Large Language Models demonstrate strong capabilities in single-turn instruction following but suffer from Lost-in-Conversation (LiC), a degradation in performance as information is revealed progressively in multi-turn settings. Motivated by the current progress on Reinforcement Learning with Verifiable Rewards (RLVR), we propose Curriculum Reinforcement Learning with Verifiable Accuracy and Abstention Rewards (RLAAR), a framework that encourages models not only to generate correct answers, but also to judge the solvability of questions in the multi-turn conversation setting. Our approach employs a competence-gated curriculum that incrementally increases dialogue difficulty (in terms of instruction shards), stabilizing training while promoting reliability. Using multi-turn, on-policy rollouts and a mixed-reward system, RLAAR teaches models to balance problem-solving with informed abstention, reducing premature answering behaviors that cause LiC. Evaluated on LiC benchmarks, RLAAR significantly mitigates LiC performance decay (62.6% to 75.1%) and improves calibrated abstention rates (33.5% to 73.4%). Together, these results provide a practical recipe for building multi-turn reliable and trustworthy LLMs.

Third, a generative model is obtained by combining the estimated distribution with the decoder part of the AE.

This is the most common metric in the machine learning community.

Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we introduce the vine computational graph, a DAG that abstracts the multilevel vine structure and associated computations. On this foundation, we devise new algorithms for conditional sampling, efficient sampling-order scheduling, and constructing vine structures for customized conditioning variables. We implement these ideas in torchvinecopulib, a GPU-accelerated Python library built upon PyTorch, delivering improved scalability for fitting, sampling, and density evaluation. Our experiments illustrate how gradient flowing through the vine can improve Vine Copula Autoencoders and that incorporating vines for uncertainty quantification in deep learning can outperform MC-dropout, deep ensembles, and Bayesian Neural Networks in sharpness, calibration, and runtime. By recasting vine copula models as computational graphs, our work connects classical dependence modeling with modern deep-learning toolchains and facilitates the integration of state-of-the-art copula methods in modern machine learning pipelines.

Bennett's tree kangaroos, one of Australia's most mysterious marsupials, have long eluded researchers. Our new study, published in Australian Mammalogy today, has achieved a breakthrough: using thermal drones to detect these rare animals with unprecedented efficiency. Tree kangaroos are found only in the tropical rainforests of Australia and New Guinea. Unlike their ground-dwelling relatives, they spend their lives in treetops, feeding on leaves and vines. Their dependence on rainforest trees makes them vulnerable to deforestation and climate change.

A robot that can grow around trees or rocks like a vine could be used to make buildings or measure pollution in hard-to-reach natural environments. Vine-like robots aren't new, but they are often designed to rely on just a single sense to grow upwards, such as heat or light, which means they don't work as well in some settings as others. Emanuela Del Dottore at the Italian Institute of Technology and her colleagues have developed a new version, called FiloBot, that can use light, shade or gravity as a guide. It grows by coiling a plastic filament into a cylindrical shape, adding new layers to its body just behind the head that contains the sensors. "Our robot has an embedded microcontroller that can process multiple stimuli and direct the growth at a precise location, the tip, ensuring the body structure is preserved," she says.

The vine-like Filobot was inspired by plants.Credit: Del Dottore et al., Sci. Researchers have demonstrated a robot that grows like a vine in response to stimuli such as light and pressure. The machine -- named FiloBot -- has a head that prints its body by melting and extruding plastic, which then solidifies as it cools. The robot's head is connected to a base by a thin hose, through which it receives a fresh supply of plastic from a spool. FiloBot's growth rate is slow -- its body elongates by just a few millimeters each minute.

Plant diseases are major causes of production losses and may have a significant impact on the agricultural sector. Detecting pests as early as possible can help increase crop yields and production efficiency. Several robotic monitoring systems have been developed allowing to collect data and provide a greater understanding of environmental processes. An agricultural robot can enable accurate timely detection of pests, by traversing the field autonomously and monitoring the entire cropped area within a field. However, in many cases it is impossible to sample all plants due to resource limitations. In this thesis, the development and evaluation of several sampling algorithms are presented to address the challenge of an agriculture-monitoring ground robot designed to locate insects in an agricultural field, where complete sampling of all the plants is infeasible. Two situations were investigated in simulation models that were specially developed as part of this thesis: where no a-priori information on the insects is available and where prior information on the insects distributions within the field is known. For the first situation, seven algorithms were tested, each utilizing an approach to sample the field without prior knowledge of it. For the second situation, we present the development and evaluation of a dynamic sampling algorithm which utilizes real-time information to prioritize sampling at suspected points, locate hot spots and adapt sampling plans accordingly. The algorithm's performance was compared to two existing algorithms using Tetranychidae insect data from previous research. Analyses revealed that the dynamic algorithm outperformed the others.

Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.