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9,568 papers
These findings show that aggregate detection metrics conceal persistent, model dependent heterogeneity in language model failures and motivate regime conditioned evaluation.
Treating intrinsic self-correction as a revision policy rather than as a uniformly beneficial second pass is suggested, and evaluating it through both the corrections it recovers and the errors it introduces.
Across 4,500 full-duplex calls and five voice configurations, Spanish, Portuguese, and Hindi remain within 3.2 task-completion points of English, but Korean and Mandarin fall by 14.7 and 8.4 points, respectively.
Although uniform diffusion language models (UDLMs) represent a promising diffusion paradigm, scaling them remains challenging. We identify the core obstacle as an over-uniform training objective and condition-target confusion during sampling. To address these, we propose Less Uniform Diffusion (LUDI), a novel UDLM fram...
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Large language model search agents are often trained with synthetic questions whose difficulty is increased through larger evidence graphs, additional hops, and longer trajectories. These global properties, however, are only indirect proxies for the local retrieval capabilities required during search. To address this m...
To help researchers understand how to run statistics over LLM judges responsibly, guidance and tooling for the statistical analysis of mixed human-AI judge designs are presented, and recommendations for what CI, p-value, and FWER correction methods to use for small-sample AI evaluations are derived, and researchers are...
This paper evaluates six strong browser-use agents, three GUI-only agents that see only screenshots, and humans, evaluating the construction of challenging instances from tasks agents already solve, turning difficulty into a programmable property of the environment.
Neighbor information improves individual prediction in all 16 public-data settings and pooled collective forecasts on held-out questions, although collective gains depend on transfer conditions.
Lookahead-R is proposed, a planning-based framework that reformulates tool retrieval as a resource-constrained sequential decision-making problem that introduces a lightweight execution-aware surrogate world model that jointly predicts tool execution success, latency cost, and semantic utility—without invoking real API...
TRACE is presented, a deployable tree-relational enhancement framework for oncology LLMs that separates expensive offline structure learning from lightweight online inference, and suggests that explicit, updatable medical structure is a practical path toward more accurate and auditable oncology LLM deployment.
A multi-agent framework in which a story agent, an image agent, and a critic agent collaborate to produce fake social media posts that plausibly counter true news is introduced, providing a foundation for developing robust defenses against social media fake news.
Memory Adaptation for Task-Conditioned Execution (MATE) is introduced, a deterministic post-retrieval procedure that converts trajectories into execution-oriented memory and supports memory adaptation as a distinct stage between retrieval and embodied execution.
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A Blog post by IBM Research on Hugging Face
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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