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6,499 papers
This system discovers causally linked action pairs directly from the training data, where one action produces exactly the condition that the next one requires, and turns each pair into a new operator, and not only accelerates planning but, in certain domains, determines solvability in practice.
It is established that multi-winner voting with argumentative ballots (MVArg) is strictly more expressive than multi-winner voting with approval ballots (MV) and the notions of cohesion and justified representation are conservative generalisations of their counterparts in MV.
A hybrid GAN-guided diffusion framework that uses a pretrained Wasserstein GAN with gradient penalty (WGAN-GP) as a feature prior for conditional diffusion-based image restoration that consistently improves the quality of both degraded and low-resolution images.
Meta-Ctrl is proposed, a constrained-decoding framework that guarantees the encoded constraints while preserving the base LM's plan quality, and is demonstrated on a real tabletop robot, where every generated plan satisfies its preconditions and goals by construction.
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This work introduces Vis-Poison, a novel visual knowledge poisoning attack where the poisoned image itself is the attacker-controlled payload, without manipulating captions, summaries, metadata, or other associated text.
JuryProbe is introduced, an empirical consensus-risk diagnostic for reference-free factuality judge panels, paired with a calibration-based routing policy, which estimates consensus risk from a labeled calibration probe using false-negative-only (FN-only) judge correlation and false-consensus lift.
PolyComp, a procedurally generated and verified benchmark that stresses visual recognition and compositional spatial reasoning, is introduced, and the observed accuracy spread across geometry families is larger than across presentation formats.
RecoverFly is proposed, a failure-aware RL post-training framework for end-to-end UAV-VLA policies that adapts token-level RL for stable optimization of grammar-constrained autoregressive UAV actions, revisits unresolved failure cases to strengthen corrective learning and sample utilization, and combines a two-stage long-tail scene curriculum with reference-policy regularization to improve scene adaptation while preserving acquired capabilities.
The proposed Barycentric Rational Forecasting with Chebyshev Enhancement (BRACE) maintains a local sliding window to cache sparse historical features and leverages adapted Chebyshev weights to formulate a barycentric rational function, directly aggregating these raw features to ensure numerical stability.
This work introduces ED-CSP, a machine learning framework that predicts crystal structures from chemical composition, atom count, and multiple detector-plane ED spot sets and establishes a benchmark for generative crystal structure prediction from sparse ED observations and provides a foundation for future transfer to experimental data.
This work introduces Sieve, a search-inspect-fetch strategy driven by a Boolean Query Language (BQL): it searches webpage fields to filter candidates, uses an interchangeable ranker to order them, presents structure-rich result cards for inspection, and fetches only selected sections.
This case study shows how locking the evaluation, harmonizing the measured endpoint, and separating primary from secondary evidence can change the inference supported by an AI benchmark.
Machine Intelligence
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A Blog post by IBM Research on Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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