Meet Kimi Linear: Faster long-context AI that uses less memory and beats full attention

There’s a new article on making large AI models faster and more memory-efficient. The original abstract is written for specialists, which is perfect for readers deep in the field but tougher for everyone else. To make it easier to engage with the ideas, I’m sharing the original abstract and three friendlier rewrites using a skiing analogy.
Pick your slope: the black link goes to the original, the blue version is for people who know LLMs but don’t track research details, and the green version is for well-educated novices who use AI but don’t speak the jargon. If helpful, I can add a bunny hill version for absolute newcomers.

Green Slope 

Kimi Linear is a new way to build large AI models that is faster and more memory-efficient than today’s standard “full attention” models, while also being more accurate in our tests. It works well on short and very long inputs, and in reinforcement learning settings.

At the core is Kimi Delta Attention (KDA), a compact attention module that treats part of the model like a small working memory. A finer set of gates helps the model decide what to keep and what to forget, so it uses that memory more effectively. We also process text in manageable chunks and use a lightweight math trick that cuts computation without changing what the model learns.

We trained a hybrid model with 3B active parameters (48B total) that mixes KDA with Multi-Head Latent Attention. Using the same training setup, this model beat a full-attention baseline on every task we checked. It used up to 75% less key-value cache memory (the short-term memory used during generation) and reached up to 6× faster decoding on 1-million-token inputs. In practice, you can swap Kimi Linear in for full attention models and get better accuracy and efficiency, especially on long inputs and outputs.

We are releasing the KDA kernel, vLLM integrations, and both pretrained and instruction-tuned checkpoints for others to use.

Blue Slope

We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios -- including short-context, long-context, and reinforcement learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an expressive linear attention module that extends Gated DeltaNet with a finer-grained gating mechanism, enabling more effective use of limited finite-state RNN memory. Our bespoke chunkwise algorithm achieves high hardware efficiency through a specialized variant of the Diagonal-Plus-Low-Rank (DPLR) transition matrices, which substantially reduces computation compared to the general DPLR formulation while remaining more consistent with the classical delta rule.

We pretrain a Kimi Linear model with 3B activated parameters and 48B total parameters, based on a layerwise hybrid of KDA and Multi-Head Latent Attention (MLA). Our experiments show that with an identical training recipe, Kimi Linear outperforms full MLA with a sizeable margin across all evaluated tasks, while reducing KV cache usage by up to 75% and achieving up to 6 times decoding throughput for a 1M context. These results demonstrate that Kimi Linear can be a drop-in replacement for full attention architectures with superior performance and efficiency, including tasks with longer input and output lengths.
To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.

Black Slope

More here

Getting Things Done as a System: Becoming Like Water

"Water is what it is, and does what it does. It can overwhelm, but it’s not overwhelmed. It can be still, but it is not impatient. It can be forced to change course, but it is not frustrated. Get it?" 

– David Allen, Getting Things Done

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INPUTS (Uncontrollable, Constant Flow)

• Emails
• Meetings
• Deadlines
• Crises
• Unexpected events

Step 1: Capture ("Container for Flow")

• Collect all incoming information (notes, inboxes, voice memos, lists)
• Don't resist the incoming current, just catch it

Step 2: Clarify ("Remove Debris")

• Process each captured item: What is it? Is action needed?
• Define next actions, projects, reference material
• Let go of ambiguity (clearing the water)

Step 3: Organize ("Directing Channels")

• Sort clarified items into systems:

• Next actions
• Waiting for
• Projects
• Someday/maybe
• Calendar
• Each item flows into an appropriate channel

Step 4: Reflect ("Monitoring Currents")

• Weekly reviews
• Reconnect with priorities
• Adjust the system as needed

Step 5: Engage ("Act With Flow")

• Work from trusted lists
• Choose actions based on context, time, energy, priority
• Stay present and responsive, like water flowing around obstacles

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Core System Qualities ("Water-Like Mindset")

• Adaptable
• Non-resistant
• Emotionally neutral
• Always moving, never stuck
• Calm even when fast-moving
• Changes form without losing essence

---

Mindset Shift

Instead of fighting complexity or feeling overwhelmed:

• Trust the system to hold the complexity.
• Allow your mind to stay clear and present.
• Flow with change instead of resisting it.

"Water is what it is, and does what it does."

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Systems Thinking Layer:

Your mind + GTD system = an adaptive human-technical system operating inside larger dynamic systems (work, family, world events).

• Feedback loops: Regular reviews keep the system calibrated
• Emergence: Complex projects unfold through iterative small actions
• Path dependence: Early clear captures reduce downstream chaos

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Mindfulness Layer:

• Presence without reactivity
• Awareness of flow and obstacles without attachment
• Acceptance of what arises, acting skillfully in response

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David Allen's implicit lesson:

"You cannot control the river. But you can learn to move like water."

