Ask-E: An Environment for Calibrated Question Generation | Paper Citation Record

Observation efa5d81a-ea7e-4542-bf75-bbb5206e26c7 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

Ask-E: An Environment for Calibrated Question Generation Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.500125Z

Source-reported events for the cited work

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Observation c656d1ed-f976-4f43-8346-17488b02d967 · outbound

This paper cites Aimo validation aime.

Ask-E: An Environment for Calibrated Question Generation Aimo validation aime

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.835244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1c4e43f-e11c-44d2-9268-e801337f9a0e · outbound

This paper cites Analysis of llms for educational question classification and generation.

Ask-E: An Environment for Calibrated Question Generation Analysis of llms for educational question classification and generation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.826511Z

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Observation fc7fe6e0-3885-4b75-bf24-b4e8498a683c · outbound

This paper cites System card: Claude opus 4.7.

Ask-E: An Environment for Calibrated Question Generation System card: Claude opus 4.7

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.817292Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1a194d04-16af-48b1-8f22-e8867c104af6 · outbound

This paper cites System card: Claude opus 5.

Ask-E: An Environment for Calibrated Question Generation System card: Claude opus 5

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.807969Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cd994603-6283-4096-9268-0d8779c6a027 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Ask-E: An Environment for Calibrated Question Generation Constitutional AI: Harmlessness from AI Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.522196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d11c07f1-1814-4591-8be7-dcb130a3dd06 · outbound

This paper cites Verifiers: Environments for llm reinforcement learning.

Ask-E: An Environment for Calibrated Question Generation Verifiers: Environments for llm reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.798183Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2a33e1fc-0531-41ec-b56d-5657949b5c17 · outbound

This paper cites MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention.

Ask-E: An Environment for Calibrated Question Generation MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.529325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c36d90-5999-4462-809d-ca347e4bc748 · outbound

This paper cites Self-Questioning Language Models.

Ask-E: An Environment for Calibrated Question Generation Self-Questioning Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.533353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b70dd9b-661e-499b-91e7-0b64f3646c59 · outbound

This paper cites Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.

Ask-E: An Environment for Calibrated Question Generation Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.537113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6e85dcd-be73-4c7e-87ee-4d373da1fed9 · outbound

This paper cites U-math: A university-level benchmark for evaluating mathematical skills in llms.

Ask-E: An Environment for Calibrated Question Generation U-math: A university-level benchmark for evaluating mathematical skills in llms

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.541212Z

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Unavailable: canonical work link unavailable.

Observation 5fe46c7a-337b-4587-975b-ffe74e458367 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Ask-E: An Environment for Calibrated Question Generation Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.544790Z

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Observation bfbf7f72-bd7f-46fb-9747-130752a64c9e · outbound

This paper cites Deepseek-v4: Towards highly eﬀicient million-token context intelligence, 2026.

Ask-E: An Environment for Calibrated Question Generation Deepseek-v4: Towards highly eﬀicient million-token context intelligence, 2026

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.788567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6fd9ef3-91cf-456e-80bf-b6fa8b9a2bb5 · outbound

This paper cites Beyond benchmarks: Matharena as an evaluation platform for mathematics with llms.

Ask-E: An Environment for Calibrated Question Generation Beyond benchmarks: Matharena as an evaluation platform for mathematics with llms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.779230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 502b1b30-aeec-434c-a469-96a9eae78ce8 · outbound

This paper cites How useful are educational questions generated by large language models? InInternational Conference on Artificial Intelligence in Education, pages 536–542.

Ask-E: An Environment for Calibrated Question Generation How useful are educational questions generated by large language models? InInternational Conference on Artificial Intelligence in Education, pages 536–542

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.769622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b97c7e30-5b05-44f8-b95d-d1498fbee8f1 · outbound

This paper cites When judgment becomes noise: How design failures in llm judge benchmarks silently undermine validity.

Ask-E: An Environment for Calibrated Question Generation When judgment becomes noise: How design failures in llm judge benchmarks silently undermine validity

Reference 16

Resolution: verified exact; raw_fallback, observed 2026-08-10T18:11:51.415104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5e14ca1b-cd89-40ca-be09-6ed819885903 · outbound

This paper cites Riemann-Bench: A Benchmark for Moonshot Mathematics.

