Hugging Face Trending Papers
Prism-1: Position- and Rival-Independent Scoring for System One Models
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arXiv AI
Oct 16
Prism-1: Position- and Rival-Independent Scoring for System One Models
Prism-1 introduces Position- and Rival-Independent Scoring for System One models, ensuring that option ordering and set changes do not affect decision probabilities. It achieves this by reading each option in its own block at shared positions, preserving softmax independence across options. Experiments on Qwen3-8B and Gemma 4 12B show Prism-1 maintains baseline accuracy while eliminating sensitivity to shuffling and irrelevant options.
arXiv AI
Oct 15
Consider, Then Choose: Order-Invariant Decisions in System One Models
arXiv:2610.18233v1 Announce Type: new Abstract: System One models answer typed questions in a single forward pass and promise the same answer for the same input. For choice questions, this promise fa...
arXiv AI
Oct 16
Verdict: Encode Once, Decide Many
Verdict-MQ is an open encoder that processes a single piece of state once and then answers multiple typed questions about that state, avoiding separate passes for each question. By using a structured attention mask, it ensures the state encoding is independent of question order and prevents questions from influencing one another. On the typed‑decisions benchmark, Verdict‑MQ outperforms a larger specialist model, achieving higher accuracy and better calibration, while on other benchmarks it shows mixed results compared to fine‑tuned baselines.
arXiv Machine Learning
Oct 8
Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation
The study investigates whether adaptive layer-skipping and repetition programs truly improve language‑model inference or merely exploit input‑blind controls. Using 32 such programs on Qwen3‑4B‑Base and Llama‑3.1‑8B across 4,413 multiple‑choice items, the authors compare real program gains to those from input‑blind perturbations and fixed letter offsets. Results show that input‑blind controls can produce comparable or even larger headroom, and that real programs only outperform controls in specific menu configurations, indicating that the apparent benefits may not be due to the actual layer computation choices.
arXiv Machine Learning
1d ago
Joint or Per-Option? Compact Encoders for Closed-Set Decisions from 17 Million to 1 Billion Parameters
The paper investigates how compact an encoder can be for closed‑set decision tasks in language‑model pipelines, comparing joint versus per‑option scoring. In a 32‑million‑parameter study, per‑option scoring outperforms joint scoring by 5.8 chance‑corrected skill points on a 37‑benchmark suite, and two external benchmarks corroborate this advantage. Across six released joint encoders ranging from 17 million to 1 billion parameters, the 68‑ and 150‑million‑parameter models surpass a 435‑million‑parameter DeBERTa‑v3 zero‑shot classifier on 209 overlapping tasks and match it on 145 unseen tasks, while the smallest model processes a median request in 14 ms versus 728 ms for the classifier.
arXiv AI
Aug 18
Gated Against One Model, Open to the Next: Option-Only Solvability in Legal Multiple-Choice Benchmarks
arXiv:2608. 15428v1 Announce Type: cross Abstract: Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question.