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Martin Andrews

dblp:172/1182 · DBLP profile ↗
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3ranked-venue papers
3as first author
1since 2021 · last 2025
0000-0002-0851-7827ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 61% Question answering and dialogue systems · 30% Knowledge representation and reasoning · 9%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Question answering and dialogue systems
answer verification
0.912025
A Reasoning-Based Approach to Cryptic Crossword Clue Solving · ICML 2025
Natural language and speech › Language models and text generation › large language model reasoning
cryptic crossword solving
0.912025
A Reasoning-Based Approach to Cryptic Crossword Clue Solving · ICML 2025
Natural language and speech › Language models and text generation
large language model reasoning
0.912025
A Reasoning-Based Approach to Cryptic Crossword Clue Solving · ICML 2025

Methods — techniques the papers use, named apart from their topics

verifier system · 0.9large language model · 0.9
YearPublicationVenuePosition
2025 A Reasoning-Based Approach to Cryptic Crossword Clue Solving
abstract
Cryptic crossword clues are challenging language tasks for which new test sets are released daily by major newspapers on a global basis. Each cryptic clue contains both the definition of the answer to be placed in the crossword grid (in common with regular crosswords), and ‘wordplay’ that *proves* that the answer is correct (i.e. a human solver can be confident that an answer is correct without needing crossing words as confirmation). This work describes an LLM-based reasoning system built from open-licensed components that solves cryptic clues by (i) hypothesising answers; (ii) proposing wordplay explanations; and (iii) using a verifier system that operates on codified reasoning steps. Overall, this system establishes a new state-of-the-art performance on the challenging Cryptonite dataset of clues from The Times and The Telegraph newspapers in the UK. Because each proved solution is expressed in Python, interpretable wordplay reasoning for proven answers is available for inspection.
Martin Andrews, Sam Witteveen
ICML1
2016 Compressing Word Embeddings
Martin Andrews
ICONIP (4)1
2015 Proceedings in Adaptation, Learning and Optimization
Martin Andrews
IES1