Ask-E: An Environment for Calibrated Question Generation Riemann-Bench: A Benchmark for Moonshot Mathematics

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.566726Z

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Observation 3a3848f3-e475-4b3c-b983-4fa9d7185d29 · outbound

This paper cites The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains.

Ask-E: An Environment for Calibrated Question Generation The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.571844Z

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Observation 1908bcf6-1392-4b46-ba21-976d9c9edb02 · outbound

This paper cites Gemini 3 flash model card.

Ask-E: An Environment for Calibrated Question Generation Gemini 3 flash model card

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.759179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 53591b83-6564-4599-aa8e-4a31887a1be7 · outbound

This paper cites Gemini 3.1 flash-lite model card.

Ask-E: An Environment for Calibrated Question Generation Gemini 3.1 flash-lite model card

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.749215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6c9421a9-4842-4779-b5b4-54b16209aaee · outbound

This paper cites Gemini 3.1 pro model card.

Ask-E: An Environment for Calibrated Question Generation Gemini 3.1 pro model card

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.739070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3a2faedb-eef0-4eec-a377-aa6d04d4d05e · outbound

This paper cites The Llama 3 Herd of Models.

Ask-E: An Environment for Calibrated Question Generation The Llama 3 Herd of Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.586404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0689c12c-473c-4bad-94a5-3b7222610bc0 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Ask-E: An Environment for Calibrated Question Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.590169Z

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Unavailable: canonical work link unavailable.

Observation cc36bd4f-8cc3-4f4a-ae6d-c12fcbb776d3 · outbound

This paper cites Curiosity-driven Red-teaming for Large Language Models.

Ask-E: An Environment for Calibrated Question Generation Curiosity-driven Red-teaming for Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.594053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 717ccbca-0a41-4166-934d-a655cdbebf2b · outbound

This paper cites R-Zero: Self-Evolving Reasoning LLM from Zero Data.

Ask-E: An Environment for Calibrated Question Generation R-Zero: Self-Evolving Reasoning LLM from Zero Data

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.597606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 294e66dd-40f2-4607-a481-a6879d9ca561 · outbound

This paper cites Prime-rl, 2025.

Ask-E: An Environment for Calibrated Question Generation Prime-rl, 2025

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.728622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6981e59b-bf3b-4d63-a84e-ae727692eeb7 · outbound

This paper cites Dynabench: Rethinking benchmarking in nlp.

Ask-E: An Environment for Calibrated Question Generation Dynabench: Rethinking benchmarking in nlp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.715982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 446113b1-2f3f-45c2-8d67-7db549fc3486 · outbound

This paper cites Language self-play for data-free training.

Ask-E: An Environment for Calibrated Question Generation Language self-play for data-free training

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.608630Z

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Observation 13df65dd-8607-4b72-b58b-5ff62c1e0e6d · outbound

This paper cites Gon- zalez, Hao Zhang, and Ion Stoica.

Ask-E: An Environment for Calibrated Question Generation Gon- zalez, Hao Zhang, and Ion Stoica

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.704162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8967a8b7-97f8-4b70-9447-c1069477f143 · outbound

This paper cites Math-verify: Math verification library, 2025.

Ask-E: An Environment for Calibrated Question Generation Math-verify: Math verification library, 2025

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.694071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 470ef701-a4ee-4aab-bebe-9a93953c26d2 · outbound

This paper cites Rewardbench: Evaluating reward models for language modeling.

Ask-E: An Environment for Calibrated Question Generation Rewardbench: Evaluating reward models for language modeling

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.683200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4db5a174-e386-428a-8630-b85c6a3e28d2 · outbound

This paper cites Questbench: Can llms ask the right question to acquire informa- tion in reasoning tasks? arXiv preprint arXiv:2503.22674, 2025.

Ask-E: An Environment for Calibrated Question Generation Questbench: Can llms ask the right question to acquire informa- tion in reasoning tasks? arXiv preprint arXiv:2503.22674, 2025

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.621902Z

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Observation 801b1703-644c-45e4-a82d-4a68979013a2 · outbound

This paper cites AutoBencher: Towards Declarative Benchmark Construction.

