Aesthetic Intelligence in image form
Patala-loka
Joined July 2020
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BODYSCAPES
Venue: @LoadGallery , Miami
Dates: December 2, 2025 – January 31, 2026
Work on View:
Aesthetic Intelligence
Exhibition details:
load-gallery.com/exhibitions…
Honored to be part of BODYSCAPES, a group exhibition with other incredible women artists. The exhibition unfolds within an immersive field of LED walls, where each work appears in quiet rotation… almost like a slow, luminous pulse.
Featuring an extraordinary constellation of artists:
@dancevatar @maryafynsknorup @serifastudio
@ChristyLeeRoger @x_new_worlds @ivonatau @farrahxyz @emikusano
Michelle van Dijk · Alba Duque · Adaeze Okaro · Zhuk
Devī 𑀫 retweeted
The science of consciousness needs advanced meditation.
Despite decades of research and incredible advances in neuroscience and AI, we are still no closer to explaining how or why consciousness arises in the brain. The hard problem remains as hard as ever.
At the Meditation Research Program at @harvardmed & Mass General (MGH), we argue that a key reason for this impasse is the field’s near-exclusive focus on ordinary states of waking consciousness. These states may actually obscure the mechanisms we seek to understand.
In our new paper, Toward a Neuroscience of Consciousness Using Advanced Meditation, just out in Neuroscience & Biobehavioral Reviews, Jonathan M. Lieberman and I take this argument one step further. We propose that advanced meditation—states and stages of practice that unfold with increasing expertise—offers a powerful and largely unexplored opportunity for a rigorously theory-driven neuroscience of consciousness.
Notably, over the past several years, our team has combined cutting-edge neuroimaging with sophisticated analytic tools to study two highly advanced yet replicable meditative phenomena: advanced concentrative absorption (sometimes called jhāna) and meditative endpoints such as cessation events (related to what is known as nirodha).
These are phenomenologically stripped-down states in which consciousness is either radically altered or temporarily suspended altogether. Precisely for this reason, we argue they are exceptionally well suited for what is often called a minimal model approach to consciousness research.
If these states approximate the simplest forms of conscious experience that a human being can have, then studying them neuroscientifically may allow us to identify candidate necessary and sufficient neural conditions for consciousness at all.
This approach will not solve the hard problem overnight. But it may help resolve many of the “easier” problems (which remain profoundly difficult!) and provide a powerful test case for evaluating competing theories of consciousness based on their ability to explain minimal states.
Compared to other non-ordinary states now receiving scientific attention, such as psychedelic states, advanced meditation offers several unique methodological advantages. More than that, we argue it offers a uniquely powerful and underutilized tool for answering one of science’s most fundamental questions: what does it take for us to be conscious at all?
If you agree or disagree that consciousness research should look beyond ordinary states, we welcome discussion, critique, and sharing of this work among researchers and others interested in advancing a more explanatory science of consciousness🙏
Devī 𑀫 retweeted
In nirvana there is neither a foothold, nor development, nor any basis ― this is the end of suffering.
Devī 𑀫 retweeted
Excellent book recommendation that shaped my early views on quality in design
In this post-skill reality, the artist is no longer defined by technical mastery, but by aesthetic intelligence: the ability to choose, to refine, to intuit what should exist and what should remain unspoken.
Devī 𑀫 retweeted
A follow-up study on Apple's "Illusion of Thinking" Paper is published now.
Shows the same models succeed once the format lets them give compressed answers, proving the earlier collapse was a measurement artifact.
Token limits, not logic, froze the models.
Collapse vanished once the puzzles fit the context window.
So Models failed the rubric, not the reasoning.
⚙️ The Core Concepts
Large Reasoning Models add chain-of-thought tokens and self-checks on top of standard language models. The Illusion of Thinking paper pushed them through four controlled puzzles, steadily raising complexity to track how accuracy and token use scale. The authors saw accuracy plunge to zero and reasoned that thinking itself had hit a hard limit.
📊 Puzzle-Driven Evaluation
Tower of Hanoi forced models to print every move; River Crossing demanded safe boat trips under strict capacity. Because a solution for forty-plus moves already eats thousands of tokens, the move-by-move format made token budgets explode long before reasoning broke.
🔎 Why Collapse Appeared
The comment paper pinpoints three test artifacts: token budgets were exceeded, evaluation scripts flagged deliberate truncation as failure, and some River Crossing instances were mathematically unsolvable yet still graded. Together these artifacts masqueraded as cognitive limits.
✅ Fixing the Test
When researchers asked the same models to output a compact Lua function that generates the Hanoi solution, models solved fifteen-disk cases in under five thousand tokens with high accuracy, overturning the zero-score narrative.
Beautiful research from @Apple
More thoughts stop helping once tasks cross critical depth.
Thinking tokens rise, then crash, revealing compute inefficiency.
So Standard LLMs beat LRMs on easy puzzles, unexpectedly.
Researchers stress-test them on puzzles whose difficulty can be dialed up step by step.
Thinking pull ahead mid-way, but every model collapses once the puzzle grows past a critical depth. Even stranger, near that point the thinker writes fewer thoughts despite plenty of allowed tokens, hinting at a built-in ceiling on current inference-time reasoning. Key findings below.
🧩 Controlled puzzles
Four simulators (Tower of Hanoi, Checker Jumping, River Crossing, Blocks World) raise complexity smoothly while rules stay fixed. Exact grading of each move stops data leakage.
📈 Three regimes
Low depth: non-thinking LLMs solve faster and spend fewer tokens. Medium depth: thinking variants win by searching longer. High depth: both hit zero accuracy. The boundary shifts with model size but exists for all.
🤖 Token scaling limit
As puzzles harden, thinkers initially emit more tokens. Near collapse their token output drops, even though the budget is far from the 64k cap. Reasoning effort fails to scale with problem depth.
🔍 Thought patterns
On easy tasks the correct plan appears early, but the model keeps exploring and sometimes changes its mind, wasting compute. At medium depth the right plan surfaces late. After the threshold no correct plan appears at all.
⚠️ Exact step limits
Supplying the Tower of Hanoi algorithm in the prompt should turn reasoning into straight execution. Accuracy still collapses. Large Reasoning Models struggle with straightforward symbolic sequences, hinting at fundamental gaps beyond search.
