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Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read Masked reconstruction training lets autoregressive LLMs learn new facts from plain documents, with no paraphrase augmentation, and removes the reversal curse.

desk verdict The masked fine-tuning idea is genuinely new and the demasking objective clearly helps, but the headline QA metric (ROUGE-1 recall) is loose enough that the 'closes the gap' claim needs re-scoring before I'd trust the size of the effect. read the letter →

arxiv 2510.09885 v6 pith:RADVO4ZG submitted 2025-10-10 cs.CL cs.AI

Xu Pan , Ely Hahami , Jingxuan Fan , Ziqian Xie , Haim Sompolinsky This is my paper · ORCID
classification cs.CLcs.AI
keywords knowledgeinjectionmaskedfine-tuningreversalcursediffusionlanguagemodelsautoregressivedataefficiencysupervisedquestionanswering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Autoregressive large language models are hard to update with new facts: fine-tuning on new documents often fails to generalize to question answering, and questions that reverse the order of the training text (the 'reversal curse') remain near-impossible without expensive paraphrase augmentation. This paper shows that masked diffusion LLMs, which learn by reconstructing masked tokens, do not have this problem: after fine-tuning on the same documents they answer both forward and backward questions accurately, with no paraphrases. To bring this advantage to autoregressive LLMs, the authors propose 'masked fine-tuning': mask a random fraction of the document, prompt the model to recover the full text, and train against the original document. Across four autoregressive models and three knowledge datasets, this single change lifts backward QA accuracy from near zero to above 0.9 and forward accuracy into the high 0.9s, closing the gap with diffusion LLMs. The same masked objective also improves supervised fine-tuning on math tasks, suggesting a general training recipe rather than a knowledge-specific trick.

What carries the argument

The central object is the masked fine-tuning paradigm: during fine-tuning, a random fraction t of each document's tokens (uniformly sampled from 0.05 to 0.95) is replaced with a special [MASK] token; the masked document is placed in the prompt with an instruction to 'return the recovered masked passage,' and the original unmasked document serves as the supervised target, with the autoregressive loss applied only to the assistant response tokens. This emulates the mask-reconstruction loss of masked diffusion LLMs without changing the autoregressive architecture or the optimization target. The random mask ratio is crucial: it modulates task difficulty, and a control experiment with random toke

What would settle it

Run a strict exact-match evaluation on the same fine-tuned models—parse the generated answer and require it to contain exactly the ground-truth entity (e.g., a name or date) with no additional sentences—and compare masked fine-tuning against vanilla fine-tuning on the Wiki and NameDescription datasets; if the previously reported margins (e.g., 0.980 vs 0.377 forward) collapse, the central claim fails.

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Extended reading notes

Core claim

The central claim is that the demasking objective alone—having the model reconstruct a randomly masked document, with the unmasked document as the supervised target—transfers the data-efficiency advantage of masked diffusion LLMs to autoregressive LLMs during fine-tuning. The paper reports that without any paraphrase augmentation, masked fine-tuning on the Wiki dataset raises forward accuracy from 0.377 to 0.980 and backward accuracy from 0.282 to 0.930 for an 8-billion-parameter autoregressive model, and lifts NameDescription backward accuracy from near zero to 0.90–0.95. This holds across four model sizes/families and three datasets, and a control experiment replacing the masked prompt wit

Load-bearing premise

The entire argument rests on the evaluation metric: accuracy is measured as ROUGE-1 recall without a threshold, so a model that merely regurgitates the training passage scores near-perfect; if this metric does not reflect genuine question answering, the reported knowledge-injection gains may be illusory.

