Paper Citation Record · LEDGER

HellaSwag: Can a Machine Really Finish Your Sentence?

As of 20 July 2026, this Paper Citation Record lists 19 of 19 outbound references and 100 inbound Pith citation observations for arXiv:1905.07830.

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

pith.paper-citation-record.v1
1905.07830 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-11T02:56:24.524623Z

measured 119 of 119 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-20T06:30:07.809122+00:00

measured 100 of 161 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T20:29:33.439034Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T06:15:00.866473Z

Reference resolution

19 of 19 outbound references displayed

verified exact4
verified fuzzy15
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 912af8e6-8196-459d-a07a-1cb673ba611d · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.602585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 85513773-0ff3-4a6a-9e18-b3269001ac07 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.652943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 8778f00c-85ea-4a26-bcee-9e36151d5170 · outbound

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

HellaSwag: Can a Machine Really Finish Your Sentence? BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-11T02:56:24.572884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation e60d9f21-6430-40b6-b67c-67d4d4b3f441 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.656359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 71b8f73c-d6f8-4cce-9700-fec50553f9b6 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.659813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation da80e24e-e09e-4c56-a926-cbfe7d70147a · outbound

This paper cites Bowman, and Noah A.

HellaSwag: Can a Machine Really Finish Your Sentence? Bowman, and Noah A

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.586947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 2150134c-c0dd-428d-b094-818aea921c5c · outbound

This paper cites The Curious Case of Neural Text Degeneration.

HellaSwag: Can a Machine Really Finish Your Sentence? The Curious Case of Neural Text Degeneration

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:18:24.042352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation d3de143c-e1ab-44fb-b4d4-f00292e6c64a · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.594863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7b1d4576-6284-4856-9558-4304ea5b1228 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.598429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 4281ab5a-7986-4f71-9a31-1265a36444fe · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.646292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0811bea8-75ce-492c-a8d1-cbe8df45b5cc · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.607266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7e943aa8-7989-48d6-bf52-ef95b833b742 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.614198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 02da32f4-f9b2-4108-9e76-f601166881b5 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.618308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0e6cf0b7-48c1-45b5-bdb3-a069ae35d777 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.622128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation ba4fd734-2246-4979-b78b-af896941ccdc · outbound

This paper cites Courville, and Bernt Schiele.

HellaSwag: Can a Machine Really Finish Your Sentence? Courville, and Bernt Schiele

Reference 15

Resolution: verified exact; doi, observed 2026-05-11T02:56:24.560281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation ad98c15d-cf63-4e22-8dfd-4c50b322db35 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.629053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 9eed7b94-3efc-45d8-9674-77c80195fdb8 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.638403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 532d5699-e3b5-44b1-959d-f32720a30209 · outbound

This paper cites an unresolved cited work.

HellaSwag: Can a Machine Really Finish Your Sentence? Unresolved cited work

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-11T02:56:24.642419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 19bce4ea-a8ac-4e6a-be21-1afe445485cf · outbound

This paper cites Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books.

HellaSwag: Can a Machine Really Finish Your Sentence? Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.582651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Pith citing papers

Observation 3ae0549a-93e1-49fb-88b5-5af7bbae5b88 · inbound

Language Models are Few-Shot Learners cites this paper.

Language Models are Few-Shot Learners HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation c811d2ca-236b-411f-9881-772d3244f2d4 · inbound

Measuring Massive Multitask Language Understanding cites this paper.

Measuring Massive Multitask Language Understanding HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 289

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 70a2da1d-73e4-4b30-8c1a-ac346edf9883 · inbound

A General Language Assistant as a Laboratory for Alignment cites this paper.

A General Language Assistant as a Laboratory for Alignment HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 254

Resolution: verified exact; local_arxiv, observed 2026-05-11T14:22:59.353948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation fa0a6e27-06a4-44ce-9b6e-daa20e0af9c1 · inbound

PaLM: Scaling Language Modeling with Pathways cites this paper.

PaLM: Scaling Language Modeling with Pathways HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 173

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 26a16eed-6774-4b15-80bd-9a99e39b7af6 · inbound

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena cites this paper.

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 6d9fd14b-6124-470b-9b36-75af240316b0 · inbound

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning cites this paper.

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T08:26:41.996551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 12c6b80d-0ca6-4a59-9934-f8d2275f9ddf · inbound

Chain-of-Verification Reduces Hallucination in Large Language Models cites this paper.

