a68a-825ff5a528a9 · inbound

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark cites this paper.

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:51:05.307004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0d258557-47dd-4de1-85a6-5c5b69fa9df4 · inbound

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence cites this paper.

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-16T01:06:07.910156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation de55283b-4306-4471-b9bd-87a58a89dd0a · inbound

LiveBench: A Challenging, Contamination-Limited LLM Benchmark cites this paper.

LiveBench: A Challenging, Contamination-Limited LLM Benchmark Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-15T04:48:26.500818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 07bff1ea-da89-472d-ad0b-613b07be387b · inbound

Reinforcement Learning for LLM Post-Training: A Survey cites this paper.

Reinforcement Learning for LLM Post-Training: A Survey Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:35:50.299816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c50530f3-288b-4c2e-a742-ac28c1f143b2 · inbound

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models cites this paper.

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 271

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T06:38:37.108197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 60b36141-770e-44b0-8135-0e137ef45a1e · inbound

mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models cites this paper.

mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 77

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T06:20:36.319194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 42c61861-9c70-4723-982d-d25e9f19a944 · inbound

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model cites this paper.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T21:14:04.976702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cfb8def-264d-444d-84a0-a691b3a8f32a · inbound

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization cites this paper.

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-05-16T09:16:17.448881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2798ba0c-1687-4752-802c-a3c2dcf5fba7 · inbound

Ultra-Sparse Memory Network cites this paper.

Ultra-Sparse Memory Network Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T17:40:11.858088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42aa4d11-1f1f-458a-b147-2bdf34e8ff59 · inbound

SparseInfer: Training-free Prediction of Activation Sparsity for Fast LLM Inference cites this paper.

SparseInfer: Training-free Prediction of Activation Sparsity for Fast LLM Inference Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T17:20:31.998935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abd24fd7-f66e-41d7-8f77-b9a025491119 · inbound

A Survey on Human-Centric LLMs cites this paper.

A Survey on Human-Centric LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T16:42:07.803258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e2ee037-24a5-499c-8b7d-106542d005ee · inbound

INTELLECT-1 Technical Report cites this paper.

INTELLECT-1 Technical Report Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T04:44:26.595005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d27fd8c6-5f55-4951-986c-35a6f51d64c6 · inbound

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization cites this paper.

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T22:52:15.171771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a1352d5-c874-4066-9051-63d8d304021d · inbound

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation cites this paper.

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T22:37:56.294266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da0a4435-febc-4a1d-9d56-ecac1858699a · inbound

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling cites this paper.

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 225

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c18562d5-1b2c-48ca-959c-f8b2d384699c · inbound

Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation cites this paper.

Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T21:14:43.304595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26c89f66-151c-430b-b17b-827b4403e3d3 · inbound

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need cites this paper.

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T19:11:48.507975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8447ce42-b0ea-44f2-adf6-2e3dd18b9bb8 · inbound

From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents cites this paper.

From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-11T18:27:26.435247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c037a59-b852-4e65-be2d-3d774f1c1c0f · inbound

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers cites this paper.

GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T16:52:28.882333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b93d489-5b7d-40f2-9c71-449c20c378d5 · inbound

Codenames as a Benchmark for Large Language Models cites this paper.

Codenames as a Benchmark for Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T15:04:39.150171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21adfeca-1a8f-4787-92bc-2f1998ab195b · inbound

C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness cites this paper.

C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T14:47:03.996961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63fe2f30-9075-4174-899b-f79055ea0f04 · inbound

Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge cites this paper.

Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T14:04:26.362213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb619b33-7315-48c9-baeb-8f9a99d883b3 · inbound

Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference cites this paper.

Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T13:49:12.674400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d04ef4d7-7c82-4939-a46a-3e5571cc9b40 · inbound

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs cites this paper.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.021871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38d0ece0-5b9d-4acd-89ef-12140dc30d40 · inbound

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment cites this paper.

Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T12:16:31.980152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99eb43db-ffe6-4276-9c02-268a8ee5af72 · inbound

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis cites this paper.

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T11:59:28.415460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd0be1f3-7eab-4981-b200-e6298c3800e8 · inbound

Language Models as Continuous Self-Evolving Data Engineers cites this paper.

