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9,568 papers
An audit of SycEval, which reports that objections raised before a model answers (preemptive) cause more caving than those raised after (in-context), and attributes the gap to timing, and builds the missing comparison and tests it on multiple-choice questions and SycEval's own free-form pipeline.
Experimental results show that ARSM maintains the task performance while simultaneously reducing token consumption, offering a practical, cost-effective route toward scalable autonomous agents for long-horizon tasks.
This work proposes A-B-D to infer traits bottom-up from behavioral data (B-data), namely how agents act on their environment and communicate with users, as recorded in existing trajectories, and offers a new lens for understanding AI personality.
A Chinese Human-AI Collaborative Text Detection Benchmark (C-HAT-Bench), a unified benchmark that links human-written source texts from five domains to variants produced using six generative models under Prefix-Conditioned Continuation as a reference setting and three collaborative production modes.
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This work proposes an inference-time, plug-and-play Self-Contrastive Steering (SCS) method that constructs unconditional general text embeddings and uses them to refine conditional text embeddings, making them more focused on the target condition.
Rep2Act, a representation-to-action alignment method that translates latent answerability awareness into explicit abstention decisions, is introduced, a representation-to-action alignment method that translates latent answerability awareness into explicit abstention decisions.
This paper proposes ExpVoyager, a novel framework in which a skill curator navigates raw experience across different views and resolutions, continually identifying reusable procedural knowledge from what it observes while tracking remaining knowledge needs that guide where to navigate next.
Large language models are increasingly used to edit human-written text rather than generate entire texts from scratch. Conventional AI-text detectors mainly distinguish human-written from fully AI-generated text, while recent methods for AI-edited text typically provide only a text-level label or editing-degree score....
Pass@k measures whether a model reaches a correct answer under repeated sampling, but never how: a lucky guess counts the same as sound reasoning. CoT-Pass@k was proposed to close that gap, adding an LLM-as-judge that must assess a solution's reasoning chain before it counts. Its value rests entirely on one assumption:...
The authors collect 40,891 hallucination phrases in 100 languages, choose a text-to-speech system by measurement, and synthesise those phrases as positives, so the model meets the same text both as something to suppress and as something to transcribe.
Instead of post-hoc diagnosis, E-Closure is introduced to supervise three dependencies for reliable reasoning during training, combining generation supervision on supported original and counterfactual responses with bidirectional switching constraints.
While large language models (LLMs) are increasingly deployed in long-context scenarios, lengthy prompts can increase inference costs and latency and exacerbate the ``lost-in-the-middle''phenomenon. Selective prompt compression offers a model-agnostic approach to alleviating these issues. However, methods based on fixed...
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A Blog post by Ai2 on Hugging Face
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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