The moment we start thinking about drone vision analytics through a tokens‑per‑watt‑per‑dollar lens, the conversation shifts from “How smart is the model?” to “How much intelligence can I afford to deploy per joule, per inference, per mission?” It’s a mindset borrowed from high‑performance computing and edge robotics, but it maps beautifully onto language‑model‑driven aerial analytics because every component in the pipeline—vision encoding, reasoning, retrieval, summarization—ultimately resolves into tokens generated, energy consumed, and dollars spent.

In a traditional CNN or YOLO‑style detector, the economics are straightforward: fixed FLOPs, predictable latency, and a cost curve that scales linearly with the number of frames. But once we introduce a language model into the loop—especially one that performs multimodal reasoning, generates explanations, or orchestrates tools—the cost profile becomes dominated by token generation. A single high‑resolution drone scene might require only a few milliseconds of GPU time for a detector, but a vision‑LLM describing that same scene in natural language could emit hundreds of tokens, each carrying a marginal cost in energy and cloud billing. The brilliance of the tokens‑per‑watt‑per‑dollar framing is that it forces us to quantify that trade‑off rather than hand‑wave it away.

In practice, the most cost‑effective systems aren’t the ones that minimize tokens or maximize accuracy in isolation, but the ones that treat tokens as a scarce resource to be spent strategically. A vision‑LLM that produces a verbose paragraph for every frame is wasteful; a model that emits a compact, schema‑aligned summary that downstream agents can act on is efficient. A ReAct‑style agent that loops endlessly, generating long chains of thoughts, burns tokens and watts; an agent that uses retrieval, structured tools, and short reasoning bursts can deliver the same analytic insight at a fraction of the cost. The economics become even more interesting when we consider that drone missions often run on edge hardware or intermittent connectivity, where watt‑hours are literally the limiting factor. In those settings, a model that can compress its reasoning into fewer, more meaningful tokens isn’t just cheaper—it’s operationally viable.

This mindset also reframes the role of model size. Bigger models are not inherently better if they require ten times the tokens to reach the same analytic conclusion. A smaller, domain‑tuned model that produces concise, high‑signal outputs may outperform a frontier‑scale model in tokens‑per‑watt‑per‑dollar terms, even if the latter is more capable in a vacuum. The same applies to agentic retrieval: if an agent can answer a question by issuing a single SQL query over a scenes catalog rather than generating a long chain of speculative reasoning, the cost savings are immediate and measurable. The most elegant drone analytics pipelines are the ones where the language model acts as a conductor rather than a workhorse—delegating perception to efficient detectors, delegating measurement to structured queries, and using its own generative power only where natural language adds genuine value.

What emerges is a philosophy of frugality that doesn’t compromise intelligence. We design prompts that elicit short, structured outputs. We build agents that reason just enough to choose the right tool. We fine‑tune models to reduce verbosity and hallucination, because every unnecessary token is wasted energy and wasted money. And we evaluate pipelines not only on accuracy or latency but on how many tokens they burn to achieve a mission‑level result. In a world where drone fleets may run thousands of analytics queries per hour, the difference between a 20‑token answer and a 200‑token answer isn’t stylistic—it’s economic.

Thinking this way turns language‑model‑based drone vision analytics into an optimization problem: maximize insight per token, minimize watt‑hours per inference, and align every component of the system with the reality that intelligence has a cost. When we design with tokens‑per‑watt‑per‑dollar in mind, we end up with systems that are not only smarter, but leaner, more predictable, and more deployable at scale.

 This is a summary of the book titled “Changing the Game: Discover How Esports and Gaming are Redefining Business, Careers, Education, and the Future” written by Lucy Chow and published by River Grove Books, 2022.

For decades, video gaming has been burdened by the stereotype of the reclusive, underachieving gamer—a perception that has long obscured the profound social and financial benefits that gaming can offer. In this book, the author together with 38 contributing experts, sets out to dismantle these misconceptions, offering a comprehensive introduction to the world of esports and gaming for those unfamiliar with its scope and impact.