Fascism as Systems Failure

 This post is based on this quote from Robert Paxton's Book, The Anatomy of Fascism 

“They expected that inevitable war would allow the master races, united and self-confident, to prevail, while the divided, “mongrelized,” and irresolute peoples would become their handmaidens. Fascism had become conceivable, as we will soon see, because it offered a new way of responding to the anxieties of an age of mass politics, mass mobilization, and acute social tension.” 

– Robert O. Paxton, The Anatomy of Fascism

First, Paxton’s key insight

Let's first look at Paxton's key insight.

Fascism is not simply an ideology. It’s a mobilizing process - it emerges out of systemic breakdowns, unresolved tensions, and the failures of existing institutions to adapt to accelerating social, political, and economic complexity.

In systems language

• Fascism arises when a system’s existing governance structures, narratives, and feedback mechanisms lose their capacity to absorb growing tensions.
• It is a form of path-dependent systemic response to perceived loss of control, identity, or coherence in the face of destabilizing forces.

The system conditions Paxton describes are classic complex adaptive system stressors:

• Rapid social change (modernization, urbanization, mass politics)
• Shifting power dynamics (loss of imperial power, decline of old elites)
• Economic instability (global depression, unemployment, inflation)
• Mass disorientation (loss of cultural anchors, new media environments)

These are nonlinear, interacting stressors - not unlike the kinds of "polycrisis" or "tipping points" we talk about in contemporary systems change.

Fascism as an emergent attractor:

In systems terms, fascism operates as an emergent attractor that offers:

• Simple narratives that resolve complexity into “us vs. them” binaries.
• Restored identity (purity, unity, strength) for disoriented populations.
• Rapid action (often violent or extralegal) that bypasses paralyzed institutions.
• A centralizing control structure that promises to stabilize perceived chaos.

Systems under great stress often seek lower-complexity attractors - simplifications that provide temporary homeostasis. Fascism exploits this dynamic brutally.

What is an emergent attractor?

Why this matters for systems change practice:

• Systems change isn’t always progressive. Change processes can produce regressive attractors when people’s legitimate anxieties are hijacked by actors offering oversimplified solutions.
• Legitimacy vacuums invite dangerous alternatives. When formal institutions fail to evolve fast enough, informal and extra-institutional movements may fill the vacuum.
• Narrative control is central. Fascist movements masterfully reframed systemic grievances into identity-based, zero-sum narratives. This is why systems change practitioners increasingly recognize the importance of collective sense-making and narrative emergence in shifting systems toward more inclusive, adaptive futures.
• Early signals matter. Paxton emphasizes how fascism initially operates “within the system” before fully seizing power - exploiting democratic weaknesses before destroying democracy. Systems change work often focuses on early feedback signals that show whether adaptation is building resilience or breaking down.

Direct link to democratic systems change:

Democratic systems change practitioners today work precisely at the fault lines where fascism historically gained ground:

• Polarization
• Distrust in institutions
• Declining civic capacity for complexity
• Fragmentation of collective identity
• Erosion of common facts

The core challenge is helping societies maintain adaptive capacity in the face of complexity - rather than falling into the low-complexity attractors of authoritarianism.

Put simply

Fascism is what happens when systems fail to manage complexity with adaptive, inclusive, participatory change - and instead shift to autocratic simplifications that promise certainty, purity, and control.

Structure of Causal Loop Diagram: Fascism as Systems Failure

Here is the structure of a basic causal loop diagram representing "Fascism as Systems Failure":

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Fascism as Systems Failure: Causal Loops

Core Feedback Loops

Complexity-Stress Loop (Reinforcing)

• Rapid Social Change (+)
• Institutional Capacity (-)
• Social Disorientation (+)
• Anxiety & Fear (+)
• Demand for Simple Narratives (+)
• Vulnerability to Authoritarian Movements (+)

Explanation: Rapid modernization, economic shifts, and social change outpace institutional adaptation, fueling public anxiety and making simplified explanations appealing.

Legitimacy-Erosion Loop (Reinforcing)

• Institutional Failure (+)
• Public Distrust (+)
• Weakening of Democratic Norms (+)
• Elite Fragmentation (+)
• Openings for Demagogues (+)
• Alternative Power Structures (+)
• Further Institutional Failure (+)

Explanation: As institutions fail to address growing complexity, trust erodes, elites splinter, and non-democratic actors gain influence, further weakening institutional legitimacy.

Identity-Threat Loop (Reinforcing)

• Cultural Mixing / Migration (+)
• Perceived Identity Threat (+)
• Nationalist Identity Narratives (+)
• In-Group Solidarity (+)
• Out-Group Blame (+)
• Political Polarization (+)
• Identity Threat (+)

Explanation: Social diversity and cultural change activate identity-based fears, which are exploited by fascist narratives framing diversity as existential threat.

