Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T21:04:56.494300Z
Paper Citation Record · LEDGER
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach
As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2508.09510.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
18 of 18 outbound references displayed
- verified exact4
- verified fuzzy3
- unresolved11
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 9c2643f2-fa7c-439a-a955-50503693ef6c · outbound
This paper cites GPT-4 Technical Report.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach GPT-4 Technical Report
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.268670Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ed05135-6e40-4af4-a186-a02bb92c325d · outbound
This paper cites and Gepperth, A.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach and Gepperth, A
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T21:04:57.164552Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5ff9763c-8a08-4889-babb-d5ffa06dbcd7 · outbound
This paper cites Progressive Prompts: Continual Learning for Language Models.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Progressive Prompts: Continual Learning for Language Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.950859Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5f9c952-fb7e-47c4-887a-931c559cc5e9 · outbound
This paper cites Dynamics of Instruction Fine-Tuning for Chinese Large Language Models.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Dynamics of Instruction Fine-Tuning for Chinese Large Language Models
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T21:04:56.863336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 020828a8-8a8c-41dd-b6a4-b97dd38bfe04 · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach LLaMA: Open and Efficient Foundation Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:56.207497Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3472e23-18fd-4c6e-86dc-2b8e9399d0ec · outbound
This paper cites InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:56.244506Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0c66f81-b060-4f87-aca6-3dae62b51558 · outbound
This paper cites ConTinTin: Continual Learning from Task Instructions.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach ConTinTin: Continual Learning from Task Instructions
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T21:04:56.763344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 39638a4d-9228-4b7b-b6dc-c9111d4e3399 · outbound
This paper cites CITB: A Benchmark for Continual Instruction Tuning.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach CITB: A Benchmark for Continual Instruction Tuning
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:56.434886Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe62f664-ac75-43b3-ac45-7a86e7c8822d · outbound
This paper cites Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T21:04:56.636808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 665d0619-f70d-4b87-9b97-45340199e228 · outbound
This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Reference 2000
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.667709Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7f90ada-8ee0-4439-9249-57b18df9a507 · outbound
This paper cites and Gepperth, A.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach and Gepperth, A
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T21:04:57.294994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e3bac6bf-ace4-4f62-a5b7-c166b0985b93 · outbound
This paper cites Cross-Task Generalization via Natural Language Crowdsourcing Instructions.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Cross-Task Generalization via Natural Language Crowdsourcing Instructions
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.745015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b6af2b75-2fc6-4134-a5d9-3a469c5fa4d6 · outbound
This paper cites Fine-tuned Language Models are Continual Learners.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Fine-tuned Language Models are Continual Learners
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.994653Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 80ee8a7e-5e47-48d9-b4e7-f02e04d7e691 · outbound
This paper cites Continual Learning with Fully Probabilistic Models.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Continual Learning with Fully Probabilistic Models
Reference 2021
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T21:04:57.005922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a12959f0-7b28-4cd4-b13f-f5dc42eef4a5 · outbound
This paper cites Continual Learning of Large Language Models: A Comprehensive Survey.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Continual Learning of Large Language Models: A Comprehensive Survey
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:56.078739Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 754d0197-98e4-4be0-a0c4-056355b2a8ad · outbound
This paper cites and Gepperth, A.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach and Gepperth, A
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T21:04:57.482153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 31a0294c-5f7f-4a03-897f-a4a1634f8fa3 · outbound
This paper cites Crafting papers on machine learning.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Crafting papers on machine learning
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.568387Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22524fca-3a90-4b26-9de1-9717c59f5342 · outbound
This paper cites Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal.
Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:04:55.398019Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.