Today, gaming is no longer a fringe activity but a central pillar of the social, cultural, and economic mainstream. With three billion players worldwide, video games have become a global phenomenon, connecting people across continents and cultures. The rise of esports—competitive gaming at a professional level—has been particularly striking. Esports tournaments now rival traditional sporting events in terms of viewership and excitement. In 2018, for example, a League of Legends tournament drew more viewers than the Super Bowl, underscoring the immense popularity and reach of these digital competitions. The esports experience is multifaceted, encompassing not only playing but also watching professionals, or streamers, perform on platforms like Twitch, which attracts an estimated 30 million viewers daily.

Despite its growing popularity, gaming has often been dismissed by mainstream media as trivial or even dangerous, largely due to concerns about violent content. However, Chow and her contributors argue that most video games are suitable for a wide range of ages and that gaming itself is becoming increasingly mainstream. The industry is reshaping the future of work, education, and investment opportunities. During the COVID-19 pandemic, the World Health Organization even endorsed active video gaming as beneficial for physical, mental, and emotional health. The US Food and Drug Administration approved a video game as a prescription treatment for children with ADHD, and recent research suggests that virtual reality games may help diagnose and treat Alzheimer’s disease and dementia.

Participation in esports fosters valuable life skills such as teamwork, resilience, and persistence. Multiplayer competitions satisfy the human desire to gather, play, and support favorite teams. Yet, a survey of high school students in Australia and New Zealand revealed that while most celebrated gaming achievements with friends, very few shared these moments with parents or teachers, highlighting a generational gap in understanding gaming’s value. Competitive gaming, according to experts like Professor Ingo Froböse of the German Sports University Cologne, demands as much from its participants as traditional sports do from athletes, with similar physical and mental exertion. Esports also help players develop critical thinking, memory, eye-hand coordination, and problem-solving abilities.

Educational institutions have recognized the potential of gaming and esports. Universities now offer more than $16 million in esports scholarships, and high schools and colleges have established esports teams to encourage students to explore related academic and career opportunities. Some universities even offer degrees in esports management, and the field encompasses a wide range of career paths, from game design and programming to event management and streaming. The industry is vast and diverse, with researcher Nico Besombes identifying 88 different types of esports jobs. Esports is also a borderless activity, uniting people from different backgrounds and cultures.

The book also addresses gender dynamics in gaming. Traditionally, video game development has been male-dominated, and female characters have often been marginalized or objectified. While tournaments do not ban female players, hostile treatment by male competitors has limited female participation. Initiatives like the GIRLGAMER Esports Festival have sought to create more inclusive environments, and organizations such as Galaxy Race have assembled all-female teams, helping to shift the industry’s culture. Encouraging girls to play video games from a young age can have a significant impact; studies show that girls who game are 30% more likely to pursue studies in science, technology, engineering, and mathematics (STEM). The rise of casual and mobile games has brought more women into gaming, and women now make up 40% of gamers, participating in events like TwitchCon and the Overwatch League Grand Finals.

Gaming is inherently social. More than 60% of gamers play with others, either in person or online, and research indicates that gaming does not harm sociability. In fact, it can help alleviate loneliness, foster new friendships, and sustain existing ones. The stereotype of the antisocial gamer has been debunked by studies showing that gamers and non-gamers enjoy similar levels of social support. Online gaming, with its sense of anonymity, can even help players overcome social inhibitions. Gaming builds both deep and broad social connections, exposing players to new experiences and perspectives.

Esports has also attracted significant investment from major sports leagues, gaming companies, and global corporations. Brands like Adidas, Coca-Cola, and Mercedes sponsor esports events, and even companies with no direct link to gaming see value in associating with the industry. Sponsorships are crucial to the esports business model, supporting everything from tournaments to gaming cafes. The industry is now a multibillion-dollar enterprise, with elite players, large prize pools, and a dedicated fan base.

Looking ahead, machine learning and artificial intelligence are poised to drive further growth in esports, while advances in smartphone technology are making mobile gaming more competitive. Esports is also exploring new frontiers with virtual reality, augmented reality, and mixed reality, offering immersive experiences that blend the digital and physical worlds. Games like Tree Tap Adventure, which combines AR features with real-world environmental action, exemplify the innovative potential of gaming.

