Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-22T09:44:55.200796Z
Paper Citation Record · LEDGER
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
As of 5 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 9 inbound Pith citation observations for arXiv:2605.06597.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T03:23:31.294548Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-03T16:28:38.401236Z
Reference resolution
57 of 57 outbound references displayed
- verified exact23
- verified fuzzy33
- unresolved0
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bf0ea2f4-2e3e-4399-9e4b-4c937499208d · outbound
This paper cites Visual instruction tuning.NeurIPS, 36:34892–34916.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Visual instruction tuning.NeurIPS, 36:34892–34916
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.163536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c98d2f70-6f33-4c48-b084-62748434ec6a · outbound
This paper cites Alpaca: A strong, replicable instruction-following model.Stanford Center for Research on Foundation Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Alpaca: A strong, replicable instruction-following model.Stanford Center for Research on Foundation Models
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.167768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0a995058-3ef6-4a69-a645-13d18fe14540 · outbound
This paper cites Visual program distillation: Distilling tools and programmatic reasoning into vision-language models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Visual program distillation: Distilling tools and programmatic reasoning into vision-language models
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.179386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 79763869-7775-4b2e-a92f-1984029b6604 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.597636Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 452e58b1-a3eb-4097-9d4c-bb9b2b72d130 · outbound
This paper cites Simpo: Simple preference optimization with a reference-free reward.NeurIPS, 37:124198–124235.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Simpo: Simple preference optimization with a reference-free reward.NeurIPS, 37:124198–124235
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.171751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a7239eca-27d9-481d-9683-0579ddc576d0 · outbound
This paper cites Qwen3 Technical Report.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Qwen3 Technical Report
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.549762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation adfeed04-bfc1-4550-ac49-1bb7fcc64853 · outbound
This paper cites Distilling the Knowledge in a Neural Network.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Distilling the Knowledge in a Neural Network
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.516034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation feebbecd-09b7-4906-ad7f-a3b71c119d03 · outbound
This paper cites Gpt4all: An ecosystem of open source compressed language models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Gpt4all: An ecosystem of open source compressed language models
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.280979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 54fb25f6-94c1-47db-bd32-ab54b844ee8d · outbound
This paper cites AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.555101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fd6d12e2-bdf2-4c92-b4bd-209b5e107691 · outbound
This paper cites Self-Distillation Enables Continual Learning.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Self-Distillation Enables Continual Learning
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.561041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4e182904-aa5b-46b5-82da-299837eff201 · outbound
This paper cites A Survey on Knowledge Distillation of Large Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models A Survey on Knowledge Distillation of Large Language Models
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.567310Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0f6137cb-ba39-4d5e-a5e3-64f51e2d03e4 · outbound
This paper cites Companioncast: A multi-agent conversational ai framework with spatial audio for social co-viewing experiences.ACM CHI 2026 Workshop on Human-Agent Collaboration.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Companioncast: A multi-agent conversational ai framework with spatial audio for social co-viewing experiences.ACM CHI 2026 Workshop on Human-Agent Collaboration
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.220973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 09f8ed6e-d735-4bbe-abea-30cf4ca3c3ea · outbound
This paper cites Harnessing the wisdom of the inner crowd.Trends in cognitive sciences, 18(10):504–506.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Harnessing the wisdom of the inner crowd.Trends in cognitive sciences, 18(10):504–506
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.260394Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation affd457a-f51a-490b-9c43-9ec4bacaaf54 · outbound
This paper cites Instruction induction: From few examples to natural language task descriptions.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Instruction induction: From few examples to natural language task descriptions
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.198913Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 690b4091-221e-417e-9523-b8ea31a326aa · outbound
This paper cites Wordnet: a lexical database for english.Communications of the ACM, 38(11):39–41.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Wordnet: a lexical database for english.Communications of the ACM, 38(11):39–41
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.202799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 676ec211-4cba-4d4d-a5d7-cd2552158569 · outbound
This paper cites Ppdb: The paraphrase database.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Ppdb: The paraphrase database
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.206805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 27356a45-645f-49ea-b48a-ee4953e9dada · outbound
This paper cites Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.195063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b63e53ca-c56c-4d8b-9a0a-d8119bd922da · outbound
This paper cites Less is more: Task-aware layer-wise distillation for language model compression.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Less is more: Task-aware layer-wise distillation for language model compression
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.187277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5e85aa15-7aca-4e74-b8ee-37139a97f668 · outbound
This paper cites Minilmv2: Multi-head self- attention relation distillation for compressing pretrained transformers.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Minilmv2: Multi-head self- attention relation distillation for compressing pretrained transformers
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.191290Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5bd81e30-32c1-4838-8cb1-d6ad9186bd3d · outbound
This paper cites On-policy distillation of language models: Learning from self-generated mistakes.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models On-policy distillation of language models: Learning from self-generated mistakes
