ibl.ai

Google: Agents Companion

Summary of https://www.kaggle.com/whitepaper-agent-companion
This technical document, the Agents Companion, explores the advancements in generative AI agents, highlighting their architecture composed of models, tools, and an orchestration layer, moving beyond traditional language models.
It emphasizes Agent Ops as crucial for operationalizing these agents, drawing parallels with DevOps and MLOps while addressing agent-specific needs like tool management.
The paper thoroughly examines agent evaluation methodologies, covering capability assessment, trajectory analysis, final response evaluation, and the importance of human-in-the-loop feedback alongside automated metrics. Furthermore, it discusses the benefits and challenges of multi-agent systems, outlining various design patterns and their application, particularly within automotive AI.
Finally, the Companion introduces Agentic RAG as an evolution in knowledge retrieval and presents Google Agentspace as a platform for developing and managing enterprise-level AI agents, even proposing the concept of "Contract adhering agents" for more robust task execution.
Agent Ops is Essential: Building successful agents requires more than just a proof-of-concept; it necessitates embracing Agent Ops principles, which integrate best practices from DevOps and MLOps, while also focusing on agent-specific elements such as tool management, orchestration, memory, and task decomposition.
Metrics Drive Improvement: To build, monitor, and compare agent revisions, it is critical to start with business-level Key Performance Indicators (KPIs) and then instrument agents to track granular metrics related to critical tasks, user interactions, and agent actions (traces). Human feedback is also invaluable for understanding where agents excel and need improvement.
Automated Evaluation is Key: Relying solely on manual testing is insufficient. Implementing automated evaluation frameworks is crucial to assess an agent's core capabilities, its trajectory (the steps taken to reach a solution, including tool use), and the quality of its final response. Techniques like exact match, in-order match, and precision/recall are useful for trajectory evaluation, while autoraters (LLMs acting as judges) can assess final response quality.
Human-in-the-Loop is Crucial: While automated metrics are powerful, human evaluation provides essential context, particularly for subjective aspects like creativity, common sense, and nuance. Human feedback should be used to calibrate and validate automated evaluation methods, ensuring alignment with desired outcomes and preventing the outsourcing of domain knowledge.
Multi-Agent Systems Offer Advantages: For complex tasks, consider leveraging multi-agent architectures. These systems can enhance accuracy through cross-checking, improve efficiency through parallel processing, better handle intricate problems by breaking them down, increase scalability by adding specialized agents, and improve fault tolerance. Understanding different design patterns like sequential, hierarchical, collaborative, and competitive is important for choosing the right architecture for a given application.

UC San Diego: Large Language Models Pass the Turing Test

Summary of https://arxiv.org/pdf/2503.23674
Researchers Cameron R. Jones and Benjamin K. Bergen investigated whether advanced large language models (LLMs) can pass the standard three-party Turing test. Their study involved human interrogators conversing with both a human and an AI, then judging which was human.
The findings indicate that GPT-4.5, when prompted to adopt a persona, was identified as human significantly more often than the actual human participant, marking the first empirical evidence of an AI passing this rigorous version of the test.
While other models like LLaMa-3.1 showed some human-like qualities, only the persona-prompted models consistently fooled human judges. The study also explored the strategies used by interrogators and the implications of these results for our understanding of AI capabilities and their societal impact.
This study provides the first empirical evidence that a Large Language Model (LLM), specifically GPT-4.5 when prompted to adopt a humanlike persona, can pass a standard three-party Turing test. In the study, GPT-4.5-PERSONA was judged as human 73% of the time, significantly more often than the actual human participants.
Prompting with a humanlike persona was crucial for success. While GPT-4.5-PERSONA and LLAMA-PERSONA were not reliably distinguished from humans, the same models without the persona prompt (GPT-4.5-NO-PERSONA and LLAMA-NO-PERSONA) performed significantly worse, often being identified as AI at rates significantly below chance in the undergraduate study.
The study compared the performance of several AI systems, including GPT-4.5, LLaMa-3.1-405B, GPT-4o, and ELIZA. The baseline models, GPT-4o-NO-PERSONA and ELIZA, had significantly lower win rates, indicating that interrogators could generally distinguish them from humans. This suggests the interrogators were not simply guessing randomly.
The research indicates that interrogators often relied on social, emotional, and linguistic cues rather than traditional measures of knowledge and reasoning when trying to distinguish between humans and AI. Interestingly, providing strange prompts or using "jailbreaks" was the most effective strategy for interrogators, while asking about the weather or human experiences was least effective.
The findings have significant social and economic implications, suggesting that contemporary LLMs could potentially substitute for humans in short conversations, raising concerns about deception, misinformation, and the potential undermining of real human interaction. The study also found that general knowledge about LLMs and frequent chatbot interaction did not consistently improve participants' ability to distinguish AI from humans.