This book reveals how gaming and esports are reshaping business, careers, education, and society at large. Far from being a trivial pastime, gaming is a dynamic, inclusive, and transformative force that connects people, fosters skills, and opens new opportunities for the future.

 This is  a summary of a book titled “We are eating the Earth: the race to fix our food system and save our climate” written by Michael Grunwald and published by Simon and Schuster in 2025.

In “We are eating the Earth: the race to fix our food system and save our climate,” Michael Grunwald embarks on a compelling journey through the tangled web of food production, land use, and climate change. The book opens with a stark warning: humanity stands at a crossroads, and the choices we make about how we produce and consume food will determine whether we avert or accelerate a climate disaster. For years, the global conversation about climate mitigation has centered on replacing fossil fuels with cleaner energy sources. Yet, as Grunwald reveals, this focus overlooks a critical truth—our current methods of land use and food production account for a full third of the climate burden. The story unfolds as a true-life drama, populated by scientists, policymakers, and activists, each wrestling with the complexities of science and politics, and each striving to find solutions before it’s too late.

Grunwald’s narrative draws readers into the heart of the problem: the way we produce food and use land must change. He explores the paradoxes and unintended consequences of well-intentioned climate policies. For example, the idea of using crops to replace fossil fuels—once hailed as a climate-friendly innovation—proves to be counterproductive. The production of ethanol from corn, which gained popularity in the 1970s and surged again in the early 2000s, was promoted as a way to reduce dependence on foreign oil and lower greenhouse gas emissions. However, as former Environmental Defense Fund attorney Tim Searchinger discovered, the reality is far more complex. Ethanol production not only fails to deliver the promised climate benefits, but also increases demand for farmland, leading to deforestation and the loss of natural carbon sinks. The research that supported biofuels often neglected the fact that natural vegetation absorbs more carbon than farmland, and the push for biofuels has threatened rainforests and contributed to food insecurity.

The book also examines the environmental harm caused by burning wood for fuel. Policies in the European Union and elsewhere encouraged the use of biomass, primarily wood, to generate electricity, under the mistaken belief that it was climate-friendly. In reality, burning wood releases carbon and diminishes the land’s future capacity to absorb it. The way carbon loss is accounted for—at the site of tree cutting rather than where the wood is burned—has led to flawed policies that exacerbate climate change rather than mitigate it. Even as the US Environmental Protection Agency initially rejected the climate benefits of biomass, political shifts reversed this stance, further complicating efforts to address the crisis.

Grunwald’s exploration of food production reveals a host of challenges. Meeting the world’s growing demand for food without increasing greenhouse gases or destroying forests is no easy task. Raising animals for meat and dairy requires far more cropland than growing plants, and animal products account for half of agriculture’s climate footprint. Searchinger’s message—“Produce, Reduce, Protect, and Restore”—serves as a guiding principle for climate-friendly strategies. These include making animal agriculture more efficient, improving crop productivity, enhancing soil health, reducing emissions, and curbing population growth. The book highlights the importance of reducing methane from rice cultivation, boosting beef yields while cutting consumption, restoring peat bogs, minimizing land use for bioenergy, cutting food waste, and developing plant-based meat substitutes.

The narrative delves into the promise and pitfalls of meat alternatives. While companies have invested heavily in alternative proteins, the path to scalable, affordable, and palatable meat replacements has been fraught with difficulty. The rise and fall of fake meat products follow the Gartner Hype Cycle, with initial excitement giving way to disappointment and skepticism about their environmental benefits. For many, meat replacements serve as a transitional product, but the future of the industry remains uncertain, as scaling up remains a significant hurdle.

Regenerative agriculture, once seen as a panacea, is scrutinized for its limitations. Practices such as reduced chemical use, less tilling, and managed grazing do help store carbon and provide social and economic benefits. However, Searchinger argues that regenerative agriculture alone cannot solve the climate crisis, as much of its benefit comes from taking land out of production, which can inadvertently increase pressure to convert more open land into farms.

