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

PublishingResearch ArticleUnder peer review

Dense Retrieval for Code-Mixed Queries: A study of Hindi–English and Yoruba–English search

Oluwaseun Adebayo, Elias Wagner, Ravi Menon

Version 1 · 0 reviewer reports · 5 October 2026 · 10.xxxx/ctm.pb.2026.0009

Users frequently mix languages within a single query, but retrieval benchmarks rarely reflect this. We construct code-mixed query sets for two language pairs and evaluate dense retrievers under different tokenisation and fine-tuning strategies. Adapting the query encoder with synthetic code-mixed data improves recall@20 by 9 points over a multilingual baseline, while transliteration normalisation helps for romanised queries but hurts for native-script queries.

Abstract PDF 640 1880 cited

PublishingResearch ArticleUnder peer review

Quantisation-Aware Distillation for On-Device Assistants

Daniel Okafor, Yuki Saito, Priya Venkataraman

Version 1 · 1 reviewer report · 28 August 2026 · 10.xxxx/ctm.pb.2026.0006

Running assistant-grade language models on phones requires both small models and low-precision arithmetic, yet distillation and quantisation are usually applied one after the other. We train a 1.3B-parameter student with distillation losses computed through simulated 4-bit quantisation, so that the student learns to be robust to its own rounding error. Compared with distilling first and quantising afterwards, the joint procedure recovers 41% of the accuracy lost to 4-bit quantisation, reaches 94% of the full-precision student on a suite of assistant tasks, and runs 3.1× faster on a mid-range mobile processor.

Abstract PDF 2,144 7862 cited

PublishingResearch ArticlePeer reviewed

Statistical Power in Agent Benchmarks: How Many Runs Are Enough?

Hana Kovács, Kofi Mensah, Carlos Medina

Version 2 · 3 reviewer reports · 14 August 2026 · 10.xxxx/ctm.pb.2026.0008

Most agent benchmarks report one run per task, yet agents are stochastic. We analyse run-to-run variance on four public agent benchmarks and derive the number of repeated runs needed to detect a given difference between two systems at conventional power. For differences smaller than five points, single-run comparisons have less than 40% power on all four benchmarks. We provide a sequential testing procedure that stops early when the evidence is clear, and show that it halves the cost of repeated evaluation while keeping the false-positive rate below 5%.

Abstract PDF 2,740 1,1033 cited

PublishingResearch ArticlePeer reviewed

Refusal Calibration in Instruction-Tuned Language Models: When should a model decline, and how well do current models know?

Nadia Haddad, Hana Kovács

Version 2 · 3 reviewer reports · 9 July 2026 · 10.xxxx/ctm.pb.2026.0007

Instruction-tuned models decline requests for many reasons, but little is known about how well their refusals track the actual risk of a request. We build a labelled set of 6,400 requests spanning benign, borderline and harmful categories and measure calibration of refusal decisions across eight open models. Refusal rates correlate only weakly with annotated risk for borderline requests, and over-refusal is concentrated in a handful of phrasing patterns rather than topics. A light-weight calibration step that uses the model's own confidence reduces over-refusal by 31% at no measurable cost to harmful-request refusal. We release the labelled set and the annotation guidelines.

Abstract PDF 3,180 1,2604 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