Elon University: Being Human in 2035 – How Are We Changing in the Age of AI?

Summary of https://imaginingthedigitalfuture.org/wp-content/uploads/2025/03/Being-Human-in-2035-ITDF-report.pdf
This Elon University Imagining the Digital Future Center report compiles insights from a non-scientific canvassing of technology pioneers, builders, and analysts regarding the potential shifts in human capacities and behaviors by 2035 due to advanced AI. Experts anticipate blurred boundaries between reality and fiction, human and artificial intelligence, and human and synthetic creations, alongside concerns about eroding individual identity, autonomy, and critical thinking skills.
The report explores both optimistic visions of AI augmenting human potential and creativity and pessimistic scenarios involving increased dependence, social division, and the erosion of essential human qualities like empathy and moral judgment. Ultimately, it highlights the critical need for ethical development, regulation, and education to navigate the profound societal changes anticipated in the coming decade.
A significant majority of experts anticipate deep and meaningful or even fundamental and revolutionary change in people’s native operating systems and operations as humans broadly adapt to and use advanced AI by 2035.
Experts predict mostly negative changes in several core human traits and behaviors by 2035, including social and emotional intelligence, the capacity for deep thinking, trust in shared values, empathy, mental well-being, sense of agency, and sense of identity and purpose.
Conversely, pluralities of experts expect mostly positive changes in human curiosity and capacity to learn, decision-making and problem-solving abilities, and innovative thinking and creativity due to interactions with AI.
Many experts express concern about the potential for AI to be used in ways that de-augment humanity, serving the interests of tool builders and those in power, potentially leading to a global sociotechnical dystopia. However, they also see the potential for AI to augment human intelligence and bring about universal enlightenment if the direction of development changes.
The experts underscore the critical importance of how humans choose to integrate AI into their lives and societies. They emphasize the need for ethical considerations, human-centered design, the establishment of human values in AI development and policy, and the preservation of human agency to ensure AI serves humanity's flourishing rather than diminishing essential human capacities.

Bain & Company: Nvidia GTC 2025 – AI Matures into Enterprise Infrastructure

Summary of https://www.bain.com/globalassets/noindex/2025/bain_article_nvidia_gtc_2025_ai_matures_into_enterprise_infrastructure.pdf
Nvidia's GTC 2025 highlighted a significant shift in AI, moving from experimental phases to becoming core enterprise infrastructure. The event showcased how data remains crucial, but AI itself is now a data generator, leading to new insights and efficiencies.
Furthermore, smaller, specialized AI models are gaining prominence, offering cost advantages and improved control. While fully autonomous AI agents are still rare, structured semi-autonomous systems with human oversight are becoming standard.
Finally, the conference underscored the growing importance of digital twins, video analytics, and accessible off-the-shelf tools in democratizing enterprise AI adoption and fostering cross-functional collaboration through simulation.
AI has matured beyond pilot projects and is now being deployed at scale within the core operations of enterprises. Companies are re-architecting how they compete by moving AI from innovation teams into the business core.
Data remains both a critical challenge and a significant opportunity for AI success. Successful AI deployments rely on clean, connected, and accessible data. Furthermore, AI is now generating a new layer of data through insights and generative applications.
The trend is shifting towards smaller, specialized AI models that are more cost-effective and offer better control, latency, and privacy. Techniques like quantization, pruning, and RAG are facilitating this shift, although deploying and managing these custom models presents new operational complexities.
Agentic AI is gaining traction, but its successful implementation hinges on structure, transparency, and human oversight. While fully autonomous agents are rare, semiautonomous systems with built-in safeguards and orchestration platforms are becoming the near-term standard.
Digital twins and simulation have moved from innovation showcases to everyday enterprise tools, enabling faster rollout cycles, lower risk, and more informed decision-making. Simulation is also evolving into a collaboration platform for cross-functional teams.