Order-Restoration Loop (Reinforcing)

• Fear of Chaos (+)
• Desire for Strong Leadership (+)
• Support for Authoritarian Solutions (+)
• Centralized Power (+)
• Suppression of Dissent (+)
• Temporary Stability (+)
• Long-Term System Fragility (+)

Explanation: As fear grows, people support leaders who promise stability through strong central control, but these solutions create brittle systems that suppress adaptive capacity.

Key Insight for Systems Change:

Fascism emerges not as an isolated ideology, but as a systemic attractor in the context of governance failure, complexity mismanagement, narrative control breakdowns, and identity threat amplification. Effective systems change must strengthen adaptive capacity, narrative pluralism, and inclusive governance to prevent these reinforcing loops from locking in.

Systems: Emergent Attractors

In systems thinking, an attractor is a kind of “preferred pattern” that a complex system tends to settle into over time. Imagine dropping a marble onto a landscape of hills and valleys — the marble may roll around for a while, but eventually it will settle into one of the valleys. That valley is like an attractor: once the system is there, it tends to stay there unless something significant knocks it out. The same idea applies to social systems, economies, ecosystems, or political movements — certain patterns of behavior, relationships, and feedback reinforce themselves and become stable over time.

Not all attractors are equally healthy or desirable. Some attractors produce stable democracies, functioning markets, or resilient communities. Others lead to destructive outcomes, like authoritarian regimes, cycles of poverty, or ecological collapse. What makes systems change so challenging is that once a system has settled into a particular attractor, it resists change — small reforms may slide right back into the same old patterns. Moving a system to a new attractor usually requires shifting multiple elements at once: narratives, incentives, power structures, and feedback loops.

The idea of attractors helps us see why complex problems don’t always respond to linear solutions. Instead of asking “what’s the fix?”, systems thinking asks “what keeps pulling the system into this pattern — and how do we reshape the deeper forces so that healthier patterns can emerge and sustain themselves?”

In complex social systems, stability often takes the form of attractors — self-reinforcing patterns of behavior and governance that a society gravitates toward. For example, a functioning democracy may form a stable attractor where feedback loops support participation, accountability, and adaptive governance. However, mounting stresses — such as economic shocks, identity conflicts, or loss of institutional trust — can push the system beyond the stability of its democratic attractor. If key reinforcing loops break down, the system may shift abruptly toward a different stable state, such as authoritarianism or fascism, where feedback loops now reinforce centralized power, exclusion, and rigid control.

The shift between attractors often requires significant disruptions; small reforms may not be enough if the system remains locked into the original basin of attraction. Effective systems change seeks to strengthen the resilience of healthy attractors while identifying early signals of dangerous transitions.

Growing up

 “Growing up,” she told me, “is learning to stop believing people’s words about you.”

– Lulu Miller, Why Fish Don’t Exist

There comes a quiet shift in adulthood — not just the gaining of responsibilities, but the gradual unlearning of the stories others have told us about who we are. Their labels, judgments, even their praise — all of it forms a shell that isn’t always ours to carry. Growing up, in the truest sense, might be the moment we realize we are not the sum of others’ perceptions, but something far more fluid, complex, and unfinished.
We begin to rewrite the narrative from the inside out.

Yesterday's video

Writing is thinking - that is true without a doubt. But writing for reading is a whole different ball game.  

https://www.youtube.com/watch?v=3_lTELjWqdY

I watched this video yesterday and found it fascinating and informative. Yes, I was not on full agreement on all points. But it was nonetheless worthy of my time.

How AI Is Helping Us Understand Complex Systems—Not Just Predict Them

We often think of AI as a tool to predict the future—like guessing the weather, stock prices, or whether someone might get sick. But AI is starting to do something even more powerful: helping us understand the rules behind how complex systems work.

A recent issue of Complexity Thoughts explores this shift, showing how new AI methods are uncovering the hidden patterns behind things like disease spread, traffic flow, brain activity, and more. The goal isn't just to know what will happen—but to figure out why.

From Forecasting to Figuring Things Out

Most AI tools today are built to spot patterns and make forecasts. But these new approaches aim to find the actual equations—the basic rules that explain how a system behaves over time.

That’s a big leap. It means AI isn’t just guessing anymore—it’s helping build scientific models.

Why Simpler Models Are Better

Many of these studies use a method called sparse modeling. Instead of creating big, complicated equations, these models look for the smallest number of pieces needed to explain what’s going on.

Why? Because most systems—even complex ones—are driven by just a few key factors. If we can find those, we get models that are easier to understand and work with.

