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BOND’s latest report on Trends – Artificial Intelligence (May 2025) presents a comprehensive data-driven snapshot of the current state and rapid evolution of AI technology. The report highlights some striking trends underscoring the unprecedented velocity of AI adoption, technological improvement, and market impact. This article reviews several key findings from the report and explores their implications for the AI ecosystem. Explosive Adoption of Open-Source Large Language Models One of the standout observations is the remarkable uptake of Meta’s Llama models. Over an eight-month span, Llama downloads surged by a factor of 3.4×, marking an unprecedented developer adoption curve for any open-source large language model (LLM). This acceleration highlights the expanding democratization of AI capabilities beyond proprietary platforms, enabling a broad spectrum of developers to integrate and innovate with advanced models. Source: https://ift.tt/LJmsjEI The rapid acceptance of Ll...

Large language models (LLMs), with billions of parameters, power many AI-driven services across industries. However, their massive size and complex architectures make their computational costs during inference a significant challenge. As these models evolve, optimizing the balance between computational efficiency and output quality has become a crucial area of research. The core challenge lies in how LLMs handle inference. Every time an input is processed, the entire model is activated, which consumes extensive computational resources. This full activation is unnecessary for most tasks, as only a small subset of neurons contribute meaningfully to the final output. Existing sparse activation methods attempt to address this by selectively deactivating less important neurons. However, these approaches often focus only on the magnitude of hidden states while ignoring the critical role of weight matrices in propagating errors through the network. This oversight leads to high approximation ...

The customer experience (CX) paradigm within B2B technology is undergoing a substantive evolution, propelled by advances in agentic AI. Cisco’s recent Agentic AI Report provides a rigorous assessment of how AI agents—characterized by autonomous decision-making, contextual awareness, and adaptive learning—are fundamentally reshaping CX, delivering a level of personalization, proactivity, and predictive capability previously unattainable. Agentic AI: Autonomous Agents Driving CX Innovation Agentic AI refers to systems endowed with agents, enabling them to retain memory, reason about tasks, and autonomously select actions to optimize outcomes with minimal human intervention. This is a marked departure from conventional AI tools, allowing these agents to engage in dynamic, multi-step workflows that span the technology lifecycle. Cisco’s data indicates a swift trajectory toward agentic AI integration: enterprises anticipate that 56% of their interactions with technology partners will be ...

Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language models (LLMs) attempt to mimic through structured approaches such as chain-of-thought (CoT) prompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem. A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This ca...

In this tutorial, we implement the Agent Communication Protocol (ACP) through building a flexible, ACP-compliant messaging system in Python, leveraging Google’s Gemini API for natural language processing. Beginning with the installation and configuration of the google-generativeai library, the tutorial introduces core abstractions, message types, performatives, and the ACPMessage data class, which standardizes inter-agent communication. By defining ACPAgent and ACPMessageBroker classes, the guide demonstrates how to create, send, route, and process structured messages among multiple autonomous agents. Through clear code examples, users learn to implement querying, requesting actions, and broadcasting information, while maintaining conversation threads, acknowledgments, and error handling. Copy Code Copied Use a different Browser import google.generativeai as genai import json import time import uuid from enum import Enum from typing import Dict, List, Any, Optional from data...

State-of-the-art models show human-competitive accuracy on AIME, GPQA, MATH-500, and OlympiadBench, solving Olympiad-level problems. Recent multimodal foundation models have advanced benchmarks for disciplinary knowledge and mathematical reasoning. However, these evaluations miss a crucial aspect of machine intelligence: physical reasoning, which requires integrating disciplinary knowledge, symbolic operations, and real-world constraints. Physical problem-solving differs fundamentally from pure mathematical reasoning as it demands models to decode implicit conditions in questions. For example, interpreting “smooth surface” as zero friction coefficient, and maintaining physical consistency across reasoning chains because physical laws remain constant regardless of reasoning trajectories. MLLM shows excellent visual understanding by integrating visual and textual data across various tasks, motivating exploration of its reasoning abilities. However, uncertainty remains regarding whether ...

In recent months, there has been growing interest in applying diffusion models—originally designed for continuous data, such as images—to natural language processing tasks. This has led to the development of Discrete Diffusion Language Models (DLMs), which treat text generation as a denoising process. Unlike traditional autoregressive models, DLMs enable parallel decoding and provide better control over structure, offering advantages such as flexible initialization of entire sequences, explicit control over output format, and improved infilling through bidirectional attention. Furthermore, their non-sequential nature opens the door to faster generation. Despite these benefits, most current multimodal large language models (MLLMs)—such as LLaMA, Qwen-VL, and InternVL—still rely solely on autoregressive methods. Work in diffusion-based language models has explored both continuous and discrete diffusion spaces. Continuous approaches, such as DiffuSeq and SED, use embedding or relaxed cat...

In natural language processing (NLP), RL methods, such as reinforcement learning with human feedback (RLHF), have been utilized to enhance model outputs by optimizing responses based on feedback signals. A specific variant, reinforcement learning with verifiable rewards (RLVR), extends this approach by utilizing automatic signals, such as mathematical correctness or syntactic features, as feedback, enabling the large-scale tuning of language models. RLVR is especially interesting because it promises to enhance models’ reasoning abilities without needing extensive human supervision. This intersection of automated feedback and reasoning tasks forms an exciting area of research, where developers aim to uncover how models can learn to reason mathematically, logically, or structurally using limited supervision. A persistent challenge in machine learning is building models that can reason effectively under minimal or imperfect supervision. In tasks like mathematical problem-solving, where t...

Human reasoning naturally operates through abstract, non-verbal concepts rather than strictly relying on discrete linguistic tokens. However, current LLMs are limited to reasoning within the boundaries of natural language, producing one token at a time through predefined vocabulary. This token-by-token approach not only restricts the expressive capacity of the model but also limits the breadth of reasoning paths it can explore, especially in ambiguous or complex scenarios. Standard Chain-of-Thought (CoT) methods exemplify this limitation, forcing the model to commit to a single path at each step. In contrast, human cognition is more flexible and parallel, allowing for simultaneous consideration of multiple ideas and delaying verbalization until concepts are fully formed. This makes human reasoning more adaptable and robust in dealing with uncertainty. To address these limitations, researchers have proposed transitioning from token-based reasoning to reasoning within a continuous conce...