Editorial extensions

If this is right

  • If masked fine-tuning works as described, parametric knowledge injection no longer requires paraphrase-augmented training data, removing a major computational bottleneck in updating LLMs with new facts.
  • The method transfers the reversal-curse resistance of diffusion LLMs to autoregressive models, so models can answer questions that invert the information order of their training text.
  • Because the same demasking objective improves supervised fine-tuning on GSM8K and MATH across two models, the benefit is not limited to factual QA and may be a general-purpose training objective.
  • The convergence analysis indicates masked fine-tuning reaches higher final accuracy at comparable total compute, despite roughly doubling per-step FLOPs, because it needs fewer training steps.
  • This offers a practical path to lifelong learning: a small amount of new text, masked and presented as a reconstruction task, can update a deployed autoregressive LLM without relying on external retrieval or paraphrase generation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the reported 'accuracy' is ROUGE-1 recall without an exact-match constraint, and the appendix shows successfully fine-tuned models often reproducing the entire source passage; a strict exact-answer metric could substantially shrink the reported margins and should be run before assuming the knowledge-injection conclusion.
  • Beyond the paper: a sharper test of genuine knowledge acquisition would be to mask the answer token itself in the prompt during fine-tuning and check whether the model can still produce it at inference; if it cannot, the method may be inducing passage-level memory rather than a reusable association.
  • Beyond the paper: the fixed-mask-ratio experiments suggest a sweet spot around t ≈ 0.5–0.75; annealing the mask ratio or using a curriculum that progresses from lightly to heavily masked documents may further improve data efficiency and reduce the seed-to-seed variance visible in the paper's training curves.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper studies post-training knowledge injection in autoregressive LLMs (arLLMs) versus masked diffusion LLMs (dLLMs). Across three datasets (NameDescription, Biography, Wiki), it reports that dLLMs achieve strong forward and backward QA accuracy without paraphrase augmentation, whereas arLLMs need paraphrases, and even then fail on backward questions. Motivated by the dLLM's demasking objective, the authors propose 'masked fine-tuning' for arLLMs: the model is prompted with a randomly masked document and trained to reconstruct the original text, using the original document as the supervised target. They report that this method closes most of the QA accuracy gap between arLLMs and dLLMs without paraphrases, at roughly twice the per-step training cost but with higher converged accuracy. They further extend the idea to math SFT (GSM8K, MATH) and, in the abstract, claim a result on the 1.2M-sample GPQA-diamond dataset that does not appear in the body. The main tables report QA accuracy via ROUGE-1 recall with no threshold; appendix generation examples show passages reproduced verbatim, which motivates the central concern about validity of the reported metric.

Significance. If the central result is borne out under a strict QA metric, the paper is significant: it offers a simple, compute-compatible way to improve knowledge-injection data efficiency in existing arLLMs, with a clean random-token control suggesting the effect is due to the demasking content rather than prompt format. The study is broad (four arLLMs, one dLLM, three knowledge datasets, plus two math datasets with exact-match evaluation), and the code/dataset repository is promised, which helps reproducibility. The empirical breadth and the existence of a control experiment are genuine strengths. However, the headline QA numbers rest on an unusually lax metric, and one advertised large-scale result (GPQA-diamond) is missing from the body. The significance of the paper therefore depends on whether the reported QA gains survive re-scoring with answer-level metrics; the paper's own exact-match math results (+0.003 to +0.049) suggest this is a real risk.

major comments (3)
  1. [Section 3, Appendix A.7] The 'accuracy' metric is ROUGE-1 recall (proportion of ground-truth words appearing in the generated text) with no length threshold. The generation examples in A.7 make the risk concrete: on the Wiki forward question, masked Llama 8B outputs the full training article, and on Biography, the dLLM emits a long passage containing the answer name; such outputs score near 1.0 by construction because the ground-truth words occur in the verbatim or near-verbatim passage. Since Table 1 compares conditions that differ greatly in verbosity (masked fine-tuning tends to reproduce whole passages; vanilla arLLM fine-tuning often produces shorter or off-topic text), the reported gaps—e.g., 0.377 vs. 0.980 on Wiki forward—may substantially overestimate the advantage. The paper's own exact-match math results in Section 8/Table 3 show only small gains (+0.003 to +0.049), which is consistent with the concer
  2. [Abstract, Section 8] The abstract states: 'on a large-scale knowledge-intensive dataset (1.2M samples), masked SFT achieves the best downstream accuracy on GPQA-diamond among all fine-tuning variants.' No GPQA-diamond experiment, dataset, table, or evaluation protocol appears anywhere in the body or appendices; Section 8 and Appendix A.5 cover only GSM8K and MATH. This is a load-bearing result used to support the claim of broader applicability. Either add the experiment with full details or remove the claim from the abstract.
  3. [Section 9, Table 4] The text claims that masked fine-tuning (condition 5) 'converges at the highest accuracy with more than 2×convergence rate of all the other methods.' Table 4 reports fitted convergence rates k (Fwd/Bwd): condition (1) 0.0350/0.1337, (2) 0.0069/0.0130, (3) 0.0151/0.0495, (4) 0.0052/0.0081, (5) 0.0032/0.0029. Condition (5) has the lowest k in both directions, so the claim is contradicted by the paper's own table. The cost-comparison conclusion should be revised; if the intended argument is that condition (5) reaches a higher converged accuracy despite a lower rate, state that directly with the supporting numbers.
minor comments (5)
  1. [Section 3, Appendix A.3] Main text says the Wiki dataset contains 94 articles; Appendix A.3 says the filtering results in 96 wiki articles. Please reconcile.
  2. [Figure 2 and Tables 1, 5] The tables report accuracy from 'best checkpoints' chosen by total accuracy on the evaluation set. Please report the selection rule explicitly and include variance across seeds or random restarts for all methods, not only for dLLM and masked arLLM (Figures 10–11).
  3. [Section 6, Equation (2)] The loss in Eq. (2) is written with m_t as a selector but the notation is not fully defined (token indices vs. a binary vector). Clarify the indexing and the normalization by the sum of m_t.
  4. [Section 3] The paper cites 'Lin et al., 2024' for the ROUGE-1 score; the standard reference is Lin (2004). Please correct the citation.
  5. [Appendix A.4] Learning rates differ across models and datasets, and some are chosen by 'smooth gains' in the text. Please state the hyperparameter search protocol more concretely (especially the number of points and the criterion for 'smooth').