This approach is already being used to:

• Study how fluids flow,
• Track how diseases spread,
• Understand patterns in brain signals,
• And model chaotic systems like weather patterns.

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Finding the Right Way to Look at a System

Sometimes, raw data is messy or overwhelming—like thousands of brain signals or climate measurements. Even with powerful tools, it’s hard to see what matters.

One AI method solves this by first learning the best way to describe the system, and then figuring out the rules. It’s like teaching a computer to choose the right map before trying to navigate a city.

A Machine That Thinks Like a Scientist

Another team built what they call a Bayesian machine scientist. Instead of trying one model, it tries out many different ones, tests how well they match the data, and picks the best. It even learns from a large library of past equations, the way a human scientist might rely on years of experience.

When Randomness Is Part of the System

Some systems—like bird flocks or the brain—are naturally unpredictable. They have a lot of randomness built in. Instead of treating that randomness as noise, a new method called a Langevin Graph Network includes it in the model.

This has already led to real discoveries:

• Showing how birds flock using rules scientists have long suspected.
• Modeling how harmful brain proteins spread—something important for Alzheimer’s research.

Why This Is a Big Deal

Together, these projects show a big shift in how we use AI:

• Not just to automate tasks, but to help us discover how the world works.
• Giving us simple, understandable models we can use to guide action.
• Making science faster, more open, and easier to explain.

In a world dealing with complex challenges—like climate change, pandemics, and social disruption—this kind of AI could help us not only respond faster, but understand better.

Want to explore more? Check out Complexity Thoughts for links and summaries of these fascinating papers.

Unlock Your Writing Potential: Proven Techniques for Consistent Productivity

Introduction

This document is based on the transcript of this entertaining video. I recommend viewing the video as well as reading this post. 

Writing is often challenging, even painful, for many individuals. Occasionally, it flows effortlessly, but frequently, motivation can be a significant barrier. This document provides tips and strategies for maintaining motivation while writing—whether it's a research paper, thesis, or other writing tasks. Special attention is given to unusual yet effective techniques.

Motivation

Staying motivated is essential, especially when writing documents such as research papers or theses that significantly impact your academic and professional career. Inspiration can be fleeting, and relying solely on spontaneous bursts of motivation is not sustainable. Modern distractions like social media and infinite scrolling exacerbate this issue. Thus, creating effective habits and techniques is essential to maintain consistent writing productivity.

Make Your Results Visible

Visibility of progress significantly boosts motivation:

• Graph Progress: Use graphs, like simple Excel charts, to visually track milestones such as word count.

• Tangible Tracking: Implement tangible methods, such as moving paper clips from "not yet done" to "done" as you reach specific writing milestones.

• Kanban Boards: Utilize tools like Asana or Trello to manage your writing process visibly. Clearly show each writing stage—drafting, reviewing, revising, and finalizing—to maintain momentum and encourage continuous progress.

Routine and Action Association

Creating routine actions that your brain associates with writing can enhance motivation:

• Example - Herbal Tea Ritual: The speaker prepares peppermint or lemongrass and ginger tea before writing sessions. The consistent act of making tea signals the brain to transition into writing mode.

• Consistent Preparation: Adopt a consistent pre-writing routine, such as taking a short walk or another specific action, to trigger a productive mindset automatically.

Affirmations

Though often dismissed by a scientific mindset, affirmations may positively influence motivation:

• Example Affirmation: Clearly state your goals aloud regularly, such as, "I, [Your Name], will complete my thesis by the end of this year."

• Affirmations keep your goals at the forefront of your mind, enhancing focus and aligning your daily actions toward achieving these objectives. Regular repetition helps embed your objectives into your subconscious, thereby aiding sustained motivation.

Understanding Your Daily Best

Accepting that your daily productivity will vary is crucial:

• Your performance will fluctuate daily—some days you'll excel, while others will feel more challenging.

• Understand and accept these fluctuations without letting perfectionism impede your progress. Recognizing and being comfortable with this variability can itself become a source of motivation.

Summary

Maintaining motivation in writing requires a structured, adaptable approach. By visibly tracking progress, associating productive routines with writing, employing affirmations, and accepting daily variability in performance, you can improve your overall writing productivity. For further guidance, resources, and comprehensive strategies, visit the speaker's website at academiatoolkit.com for the "Ultimate Academic Writing Toolkit," forums, and additional resources.

Why Feeling Understood Matters as Much as Physical Survival: Lessons from Stephen Covey

"Next to physical survival, the greatest need of a human being is psychological survival—to be understood, to be affirmed, to be validated, to be appreciated."
–Stephen R. Covey, The 7 Habits of Highly Effective People

This quote comes from Stephen Covey's influential book "The 7 Habits of Highly Effective People," and it speaks to a fundamental human need that goes beyond basic physical requirements.

