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Preprints: 9
Authors: 16
Downloads: 6,199

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Short CommunicationNot peer reviewed

Speculative Decoding Under Memory Constraints

Rahul Desai, Yuki Saito

Version 1 · 5 November 2026 · 10.xxxx/ctm.pp.2026.0009

Speculative decoding accelerates generation by letting a small draft model propose tokens that a larger model verifies, but the draft model competes with the target model for scarce memory on edge devices. We characterise the trade-off between draft-model size, speed-up and memory footprint, and propose a draft that shares the target model's embedding table and lower layers. The shared draft reduces extra memory from 11% to 3% of the target model and retains 88% of the speed-up of an independent draft.

Abstract PDF 437 1210 cited

Short CommunicationNot peer reviewed

Cheap Reward Models for Small-Scale RLHF Experiments

Jonas Berg, Anders Holm

Version 1 · 29 October 2026 · 10.xxxx/ctm.pp.2026.0008

Preference-based fine-tuning is expensive to study because reward models are large. We show that a reward model distilled to 1% of the size of the original recovers 94% of its ranking accuracy on held-out preference pairs, and that policies trained against the small model transfer to the large one with a modest loss. The result lowers the cost of reward-modelling experiments for groups with limited compute.

Abstract PDF 1,480 5120 cited

Research ArticleNot peer reviewed

Mining Failure Modes from Agent Trajectories at Scale

Aisha Rahman, Lena Hartwell

Version 1 · 21 October 2026 · 10.xxxx/ctm.pp.2026.0007

Agent developers inspect failed runs by hand, which does not scale beyond a few dozen trajectories. We propose a clustering approach that groups failures by the step at which the trajectory diverges from a successful reference run, and label the clusters with short natural-language descriptions. On 18,000 trajectories from three agent frameworks, the method recovers 83% of failure categories identified by manual analysis and surfaces four categories that were previously unreported.

Abstract PDF 2,065 6901 cited

Review ArticleNot peer reviewed

A Taxonomy of Evaluation Leakage in Language-Model Benchmarks

Nadia Haddad, Ingrid Novak, Kofi Mensah

Version 2 (updated) · 14 October 2026 · 10.xxxx/ctm.pp.2026.0004

Benchmark scores can be inflated when test material reaches the training data or when evaluation choices quietly favour a model. We review 140 papers and incident reports and organise the mechanisms of evaluation leakage into a taxonomy with four top-level classes and eleven sub-classes. For each class we summarise how it can be detected, how often it has been reported, and what practices reduce its likelihood.

Abstract PDF 3,350 1,4105 cited

Research ArticleNot peer reviewed

Prompt-Injection Resilience in Browser-Embedded Assistants

Marcus Tan, Sofia Lindqvist, Lena Hartwell

Version 1 · 2 October 2026 · 10.xxxx/ctm.pp.2026.0006

Assistants embedded in web browsers read page content on the user's behalf, which exposes them to indirect prompt injection: instructions hidden in a page that the assistant mistakes for the user's. We build a test suite of 600 injected pages across seven attack styles and measure three defences: instruction-hierarchy prompting, a classifier that screens page text, and permission gating of sensitive actions. No single defence is sufficient; combining a screening classifier with gating of actions that write or send data lowers attack success from 62% to 4% while preserving 93% of benign task success.

Abstract PDF 912 2880 cited

Research ArticleNot peer reviewed

Tokenizer Effects on Low-Resource Language Adaptation

Oluwaseun Adebayo, Mei Tanaka

Version 1 · 12 September 2026 · 10.xxxx/ctm.pp.2026.0005

Adapting a pretrained model to a low-resource language often begins with extending the tokenizer, yet the choice of extension method is rarely compared. We evaluate five vocabulary-extension strategies on six languages and find that fertility reduction alone does not predict downstream accuracy. Strategies that initialise new embeddings from semantically related tokens consistently outperform random initialisation, at the same training budget.

Abstract PDF 1,890 6032 cited

Technical ReportNot peer reviewed

Position Bias in Long-Context Retrieval: A Replication Study

Elias Wagner, Tomás Ibarra

Version 1 · 19 August 2026 · 10.xxxx/ctm.pp.2026.0003

Several studies report that language models use information in the middle of a long context less reliably than information at the start or end. We replicate these findings across eleven models and four context lengths using a controlled retrieval task. The effect is reproducible but smaller than originally reported, and largely disappears when the task format is held fixed across lengths. We release the harness and the full per-position results.

Abstract PDF 2,210 7401 cited

Technical ReportNot peer reviewed

Reproducibility-Checklist Compliance in Machine-Learning Papers: A technical report on what authors state and what they release

Ingrid Novak, Kofi Mensah

Version 1 · 27 July 2026 · 10.xxxx/ctm.pp.2026.0002

Many machine-learning venues now require authors to complete a reproducibility checklist. We audited 240 papers that ticked the item “code is provided” and checked whether the linked repositories existed, ran and reproduced the headline table. 87% had a reachable repository, 58% contained instructions sufficient to run training or evaluation, and 31% reproduced the headline result within the reported variance. Compliance was higher for papers reporting variance across seeds. We recommend checks that can be automated at submission time.

Abstract PDF 2,455 9314 cited