Language Models as Continuous Self-Evolving Data Engineers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T11:40:52.554445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e92359ad-a340-49ab-85d3-99b5a7882fcc · inbound

Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying cites this paper.

Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T11:37:23.437515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36bc1ab2-a1db-49a9-a90f-bcb34ab95f26 · inbound

Multi-matrix Factorization Attention cites this paper.

Multi-matrix Factorization Attention Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T00:55:04.003033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88b4f048-4e97-4b13-b09a-0352066c6f09 · inbound

Aligning Large Language Models for Faithful Integrity Against Opposing Argument cites this paper.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T22:35:43.939107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae3590d3-ca72-456d-945f-488df1a5c8f7 · inbound

InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion cites this paper.

InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T22:08:35.194067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7f41a0a-449a-4f73-bbb6-ff013a3286e5 · inbound

A Survey on Large Language Models with some Insights on their Capabilities and Limitations cites this paper.

A Survey on Large Language Models with some Insights on their Capabilities and Limitations Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 218

Resolution: unresolved; no resolver link, observed 2026-08-10T22:17:56.251776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c19a6f9-f116-45e4-8275-9764fc426a7e · inbound

Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark cites this paper.

Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:59.717663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0df76838-29dd-462a-9686-812e9eef82e6 · inbound

TAPO: Task-Referenced Adaptation for Prompt Optimization cites this paper.

TAPO: Task-Referenced Adaptation for Prompt Optimization Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:57:57.261868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5461f440-0748-4495-9662-88d161f2d8f0 · inbound

ViBidirectionMT-Eval: Machine Translation for Vietnamese-Chinese and Vietnamese-Lao language pair cites this paper.

ViBidirectionMT-Eval: Machine Translation for Vietnamese-Chinese and Vietnamese-Lao language pair Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T20:25:06.274531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfcacde6-05fc-4e24-8076-b7417dd6e3bf · inbound

Domain Adaptation of Foundation LLMs for e-Commerce cites this paper.

Domain Adaptation of Foundation LLMs for e-Commerce Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:29.704958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 208f4aa8-0c4b-45f6-8b11-a4772b66bdd9 · inbound

DNA 1.0 Technical Report cites this paper.

DNA 1.0 Technical Report Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T19:04:35.011026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbd223b4-5fec-4f2a-a188-f0e9c7955c2d · inbound

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

RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems? Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T18:30:56.673473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e1237a-0aaf-425e-955c-4d8a3b1f9292 · inbound

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities cites this paper.

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T17:34:52.494040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89aae155-c70d-49c6-a29d-b73f2fcc3f50 · inbound

Spurious Forgetting in Continual Learning of Language Models cites this paper.

Spurious Forgetting in Continual Learning of Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T16:13:49.393292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be5bb4b6-fd4f-4e69-9f89-70d457fa39e0 · inbound

Adaptive Testing for LLM-Based Applications: A Diversity-based Approach cites this paper.

Adaptive Testing for LLM-Based Applications: A Diversity-based Approach Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T15:57:28.884092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb2f21f-e0ef-42ee-8ea8-e223838bbc8b · inbound

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models cites this paper.

Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T15:50:21.983664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cae2543c-5c9c-464f-9500-08e524451f5c · inbound

On the Reasoning Capacity of AI Models and How to Quantify It cites this paper.

On the Reasoning Capacity of AI Models and How to Quantify It Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:37.480762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f12650d-6bb5-41b9-b0a4-f2d72c38eb7e · inbound

StaICC: Standardized Evaluation for Classification Task in In-context Learning cites this paper.

StaICC: Standardized Evaluation for Classification Task in In-context Learning Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T14:06:14.467857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91f144c8-4bea-4f08-9802-e3d79dc3f769 · inbound

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow cites this paper.

LLM-AutoDiff: Auto-Differentiate Any LLM Workflow Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T11:33:24.669461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c70adb-9721-44a4-a224-80e0b12dad0b · inbound

BTS: Harmonizing Specialized Experts into a Generalist LLM cites this paper.

BTS: Harmonizing Specialized Experts into a Generalist LLM Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T21:59:01.305357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ff5323-6dbe-4dc2-9d6f-bc7aaffc4c21 · inbound

Process Reinforcement through Implicit Rewards cites this paper.