Grunwald also explores technological innovations that could help increase crop yields and reduce the land needed for food production. Artificial fertilizers have boosted yields but are costly pollutants. New approaches, such as introducing nitrogen-fixing microbes, offer hope for more sustainable agriculture. Advances in animal agriculture, including high-tech farming techniques and gene editing, show promise for increasing efficiency and reducing emissions, though resistance to these innovations persists. Aquaculture, too, presents opportunities and challenges, as fish are more efficient than land animals but raising them in captivity introduces new problems.

Gene editing emerges as a beacon of hope, with scientists experimenting to enhance crop yields, combat pests, and improve food quality. The development of drought- and flood-resistant trees like pongamia, and the investment in biofuels and animal feed, illustrate the potential of biotechnology, even as skepticism and financial barriers remain.

Throughout the book, Grunwald emphasizes the difficulty of changing agriculture. Precision farming and other tech advances have made megafarms more productive and environmentally friendly, but these gains are not enough to meet global food demands, especially as climate change complicates implementation. Vertical farms and greenhouses offer solutions for some crops, but scaling these innovations is slow and challenging.

Grunwald’s narrative is one of cautious optimism. He points to Denmark as an example of how climate-friendly policies—taxing agricultural emissions, restoring natural lands, and encouraging less meat consumption—can make a difference. The ongoing struggle between food production and climate damage is complex, with trade-offs involving animal welfare, plastic use, and political opposition to climate action. Yet, Grunwald insists that even imperfect solutions can move us in the right direction. More funding for research, ramping up existing technologies, and linking subsidies to forest protection are among the measures that could help. In the end, innovation, grounded in reality and supported by sound policy, remains humanity’s best hope for saving both our food system and our climate.

 Across aerial drone analytics, the comparison between vision‑LLMs and classical CNN/YOLO detectors is beginning to look like a trade‑off between structured efficiency and semantic flexibility rather than a simple accuracy leaderboard battle. YOLO’s evolution from v1 through v8 and into transformer‑augmented variants has been driven by exactly the kinds of requirements that matter in urban aerial scenes—real‑time detection, small object robustness, and deployment on constrained hardware. The comprehensive YOLO survey by Terven and Cordova‑Esparza systematically traces how each generation improved feature pyramids, anchor strategies, loss functions, and post‑processing to balance speed and accuracy, and emphasizes that YOLO remains the de facto standard for real‑time object detection in robotics, autonomous vehicles, surveillance, and similar settings. Parking lots in oblique or nadir drone imagery—dense, small, often partially occluded cars—fit squarely into the “hard but well‑structured” regime these models were built for.

Vision‑LLMs enter this picture from a different direction. Rather than optimizing a single forward pass for bounding boxes, they integrate large‑scale image–text pretraining and treat detection as one capability inside a broader multimodal reasoning space. The recent review and evaluation of vision‑language models for object detection and segmentation by Feng et al. makes that explicit: they treat VLMs as foundational models and evaluate them across eight detection scenarios—including crowded objects, domain adaptation, and small object settings—and eight segmentation scenarios. Their results show that VLM‑based detectors have clear advantages in open‑vocabulary and cross‑domain cases, where the ability to reason over arbitrary text labels and semantically rich prompts matters. However, when we push them into conventional closed‑set detection benchmarks, especially with strict localization requirements and dense scenes, specialized detectors like YOLO and other CNN‑based architectures still tend to outperform them in raw mean Average Precision and efficiency. In other words, VLMs shine when we want to say “find all the areas that look like improvised parking near stadium entrances” even if we never trained on that exact label, but they remain less competitive if the task is simply “find every car at 0.5 IoU with millisecond latency.”