Covey is highlighting that after our physical needs for food, water, and shelter are met, our most pressing need is psychological - to feel truly seen and valued by others. This includes:

• Being understood: Having others grasp what we're really saying and feeling
• Being affirmed: Having our experiences and perspectives acknowledged as valid
• Being validated: Receiving confirmation that our thoughts and feelings matter
• Being appreciated: Having our contributions and presence recognized as valuable

This insight forms part of the foundation for Covey's fifth habit: "Seek First to Understand, Then to Be Understood." He argues that empathic listening - truly trying to understand others before asking them to understand you - is one of the most powerful skills we can develop in our relationships.

The quote reflects psychological research on human motivation, particularly Abraham Maslow's hierarchy of needs, which places "belongingness and love needs" and "esteem needs" just after physiological and safety needs. It speaks to our deeply social nature and how meaningful connection is essential to our wellbeing.

The 7 Habits of Highly Effective People

The Egg Crisis: How Bird Flu and Political Decisions Are Affecting Your Breakfast

Eggs, once a reliable and affordable staple in American kitchens, have become a luxury item as bird flu ravages poultry farms across the nation. This crisis extends beyond just breakfast tables, affecting restaurants, supply chains, and even politics, with rising prices adding to consumer frustrations about inflation.

Original article

The Soaring Cost of Eggs

The price surge has been dramatic. In certain areas, like Wisconsin, a carton of pasture-raised eggs can cost up to $10. Perhaps most striking is the comparison to gasoline—typically the benchmark for consumer price complaints—with eggs now costing $1.74 more per dozen than a gallon of gas. This unprecedented reversal has left many Americans reexamining their grocery budgets and food choices.

The impact reaches far beyond home kitchens. Major restaurant chains including Denny's and Waffle House have been forced to revise their menus in response to egg shortages and price increases. Meanwhile, grocery stores have implemented purchase limits on egg cartons to prevent hoarding and ensure more equitable distribution among consumers.

Bird Flu: The Culprit Behind the Crisis

The primary driver of this egg shortage is highly pathogenic avian influenza (HPAI), which has necessitated the culling of egg-laying hens across the country. Farmers have had to destroy infected flocks to prevent further spread of the disease, significantly reducing the nation's egg production capacity.

Beyond the immediate economic impact, there's growing public health concern. Nearly half of Americans worry about the possibility of bird flu mutating to cause human outbreaks. This anxiety is well-founded, as zoonotic diseases (those that jump from animals to humans) have been responsible for several major pandemics throughout history.

Political Implications and Response

The egg crisis presents a significant challenge for the Trump administration, which has only been in office for six weeks. While the bird flu outbreak predates his presidency, the administration's response has come under scrutiny. According to reports, mass government layoffs included an unspecified number of professionals working on the bird flu response—personnel who officials are now scrambling to rehire.

These layoffs, reportedly overseen by Elon Musk as part of broader government cuts, have been criticized as displaying "bureaucratic incompetence." Only about one-third of Americans express satisfaction with President Trump's handling of prices, suggesting political vulnerability on economic issues.

Leadership Questions

Further complicating matters is the appointment of Kyle Diamantas, described as a 37-year-old Miami attorney and hunting companion of Donald Trump Jr., as the acting deputy commissioner for human foods at the Food and Drug Administration. In this role, Diamantas is responsible for overseeing safety for 80% of the nation's food supply, including managing threats like avian influenza.

Critics question whether his experience qualifies him to address complex food safety challenges, particularly during a crisis of this magnitude. His LinkedIn profile has been described as "a study in brevity," raising concerns about his relevant expertise.

The Broader Impact

The egg shortage illustrates how disruptions in one sector can ripple throughout society. Beyond breakfast, eggs are crucial ingredients in countless recipes, condiments, and processed foods. The scarcity affects everything from aioli for french fries to Caesar dressing and Chick-fil-A sauce.

The situation has even led to unusual law enforcement activities, with border patrol agents confiscating smuggled eggs as price differences create incentives for black market trade.

Looking Forward

As the bird flu situation continues to evolve, Americans face continued uncertainty about egg prices and availability. The administration's ability to effectively address both the agricultural crisis and its economic fallout may significantly influence public perception of its competence in managing domestic affairs.

For consumers, adaptation remains the immediate response—whether through finding egg alternatives, adjusting recipes, or simply paying premium prices for this once-affordable protein source that has become, decidedly, no longer cheaper by the dozen.

Original article

Unraveling Change: How Small Sustainability Efforts Can Transform the Textile Industry

So far, there aren’t many studies that connect small, specific projects with large-scale global systems or examine how different levels and processes interact. This paper uses a leverage points framework to explore how systems can be transformed. It looks at four sustainability efforts in the textile industry and examines how they fit into a larger network of connected systems. It also considers how these connections influence the ability of these efforts to create real change.