Process Reinforcement through Implicit Rewards Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 131

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T20:23:31.037818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1568f826-7b6a-4846-b1cf-8bc21689a904 · inbound

Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression cites this paper.

Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:17:31.061104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ff57f1f-cd73-4af4-902b-9fd4fde3f128 · inbound

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model cites this paper.

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 110

Resolution: metadata mismatch; local_arxiv, observed 2026-05-13T17:30:02.885190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5c3ee4f6-2317-4594-92e0-52fca4eafe53 · inbound

Minerva: A Programmable Memory Test Benchmark for Language Models cites this paper.

Minerva: A Programmable Memory Test Benchmark for Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T05:02:35.797402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cecd3773-fd12-44e2-90f6-cbc869d0af81 · inbound

It's All in The [MASK]: Simple Instruction-Tuning Enables BERT-like Masked Language Models As Generative Classifiers cites this paper.

It's All in The [MASK]: Simple Instruction-Tuning Enables BERT-like Masked Language Models As Generative Classifiers Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T00:49:12.213813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9b77044-99b1-45fe-9c5d-727777069825 · inbound

CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance cites this paper.

CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T12:16:27.416734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00f716b3-e7bf-4484-b15f-2057fbb985da · inbound

Automatic Evaluation of Healthcare LLMs Beyond Question-Answering cites this paper.

Automatic Evaluation of Healthcare LLMs Beyond Question-Answering Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-08T14:46:29.688673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a928af93-6277-4e58-a27c-acd6cafd6f1f · inbound

KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems cites this paper.

KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T13:08:22.864918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2c5ac7a-a6e2-4931-a46e-a312c619e32d · inbound

CryptoX : Compositional Reasoning Evaluation of Large Language Models cites this paper.

CryptoX : Compositional Reasoning Evaluation of Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T18:34:26.786749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83636a88-52f1-4891-b66d-98e554e2fb56 · inbound

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters cites this paper.

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T13:44:00.641561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6921b2ba-1521-4e3d-8b4b-623878249861 · inbound

Salamandra Technical Report cites this paper.

Salamandra Technical Report Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 186

Resolution: unresolved; no resolver link, observed 2026-08-08T04:58:34.389582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45500c75-d4ab-429d-81d9-7d08780ac842 · inbound

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models cites this paper.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.662978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4e11429-2c0e-4206-8140-eadeb4db496b · inbound

Large Language Diffusion Models cites this paper.

Large Language Diffusion Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f88203ad-f0c2-4061-94a4-2b92f6933b7b · 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 Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 239

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T08:02:23.630557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a083efec-094a-4cb1-925f-85ccbfbf1517 · inbound

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention cites this paper.

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 73

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T23:46:30.126324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cfedbd3e-8392-472e-8469-63f2cc848781 · inbound

InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models cites this paper.

InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:15:24.133279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7867c7a8-2bc5-40d9-92e9-293fb68fe583 · inbound

MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space cites this paper.

MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:23.497019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0961d143-6f64-4c50-985e-aec82a16c75b · inbound

FarsEval-PKBETS: A new diverse benchmark for evaluating Persian large language models cites this paper.

FarsEval-PKBETS: A new diverse benchmark for evaluating Persian large language models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:47:09.996193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1652c657-4c63-43ea-8e8d-05ef74895200 · inbound

Trillion 7B Technical Report cites this paper.

Trillion 7B Technical Report Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:06.079010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d90395e-ccc4-4a76-8e28-8fe87d62527f · inbound

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism cites this paper.

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:29.443099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 187cc37e-634a-4aa6-92e8-8c403a0c25f0 · inbound

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges cites this paper.

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T06:05:12.114299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b2eb093-5e1e-4e85-aee7-f00ab4adabc8 · inbound

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review cites this paper.

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 131

Resolution: verified exact; local_arxiv, observed 2026-05-15T02:57:38.501204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 050e536f-97e7-4336-ac09-e6c678f15308 · inbound

Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection cites this paper.

Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T05:30:51.296395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e6519e7-9c1c-4eed-80db-d4a23fefdf07 · inbound

100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models cites this paper.

100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-16T04:43:44.486624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.