A qualitative comparison of vision and vision‑language models in object detection underscores this pattern from a different angle. Rather than only reporting mAP values, Rakic and Dejanovic analyze how vision‑only and vision‑language detectors behave when confronted with ambiguous, cluttered, or semantically nuanced scenes. They note that VLMs are better at leveraging contextual cues and language priors—understanding that cars tend to align along marked lanes, or that certain textures and shapes co‑occur in parking environments—but can suffer from inconsistent localization and higher computational overhead, especially when used in zero‑shot or text‑prompted modes. CNN/YOLO detectors, by contrast, exhibit highly stable behavior under the same conditions once they are trained on the relevant aerial domain: their strengths are repeatability, tight bounding boxes, and predictable scaling with resolution and hardware. For an analytics benchmark that cares about usable detections in urban parking scenes, this suggests that YOLO‑style models will remain our baseline for “hard numbers,” while VLMs add a layer of semantic interpretability and open‑vocabulary querying on top.

The VLM review goes further by explicitly varying finetuning strategies—zero‑prediction, visual fine‑tuning, and text‑prompt tuning—and evaluating how they affect performance across different detection scenarios. One of their core findings is that visual fine‑tuning on domain‑specific data significantly narrows the gap between VLMs and classical detectors for conventional tasks, while preserving much of the open‑vocabulary flexibility. In a drone parking‑lot scenario, that means a VLM fine‑tuned on aerial imagery with car and parking‑slot annotations can approach YOLO‑like performance for “find all cars” while still being able to answer richer queries like “highlight illegally parked vehicles” or “find under‑utilized areas in this lot” by combining detection with relational reasoning. But this comes at a cost: model size, inference time, and system complexity are higher than simply running a YOLO variant whose entire architecture has been optimized for single‑shot detection.

For aerial drone analytics stacks like the ones we are exploring, the emerging consensus from these surveys is that vision‑LLMs and CNN/YOLO detectors occupy complementary niches. YOLO and related CNN architectures provide the backbone for high‑throughput, high‑precision object detection in structured scenes, with well‑understood trade‑offs between mAP, speed, and parameter count. Vision‑LLMs, especially when lightly or moderately fine‑tuned, act as semantic overlays: they enable open‑vocabulary detection, natural‑language queries, and richer scene understanding at the cost of heavier computation and less predictable performance on dense, small‑object detection. The qualitative comparison work reinforces that VLMs are most compelling when the question isn’t just “is there a car here?” but “what does this pattern of cars, markings, and context mean in human terms?”. In a benchmark for urban aerial analytics that includes tasks like parking occupancy estimation, illegal parking detection, or semantic tagging of parking lot usage, treating YOLO‑style detectors as the quantitative ground‑truth engines and VLMs as higher‑level interpreters and judges would be directly aligned with what the current research landscape is telling us.

 Problem: Count the number of ways to climb up the staircase and we can modify the number of steps at any time to 1 or 2

Solution: int getCount(int n)

{

    int [] dp = new int[n+2];

    dp [0] = 0;

    dp [1] = 1;

    dp [2] = 2;

    for (int k = 3; k <= n; k++) {

                 dp [k] = dp [k-1] + dp [k-2];

    }

   return dp [n];

}

Problem: Rotate a n x n matrix by 90 degrees:

Solution:

static void matrixRotate(int[][] A, int r0, int c0, int rt, int ct)

        {

            if (r0 >= rt) return;

            if (c0 >= ct) return;

            var top = new int[ct-c0+1];

            int count = 0;

            for (int j = 0; j <= ct-c0; j++){

                  top[count] = A[0][j];

                  count++;

            }

            count--;

            for (int j = ct; j >= c0; j--)

            A[c0][j] = A[ct-j][0];

            for (int i = r0; i <= rt; i++)

            A[i][c0] = A[rt][i];

            for (int j = c0; j <= ct; j++)

            A[rt][j] = A[ct-j][ct];

            for (int i = rt; i >= r0; i--) {

                   A[i][ct] = top[count];

                   count--;

            }

            matrixRotate(A, r0+1, c0+1, rt-1, ct-1);

        }

// Before:

1 2 3

4 5 6

7 8 9

// After:

