Demo Data from EDBT 2026 / reviewers in the wild / expert
Martin Andrews
dblp:172/1182
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems
answer verification |
0.9 | 1 | 2025 | 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 |
0.9 | 1 | 2025 | A Reasoning-Based Approach to Cryptic Crossword Clue Solving · ICML 2025 |
Natural language and speech › Language models and text generation
large language model reasoning |
0.9 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Reasoning-Based Approach to Cryptic Crossword Clue SolvingabstractCryptic 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 |
ICML | 1 |
| 2016 | Compressing Word Embeddings
Martin Andrews |
ICONIP (4) | 1 |
| 2015 | Proceedings in Adaptation, Learning and Optimization
Martin Andrews |
IES | 1 |