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9,568 papers
NLPG is proposed, an external policy-memory method for improving a fixed agent without changing its model parameters or program structure that outperforms the strongest listed baseline for each benchmark by 8.71 percentage points on average.
CHI (Composite Hallucination Index), the first unified hallucination metric that decomposes faithfulness errors into three orthogonal dimensions, provides practitioners with a decomposable, interpretable, and efficient faithfulness metric suitable for both offline evaluation and online monitoring of summarization syste...
This work replaces true next-observation targets with in-distribution mismatched observations and introduces a setting that replaces prediction-based rewards with independent random signals, which expands task coverage even when the reward carries no environment information.
This work proposes SHED, a two-stage workflow for inferring a robust and compact structure that improves F-1 scores by 13%--68% over non-LLM baselines and 9%--15% over expensive LLM-based approaches.
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Diffusion large language models (dLLMs) generate text by iterative unmasking. At each denoising step, a dLLM proposes a token at every masked position, but the decoder commits only a confident subset of these proposals. Trace-based on-policy distillation (TOPD) builds on this process by matching the student to a strong...
BERT4DTI is presented, which encodes SMILES strings with ChemBERTa and amino-acid sequences with ProtBERT, applies bidirectional mutual attention between token-level representations, and classifies the resulting interaction features using convolutional layers and a multilayer perceptron.
This work studies failure disclosure: whether a model admits that an attempted solution failed rather than staying silent or presenting it as successful, and makes reporting substantially more consistent, though its effect on task performance depends on the setting.
This property, coherence, is tested with a battery of logically linked questions that needs no labels, and finds that the checks need no labels expose biases that appear only when question forms are compared, and inconsistencies within items.
Constrained-option scoring reads a model's probabilities for a fixed set of permitted answers, so it returns a score even when the model is about to write something else. We study a prompt that quotes a multiple-choice item, ending in the item's own answer instruction, and asks for a forecast of whether a reader model...
This paper introduces MedRouter, an agentic system that uses an embedding-based multi-label router to select and query specialist LLMs, then passes their responses to a generator to produce the final answer, and proposes SCALE (Specialist Competence-Aware Learning), a two-stage training framework.
A recast long-duration ECG interpretation as a long-horizon, online (streaming, causal) sequential decision process and introduces ECG-Scroll, which evaluates a signal-threshold rule agent alongside off-the-shelf LLM agents online, characterizing how they use memory, tools, and planning and where the benchmark's head-r...
SLU encompasses a diverse set of tasks, including ISLR, CSLR, and SLT. Although these tasks share basic semantic and linguistic foundations, they are typically addressed with task-specific architectures and training pipelines, which hinders knowledge sharing and requires costly pretraining and finetuning for each task....
Natural Language Processing
Our most capable vision-language-action model enhances robot dexterity, reliability, and safety
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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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