Ask-E: An Environment for Calibrated Question Generation AutoBencher: Towards Declarative Benchmark Construction

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.625286Z

Source-reported events for the cited work

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Observation eccba2ec-b5c9-4966-9b61-602506596432 · outbound

This paper cites Ministral 3.

Ask-E: An Environment for Calibrated Question Generation Ministral 3

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.629131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 830bbac3-fa23-4507-8b8c-9dc6609eb7a9 · outbound

This paper cites Spiral: Self-play on zero-sum games incentivizes reasoning via multi-agent multi-turn reinforcement learning.

Ask-E: An Environment for Calibrated Question Generation Spiral: Self-play on zero-sum games incentivizes reasoning via multi-agent multi-turn reinforcement learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.632730Z

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Unavailable: canonical work link unavailable.

Observation 6df9bb33-e2e9-4959-8628-844643a2d72e · outbound

This paper cites Spice: Self-play in corpus environments improves reasoning.

Ask-E: An Environment for Calibrated Question Generation Spice: Self-play in corpus environments improves reasoning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.635959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0f1b392-dc92-424d-93f5-f5ee02f51058 · outbound

This paper cites Decoupled Weight Decay Regularization.

Ask-E: An Environment for Calibrated Question Generation Decoupled Weight Decay Regularization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.639187Z

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Observation bd7f4f53-3487-4a99-8e93-2d96dcf06b93 · outbound

This paper cites Towards robust mathematical rea- soning.

Ask-E: An Environment for Calibrated Question Generation Towards robust mathematical rea- soning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.672607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4d3cd2da-5fee-41a4-ad8e-252993644ade · outbound

This paper cites Learning to ask informative questions: En- hancing llms with preference optimization and expected information gain.

Ask-E: An Environment for Calibrated Question Generation Learning to ask informative questions: En- hancing llms with preference optimization and expected information gain

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.661795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1b9e3709-9074-4b7a-964f-24c0a015d9fa · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

Ask-E: An Environment for Calibrated Question Generation GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.649079Z

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Observation 2337c8a9-bd16-4d52-b42b-e13741159a0b · outbound

This paper cites gpt-oss-120b & gpt-oss-20b model card, 2025.

Ask-E: An Environment for Calibrated Question Generation gpt-oss-120b & gpt-oss-20b model card, 2025

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.650788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 77a9dd4d-8c7f-40d2-9ff4-9b79293444a0 · outbound

This paper cites Aime 2025.

Ask-E: An Environment for Calibrated Question Generation Aime 2025

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.641053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b58bcdae-2001-4096-a004-b3c98b8db571 · outbound

This paper cites How to Get Your LLM to Generate Challenging Problems for Evaluation.

Ask-E: An Environment for Calibrated Question Generation How to Get Your LLM to Generate Challenging Problems for Evaluation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.660114Z

Source-reported events for the cited work

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Observation 729b13b6-6126-4d9b-9e34-362ce2f64700 · outbound

This paper cites Do reasoning models ask better questions? a formal information-theoretic analysis on multi-turn llm games.

Ask-E: An Environment for Calibrated Question Generation Do reasoning models ask better questions? a formal information-theoretic analysis on multi-turn llm games

Reference 44

Resolution: verified exact; raw_fallback, observed 2026-08-10T18:11:51.021984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c1579d25-0b44-48db-9f2b-2aae01ab2e96 · outbound

This paper cites Qwen3.5: Towards native multimodal agents, February 2026.

Ask-E: An Environment for Calibrated Question Generation Qwen3.5: Towards native multimodal agents, February 2026

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.667571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ade2654e-b2f0-4134-95f6-032253e180fa · outbound

This paper cites AI-Assisted Generation of Difficult Math Questions.

Ask-E: An Environment for Calibrated Question Generation AI-Assisted Generation of Difficult Math Questions

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.670903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 798d43d2-91fb-4ad5-8c88-5000b2d23e75 · outbound

This paper cites OpenAI GPT-5 System Card.

Ask-E: An Environment for Calibrated Question Generation OpenAI GPT-5 System Card

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.674344Z

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Unavailable: canonical work link unavailable.