Chain-of-Verification Reduces Hallucination in Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 87

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T01:06:50.324700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 1a664783-62af-4812-b999-38a53cce0dda · inbound

Efficient Streaming Language Models with Attention Sinks cites this paper.

Efficient Streaming Language Models with Attention Sinks HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 686d9b91-39ae-4033-a7d4-353010f64ba8 · inbound

Mistral 7B cites this paper.

Mistral 7B HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-24T06:14:00.117062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation fc1aee88-b190-463e-85b3-fffefa677282 · inbound

Gated Linear Attention Transformers with Hardware-Efficient Training cites this paper.

Gated Linear Attention Transformers with Hardware-Efficient Training HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 104

Resolution: verified exact; local_arxiv, observed 2026-05-15T01:15:14.220426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 01ced8c2-99b7-405b-b2d6-e7270353c4d7 · inbound

Retrieval-Augmented Generation for Large Language Models: A Survey cites this paper.

Retrieval-Augmented Generation for Large Language Models: A Survey HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 143

Resolution: verified exact; local_arxiv, observed 2026-05-24T05:13:56.467851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 58415446-0cfa-409b-98b0-ab97ceb6d6d0 · inbound

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

Gemini: A Family of Highly Capable Multimodal Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 131

Resolution: verified exact; local_arxiv, observed 2026-05-24T05:03:55.303382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 643c3264-2ee1-4af5-bcbd-6f27c2a991dd · inbound

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices cites this paper.

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 129

Resolution: verified exact; local_arxiv, observed 2026-05-16T16:35:38.178405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0e2e1151-378f-41d2-94f3-3ce700f9f533 · inbound

Mixtral of Experts cites this paper.

Mixtral of Experts HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-24T04:13:53.873071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7916776c-329e-4a3f-98e8-92e136b352c6 · inbound

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies cites this paper.

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-13T18:00:53.504555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 463b9d31-b296-4c1d-89c1-3d3cd1bd40b0 · inbound

Chameleon: Mixed-Modal Early-Fusion Foundation Models cites this paper.

Chameleon: Mixed-Modal Early-Fusion Foundation Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-11T10:03:28.156012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 159fac3a-c7e5-4d66-a20a-50ef63bda705 · inbound

SpinQuant: LLM quantization with learned rotations cites this paper.

SpinQuant: LLM quantization with learned rotations HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-05-15T15:52:34.793895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation b8c5ff66-d693-44cc-b20b-0cb6569e2320 · 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 HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-11T15:51:07.104450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation c66132cc-70b9-4fdc-a9cf-3f14aad7d924 · inbound

An Empirical Study of Mamba-based Language Models cites this paper.

An Empirical Study of Mamba-based Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-18T10:31:03.941668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 51cd2895-c154-4d50-aa19-4b0218e09961 · inbound

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing cites this paper.

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 161

Resolution: verified exact; local_arxiv, observed 2026-05-16T06:58:36.921527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 39d32921-13d0-42ed-801e-39ae97609d7c · inbound

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression cites this paper.

Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-05-24T00:13:39.546065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 402cc719-be4f-4c88-9a80-c1b71f787f09 · inbound

Refusal in Language Models Is Mediated by a Single Direction cites this paper.

Refusal in Language Models Is Mediated by a Single Direction HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 204

Resolution: verified exact; local_arxiv, observed 2026-05-13T10:47:56.168323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation ff6b2ef4-b3da-4b6f-a370-deb0954b9c61 · inbound

LaMI: Augmenting Large Language Models via Late Multi-Image Fusion cites this paper.

LaMI: Augmenting Large Language Models via Late Multi-Image Fusion HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-23T23:53:38.782626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation c1d4e26e-2af4-43a3-814a-77580b689d43 · inbound

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale cites this paper.

The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-05-13T04:36:45.548213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 6bfbd48c-d114-4fe2-91d3-8ab5138d2110 · inbound

Moshi: a speech-text foundation model for real-time dialogue cites this paper.

Moshi: a speech-text foundation model for real-time dialogue HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 109

Resolution: verified exact; local_arxiv, observed 2026-05-12T08:13:22.575133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 4b9496a2-5544-43c4-ad32-6ed50c95df3b · inbound

When Attention Sink Emerges in Language Models: An Empirical View cites this paper.