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.216209Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation aa74d1fe-6ba0-405b-a609-f948bf8eb8f1 · outbound
This paper cites Embarrassingly Simple Self-Distillation Improves Code Generation.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Embarrassingly Simple Self-Distillation Improves Code Generation
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-06-26T01:15:18.478768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d76df9da-c4c0-4508-b293-3203f1d0e9a9 · outbound
This paper cites Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.492347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8bd939ff-4c9c-485f-b18d-317011d7f493 · outbound
This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Learn to explain: Multimodal reasoning via thought chains for science question answering
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.183370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 18c2b5ce-25ac-41e2-9d92-81ced4002c61 · outbound
This paper cites Gpqa: A graduate-level google-proof q&a benchmark.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Gpqa: A graduate-level google-proof q&a benchmark
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.155795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bbe7495d-9f1d-4513-ba20-eed4106fe9a9 · outbound
This paper cites Explain yourself! leveraging language models for commonsense reasoning.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Explain yourself! leveraging language models for commonsense reasoning
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.143790Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 26f891b1-2a16-4c37-b22c-007057030dd4 · outbound
This paper cites Commonsenseqa: A question answering challenge targeting commonsense knowledge.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Commonsenseqa: A question answering challenge targeting commonsense knowledge
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.151593Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ad08a83d-20c2-4414-92a8-da1dbdd154b3 · outbound
This paper cites Program Synthesis with Large Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Program Synthesis with Large Language Models
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.498232Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0e6ce38b-40ab-4ee0-ba29-26ac2bfca03d · outbound
This paper cites Evaluating Large Language Models Trained on Code.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Evaluating Large Language Models Trained on Code
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.538387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5734dbab-b3aa-4507-a0bd-34ec4b2b9f28 · outbound
This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.618119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1af10851-4a66-42a4-96f2-14c0eb70ff20 · outbound
This paper cites Qwen2.5: A party of foundation models, September 2024.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Qwen2.5: A party of foundation models, September 2024
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.255839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2f9919d1-4cc9-4ebf-9290-14826dfbd85e · outbound
This paper cites The Llama 3 Herd of Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models The Llama 3 Herd of Models
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.607286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e71ff4a9-1e8d-432f-93cc-b585791c35e7 · outbound
This paper cites Gemma 3 Technical Report.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Gemma 3 Technical Report
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.612179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 69b8405f-d7e6-413f-b4b2-c05a0a50f864 · outbound
This paper cites How context affects language models’ factual predictions.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models How context affects language models’ factual predictions
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.264776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 022942bc-4799-4885-9bcf-e5201d127ae0 · outbound
This paper cites Lost in the middle: How language models use long contexts.TACL, 12:157–173.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Lost in the middle: How language models use long contexts.TACL, 12:157–173
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.272905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d166c954-85cf-47ba-bf87-185eee76ecf2 · outbound
This paper cites Federated continual learning via knowledge fusion: A survey.TKDE, 36(8):3832–3850.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Federated continual learning via knowledge fusion: A survey.TKDE, 36(8):3832–3850
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.251515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f9bdec3d-0a40-4a42-94e0-870d806c4632 · outbound
This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Catastrophic interference in connectionist networks: The sequential learning problem
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.243205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a9643c08-bce2-4d10-9ccb-d46cbe9a4ff0 · outbound
This paper cites A comprehensive survey of continual learning: Theory, method and application.TPAMI, 46(8):5362–5383.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models A comprehensive survey of continual learning: Theory, method and application.TPAMI, 46(8):5362–5383
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.247569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 19ca7aab-eeb8-4445-96c9-b8665d94bdeb · outbound
This paper cites MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-06-02T02:04:13.222420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation dfe4168b-633c-4021-b0c2-d5ca2a3e244e · outbound
This paper cites A Survey of On-Policy Distillation for Large Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models A Survey of On-Policy Distillation for Large Language Models
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.585618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 2316f8ab-056a-4058-9cdc-b4901d134cb5 · outbound
This paper cites Privileged Information Distillation for Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Privileged Information Distillation for Language Models
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.602765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9e133665-145b-464c-bf79-f937d682d39e · outbound
This paper cites Minillm: Knowledge distillation of large language models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Minillm: Knowledge distillation of large language models
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.277233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 89b6c35c-e2fe-417e-bfc9-bb569f5be156 · outbound
This paper cites Distillm: Towards streamlined distillation for large language models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Distillm: Towards streamlined distillation for large language models
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.268715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cc193bb3-b8f8-47c2-a837-69a382876f16 · outbound
This paper cites Entropy-Aware On-Policy Distillation of Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Entropy-Aware On-Policy Distillation of Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:02:00.585154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6830b879-c527-4129-aff8-a3cbfaa58112 · outbound
