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Titles, abstracts, authors, keywords and DOIs across all five collections.

PreprintsResearch ArticleNot peer reviewed

Mining Failure Modes from Agent Trajectories at Scale

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

PreprintsResearch 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

PreprintsResearch 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

PublishingResearch ArticlePeer reviewed

Scaling Behaviour of Parameter-Efficient Fine-Tuning Across Model Sizes

Priya Venkataraman, Daniel Okafor

Version 2 · 3 reviewer reports · 6 March 2026 · 10.xxxx/ctm.pb.2026.0002

Parameter-efficient fine-tuning (PEFT) methods adapt large models by updating a small fraction of weights, but most evidence comes from a single model size. We fit scaling curves for low-rank adaptation across six base-model sizes spanning two orders of magnitude and four downstream tasks. The optimal adapter rank grows sub-linearly with model width, and the gap between PEFT and full fine-tuning shrinks as models grow, closing to within 0.6 points at the largest size. A simple rule that sets the rank proportional to the square root of the hidden dimension recovers 96% of the best rank found by grid search. We provide fitted coefficients, discuss threats to validity, and note that results on instruction-following tasks differ from those on classification.

Abstract PDF 3,902 1,5445 cited

PublishingResearch ArticlePeer reviewed

Retrieval-Augmented Tool Selection for Long-Horizon Language Agents: Choosing from thousands of tools without placing them all in the prompt

Lena Hartwell, Tomás Ibarra, Marcus Tan

Version 2 · 4 reviewer reports · 27 February 2026 · 10.xxxx/ctm.pb.2026.0001

Language agents that act through external tools are limited by the size of their prompt: listing hundreds of tool descriptions crowds out the task and degrades accuracy. We study tool selection as a retrieval problem and propose a two-stage selector that (i) retrieves a short list of candidate tools using an embedding of the evolving task state and (ii) lets the model re-rank candidates with a single constrained call. Across a synthetic suite of 4,000 tools and three long-horizon task families, the selector keeps task success within 2.1 points of an oracle that sees only the correct tools, while reducing prompt length by 87%. Ablations show that conditioning retrieval on the task state, rather than on the initial instruction alone, accounts for most of the gain. We discuss failure cases, including tools with overlapping descriptions, and release the evaluation harness.

Abstract PDF 4,821 1,9367 cited