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity; central masked-fine-tuning result is empirically controlled, though ROUGE-1 accuracy is a validity caveat.

full rationale

Walking the derivation chain, I find no step where a predicted quantity is equivalent to the training input by construction. The central claim is empirical: Eq. 2 defines a masked autoregressive loss whose target is the original document, and the paper validates the demasking objective with a random-token control (Appendix Figure 12) that keeps the format and target while removing the masked cues. This control shows the improvement is not merely a data-augmentation artifact of prepending varied text. The dLLM baseline is trained with the external objective of Eq. 1, and the arLLM/dLLM gap is measured before and after fine-tuning, so the 'closing the gap' conclusion is not an algebraic consequence of the loss. The only self-citation (Pan et al. 2025) supplies the Wiki data-collection protocol and a non-central remark about gating; neither is load-bearing for the masked-fine-tuning result. A genuine validity caveat remains: Section 3 defines accuracy as ROUGE-1 recall, and Appendix A.7 shows models often answer by emitting the full training passage, so the reported QA numbers may overstate concise-answer capability. That is an evaluation-risk concern rather than a circularity, because the paper's equations do not by construction equate the QA score to the training loss; the QA prompt differs from the masked training prompt, and the random-token control shows that the model must still use the prompt cues for retrieval. The score of 2 reflects the minor self-citation and this metric caveat, not a demonstrated circular step.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method rests on hand-chosen hyperparameters (mask distribution, learning rates, decoding config), on two prior-work premises about diffusion objectives adopted without re-derivation, and on the ROUGE-based evaluation assumption. The load-bearing items are the mask-ratio choice and the evaluation metric; no new entities are postulated.

free parameters (3)
  • mask ratio sampling distribution = U(0.05, 0.95)
    Chosen by hand in A.4 ("leave a small margin to avoid edge cases") instead of the dLLM pretraining U(0,1); the method's effectiveness is mask-ratio dependent, with t=0 failing entirely (Fig 4), so this choice is load-bearing.
  • learning rates per model = 5e-6 arLLMs; 1e-5 dLLM; 3e-6 masked Llama-8B; 5e-6 masked others
    Swept only on NameDescription (Fig 5) and then fixed across datasets; every model-condition comparison in Tables 1-3 depends on these hand-tuned values.
  • dLLM decoding settings = block length 4, low_confidence remasking
    Inference-time hyperparameters for LLaDA (A.4) that affect all dLLM QA numbers in Table 1.
assumptions (4)
  • standard math The masked diffusion loss in Eq. 1 is an ELBO on the data likelihood
    Invoked in Section 2.2 via Shi et al. 2024 to justify the dLLM fine-tuning protocol.
  • domain assumption Random mask ratios provide implicit data augmentation and are necessary for demasking to yield generative ability
    Adopted from Prabhudesai et al. 2025 and Ni & the team 2025 (Section 2.2) and used to motivate U(0.05,0.95); underpins the transfer argument in Section 6.
  • domain assumption ROUGE-1 recall of ground-truth words in generated text measures answer correctness
    Section 3 defines accuracy as the proportion of ground-truth answer words appearing in the generated answer; load-bearing because A.7 shows outputs that copy whole passages scoring high.
  • domain assumption The 2025 Wikipedia articles are effectively unknown to the base models
    Section 3/A.3: pages created in 2025 were selected to minimize leakage; pre-fine-tuning accuracies are nonzero (0.10-0.24), so leakage is reduced but not eliminated.

