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2,491 papers
ClinMPO improved performance across two complementary schemes covering ICD-11 diagnostic categories and psychiatric practice competencies and Blinded assessment by three clinicians showed improved rationale quality across CPTS criteria.
The first large-scale study of 50 quantized models evaluated on PostTrainingBiasBench, a unified benchmark of 13 closed- and open-ended bias datasets, shows that compression fundamentally alters bias patterns, requiring crucial post-quantization evaluation and interventions to ensure reliability in practice.
It is taken that TLMs encode a non-trivial amount of syntactic knowledge, which shows strong performance on formal syntactic phenomena, but weaker and more variable performance on phenomena at the syntax-semantics interface.
It is suggested that demographic conditioning in LLMs is not a cue-invariant category-level parameter but depends fundamentally on how identity is cued, reflecting responses to linguistic signals rather than stable demographic categories.
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This work introduces MentorQA, the first multilingual dataset and evaluation framework for mentorship-focused question answering from long-form videos, and defines mentorship-focused evaluation dimensions that go beyond factual accuracy, capturing clarity, alignment, and learning value.
An LLM-based pipeline to automatically annotate longitudinal information in radiology reports is proposed, which outperforms existing annotation solutions, achieving 11.3\% and 5.3\% higher F1-scores for longitudinal information detection and disease tracking, respectively.
This work introduces EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games, and evaluates memory agents with strong LMs/VLMs as backbones, using in-context prompting as baselines.
LocalNewsQA is introduced, an 18,700-item English-news benchmark that pairs the same question across two locales and scores whether a model actually switches its answer when the locale changes, and pretraining with metadata in MAPLE produces measurable switching and improves accuracy on questions whose correct answer depends on locale.
This work proposes CORE-T, a scalable, training-free framework that enriches tables with LLM-generated purpose metadata and pre-computes a lightweight table-compatibility cache, and uses 1.20x fewer total selection tokens than LLM-intensive baselines.
CoReflect is introduced, which unifies dialogue simulation and evaluation into an adaptive, iterative process that allows evaluation protocols to adapt alongside the rapidly advancing capabilities of dialogue models.
Mechanistic analyses of attention routing, MLP contributions, and layer-wise probability trajectories reveal an asymmetry: inducing copying is an easy ``reactivation''process that can be triggered at different locations in the input, while restoring recall is a ``suppression''process that is more fragile and strongly tied to object-token interventions.
EvoCtx dynamically decides whether the next reasoning transition should cross the current evidence boundary through retrieval or refine the reasoning state within the existing context, and strategically alternates between boundary expansion and intra-boundary trajectory refinement.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Algorithms & Theory
New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
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