Anthropic: Circuit Tracing – Revealing Computational Graphs in Language Models

Summary of https://transformer-circuits.pub/2025/attribution-graphs/methods.html
Introduces a novel methodology called "circuit tracing" to understand the inner workings of language models. The authors developed a technique using "replacement models" with interpretable components to map the computational steps of a language model as "attribution graphs." These graphs visually represent how different computational units, or "features," interact to process information and generate output for specific prompts.
The research details the construction, visualization, and validation of these graphs using an 18-layer model and offers a preview of their application to a more advanced model, Claude 3.5 Haiku. The study explores the interpretability and sufficiency of this method through various evaluations, including case studies on acronym generation and addition.
While acknowledging limitations like missing attention circuits and reconstruction errors, the authors propose circuit tracing as a significant step towards achieving mechanistic interpretability in large language models.
This paper introduces a methodology for revealing computational graphs in language models using Cross-Layer Transcoders (CLTs) to extract interpretable features and construct attribution graphs that depict how these features interact to produce model outputs for specific prompts. This approach aims to bridge the gap between raw neurons and high-level model behaviors by identifying meaningful building blocks and their interactions.
The methodology involves several key steps: training CLTs to reconstruct MLP outputs, building attribution graphs with nodes representing active features, tokens, errors, and logits, and edges representing linear effects between these nodes. A crucial aspect is achieving linearity in feature interactions by freezing attention patterns and normalization denominators. Attribution graphs allow for the study of how information flows from the input prompt through intermediate features to the final output token.
The paper demonstrates the application of this methodology through several case studies, including acronym generation, factual recall, and small number addition. These case studies illustrate how attribution graphs can reveal the specific features and pathways involved in different cognitive tasks performed by language models. For instance, in the addition case study, the method uncovers a hierarchy of heuristic features that collaboratively solve the task.
Despite the advancements, the methodology has several significant limitations. A key limitation is the missing explanation of how attention patterns are formed and how they mediate feature interactions (QK-circuits), as the analysis is conducted with fixed attention patterns. Other limitations include reconstruction errors (unexplained model computation), the role of inactive features and inhibitory circuits, the complexity of the resulting graphs, and the difficulty of understanding global circuits that generalize across many prompts.
The paper also explores the concept of global weights between features, which are prompt-independent and aim to capture general algorithms used by the replacement model. However, interpreting these global weights is challenging due to issues like interference (spurious connections) and the lack of accounting for attention-mediated interactions. While attribution graphs provide insights on specific prompts, future work aims to enhance the understanding of global mechanisms and address current limitations, potentially through advancements in dictionary learning and handling of attention mechanisms.

RAND: Uneven Adoption of AI Tools Among U.S. Teachers and Principals in the 2023-2024 School Year

Summary of https://www.rand.org/content/dam/rand/pubs/research_reports/RRA100/RRA134-25/RAND_RRA134-25.pdf
A RAND Corporation report, utilizing surveys from the 2023-2024 school year, investigates the adoption and use of artificial intelligence tools by K-12 public school teachers and principals. The research highlights that roughly one-quarter of teachers reported using AI for instructional planning or teaching, with higher usage among ELA and science teachers and those in lower-poverty schools.
Simultaneously, nearly 60 percent of principals indicated using AI in their jobs, primarily for administrative tasks like drafting communications. The study also found that guidance and support for AI use were less prevalent in higher-poverty schools for both educators, suggesting potential inequities in AI integration. Ultimately, the report underscores the emerging role of AI in education and recommends developing strategies and further research to ensure its effective and equitable implementation.
A significant portion of educators are using AI tools, but there's considerable variation. Approximately one-quarter of teachers reported using AI tools for instructional planning or teaching, with higher rates among ELA and science teachers, as well as secondary teachers. Notably, nearly 60 percent of principals reported using AI tools in their jobs. However, usage differed by subject taught and school characteristics, with teachers and principals in higher-poverty schools being less likely to report using AI tools.
Teachers primarily use AI for instructional planning, while principals focus on administrative tasks. Teachers most commonly reported using AI to generate lesson materials, assess students, and differentiate instruction. Principals primarily used AI to draft communications, support other school administrative tasks, and assist with teacher hiring, evaluation, or professional learning.
Disparities exist in AI adoption and support based on school poverty levels. Teachers and principals in lower-poverty schools were more likely to use AI and reported receiving more guidance on its use compared to their counterparts in higher-poverty schools. Furthermore, schools in higher-poverty areas were less likely to be developing AI usage policies. This suggests a widening gap in AI integration and the potential for unequal access to its benefits.
Educators have several concerns regarding AI use, including a lack of professional learning and data privacy. Principals identified a lack of professional development, concerns about data privacy, and uncertainty about how to use AI as major influences on their AI adoption. Teachers also expressed mixed perceptions about AI's helpfulness, noting the need to assess the quality of AI output and potential for errors.
The report highlights the need for intentional strategies and further research to effectively integrate AI in education. The authors recommend that districts and schools develop strategies to support AI use in ways that improve instruction and learning, focusing on AI's potential for differentiated instruction, practice opportunities, and student engagement. They also emphasize the importance of research to identify effective AI applications and address disparities in access and guidance, particularly for higher-poverty schools.