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Cite this review

Pith. "Pith review of Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs." pith.science (2026). https://pith.science/paper/RADVO4ZG

@misc{pith2026251009885,
  author       = {Pith},
  title        = {Pith review of: Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RADVO4ZG}},
  note         = {Machine review of arXiv:2510.09885}
}
read the original abstract

Large language models (LLMs) are often used in environments where facts evolve, yet factual knowledge updates via fine-tuning on unstructured text often suffer from 1) reliance on compute-heavy paraphrasing augmentation and 2) the reversal curse. Recent studies show diffusion large language models (dLLMs) require fewer training samples to achieve lower loss in pre-training and are more resistant to the reversal curse, suggesting dLLMs may learn new knowledge more easily than autoregressive LLMs (arLLMs). We test this hypothesis in controlled knowledge fine-tuning experiments and find that while arLLMs rely on paraphrase augmentation to generalize knowledge text into question-answering (QA) capability, dLLMs do not require paraphrases to achieve high QA accuracy. To further investigate whether the demasking objective alone can induce such a knowledge injection advantage in dLLMs regardless of their diffusion denoising paradigm, we propose masked fine-tuning for arLLMs, which prompts an arLLM to reconstruct the original text given a masked version in context. The masked fine-tuning for arLLMs substantially improves the efficacy of knowledge injection, i.e. no paraphrase needed and resistant to the reversal curse, closing the gap between arLLMs and dLLMs. We also demonstrate broader applicability: on a large-scale knowledge-intensive dataset (1.2M samples), masked SFT achieves the best downstream accuracy on GPQA-diamond among all fine-tuning variants. The demasking objective also improves SFT on math tasks, suggesting broad utility beyond factual knowledge injection.

Figures

Figures reproduced from arXiv: 2510.09885 by the authors.

Figure 1
Figure 1. A schematic summary of the results. First row: autoregressive LLM requires paraphrases [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Training dynamics of arLLM (Llama 8B), dLLM (Llada), and masked arLLM (Llama [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. An example of masked fine-tuning prompt. Random selection of text tokens are replaced [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Accuracy of using fixed mask ratio (t) in dLLM fine-tuning and arLLM masked fine￾tuning on the NameDescription dataset. Previous studies (Allen-Zhu & Li, 2024; 2025) claim that bidirectional BERT-like models strug￾gle with even forward style knowledge extraction due to…
Figure 5
Figure 5. Figure 5: Learning rate sweep of Llama-3.1-8B-instruct. We swept learning rate on the [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: Total accuracy (macro average of forward and backward accuracy) of experiments on [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: Learning dynamics of Llama-3.2-3B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: Learning dynamics of Qwen/Qwen3-4B-Instruct-2507. [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 9
Figure 9. Figure 9: Learning dynamics of Llama-3.2-3B-Instruct. [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 10
Figure 10. Figure 10: Random seed effects in Llada. Random seed determines the sampling of mask ratio and [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: Random seed effects in maksed Llama3.1 8B. Random seed determines the sampling [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]
Figure 12
Figure 12. Figure 12: To verify the advantage of masked fine-tuning of arLLMs is not simply due “data aug [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: Learning rate and epoch sweep of Llama-3.2-3B-Instruct on GSM8K dataset. [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]
Figure 14
Figure 14. Figure 14: Learning rate and epoch sweep of Qwen3-4B-Instruct-2507 on GSM8K dataset. [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: Learning rate and epoch sweep of Llama-3.2-3B-Instruct on MATH dataset. [PITH_FULL_IMAGE:figures/full_fig_p024_15.png]
Figure 16
Figure 16. Figure 16: Learning rate and epoch sweep of Qwen3-4B-Instruct-2507 on MATH dataset. [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]

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Forward citations

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    Eric Zhao, Pranjal Awasthi, and Nika Haghtalab. From style to facts: Mapping the boundaries of knowledge injection with finetuning.arXiv preprint arXiv:2503.05919,

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    A APPENDIX A.1 DATASET AND CODE AVAILABILITY The dataset and code base are available at:https://github.com/xup5/masked_arLLM. git A.2 LLMUSAGE The usage of LLM is limited to language polishing and literature search. We asked an LLM to suggest surface-level rewrites to improve ...