Observation 04dfdb9b-58a3-443b-b82c-a91d05bb4d86 · outbound

This paper cites Beyondbench: Contamination-resistant evaluation of reasoning in language models.

Ask-E: An Environment for Calibrated Question Generation Beyondbench: Contamination-resistant evaluation of reasoning in language models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.624628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 84b9f1d0-f9c0-4e3a-8793-3930d68f25c2 · outbound

This paper cites Debate, train, evolve: Self-evolution of language model reasoning.

Ask-E: An Environment for Calibrated Question Generation Debate, train, evolve: Self-evolution of language model reasoning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.614286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7b02d0c2-6db0-42b1-b74b-4e51bc486618 · outbound

This paper cites Question generation for adaptive education.

Ask-E: An Environment for Calibrated Question Generation Question generation for adaptive education

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.603605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3115606-8d7d-4b7a-9c8c-e296f1eaddaa · outbound

This paper cites OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization.

Ask-E: An Environment for Calibrated Question Generation OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.688938Z

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Observation 2fdd5fdb-76be-4f82-a8d9-9af7c4efd273 · outbound

This paper cites an unresolved cited work.

Ask-E: An Environment for Calibrated Question Generation Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.692728Z

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Unavailable: canonical work link unavailable.

Observation cef34fe4-036b-4eb9-b6c4-173021f42e1b · outbound

This paper cites Learning to ask: When llm agents meet unclear instruction.

Ask-E: An Environment for Calibrated Question Generation Learning to ask: When llm agents meet unclear instruction

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.585605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e86daf76-c118-47aa-a152-9bd2abd175f4 · outbound

This paper cites Qg-net: a data-driven question generation model for educational content.

Ask-E: An Environment for Calibrated Question Generation Qg-net: a data-driven question generation model for educational content

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.575074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3827a995-fe82-479f-827c-fad9aa34672a · outbound

This paper cites Qwen3 Technical Report.

Ask-E: An Environment for Calibrated Question Generation Qwen3 Technical Report

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.703480Z

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Observation 332e910e-b7a3-4796-8889-a5c479063466 · outbound

This paper cites CodeClash: Benchmarking Goal-Oriented Software Engineering.

Ask-E: An Environment for Calibrated Question Generation CodeClash: Benchmarking Goal-Oriented Software Engineering

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.707243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f4e9e39-805f-47c8-8735-de2c4b13afd7 · outbound

This paper cites Spell: Self-play reinforcement learning for evolving long-context language models.

Ask-E: An Environment for Calibrated Question Generation Spell: Self-play reinforcement learning for evolving long-context language models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.712039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db137e58-d118-44bd-8737-4d70c5a4cf97 · outbound

This paper cites Self-rewarding language models.

Ask-E: An Environment for Calibrated Question Generation Self-rewarding language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.564988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 305a8755-5e38-4b90-a0f2-82ade91e0d16 · outbound

This paper cites Absolute Zero: Reinforced Self-play Reasoning with Zero Data.

Ask-E: An Environment for Calibrated Question Generation Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.719307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e9ff3b-cd70-42d8-837b-aec9cd89632d · outbound

This paper cites GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?.

Ask-E: An Environment for Calibrated Question Generation GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.722837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a1ffbb2-14d1-4c9c-96a8-296db5c6fda7 · outbound

This paper cites DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks.

Ask-E: An Environment for Calibrated Question Generation DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.726572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6014fca-27b9-49a8-a167-f13dd9541fe1 · outbound

This paper cites Twinstar: A novel design for enhanced test question generation using dual-llm engine.

Ask-E: An Environment for Calibrated Question Generation Twinstar: A novel design for enhanced test question generation using dual-llm engine

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.552784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3f023ab3-82ef-463e-95ad-277d9990bd64 · outbound

This paper cites no solution.

Ask-E: An Environment for Calibrated Question Generation no solution

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:11:51.541583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b95facc-b5c9-4eab-b082-ebfa1f38030c · outbound

This paper cites Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs.

Ask-E: An Environment for Calibrated Question Generation Beyond Benchmarks: MathArena as an Evaluation Platform for Mathematics with LLMs

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-10T18:11:50.555244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

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