The study examines how 105 language models choose a single word from a large set of valid options, finding that 46% of the time they all pick the same word, "serendipity." Using 96 prompts that ask for one-word answers across various categories, the researchers measure convergence and each model’s share of the consensus. They observe that post‑training and persona‑tuned models diverge more, while heavily post‑trained assistants are more conformist, and that the field’s answer choices are more concentrated than human norms in most categories.
arXiv:2607. 12796v1 Announce Type: cross Abstract: When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks?
When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks? Asked to "pick a word -- any word," 44 models chose "serendipity" 41% of the time.
arXiv:2607. 18476v1 Announce Type: cross Abstract: When a language model must choose one answer from a large space of equally valid options, a format clause -- "Reply with JSON only" -- changes which answer it chooses.
arXiv:2608. 11247v1 Announce Type: new Abstract: Recent advances in language models have enabled collaborative settings in which multiple models leverage one another's capabilities, iteratively improving, transforming, and extending each other's outputs.
The study evaluates six frontier language models on a two‑agent <log(N)>‑Questions game using Wikipedia lead paragraphs. In each game a questioner must identify a target paragraph with exactly <log2 N> yes/no questions, while an answerer only sees the target and the question and replies with a single word. Across 408 games, the models perform similarly, with Claude Opus 5 winning 28 of 68 games and the top five models showing only marginal differences; win rates decline sharply with larger document sets, following a reliability parameter of 0.928 per question. "whyItMatters":"The results reveal how well language models can communicate under information asymmetry, highlighting that even top models struggle to extract a full bit per question and that reasoning token usage does not strongly predict success."
The paper investigates whether language models can identify sentences from their training data by using exact duplication counts from publicly released corpora for two model families, OLMo‑2 and Pythia. It finds that for typical duplication levels, models show only a weak trace of exposure, with a rank correlation near –0.08, and that strong signals only appear when a sentence appears roughly a thousand times, at which point fame rather than memory dominates. The study also demonstrates that common membership tests can be misleading, as changing a single word does not alter the model’s preference, and that controlling for register can significantly improve detector performance.
arXiv:2607. 20492v1 Announce Type: cross Abstract: Language models in production do not write prose.
arXiv:2607. 23976v1 Announce Type: cross Abstract: Appending a two-word confirmation tag to a decision question -- "Is X the better choice?
The study investigates how demographic identity is represented in a language model, using representational similarity analysis against Pew survey data across 169 demographic cells. It finds that standard last‑token read‑outs underestimate the model’s fidelity, while specific attention heads (notably L11 H16) capture demographic structure more accurately, though race‑based types remain weak. Causal interventions reveal that high fidelity does not guarantee causal use, and a 128‑dimensional probe of a single head improves alignment with survey truth but fails to recover per‑question group ordering.
arXiv:2607. 25292v1 Announce Type: new Abstract: Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's response distribution.
arXiv:2603.13911v2 Announce Type: replace Abstract: A language model can give the wrong answer even when the correct answer is decodable from its intermediate states. To study this gap between decoda...
A benchmark score means nothing without knowing what a trivial method achieves and what the best possible method could achieve. We construct both bounds for a task with a rare kind of ground truth: predicting which sentences a crowd of readers -- highlighting for their own purposes, unpaid, uninstructed, and blind to each other -- marked in 120 web documents.