When Attention Sink Emerges in Language Models: An Empirical View HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-16T17:41:03.783343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation fe1faf90-3339-4ef4-aeea-2519e289f5dd · inbound

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning cites this paper.

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-23T17:13:14.000037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 3956351a-667a-4e90-947e-1c6f09b5dc3f · inbound

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks cites this paper.

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:02:30.524964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 10f87a6e-6567-4ee9-8186-eafc2c7611f0 · inbound

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis cites this paper.

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-23T04:12:31.030193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 666a17be-c8d7-4ed0-afd4-9677545d4eaf · inbound

Large Language Diffusion Models cites this paper.

Large Language Diffusion Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation cb2eb73e-6055-43ce-87d6-30bb7c58c1e0 · 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 HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 246

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T08:02:23.678226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7ade2323-c95b-4630-ae9f-567be4c1e44d · inbound

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws cites this paper.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-23T02:52:27.001197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0a7fa03c-0c19-4f0a-97bc-7c3237f2789c · inbound

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results cites this paper.

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-23T03:22:28.156116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation cc01a80b-2949-4c6c-b35e-0da3d613abdb · inbound

LLM-Safety Evaluations Lack Robustness cites this paper.

LLM-Safety Evaluations Lack Robustness HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:27:21.405270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation cfb3d481-c59e-42aa-87a8-06bd568dc86d · inbound

PRIMETIME : Limits of LLMs in Temporal Primitives cites this paper.

PRIMETIME : Limits of LLMs in Temporal Primitives HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-22T18:36:58.907211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation dc9c4237-363d-4e59-bee0-a771280720a9 · inbound

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free cites this paper.

Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-05-12T09:04:34.956066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0ba1f7bb-c370-4d6a-8450-93971f08690e · inbound

Secure LLM Fine-Tuning via Safety-Aware Probing cites this paper.

Secure LLM Fine-Tuning via Safety-Aware Probing HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:11:35.787799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 8011c3ad-cff7-4ac8-ae5f-5058df0497ae · inbound

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction cites this paper.

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:47:07.669991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 743f9ef5-8bdc-435a-9a80-78c3835bbbe1 · inbound

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan cites this paper.

From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-19T04:57:04.351350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation e9b0b7e4-9300-4e7b-be1f-9814b73db33c · inbound

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training cites this paper.

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T03:37:00.964918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 60770117-331c-4e57-a44b-b2a208a3b0fd · inbound

Kimi K2: Open Agentic Intelligence cites this paper.

Kimi K2: Open Agentic Intelligence HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0b6fbad3-bca1-443c-9e73-449d9fa7d22f · inbound

League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models cites this paper.

League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-19T03:22:01.384163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 81b34578-08eb-4da0-abfd-7e66780b0901 · inbound

Diffusion Language Models Know the Answer Before Decoding cites this paper.

Diffusion Language Models Know the Answer Before Decoding HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-18T20:31:50.311916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 58158169-2143-48bf-8a72-6b6a24a40014 · inbound

SpikingBrain: Spiking Brain-inspired Large Models cites this paper.

SpikingBrain: Spiking Brain-inspired Large Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-18T18:51:45.722336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation a6004e3f-105d-4493-8405-ee9c850a428f · inbound

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference cites this paper.

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-05-18T17:51:42.073827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation e0d06aa8-1d43-4a4e-aca8-2ef1e6580bd3 · inbound

QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models cites this paper.

QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-05-21T21:50:41.293582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 5dcd0d58-cd5c-4e33-9433-dfeeb3882214 · inbound

HyperAdapt: Simple High-Rank Adaptation cites this paper.

HyperAdapt: Simple High-Rank Adaptation HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 41

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T13:46:25.921668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 16b768db-d7b9-4ddd-a506-2585553d7582 · inbound

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution cites this paper.

ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 264

Resolution: verified exact; local_arxiv, observed 2026-05-16T13:58:59.079515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0430ff0d-7864-45d1-a0b8-86b2bf17bd0d · inbound

BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning cites this paper.

BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:51:26.044835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 6e24e9a2-3a0e-460d-bb4d-1c0251ef9412 · inbound

Multiplayer Nash Preference Optimization cites this paper.

Multiplayer Nash Preference Optimization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-18T13:11:24.014337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation b577d23c-9ae3-49a3-99ea-751f9ca90a45 · inbound

LLM DNA: Tracing Model Evolution via Functional Representations cites this paper.

