Here we present a new class of optimality for coding systems. Members of that class are displaced linearly from optimal coding and thus exhibit Zipf's law, namely a power-law distribution of frequency ranks. Within that class, Zipf's law, the size-rank law and the size-probability law form a group-like structure. We identify human languages that are members of the class. All languages showing sufficient agreement with Zipf's law are potential members of the class. In contrast, there are communication systems in other species that cannot be members of that class for exhibiting an exponential distribution instead but dolphins and humpback whales might. We provide a new insight into plots of frequency versus rank in double logarithmic scale. For any system, a straight line in that scale indicates that the lengths of optimal codes under non-singular coding and under uniquely decodable encoding are displaced by a linear function whose slope is the exponent of Zipf's law. For systems under compression and constrained to be uniquely decodable, such a straight line may indicate that the system is coding close to optimality. We provide support for the hypothesis that Zipf's law originates from compression and define testable conditions for the emergence of Zipf's law in compressing systems.

Environment Conservation Ocean Dolphins may be getting an Alzheimer's-like disease due to this neurotoxin The neurotoxins, found in algal blooms, primarily affect the body's nervous system. Breakthroughs, discoveries, and DIY tips sent every weekday. For marine biologists, dolphins are often viewed as sentinel species, or animals that shed light on the health of the ocean . Along with whales, porpoises, and other cetacean species, dolphins are one way that researchers know to sound the alarm about environmental hazards that might affect the ocean as a whole and potentially humans. In this context, researchers have connected neurotoxins from algal blooms to brain changes associated with an Alzheimer's-like disease in dolphins in Florida.

'These strandings happen fast, and every minute counts.' Breakthroughs, discoveries, and DIY tips sent every weekday. Chipman's Cove in Wellfleet, Massachusetts can be treacherous for the sea creatures that must navigate it. Shallow bays, complex tidal flats, and an arm of land that shelters it from the greater harbor and Cape Cod Bay make it a great summer destination for humans and a notorious stranding hotspot for marine life. Early on Saturday, September 13, worried individuals called the International Fund for Animal Welfare (IFAW) stranding hotline (its 508-743 9548, in case you ever need it), warning the nonprofit that a number of dolphins were in Chipman's Cove.

A new large language model AI system may soon allow humans to converse with dolphins. Scheduled to debut in the coming months, researchers will test to see if DolphinGemma and its companion Cetacean Hearing Augmentation Telemetry (CHAT) system can translate and mimic some of the mammal's own complex vocalizations. If successful, the breakthrough may represent the culmination of over four decades' worth of work, documentation, and conservation efforts.. Dolphins are some of the Earth's smartest and most communicative animals. Their social interactions are so complex that researchers at the Wild Dolphin Project (WDP) have spent the last 40 years attempting to decipher them. In the process, WDP has amassed decades' worth of underwater audio and video documenting a single community of Atlantic spotted dolphins in the Bahamas.

This report introduces Dolphin, a large-scale multilingual automatic speech recognition (ASR) model that extends the Whisper architecture to support a wider range of languages. Our approach integrates in-house proprietary and open-source datasets to refine and optimize Dolphin's performance. The model is specifically designed to achieve notable recognition accuracy for 40 Eastern languages across East Asia, South Asia, Southeast Asia, and the Middle East, while also supporting 22 Chinese dialects. Experimental evaluations show that Dolphin significantly outperforms current state-of-the-art open-source models across various languages. To promote reproducibility and community-driven innovation, we are making our trained models and inference source code publicly available.

After nine months stuck on the International Space Station (ISS), NASA's Butch Wilmore and Suni Williams finally made it back home last night. The duo splashed down off the coast of Florida aboard SpaceX's Crew Dragon capsule, having arrived at the ISS way back in June. While Wilmore and Williams will be relieved to be back on solid ground, their return has ignited a slew of conspiracy theories - with many sceptics critical of the splashdown in particular. Upon arrival, the capsule was circled by an inquisitive pod of dolphins, which many social media commentators are describing as'fake' and computer-generated. Others have taken it even further, suggesting the entire mission footage from departure to landing was created by a sophisticated AI tool.