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    "" ND dataset Type

    Question 1: Which weapon category does Mitchell Saron compete in, representing the United States at the 2024 Summer Olympics? Answer 1: Sabre Cue used in the question: [Mitchell Saron, United States, 2024 Summer Olympics] Question 2 (reverse question of question 1): Who repres...

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    Change-order paraphrase:

    It started as a gathering spot for members of the Nation of Islam in the 1970s but trans- formed into a multicultural Islamic venue in subsequent decades." Change-order paraphrase:"Located in Altadena, California, USA, Masjid Al-Taqwa stood on Lake Ave directly opposite the El...

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    factorization curse

    For GSM8K we report the accuracy using exact match withLM Evaluation Harness’s flexible extraction. For MATH we report the accuracy using exact match withmath-verifyextraction (Kydlí ˇcek). Both extraction methods are chosen to maximize alignment with human examination. Most o...

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    Ground truth answer:

    was born on March 25, 2024?" Ground truth answer:"Owen Carole Pavy" arLLM without paraphrases (step 1280, best checkpoint):"/n /n I am a licensed Dental Assistant in Phoenix, AZ. I was hired by a company that provides dental services. My birth is commemorated in March 25, 2024...

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    Wiki dataset Original text:

    Her early moments were in Seattle, WA. She secured her degree from University of Minnesota. She set her academic sights on Dental Assistant. She was in charge of tasks at Avnet. She worked at a business located in Phoenix, AZ." Wiki dataset Original text:"The 2024-25 Men’s FIH...

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    Questions (Forward):

    The defending champions New Zealand won their second title by defeating Pakistan 6-2 in the final. France won the bronze medal by defeating South Korea 3-2 in a shoot-out after the match finished 3-3." Questions (Forward):"When was the 2024-25 Men’s FIH Hockey Nations Cup held...

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    arLLM with same-order paraphrases (step 10240, best checkpoint):

    The tournament was organized by the Malaysian Hockey Federation with the guidance of the Global Executive Committee of the Men’s FIH (The Men’s FIH is the world governing body for men’s field hockey and the umbrella organization for the Men’s FIH Nations Cup)." arLLM with same...

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    dLLM without paraphrases (step 80):

    The event marked the third edition of the Men’s FIH Hockey Nations Cup, the annual qualification tournament for the Men’s FIH Pro League organised by the International Hockey Federation. The tournament was won by the Malaysia National FIH Hockey team, who won their second titl...

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    The match was played between Malaysia and the United Arab Emirates. Malaysia won the match 3-2." dLLM with same-order paraphrases (step 5120, best checkpoint): "/n /n The 2024-25 Men’s FIH Hockey Nations Cup was the third edition of the Men’s FIH Hockey Nations Cup, the annual...

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    He was brought into the world in Elk Grove, CA. He culminated his studies at Kansas State University. He concentrated his efforts toward EMT and Paramedic. He supported the operations at HP. He practiced his profession in Palo Alto, CA." Forward question:"What is the birth dat...

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    He represented the United States at the 2024 Summer Olympics in Paris, France, in the men's sabre and men's team sabre events in July

    is an American right−handed sabre fencer. He represented the United States at the 2024 Summer Olympics in Paris, France, in the men's sabre and men's team sabre events in July

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    URL http://arxiv.org/abs/1810.04805. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for co...

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    Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M

    URLhttps://arxiv.org/abs/2001.08361. Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, and Sitan Chen. Train for the worst, plan for the best: Understanding token ordering in masked diffusions. InForty-second Interna- tional Conference on Machine Learning,

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    Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma, Mingyang Wan, Xiang Wang, Xiangnan He, and Tat-seng Chua

    URLhttps: //arxiv.org/abs/2211.11031. Houcheng Jiang, Junfeng Fang, Ningyu Zhang, Guojun Ma, Mingyang Wan, Xiang Wang, Xiangnan He, and Tat-seng Chua. Anyedit: Edit any knowledge encoded in language models.arXiv preprint arXiv:2502.05628,

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    Mem0: Building production-ready ai agents with scalable long-term memory.arXiv preprint arXiv:2504.19413,

    Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory.arXiv preprint arXiv:2504.19413,

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    a is b" fail to learn

    URLhttps: //openreview.net/forum?id=oDbiL9CLoS. Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Kor- bak, and Owain Evans. The reversal curse: Llms trained on" a is b" fail to learn" b is a".arXiv preprint arXiv:2309.12288,

    arXiv

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.