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.

pith.paper-citation-record.v1
2508.09510 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:04:56.494300Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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 2026-08-06T06:34:29.942622+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 2026-08-06T06:34:29.942622+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 2026-08-06T06:34:29.942622+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 2026-08-06T06:34:29.942622+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 2026-08-06T06:34:29.942622+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 2026-08-06T06:34:29.942622+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 2026-08-06T06:34:29.942622+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.