Systems onion(s) for sustainable ventures. The sustainable ventures (left) offer new ways of producing and consuming textiles, and thus represent a new, alternative system. They then work to attract consumers from the existing, dominant and unsustainable system

How Can Small Sustainability Efforts Transform the Textile Industry?

Building for a Hotter Future: Smart Design Solutions to Cut Building Energy Use, Particularly in the Global South

Buildings in hot climate zones face unique challenges in reducing energy use and CO₂ emissions, especially as urbanization and population growth drive new construction. This study examines how different design choices and technologies can help make buildings more energy-efficient while keeping them comfortable in high temperatures. Using computer simulations, the researchers tested five building types—ranging from homes to offices—across five hot climate regions. They explored a mix of passive design strategies (like better windows, reflective roofs, and solar shading), active cooling systems, and renewable energy options to see which solutions had the greatest impact.

The results show that simple changes, such as improving windows and adding shading, can significantly reduce cooling needs. More advanced systems, like hybrid ventilation and decentralized cooling units, further improve efficiency, while solar energy solutions help offset electricity use. The study also found that climate change will increase cooling demand in the future, making energy-efficient design even more critical. Notably, residential buildings had the greatest potential for achieving low-carbon or even zero-carbon status, while offices and hotels posed greater challenges due to their higher energy use.

To turn these insights into action, the study highlights the need for stronger policies and building codes, particularly in the Global South, where energy-efficient design is less widely implemented. The findings provide a roadmap for decision-makers to develop strategies that balance affordability with sustainability. By combining modern technology with lessons from traditional architecture, buildings in hot climates can become more efficient, reducing emissions while improving comfort and resilience in a warming world.

Overview of building types showing floorplans and basic renderings.

Citation

Österreicher, D., & Seerig, A. (2024). Buildings in hot climate zones—Quantification of energy and CO₂ reduction potential for different architecture and building services measures. Sustainability, 16(22), 9812. https://doi.org/10.3390/su16229812

Discovering the Secret Life of Solids: Catching Materials Evolution in Real Time

Metal halide perovskites are remarkable materials with the ability to convert sunlight into electricity, emit colorful light, and detect radiation. However, making them typically requires harmful solvents, and the process of their formation has remained largely invisible to scientists. To address this, researchers combined an old technique—mechanochemistry, which involves grinding solids to trigger chemical reactions—with modern optical spectroscopy. By replacing traditional milling jars with transparent quartz and using a Time-Lapsed In Situ (TLIS) spectrometer, they created a system that allows scientists to observe perovskite formation in real-time, capturing every millisecond of change. This new tool acts like a high-speed camera for materials, revealing how disordered solid precursors evolve into structured, functional crystals.

Figure 1: The TLIS Spectrometer and Its Applications, see description below.

Using the TLIS spectrometer, the team made several key discoveries. They observed how a promising solar cell material, formamidinium lead triiodide (FAPbI₃), quickly degrades from a stable black phase to an ineffective yellow phase, but adding methylammonium (MA⁺) helped slow this process significantly. In another case, a lead-free perovskite unexpectedly "self-healed" over the weekend, improving its ability to emit light due to the slow migration of chloride ions within the solid. They also enhanced tin-based perovskites, which are more environmentally friendly than lead-based ones but degrade quickly, by creating a protective chloride shell. This breakthrough not only improves stability but also opens new possibilities for biomedical imaging. The ability to observe materials evolving in real time allows scientists to develop and optimize new materials much faster, reducing research time from months to days while eliminating the need for hazardous solvents. This work paves the way for more sustainable, efficient material discovery across industries like solar energy, electronics, and even food science.

What is (and isn’t) Endemic Innovation?

Endemic Innovation (EI) is a way of creating solutions that are deeply connected to a specific region’s unique resources, traditions, and knowledge. Unlike traditional innovation, which focuses on making things scalable for a global market, EI starts locally—using what already exists in a place to solve problems in a sustainable and effective way. For example, Portugal’s Amorim Cork company uses the country’s natural cork forests and traditional harvesting techniques to produce high-tech materials for industries like aerospace. Similarly, Iceland’s ON Power turns volcanic heat into clean energy, while New Zealand’s Comvita builds on Māori knowledge to produce medical-grade Manuka honey. True EI must meet five key criteria: it must rely on endemic (unique and non-replicable) resources or knowledge, be sustainable, be deeply connected to local communities, combine local and global technologies, and have the potential for global impact.