Stanford University: Expanding Academia's Role in Public Sector AI

Summary of https://hai-production.s3.amazonaws.com/files/hai-issue-brief-expanding-academia-role-public-sector.pdf
Stanford HAI highlights a growing disparity between academia and industry in frontier AI research. Industry's access to vast resources like data and computing power allows them to outpace universities in developing advanced AI systems.
The authors argue that this imbalance risks hindering public-interest AI innovation and weakening the talent pipeline. To address this, the brief proposes increased public investment in academic AI, the adoption of collaborative research models, and the creation of new government-backed academic institutions. Ultimately, the aim is to ensure academia plays a vital role in shaping the future of AI in a way that benefits society.
Academia is currently lagging behind industry in frontier AI research because no university possesses the resources to build AI systems comparable to those in the private sector. This is largely due to industry's access to massive datasets and significantly greater computational power.
Industry's dominance in AI development is driven by its unprecedented computational resources, vast datasets, and top-tier talent, leading to AI models that are considerably larger than those produced by academia. This resource disparity has become a substantial barrier to entry for academic researchers.
For AI to be developed responsibly and in the public interest, it is crucial for governments to increase investment in public sector AI, with academia at the forefront of training future innovators and advancing cutting-edge scientific research. Historically, academia has been the source of foundational AI technologies and prioritizes public benefit over commercial gain.
The significant cost of developing advanced AI models has created a major divide between industry and academia. The expense of computational resources required for state-of-the-art models has grown exponentially, making it challenging for academics to meaningfully contribute to their development.
The growing resource gap in funding, computational power, and talent between academia and industry is concerning because it restricts independent, public-interest AI research, weakens the future talent pipeline by incentivizing students to join industry, and can skew AI policy discussions in favor of well-funded private sector interests.

University of Texas at Austin: Protecting Human Cognition in the Age of AI

Summary of https://arxiv.org/pdf/2502.12447
Explores the rapidly evolving influence of Generative AI on human cognition, examining its effects on how we think, learn, reason, and engage with information. Synthesizing existing research, the authors analyze these impacts through the lens of educational frameworks like Bloom's Taxonomy and Dewey's reflective thought theory.
The work identifies potential benefits and significant concerns, particularly regarding critical thinking and knowledge retention among novices. Ultimately, it proposes implications for educators and test designers and suggests future research directions to understand the long-term cognitive consequences of AI.
Generative AI (GenAI) is rapidly reshaping human cognition, influencing how we engage with information, think, reason, and learn. This adoption is happening at a much faster rate compared to previous technological advancements like the internet.
While GenAI offers potential benefits such as increased productivity, enhanced creativity, and improved learning experiences, there are significant concerns about its potential long-term detrimental effects on essential cognitive abilities, particularly critical thinking and reasoning. The paper primarily focuses on these negative impacts, especially on novices like students.
GenAI's impact on cognition can be understood through frameworks like Krathwohl’s revised Bloom’s Taxonomy and Dewey’s conceptualization of reflective thought. GenAI can accelerate access to knowledge but may bypass the cognitive processes necessary for deeper understanding and the development of metacognitive skills. It can also disrupt the prerequisites for reflective thought by diminishing cognitive dissonance, reinforcing existing beliefs, and creating an illusion of comprehensive understanding.
Over-reliance on GenAI can lead to 'cognitive offloading' and 'metacognitive laziness', where individuals delegate cognitive tasks to AI, reducing their own cognitive engagement and hindering the development of critical thinking and self-regulation. This is particularly concerning for novice learners who have less experience with diverse cognitive strategies.
To support thinking and learning in the AI era, there is a need to rethink educational experiences and design 'tools for thought' that foster critical and evaluative skills. This includes minimizing AI use in the early stages of learning to encourage productive struggle, emphasizing critical evaluation of AI outputs in curricula and tests, and promoting active engagement with GenAI tools through methods like integrating cognitive schemas and using metacognitive prompts. The paper also highlights the need for long-term research on the sustained cognitive effects of AI use.