This paper cites Vla-opd: Bridging offline sft and online rl for vision-language-action models via on-policy distillation.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Vla-opd: Bridging offline sft and online rl for vision-language-action models via on-policy distillation
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T09:46:22.526439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 14fd2344-1599-435d-bd3b-703575a9cc81 · outbound
This paper cites SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.510338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5789fdb2-a335-4cac-a115-a4d4bcef9fb3 · outbound
This paper cites Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models
Reference 46
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.544088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 05b67307-e2c9-418e-b6aa-1d9dfc5d00a3 · outbound
This paper cites Reinforcement Learning via Self-Distillation.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Reinforcement Learning via Self-Distillation
Reference 47
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3cef06ed-8ee4-4643-bf0a-c036eedf651d · outbound
This paper cites Energy and policy considerations for deep learning in nlp.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Energy and policy considerations for deep learning in nlp
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.211628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 365b2087-210c-4e16-9692-60d1a554051b · outbound
This paper cites Green ai.Communications of the ACM, 63(12):54–63.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Green ai.Communications of the ACM, 63(12):54–63
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.147821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 67a529f0-68e8-4f0c-be00-0203d412a9ad · outbound
This paper cites Carbon Emissions and Large Neural Network Training.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Carbon Emissions and Large Neural Network Training
Reference 50
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.579905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6f715edb-1970-493a-9ca2-a69068e8d22f · outbound
This paper cites CodeCarbon: mlco2/codecarbon v2.4.1, May 2024.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models CodeCarbon: mlco2/codecarbon v2.4.1, May 2024
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.159620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cf2ad82a-5124-4597-bd04-abe74ea98be0 · outbound
This paper cites Quantifying the Carbon Emissions of Machine Learning.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Quantifying the Carbon Emissions of Machine Learning
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:46:22.486306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c6156179-da57-4aa3-804e-22f45b2f19c5 · outbound
This paper cites Pue: a comprehensive examination of the metric.White paper, 49:52.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Pue: a comprehensive examination of the metric.White paper, 49:52
Reference 53
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 983644f3-fb4b-4c98-9db0-a49fab2f5c71 · outbound
This paper cites Lora: Low-rank adaptation of large language models.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Lora: Low-rank adaptation of large language models
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.234417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5fa592d2-4dee-44fb-a2bd-80acefbc3967 · outbound
This paper cites Decoupled weight decay regularization.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Decoupled weight decay regularization
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.238820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 34c5d18b-5985-4f76-b108-824f64325363 · outbound
This paper cites Efficient memory management for large language model serving with pagedattention.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Efficient memory management for large language model serving with pagedattention
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-22T09:46:23.225347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e4dcc63d-f6cf-4958-a8d6-ba3bf90721ae · outbound
This paper cites Beyond magic words: Sharpness-aware prompt evolving for robust large language models with tare.
UniSD: Towards a Unified Self-Distillation Framework for Large Language Models Beyond magic words: Sharpness-aware prompt evolving for robust large language models with tare
Reference 57
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-22T09:46:23.230169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation d754a85a-330c-460b-bb14-6a5ec20fa880 · inbound
A Brief Overview: On-Policy Self-Distillation In Large Language Models cites this paper.
A Brief Overview: On-Policy Self-Distillation In Large Language Models UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 20
- Resolution
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- local_arxiv, observed 2026-05-20T09:08:09.856485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eef4040c-5786-425e-8afa-e3c82e2cbf18 · inbound
A Brief Overview: On-Policy Self-Distillation In Large Language Models cites this paper.
A Brief Overview: On-Policy Self-Distillation In Large Language Models UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9a7c0620-a8d1-4073-ade6-8c4ec65a0c02 · inbound
RLCSD: Reinforcement Learning with Contrastive On-Policy Self-Distillation cites this paper.
RLCSD: Reinforcement Learning with Contrastive On-Policy Self-Distillation UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T09:07:48.368268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7286c9b8-cd70-4474-a5de-0154c3844f9d · inbound
DemoPSD: Disagreement-Modulated Policy Self-Distillation cites this paper.
DemoPSD: Disagreement-Modulated Policy Self-Distillation UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 9
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-03T16:28:38.402435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d1ef0f6e-7beb-4829-b9d1-4847ffece68d · inbound
DemoPSD: Disagreement-Modulated Policy Self-Distillation cites this paper.
DemoPSD: Disagreement-Modulated Policy Self-Distillation UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f0d3291-79e1-4397-83d1-991b1d8df601 · inbound
DemoPSD: Disagreement-Modulated Policy Self-Distillation cites this paper.
DemoPSD: Disagreement-Modulated Policy Self-Distillation UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 9
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2fff4f34-bfc2-4241-9075-c35dec88808d · inbound
FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry cites this paper.
FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 82
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e908b5a8-ebe8-493b-824f-e95ed19fa9c7 · inbound
Group-Reflective Self-Distillation for Agentic Reinforcement Learning cites this paper.
Group-Reflective Self-Distillation for Agentic Reinforcement Learning UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 29
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58e5bc50-b9d5-4850-be04-0ca605e48e6d · inbound
Group-Reflective Self-Distillation for Agentic Reinforcement Learning cites this paper.
Group-Reflective Self-Distillation for Agentic Reinforcement Learning UniSD: Towards a Unified Self-Distillation Framework for Large Language Models
Reference 29
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.