7 4 1

8 5 2

9 6 3

// Before

1 2

3 4

// After

3 1

4 2

 Across aerial drone analytics, the comparison between vision‑LLMs and classical CNN/YOLO detectors is beginning to look like a trade‑off between structured efficiency and semantic flexibility rather than a simple accuracy leaderboard battle. YOLO’s evolution from v1 through v8 and into transformer‑augmented variants has been driven by exactly the kinds of requirements that matter in urban aerial scenes—real‑time detection, small object robustness, and deployment on constrained hardware. The comprehensive YOLO survey by Terven and Cordova‑Esparza systematically traces how each generation improved feature pyramids, anchor strategies, loss functions, and post‑processing to balance speed and accuracy, and emphasizes that YOLO remains the de facto standard for real‑time object detection in robotics, autonomous vehicles, surveillance, and similar settings. Parking lots in oblique or nadir drone imagery—dense, small, often partially occluded cars—fit squarely into the “hard but well‑structured” regime these models were built for.

Vision‑LLMs enter this picture from a different direction. Rather than optimizing a single forward pass for bounding boxes, they integrate large‑scale image–text pretraining and treat detection as one capability inside a broader multimodal reasoning space. The recent review and evaluation of vision‑language models for object detection and segmentation by Feng et al. makes that explicit: they treat VLMs as foundational models and evaluate them across eight detection scenarios—including crowded objects, domain adaptation, and small object settings—and eight segmentation scenarios. Their results show that VLM‑based detectors have clear advantages in open‑vocabulary and cross‑domain cases, where the ability to reason over arbitrary text labels and semantically rich prompts matters. However, when we push them into conventional closed‑set detection benchmarks, especially with strict localization requirements and dense scenes, specialized detectors like YOLO and other CNN‑based architectures still tend to outperform them in raw mean Average Precision and efficiency. In other words, VLMs shine when we want to say “find all the areas that look like improvised parking near stadium entrances” even if we never trained on that exact label, but they remain less competitive if the task is simply “find every car at 0.5 IoU with millisecond latency.”

A qualitative comparison study of vision and vision‑language models in object detection underscores this pattern from a different angle. Rather than only reporting mAP values, Rakic and Dejanovic analyze how vision‑only and vision‑language detectors behave when confronted with ambiguous, cluttered, or semantically nuanced scenes. They note that VLMs are better at leveraging contextual cues and language priors—understanding that cars tend to align along marked lanes, or that certain textures and shapes co‑occur in parking environments—but can suffer from inconsistent localization and higher computational overhead, especially when used in zero‑shot or text‑prompted modes. CNN/YOLO detectors, by contrast, exhibit highly stable behavior under the same conditions once they are trained on the relevant aerial domain: their strengths are repeatability, tight bounding boxes, and predictable scaling with resolution and hardware. For an analytics benchmark that cares about usable detections in urban parking scenes, this suggests that YOLO‑style models will remain our baseline for “hard numbers,” while VLMs add a layer of semantic interpretability and open‑vocabulary querying on top.

The VLM review goes further by explicitly varying finetuning strategies—zero‑prediction, visual fine‑tuning, and text‑prompt tuning—and evaluating how they affect performance across different detection scenarios. One of their core findings is that visual fine‑tuning on domain‑specific data significantly narrows the gap between VLMs and classical detectors for conventional tasks, while preserving much of the open‑vocabulary flexibility. In a drone parking‑lot scenario, that means a VLM fine‑tuned on aerial imagery with car and parking‑slot annotations can approach YOLO‑like performance for “find all cars” while still being able to answer richer queries like “highlight illegally parked vehicles” or “find under‑utilized areas in this lot” by combining detection with relational reasoning. But this comes at a cost: model size, inference time, and system complexity are higher than simply running a YOLO variant whose entire architecture has been optimized for single‑shot detection.

For aerial drone analytics stacks like the ones we are exploring, the emerging consensus from these surveys is that vision‑LLMs and CNN/YOLO detectors occupy complementary niches. YOLO and related CNN architectures provide the backbone for high‑throughput, high‑precision object detection in structured scenes, with well‑understood trade‑offs between mAP, speed, and parameter count. Vision‑LLMs, especially when lightly or moderately fine‑tuned, act as semantic overlays: they enable open‑vocabulary detection, natural‑language queries, and richer scene understanding at the cost of heavier computation and less predictable performance on dense, small‑object detection. The qualitative comparison work reinforces that VLMs are most compelling when the question isn’t just “is there a car here?” but “what does this pattern of cars, markings, and context mean in human terms?”. In a benchmark for urban aerial analytics that includes tasks like parking occupancy estimation, illegal parking detection, or semantic tagging of parking lot usage, treating YOLO‑style detectors as the quantitative ground‑truth engines and VLMs as higher‑level interpreters and judges would be directly aligned with what the current research landscape is telling us.