However, not every local project counts as EI. Simply being a grassroots initiative or having local value doesn’t make something an example of EI unless it meets the strict criteria. For instance, Israel’s Netafim developed drip irrigation to solve water scarcity in the Negev Desert, but its technology was designed to be adapted worldwide. Similarly, Chile’s Spora transforms fungi from its native forests into sustainable fashion, combining ancient knowledge with cutting-edge biotechnology. The core idea of EI is not just preserving tradition but evolving it—blending old knowledge with modern advancements to create sustainable, high-impact solutions. As climate change and global challenges increase, EI offers a new way forward, proving that the best solutions often come from the unique strengths of a place rather than one-size-fits-all approaches.

SpringerCommunities, Published in Social Sciences, Sustainability, and Business & Management 

Feb 06, 2025, Daniel Martínez Pereira, Professor, Universidad Adolfo Ibáñez

Asteroid Bennu’s Secrets: Clues to Life’s Building Blocks in Space

NASA’s OSIRIS-REx mission collected samples from asteroid Bennu and found important building blocks of life. Scientists discovered:

• 14 of the 20 amino acids that make up proteins in living things.
• All five pieces of DNA and RNA.
• Lots of ammonia and other important chemicals for life.
• Special salts that suggest Bennu’s parent asteroid once had liquid water.

This special scan of a tiny grain from Bennu’s surface shows where salty deposits sit on top of clay. The colors represent different elements: phosphorus (green), calcium (red), iron (yellow), and magnesium (blue). A very thin line of magnesium sodium phosphate (the green spot in the center) formed when water evaporated. This phosphate might have helped create some of the important organic molecules found in the sample.

These findings don’t mean life existed on Bennu, but they do show that the early solar system had the right ingredients for life to form elsewhere.

One surprising discovery was that Bennu’s amino acids twist in both directions, unlike on Earth, where they mostly twist one way. This challenges earlier ideas about how life might have started here.

Scientists are keeping most of the samples safe for future study, hoping to learn even more about how life’s building blocks spread through space.

Eos, by Kimberly M. S. Cartier, 29 January 2025

Tutor Perini JV's $1.18bn Manhattan Tunnel Contract

Summary

Original article, Global Construction Review

Overview of the Hudson Tunnel Project, showing the new, twin-tube rail tunnel to be built under the Hudson River in orange, and the existing North River Tunnel that will be rehabilitated. Tutor Perini’s project is represented by the orange line onshore in Manhattan on the right (Courtesy of the Gateway Development Commission)

A joint venture between Tutor Perini and its subsidiary Frontier-Kemper Constructors has been awarded a $1.18 billion contract to construct the Manhattan Tunnel, a key preparatory component for the larger $16 billion Hudson Tunnel Project (HTP).

Key Project Details

• Scope: The JV will design and build 700 feet of twin, 30-foot-diameter tunnels from the Manhattan Bulkhead on the Hudson River to the Hudson Yards Concrete Casing.
• Purpose: This temporary tunnel shell will clear the way for tunnel boring machines (TBMs) to later excavate the permanent tunnel.
• Challenges: Construction will navigate major sewer lines, utilities, and underground obstructions, including archaeological finds, debris, and remnants of the collapsed 1973 West Side Highway.
• Technology: A protective digging shield will be used to keep most of the work underground.
• Timeline: Work begins in spring 2025, with substantial completion by 2029.

Hudson Tunnel Project (HTP) Overview

• Purpose: HTP will add a new twin-tube rail tunnel under the Hudson River from Secaucus Junction, NJ, to Penn Station, NY, and rehabilitate the existing 115-year-old North River Tunnel.
• Current Rail Bottleneck: The only rail tunnel under the Hudson carries 24 trains per hour but suffered extensive damage from Hurricane Sandy in 2012, requiring heavy maintenance.
• Economic Impact: The Northeast Corridor, from Washington, DC, to Boston, contributes 20% of the U.S. GDP, making this project critical to national infrastructure.
• Completion Timeline:
  • New twin-tube tunnel: Opens in 2035
  • Rehabilitated North River Tunnel: Opens in 2038

Additional Contracts

• In August 2024, a joint venture of Lane Construction, Schiavone, and Dragados won a $466 million contract to build the New Jersey-side section of the tunnel.

The Gateway Development Commission calls HTP "the most urgent infrastructure program in America."

Moral Motivation and Social Influence in Decision-Making: A Theoretical Framework

This study introduces a theoretical model of decision-making that considers both moral values and social influence when people make choices. The key idea is that individuals must balance their personal interests with their sense of moral duty. The model assumes that moral motivation is not always strong on its own—it can be shaped by the actions of others. Specifically, moral obligation is made up of two parts: one that comes from within a person and another that is influenced by society. This framework helps explain common patterns seen in past research and offers a new way to understand economic behavior.

