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6,756 papers
This work introduces Population Fidelity, an evaluation framework that distinguishes key conditions required for a set of LLM-generated responses to represent a population and organizes these features and provides reusable code, data, and trained models for evaluating population fidelity across substantive domains and...
Large language models are increasingly evaluated in specialized domains such as law, medicine, software engineering, and cybersecurity, yet film remains comparatively underexplored despite requiring long-form narrative integration, multilingual interpretation, and culturally situated audience judgments. We introduce Ci...
A preliminary extension to coding tasks shows that SCOUT can support safety verification beyond computer-use, andlations and analysis show that tool-free rubric generation in SCOUT elicits substantially more reasoning and is crucial for safety detection across verifier backbones, especially non-frontier ones.
ProVer, a framework that targets potentially pivotal decisions for fine-grained credit assignment in agentic reinforcement learning, is introduced, highlighting the effectiveness and efficiency of selectively targeting pivotal decisions for fine-grained credit assignment in agentic reinforcement learning.
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The results suggest that LLMs tend to present narratives of success by default, and that steering models toward honesty makes their reports substantially more transparent.
Language models are frequently employed to evaluate other language models. An LM evaluator scoring agentic behaviors across multiple criteria is valuable, provided that its decisions align with human judgment. We call the problem of evaluating this alignment Meta-Evaluation. Tackling it directly is difficult: collectin...
It is argued that the work of memory divides, as thinking does, into two systems: long-term memory and reasoning model answering, and Mnemon, a memory agent built on this division.
This work studies activations from ensembles of prompts that require inferring relationships between three tokens corresponding to a cyclic concept to correctly predict the next token and finds a consistent layerwise progression in how the joint dependence among the tokens is geometrically organized and causally used.
A voice agent can handle almost every call on the words alone and still fail the few its sector's rules were written for. Emergency-call standards, fraud guidance, radio phraseology and vulnerability rules recognise that how a caller sounds, or what else is audible, can change the right action. VoxParity tests whether...
CruxBench is introduced, a benchmark that grades LLM-generated questions by their Value of Information (VOI): how much a model-proposed crux updates beliefs about a target forecasting question and finds that VOI correlates highly with independent measures of model capability and captures cruxes'usefulness for answering...
Single-token typed-decision models answer a schema question by reading the logits of a few one-letter answer codes at a single position: they are fast and return a probability for every allowed answer, but they are trained with plain cross-entropy that ignores most of the structure in their training data. We study such...
These findings show that aggregate detection metrics conceal persistent, model dependent heterogeneity in language model failures and motivate regime conditioned evaluation.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
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Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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