 In the aerial drone analytics space, the comparison between plain vision‑LLMs, ReAct‑style agents, and broader agentic frameworks is about how we want our system to behave under pressure: do we want a single powerful model that “understands” scenes, or an ensemble of agents that can plan, probe, and correct themselves over time. The recent UAV‑CodeAgents work is a clean illustration of the second camp. It builds a multi‑agent framework on top of large language and vision‑language models, using the ReAct (Reason + Act) paradigm to interpret satellite imagery, ground high‑level natural language instructions, and collaboratively generate UAV trajectories. A vision‑grounded pixel‑pointing mechanism lets agents refer to precise locations on aerial maps, while a reactive thinking loop supports iterative reflection and dynamic goal revision in evolving environments. Evaluated on large‑scale fire detection missions, this ReAct+agentic stack achieves a 93% mission success rate with an average mission creation time of 96.96 seconds at a low decoding temperature, demonstrating that structured, multi‑step reasoning and tool use can deliver high reliability without blowing up latency.  

 By contrast, pure vision‑LLMs, even when multimodal and fairly large, tend to be evaluated on perceptual or question‑answer tasks rather than mission‑level outcomes. Sapkota and colleagues’ broader work on multimodal LLMs in domains like agriculture underscores the pattern: general‑purpose or domain‑adapted vision‑language models excel at flexible perception and instruction following, but their performance is typically reported as accuracy on classification, detection, or description benchmarks, not as end‑to‑end success in complex workflows. dblp In a benchmarking context like ezbenchmark, which is inspired by TPC‑H’s workload‑centric philosophy, that distinction matters. A vision‑LLM can certainly answer “What structures do we see in this tile?” or “Which parcels are likely orchards?” with impressive zero‑shot competence, but those answers are rarely tied directly to operational metrics like “Did the mission achieve its analytic goal without re‑flight?” or “How many follow‑up queries or human corrections were needed?” The agentic literature, especially around UAVs, starts from those operational questions and works backward to architecture.  CatalyzeX

 The Agentic UAVs survey by Sapkota, Roumeliotis, and Karkee makes that shift explicit. They define Agentic UAVs as systems that integrate perception, decision‑making, memory, and collaborative planning to operate adaptively in real environments, with goal‑driven behavior and contextual reasoning as first‑class design targets.  CatalyzeX In their taxonomy, vision‑LLMs and other multimodal models are enabling technologies inside a larger agentic stack rather than the entire solution. Perception components transform aerial imagery and other sensor data into structured representations; cognitive agents plan and replan missions; control agents execute actions; and communication agents manage interaction with humans and other UAVs across domains like precision agriculture, construction, disaster response, environmental monitoring, and inspection.  From an effectiveness standpoint, the survey argues that these agentic stacks surpass traditional UAV autonomy by improving mission flexibility, learning capacity, and system‑level robustness, but they also incur more architectural complexity. For a benchmark like ezbenchmark or a spatio‑temporal query engine like SpatialSky, this implies that evaluating “just the vision‑LLM” only tells part of the story; we also want metrics that capture how an agentic wrapper uses perception, memory, and planning to deliver reliable analytics over time.  CatalyzeX

 UAV‑CodeAgents sits at the intersection of these ideas and gives we quantitative hooks to work with. It exemplifies a multi‑agent ReAct framework where each agent is powered by an LLM or VLM but constrained by a structured action space: interpret imagery, reference map locations, synthesize mission code, revise plans, and coordinate with peers. The authors show that fine‑tuning Qwen2.5‑VL‑7B on 9,000 annotated satellite images substantially improves spatial grounding, which is a direct nod to the strength of vision‑LLMs as perception cores.  Yet the headline numbers—93% success rate, roughly 97‑second planning times—are achievements of the full agentic system, not the VLM alone.  If we imagine swapping that ReAct framework into an ezbenchmark workload, the effectiveness metrics we would record are not only pixel‑ or object‑level accuracies but also how many reasoning–action iterations the agents need to converge, how often they recover from ambiguous instructions without human help, and how consistently they satisfy constraints akin to TPC‑H’s query semantics when operating over a drone scenes catalog.