Reward models have become a staple in modern NLP, serving as not only a scalable text evaluator, but also an indispensable component in many alignment recipes and inference-time algorithms. However, while recent reward models increase performance on standard benchmarks, this may partly be due to overfitting effects, which would confound an understanding of their true capability. In this work, we scrutinize the robustness of reward models and the extent of such overfitting. We build **reWordBench**, which systematically transforms reward model inputs in meaning- or ranking-preserving ways. We show that state-of-the-art reward models suffer from substantial performance degradation even with minor input transformations, sometimes dropping to significantly below-random accuracy, suggesting brittleness. To improve reward model robustness, we propose to explicitly train them to assign similar scores to paraphrases, and find that this approach also improves robustness to other distinct kinds of transformations. For example, our robust reward model reduces such degradation by roughly half for the Chat Hard subset in RewardBench. Furthermore, when used in alignment, our robust reward models demonstrate better utility and lead to higher-quality outputs, winning in up to 59% of instances against a standardly trained RM.

Researchers say they have made a startling discovery in the Amazon River. But their evidence wasn't collected from the water--it could be seen from shore. After around 219 hours of observations, they can confirm that male Amazon river dolphins (Inia geoffrensis), also known as botos, often roll onto their backs and urinate over three feet into the air. The male botos appear to be peeing with a purpose. Over four years, a team from Canada's CetAsia Research Group traveled to the Amazon river, where they then closely watched river dolphin social interactions. Researchers documented a total of 36 separate instances of male botos deciding to pee while floating in the unconventional position.

Hallucinations in large language models (LLMs) pose significant challenges in tasks requiring complex multi-step reasoning, such as mathematical problem-solving. Existing approaches primarily detect the presence of hallucinations but lack a nuanced understanding of their types and manifestations. In this paper, we first introduce a comprehensive taxonomy that categorizes the common hallucinations in mathematical reasoning tasks into six types: fabrication, factual inconsistency, context inconsistency, instruction inconsistency, logical inconsistency, and logical error. We then propose FG-PRM (Fine-Grained Process Reward Model), an augmented model designed to detect and mitigate hallucinations in a finegrained, step-level manner. To address the limitations of manually labeling training data, we propose an automated method for generating fine-grained hallucination data using LLMs. By injecting hallucinations into reasoning steps of correct solutions, we create a diverse and balanced synthetic dataset for training FG-PRM, which consists of six specialized Process Reward Models (PRMs), each tailored to detect a specific hallucination type. Our FG-PRM demonstrates superior performance across two key tasks: 1) Fine-grained hallucination detection: classifying hallucination types for each reasoning step; and 2) Verification: ranking multiple LLM-generated outputs to select the most accurate solution, mitigating reasoning hallucinations. Our experiments show that FG-PRM outperforms ChatGPT-3.5 and Claude-3 on fine-grained hallucination detection and substantially boosts the performance of LLMs on GSM8K and MATH benchmarks.

This paper presents the first steps toward a soft dolphin robot using a bio-inspired approach to mimic dolphin flexibility. The current dolphin robot uses a minimalist approach, with only two actuated cable-driven degrees of freedom actuated by a pair of motors. The actuated tail moves up and down in a swimming motion, but this first proof of concept does not permit controlled turns of the robot. While existing robotic dolphins typically use revolute joints to articulate rigid bodies, our design -- which will be made opensource -- incorporates a flexible tail with tunable silicone skin and actuation flexibility via a cable-driven system, which mimics muscle dynamics and design flexibility with a tunable skeleton structure. The design is also tunable since the backbone can be easily printed in various geometries. The paper provides insights into how a few such variations affect robot motion and efficiency, measured by speed and cost of transport (COT). This approach demonstrates the potential of achieving dolphin-like motion through enhanced flexibility in bio-inspired robotics.