arXiv Machine Learning
ReLOBGen: Replayable Limit Order Book Message Generation
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Break Step: Recursive Training Resonates with Replayed Sampling Noise
arXiv:2609.11149v3 Announce Type: replace-cross Abstract: How fast does a language model degrade when trained on its own outputs? Theory traces it to gradually accumulating errors, while experiments...
arXiv AI
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Oilbird: Training-Free Speculative Decoding with Keys the Verifier Already Computes
arXiv:2608. 03839v1 Announce Type: new Abstract: Training-free speculative decoding drafts by matching an exact suffix of the context against a pool of earlier context.
arXiv AI
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SodaMem: Evidence-Grounded Temporal Graph Memory for LLM Agents
arXiv:2608. 08055v1 Announce Type: new Abstract: Large language model (LLM) agents that assist users over weeks of conversation must remember what is currently true, not merely what was once said.
arXiv Machine Learning
Jul 7
Language Generation with Replay: A Learning-Theoretic View of Model Collapse
arXiv:2603. 11784v2 Announce Type: replace Abstract: As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching a regime where much of the publicly available online text may be consumed.
arXiv Machine Learning
Sep 4
Beyond Endpoint Scores: Time- and Capacity-Conditioned Evaluation of Continual Knowledge Updating
The paper argues that evaluating continual knowledge‑updating methods solely at a final checkpoint and a single adapter rank can be misleading. By fixing a periodic hierarchy and comparing it to cumulative replay on a 24‑month Wikidata stream, the authors show that the apparent best method changes depending on the evaluation month, replay LoRA rank, and query formulation. They recommend reporting performance trajectories and capacity sweeps, and only declaring a robust winner when the ranking remains stable across the evaluation region.
arXiv AI
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Uniform Herding: Exemplar Replay with Representation Refresh
arXiv:2608. 13061v1 Announce Type: new Abstract: As the feature representation changes, replay must preserve the earlier classes.