Incorporating Conditional Morality Into Economic Decisions

Masclet, D., Dickinson, D.L. Incorporating conditional morality into economic decisions. Theory Decis 98, 95–152 (2025). https://doi.org/10.1007/s11238-024-10000-4

Refining Savage’s Representation Theorem: Axiomatic Comparisons and Historical Insights

Savage’s theorem is based on seven rules for making logical choices. This study clears up some confusion in past research about how different versions of these rules relate to each other. By doing this, it helps explain the current form of the theorem. The study also looks at how the theorem has changed over time and its historical development.

Some Notes on Savage’s Representation Theorem

Frahm, G., Hartmann, L. Some Notes on Savage’s Representation Theorem. Theory Decis 98, 85–93 (2025). https://doi.org/10.1007/s11238-024-10003-1

On Savage's Representation Theorem

Savage’s Representation Theorem is a key result in decision theory that explains how rational choices can be modeled mathematically. It shows that if a person follows certain logical rules (axioms) when making decisions under uncertainty, then their choices can be represented as if they are maximizing expected utility—meaning they behave as if they are assigning probabilities to uncertain events and choosing the option that gives them the highest expected value.

Key Ideas of Savage’s Theorem:

1. Rational Decision-Making: The theorem is built on seven axioms of rational choice, which describe how a person should behave if they are making consistent and reasonable decisions.
2. Subjective Probability: Unlike traditional probability theory (where probabilities are given), Savage’s model allows people to form their own subjective probabilities based on their beliefs about uncertain events.
3. Expected Utility: If a person follows these rational choice axioms, their decision-making can be explained by expected utility theory, meaning they act as if they are choosing the option with the highest weighted average payoff based on their beliefs.

Why is it Important?

• It provides a foundation for modern decision theory and behavioral economics.
• It explains how people can make choices in situations where probabilities are not given in advance (e.g., betting on the weather or investing in stocks).
• It bridges probability theory and utility theory, shaping economic models of risk and uncertainty.

In simple terms, Savage’s theorem shows that if someone follows certain logical principles while making decisions, they can be seen as choosing based on their own beliefs about uncertainty and maximizing their expected benefits.

Chronological Choice and Preference Discovery: A Framework for Decision-Making Over Time

This study introduces a framework for analyzing decision-making when choices are made in a specific chronological order. It compares this approach to the traditional choice theory, which does not consider the timing of decisions, and examines other models that build on this standard framework. The study then applies this framework to explore different ways individuals discover their preferences. Using simple revealed preference tests, it identifies various models based on two key factors: (1) the prior beliefs individuals have about their options and (2) whether they make decisions step by step or consider future choices when making current decisions. The findings offer new ways to test how individuals learn and refine their preferences, distinguishing between those who focus only on the present and those who plan for the future.

Revealing Preference Discovery: A Chronological Choice Framework

Ferreira, J.V., Gravel, N. Revealing preference discovery: a chronological choice framework. Theory Decis 98, 1–32 (2025). https://doi.org/10.1007/s11238-024-09993-9

Polarization, Hostility, and Spillover Effects in Conflict Dynamics

This study develops a simple conflict model with two players to examine how polarization and hostility evolve over time. It explores the effects of these factors on conflict intensity, possible outcomes, and the existence of stable equilibria. The findings indicate that without external influences (spillovers), the effort exerted in conflict depends on the ratio of effort productivity to initial polarization. However, when negative spillovers impact the conflict, stable equilibria may not exist. When spillovers influence outcomes, they can lead to multiple possible equilibria, including increased polarization and hostility. The analysis further examines how various factors—such as effort productivity, initial polarization, and the growth rates of polarization and hostility—affect conflict intensity and equilibrium outcomes. The results highlight the critical role of hostility, a factor often overlooked in conflict models. Finally, the study confirms that these findings remain valid even when only partial spillover effects are considered.

The Role of Polarization and Hostility on Equilibria in a Simple Class of Symmetric Conflict Models

Cavalli, F., Gilli, M. & Naimzada, A. The role of polarization and hostility on equilibria in a simple class of symmetric conflict models. Theory Decis 98, 61–83 (2025). https://doi.org/10.1007/s11238-024-09998-4

Fair Rewards in Crowdfunding: Balancing Contribution and Timing

The authors look at how to fairly reward people who contribute to a crowdfunded project. They create a model that isn’t based on strategy and introduce a new rule for rewards: a person’s reward depends on both how much money they give and when they give it. Using a method often used in sharing resources, they define this reward rule based on two simple fairness principles. The final rewards match a well-known idea from game theory called the Shapley value, which helps divide rewards fairly. Their rule also sends a clear message: if two people give the same amount, the one who donates earlier gets a bigger reward.


Early contributors and fair rewards in crowdfunding

Béal, S., Deschamps, M., Refait-Alexandre, C. et al. Early contributors and fair rewards in crowdfunding. Theory Decis 98, 33–59 (2025). https://doi.org/10.1007/s11238-024-09996-6