LLM DNA: Tracing Model Evolution via Functional Representations HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:21:21.286987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 54b8c98b-1d7e-4225-872a-92c6e8aac9e3 · inbound

Short window attention enables long-term memorization cites this paper.

Short window attention enables long-term memorization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-18T12:11:21.734696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0adf2b74-e90e-42bc-9898-472ef13a1cf6 · inbound

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning cites this paper.

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-18T10:46:16.964184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation a2942cd0-9d4c-4d2e-b113-e806fd83cd5b · inbound

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba cites this paper.

SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-18T09:46:12.381901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 3e18282a-a525-43b9-8ffc-343806a7f11b · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-18T05:30:55.007029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7f99cae7-e272-4acc-b555-fa1a7e4c90ac · inbound

ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning cites this paper.

ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-18T03:45:50.208525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation c227f2f4-2351-436a-b791-d0c783023f0d · inbound

NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium cites this paper.

NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-05-18T02:52:21.857362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 272feffa-d0d1-408a-8930-43a63881cfeb · inbound

SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization cites this paper.

SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-05-17T23:10:26.027386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 237e36dc-60a0-4789-815d-ab91086c5ffa · inbound

Dynamic Nested Hierarchies: Pioneering Self-Evolution in Machine Learning Architectures for Lifelong Intelligence cites this paper.

Dynamic Nested Hierarchies: Pioneering Self-Evolution in Machine Learning Architectures for Lifelong Intelligence HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-17T20:15:10.599433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 84583a25-02d8-4854-bf8f-a6a2e0e6c821 · inbound

Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling cites this paper.

Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-17T02:23:52.764576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 670c682b-11f3-421b-a294-2ffd6fe5e3e4 · inbound

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics cites this paper.

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-16T23:08:39.709512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 648685a0-f329-4c85-b95c-ccb87d6dc540 · inbound

LLaDA2.0: Scaling Up Diffusion Language Models to 100B cites this paper.

LLaDA2.0: Scaling Up Diffusion Language Models to 100B HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-14T18:53:21.194371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation f454c5e8-c8a0-43f5-8cf1-c883d272b3eb · inbound

NVIDIA Nemotron 3: Efficient and Open Intelligence cites this paper.

NVIDIA Nemotron 3: Efficient and Open Intelligence HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 96

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T01:40:42.521886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7ff7f66b-952d-42ae-8283-a4761b185e6a · inbound

Ministral 3 cites this paper.

Ministral 3 HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-14T19:12:24.818478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 333eeeba-eac8-4ca2-873b-9f59385bfa7d · inbound

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts cites this paper.

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-16T09:57:42.815859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7f9f76b0-3c43-47a4-90ae-b1c51050af6f · inbound

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models cites this paper.

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-16T08:40:46.164874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0d2af9ff-7838-4300-be50-0819dad0e484 · inbound

When control meets large language models: From words to dynamics cites this paper.

When control meets large language models: From words to dynamics HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 217

Resolution: verified exact; local_arxiv, observed 2026-05-21T14:54:13.219645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation aceb8e1a-d580-428e-aac1-70f1f5034d7c · inbound

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models cites this paper.

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-21T14:14:12.390654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 71c45eae-3bfd-49b6-84eb-b1c2b4503e9b · inbound

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization cites this paper.

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-16T06:52:28.351302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 8a57bf96-a16a-42d8-8dca-a179bc40c306 · inbound

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE cites this paper.

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-05-15T14:35:55.656086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 93fd9bf8-0237-427f-a6ec-20fdc304948c · inbound

When Does Sparsity Mitigate the Curse of Depth in LLMs cites this paper.

When Does Sparsity Mitigate the Curse of Depth in LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-14T20:29:33.439034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cdba922-cac3-4a10-88ea-4dea37cf12c1 · inbound

Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization cites this paper.

Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-05-15T10:39:56.786549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 048d6256-790a-4e86-a35a-1938e048f975 · inbound

Efficient Reasoning on the Edge cites this paper.

Efficient Reasoning on the Edge HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 154

Resolution: unresolved; no resolver link, observed 2026-07-13T23:28:12.790404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c019a93-a66a-4825-a519-39dc028327ad · inbound

Path-Constrained Mixture-of-Experts cites this paper.

Path-Constrained Mixture-of-Experts HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-15T09:19:54.315761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7cb3a10c-8a60-418b-a84a-241c229a839a · inbound

A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network cites this paper.

