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VERITAS is introduced to verbalise the model's computation: how different reasoning anchors emerge, transfer across layers, and surface in the output, to provide a richer and more interpretable lens on CoT faithfulness.
To study this phenomenon, SocioHack is introduced, a sandbox of 72 societal environments, and it is found that within these environments, reward hacking naturally emerges and leads to regulatory loophole discovery.
GraphMind, a model that jointly processes micro- and macro-level structures for novelty prediction and rationale generation, is proposed that significantly outperforms baseline LLMs in both novelty score prediction and rationale generation.
This work connects papers in a graph whose edges are typed by these four facets, derived from facet items and citation signals, and distil it into a generative retriever whose identifiers are the papers'own facet text.
Assessing scientific novelty is inherently complex, requiring evaluation of both a paper's internal structure and its contribution within the broader research landscape. Existing large language model (LLM) approaches often rely on surface-level similarity or citation retrieval, overlooking the integration between a pap...
The AppraiSal benchmark is introduced, containing 996 emotional support conversations with human-annotated mental states, including salient cognitive appraisal dimensions, and PRISM, a multi-agent probabilistic framework grounded in Bayesian Inverse Planning, designed to improve LLMs'ability to identify context-specifi...
GRADE (GRAdient Dynamics for knowlEdge gap detection), which quantifies the knowledge gap via the cross-layer rank ratio of the gradient to that of the corresponding hidden state subspace, motivated by the property of gradients as estimators of the required knowledge updates for a given target.
This work analyses the linearity and quality of MGT representations and shows that simple linear probes outperform a wide range of detectors while being substantially more sample-efficient, and demonstrates the potential of linear probes as as robust and sample-efficient MGT detectors.
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