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SWE-Verifier is introduced, a lightweight generative model that selectively triggers candidate sampling and verification that achieves comparable or higher resolution than the trajectory and all-step baselines at substantially lower cost.
AI assistants are increasingly used as advisors to guide decisions, yet little is known about how people evaluate such advice when the advisor's underlying intent conflicts with their interests. We examine how covert misalignment shapes choices in a randomized experiment (N = 233 participants; 699 observations) in whic...
Step-level reasoning evaluators are commonly based on autoregressive language models, whose causal attention restricts each step representation to the problem, previous steps, and the current step. Yet, when the complete solution is available, the validity of an earlier step may become clearer only through its downstre...
It is revealed that the energy divide persists across models, hardware, and tasks, suggesting a systemic energy inequity in multilingual LLM deployment and is recommended that the community treat energy as a first-class evaluation axis, extend reporting checklists and model cards to include it, and adopt deployment-sid...
It is argued that BBQ-style multiple-choice abstention benchmarks measure a single structural cue, and a model that solves them does not thereby become fair, and is called for evaluation suites that cover a broader spectrum of fairness alignment.
KIDBench, a benchmark for evaluating child-facing LLM safety for ages 7-11 using a LLM-as-a-Judge rubric grounded in developmental-psychology, is introduced and KIDGuardLlama, a child-safety evaluator, and KIDLlama, a child-safe response model are introduced.
AtlasNLP is introduced, a country-aware atlas of over 13,000 NLP dataset records across normalized NLP task categories, tracking both the populations represented and where datasets are produced, showing that dataset coverage is highly uneven across countries and tasks and language coverage does not imply geographic rep...
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