 The broader survey of Agentic LLMs reinforces why that ReAct pattern has become so central. It distinguishes between “plain” LLM use—where the model simply maps prompts to outputs—and agentic use, where LLMs plan, call tools, manage memory, and interact with other agents in pursuit of goals. alphaxiv.org UAV‑CodeAgents is explicitly cited as an example of this agentic turn in UAV mission planning: multi‑agent ReAct plus vision‑language grounding yields scalable, autonomous mission generation with minimal supervision. alphaxiv.org When we transfer that lens back to benchmarking, we get a natural three‑way comparison. Pure vision‑LLMs are cost‑effective for single‑step perception and natural language querying; ReAct frameworks wrap those models in explicit “think–act–observe–think” loops that can interrogate data and tools; full agentic UAV architectures, as surveyed by Sapkota et al., extend this further by embedding ReAct‑like cycles into a distributed system that includes collaboration, persistent memory, and multi‑mission learning.  alphaxiv.org  Each step up the ladder tends to increase implementation cost and complexity but also improves mission‑level robustness and adaptability in domains that look a lot like the use cases in SpatialSky and what we are sketching in ezbenchmark—multi‑tile analytics, evolving spatio‑temporal queries, and feedback‑driven missions over large areas.

 For the specific kinds of workloads in ezbenchmark and SpatialSky—workload chains over a spatial schema, spatio‑temporal pattern detection, and comparative evaluation of alternative pipelines—the existing literature suggests a division of labor rather than a straight winner. Vision‑LLMs, especially when domain‑tuned like the Qwen2.5‑VL‑7B variant in UAV‑CodeAgents, serve as powerful perception and explanation modules, mapping imagery and schema‑level metadata into natural language and structured hints.  dblp ReAct frameworks, exemplified by UAV‑CodeAgents, convert that perception into iterative planning and tool use, achieving high mission success and bounded planning time.  Agentic UAV architectures, as surveyed by Sapkota and colleagues, frame everything as part of a larger ecosystem where agents can accumulate experience, coordinate across missions, and adapt to new tasks and domains.  CatalyzeX If we encode those three regimes as configurations in ezbenchmark—vision‑LLM only, vision‑LLM+ReAct controller, and full agentic stack—we can attach metrics that reflect what the literature actually measures: task‑level accuracy and descriptive quality for the VLM, convergence behavior and mission‑success rates for ReAct, and cross‑mission adaptability and system‑level robustness for the agentic frameworks.  alphaxiv.org  CatalyzeX

 In that sense, incorporating ReAct and agentic metrics into ezbenchmark is less about chasing a trend and more about turning the UAV and agentic AI survey results into concrete benchmark dimensions. UAV‑CodeAgents gives us  model of how to quantify ReAct‑based mission planning performance in aerial scenarios, including success rates and planning time under different reasoning temperatures.  The Agentic UAVs survey gives us a taxonomy of capabilities—goal‑driven behavior, contextual reasoning, collaborative planning—that we can translate into workloads and evaluation criteria at the analytics level.  CatalyzeX And the broader Agentic LLMs perspective explains why simply swapping in a bigger or better vision‑LLM will not give us the same system‑level behavior as a ReAct or agentic framework; what matters is how the model is embedded in a loop of reasoning, action, and feedback. alphaxiv.org Together, they give us a roadmap for evolving ezbenchmark from a TPC‑H‑inspired catalog of queries into a testbed that can meaningfully compare vision‑LLMs, ReAct controllers, and full agentic UAV stacks on the very kinds of aerial analytics workloads embodied in our own repository and in systems like SpatialSky. 

#codingexercise: CodingExercise-01-07-2026.docx