University of Bristol: Alice in Wonderland – Simple Tasks Showing Complete Reasoning Breakdown in State-of-the-Art LLMs

Summary of https://arxiv.org/pdf/2406.02061
Introduces the "Alice in Wonderland" (AIW) problem, a seemingly simple common-sense reasoning task, to evaluate the capabilities of state-of-the-art Large Language Models (LLMs). The authors demonstrate that even advanced models like GPT-4 and Claude 3 Opus exhibit a dramatic breakdown in generalization and basic reasoning when faced with minor variations of the AIW problem that do not alter its core structure or difficulty.
This breakdown is characterized by low average performance and significant fluctuations in accuracy across these variations, alongside overconfident, yet incorrect, explanations. The study further reveals that standardized benchmarks fail to detect these limitations, suggesting a potential overestimation of current LLM reasoning abilities, possibly due to data contamination or insufficient challenge diversity.
Ultimately, the AIW problem is presented as a valuable tool for uncovering fundamental weaknesses in LLMs' generalization and reasoning skills that are not apparent in current evaluation methods.
Despite achieving high scores on various standardized benchmarks, many state-of-the-art Large Language Models (LLMs) exhibit surprisingly low correct response rates on the seemingly simple "Alice has brothers and sisters" (AIW) problem and its variations. Only a few large-scale closed models like GPT-4o and Claude 3 Opus show relatively better performance, while many others, including models claiming strong function, struggle significantly, sometimes even collapsing to a zero correct response rate.
The document highlights a significant discrepancy between the performance of LLMs on standardized reasoning benchmarks and on the AIW problem, suggesting that current benchmarks may not accurately reflect true generalization and basic reasoning skills. Models that score highly on benchmarks like MMLU, MATH, ARC-c, GSM8K, and HellaSwag often perform poorly on AIW, indicating a potential issue with the benchmarks' ability to detect fundamental deficits in model function. This suggests that these benchmarks might suffer from issues like test data leakage.
A key observation is the lack of robustness in SOTA LLMs, evidenced by strong performance fluctuations across structure and difficulty-preserving variations of the same AIW problem. Even slight changes in the numerical values within the problem statement can lead to drastically different correct response rates for many models. This sensitivity to minor variations points to underlying generalization deficits.
The study reveals that LLMs often exhibit overconfidence and provide persuasive, explanation-like confabulations even when their answers to AIW problems are incorrect. This can mislead users into trusting wrong responses, especially in situations where verification is difficult. Furthermore, many models struggle to properly detect mistakes and revise their incorrect solutions, even when encouraged to do so.
The AIW problem and its variations are presented as valuable tools for evaluating the robustness and generalization capabilities of LLMs, offering a method to reveal weaknesses that are not captured by standard benchmarks. The ability to create numerous diverse problem instances through variations addresses potential test set leakage issues. The introduction of a unified robustness score (R) is proposed to provide a more accurate model ranking by considering both average correct response rate and the degree of performance fluctuations across problem variations.

NIST: Adversarial Machine Learning – A Taxonomy and Terminology of Attacks and Mitigations

Summary of https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2025.pdf
This NIST report explores the landscape of adversarial machine learning (AML), categorizing attacks and corresponding defenses for both traditional (predictive) and modern generative AI systems.
It establishes a taxonomy and terminology to create a common understanding of threats like data poisoning, evasion, privacy breaches, and prompt injection. The document also highlights key challenges and limitations in current AML research and mitigation strategies, emphasizing the trade-offs between security, accuracy, and other desirable AI characteristics. Ultimately, the report aims to inform standards and practices for managing the security risks associated with the rapidly evolving field of artificial intelligence.
This report establishes a taxonomy and defines terminology for the field of Adversarial Machine Learning (AML). The aim is to create a common language within the rapidly evolving AML landscape to inform future standards and practice guides for securing AI systems.
The report provides separate taxonomies for attacks targeting Predictive AI (PredAI) systems and Generative AI (GenAI) systems. These taxonomies categorize attacks based on attacker goals and objectives (availability breakdown, integrity violation, privacy compromise, and misuse enablement for GenAI), attacker capabilities, attacker knowledge, and the stages of the machine learning lifecycle.
The report describes various AML attack classes relevant to both PredAI and GenAI, including evasion, poisoning (data and model poisoning), privacy attacks (such as data reconstruction, membership inference, and model extraction), and GenAI-specific attacks like direct and indirect prompt injection, and supply chain attacks. For each attack class, the report discusses existing mitigation methods and their limitations.
The report identifies key challenges in the field of AML. These challenges include the inherent trade-offs between different attributes of trustworthy AI (e.g., accuracy and adversarial robustness), theoretical limitations on achieving perfect adversarial robustness, and the complexities of evaluating the effectiveness of mitigations across the diverse and evolving AML landscape. Factors like the scale of AI models, supply chain vulnerabilities, and multimodal capabilities further complicate these challenges.
Managing the security of AI systems requires a comprehensive approach that combines AML-specific mitigations with established cybersecurity best practices. Understanding the relationship between these fields and identifying any unique security considerations for AI that fall outside their scope is crucial for organizations seeking to secure their AI deployments.