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7,776 papers
Large Language Models (LLMs) offer a scalable way to simulate survey respondents using demographic profiles and observed reference responses. However, the conventional approach of predicting one question per prompt repeatedly encodes the same context, limits each target to a narrow set of reference responses, and preve...
To enable bidirectional mapping between text and sign, SignFLIP adopts a symmetric architecture together with a stage-wise training strategy built on large-scale data, which shows competitive performance compared with task-specific models on both translation and generation tasks, as well as strong transferability to si...
The swap readout is proposed, which reads each student checkpoint's feature activations on its own, measuring how training changes the student's use of each feature, even for checkpoints unseen by the crosscoder.
The Distributional Answer Commitment Score (DACS) is proposed, a distributional confidence estimator that measures the entropy of the model's probability distribution over answer commitment at each reasoning step to capture how concentrated the model's answer belief is without requiring ground-truth answers or task-spe...
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Results show that self-evolving agents should place experience inside the control flow, rather than only store it as text, and show how learned conventions must reach the right execution step on long tool-use environments where learned conventions must reach the right execution step.
It is argued that this process provides a statistical-learning account of how NLMs solve the compositionality problem in language, and the possibility that this statistical learning process provides an explanatory theory on how language cognition could emerge from pure distributional statistics.
CPSE is introduced, a contract-preserving semantic extraction framework that jointly calibrates extraction prompts and field-level semantic descriptions from a few gold annotations that enables low-resource schema execution while preserving the output structure required downstream.
Recent advances in bioacoustics have been driven by large-scale corpora and standardized benchmarks, yet existing resources are overwhelmingly bird-centric and shallow per species, limiting their use for studying the structure of a single species' communication system. This gap is particularly acute for cetaceans: desp...
Most methods that optimize LLM prompts and agent workflows assume that task-specific output schemas, extraction instructions, and evaluation criteria are predefined. For scientific extraction agents, however, a short task goal may not fully determine these components, while specifying them manually is costly. We study...
The rapid advancement of Large Language Models (LLMs) imposes a thorough evaluation of their linguistic and analytical capabilities as well as constraints, particularly for a language with limited benchmark coverage such as Greek. To address the limited availability of comprehensive benchmarks in this domain, we introd...
This work introduces InfiMed2, a family of 4B and 27B generalist medical multimodal foundation models built around stage-aware data design, and curates a 55.68B-token corpus that combines broad clinical knowledge with context-rich biomedical visual evidence through source-specific processing.
TQTS-BENCH is introduced, a multi-syntax benchmark for evaluating text-to-query capabilities over TSDBs, and error analysis reveals that this performance gap mainly stems from the heterogeneous query syntaxes across different TSDBs, misinterpretation of time-specific intents, and incorrect schema linking.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
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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