A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-05-14T01:58:36.994071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation f425af20-3c31-4a73-830b-4b7f56787d9b · inbound

JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency cites this paper.

JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-05-13T19:28:09.849683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 5d216f3f-b8ac-4a3d-b6aa-4ae36b9914c4 · inbound

SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models cites this paper.

SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 1789bb92-5e9a-4cd7-8ff7-9625dbd62e34 · inbound

PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer cites this paper.

PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation c23f0ba4-0ec5-41c8-b386-7c1cbd466f62 · inbound

In-Place Test-Time Training cites this paper.

In-Place Test-Time Training HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 99230c30-5273-46db-a444-8a1d3b23a5c5 · inbound

Rethinking Residual Errors in Compensation-based LLM Quantization cites this paper.

Rethinking Residual Errors in Compensation-based LLM Quantization HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:21:01.284327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation be99f88d-9d71-40af-895b-bbdbc2ae9553 · inbound

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts cites this paper.

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-05-11T06:15:58.983126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 5379d142-fa26-445c-850b-f13e7d8bf7d6 · inbound

Winner-Take-All Spiking Transformer for Language Modeling cites this paper.

Winner-Take-All Spiking Transformer for Language Modeling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-11T11:06:03.720664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 45b236f3-d452-4e8f-9f7a-932d53641456 · inbound

Adaptive Spiking Neurons for Vision and Language Modeling cites this paper.

Adaptive Spiking Neurons for Vision and Language Modeling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 75527686-09c2-43ad-b3a3-50301e2e29d3 · inbound

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate cites this paper.

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 9b12e380-ca76-4624-9259-517569de676c · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 178

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 59f42027-5ab1-4226-a9e8-0862eee85d44 · inbound

TLoRA: Task-aware Low Rank Adaptation of Large Language Models cites this paper.

TLoRA: Task-aware Low Rank Adaptation of Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation e063e0c0-a09d-492f-9357-9a780d9b6c1d · inbound

Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models cites this paper.

Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation c2110036-1aa9-490a-96d3-3ac28d4f7bf8 · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 90

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T12:56:05.878275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation fcef31a3-c8f3-4745-a874-3bbcd0e83114 · inbound

Nexusformer: Nonlinear Attention Expansion for Stable and Inheritable Transformer Scaling cites this paper.

Nexusformer: Nonlinear Attention Expansion for Stable and Inheritable Transformer Scaling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-11T12:41:04.964359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 0ac943e3-f0d5-4899-9d4a-fd75f37a24af · inbound

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation cites this paper.

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T12:46:04.898207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 61727f5e-f828-4ad7-afb4-64d0228e2983 · inbound

SimDiff: Depth Pruning via Similarity and Difference cites this paper.

SimDiff: Depth Pruning via Similarity and Difference HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-11T13:16:22.029381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 7e244721-6975-4712-bebd-dfa7fe1bce9f · inbound

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs cites this paper.

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-05-11T21:36:18.628340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation d6fb71a1-e342-451f-8617-aaccd814e964 · inbound

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling cites this paper.

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-05-11T22:01:10.632195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 413f5a8b-3ab7-434a-b5b2-de4644a5d564 · inbound

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid cites this paper.

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 276

Resolution: verified exact; local_arxiv, observed 2026-05-11T16:56:06.226808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 8e2a8ab8-6d1b-4c8f-bc09-9947918d8ded · inbound

Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting cites this paper.

Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 9c2b7aff-98d9-4cbb-83d4-56dd89fabaed · inbound

FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression cites this paper.

FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:56:24.661063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 2ecba6d3-da27-433d-93af-f4bbba669526 · inbound

Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio cites this paper.

Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-11T16:41:05.674549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation d6d7df26-c965-4740-b509-cb5f9b48179b · inbound

MDN: Parallelizing Stepwise Momentum for Delta Linear Attention cites this paper.

MDN: Parallelizing Stepwise Momentum for Delta Linear Attention HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T16:41:10.906461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation 985d4efa-3efa-4d32-8c6d-cc956794d388 · inbound

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models cites this paper.

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-05-11T04:45:56.387728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

Observation f1444c13-962e-4285-bca7-982d0038bcce · inbound

Structured Recurrent Mixers for Massively Parallelized Sequence Generation cites this paper.

Structured Recurrent Mixers for Massively Parallelized Sequence Generation HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-12T07:56:29.158383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.