Can we build accurate world models out of large language models (LLMs)? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified environment's dynamics usually bottlenecks LLMs' performance as world models. To bridge the gap, we propose a training-free world alignment that learns an environment's symbolic knowledge complementary to LLMs. The symbolic knowledge covers action rules, knowledge graphs, and scene graphs, which are extracted by LLMs from exploration trajectories and encoded into executable codes to regulate LLM agents' policies.

We present a systematic theoretical framework that interprets masked diffusion models (MDMs) as solutions to energy minimization problems in discrete optimal transport. Specifically, we prove that three distinct energy formulationskinetic, conditional kinetic, and geodesic energyare mathematically equivalent under the structure of MDMs, and that MDMs minimize all three when the mask schedule satisfies a closed-form optimality condition. This unification not only clarifies the theoretical foundations of MDMs, but also motivates practical improvements in sampling. By parameterizing interpolation schedules via Beta distributions, we reduce the schedule design space to a tractable 2D search, enabling efficient post-training tuning without model modification. Experiments on synthetic and real-world benchmarks demonstrate that our energy-inspired schedules outperform hand-crafted baselines, particularly in low-step sampling settings.

Augmented Large Language Models (LLMs) enhance standalone LLMs by integrating external data sources through API calls. In interactive applications, efficient scheduling is crucial for maintaining low request completion times, directly impacting user engagement. However, these augmentations introduce new scheduling challenges: the size of augmented requests (in tokens) no longer correlates proportionally with execution time, making traditional size-based scheduling algorithms like Shortest Job First less effective. Additionally, requests may require different handling during API calls, which must be incorporated into scheduling. This paper presents MARS, a novel inference framework that optimizes augmented LLM latency by explicitly incorporating system-and application-level considerations into scheduling. MARS introduces a predictive, memory-aware scheduling approach that integrates API handling and request prioritization to minimize completion time. We implement MARS on top of vLLM and evaluate its performance against baseline LLM inference systems, demonstrating improvements in end-to-end latency by 27%-85% and reductions in TTFT by 4%-96% compared to the existing augmented-LLM system, with even greater gains over vLLM. Our implementation is available online.

NASA's Perseverance rover has traveled nearly 26 miles since landing on the Red Planet in 2021. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. It took the rover about an hour to take all the images necessary to compile into a single selfie. Breakthroughs, discoveries, and DIY tips sent six days a week. After five years of rolling across Mars, NASA's Perseverance rover is still going strong.

Science Space Solar System Mars For 6 days, NASA's Mars rover battled a rock More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The multi-day challenge took multiple attempts to fix. Breakthroughs, discoveries, and DIY tips sent six days a week. Curiosity got itself stuck between a rock and hard place last month, but NASA says there's no reason to fret about the intrepid Mars rover . On April 25, mission engineers were remotely piloting its robotic arm's rotary-percussive drill into a Martian rock nicknamed Atacama.

Martian clay may have held water billions of years ago. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. NASA's Curiosity Mars rover took this selfie at a location nicknamed Mary Anning after a 19th century English paleontologist. This was the site of the chemical experiment uncovering diverse organic molecules on Mars, in the Glen Torridon region, which scientists believe was a site where ancient conditions would have been favorable to supporting life, if it ever was present. Breakthroughs, discoveries, and DIY tips sent six days a week.

He told Mission Control that they saw'an island of terrain completely surrounded by darkness.' 'Up to the north, there is a very nice double crater. It looks like a snowman just sitting there,' he continued. 'On the southern edge, there is a hole.

Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment andperforming reasoning with the acquired knowledge--situated inductive reasoning, is crucial and challenging for machine intelligence. In this paper, we design Mars, an interactive environment devised for situated inductive reasoning. It introduces counter-commonsense game mechanisms by modifying terrain, survival setting and task dependency while adhering to certain principles.