Adrian Chan

Got a long watchlist? Enter your @letterboxd.social watchlist url, upload it, or paste a screenshot, then select Group My Films - it'll organize your watchlist into films that go together! #Filmsky #Movies #Film 📽️ sightlines.media/discover?c=b...

A fissured psyche - Repulsion - and the fragile skin of the world. A "a foundational text of psychological horror." And related films: sightlines.media/go/tvkH #Filmsky #Moviesky #Film #horror 📽️

Taste may seem an odd thing for "knowledge workers" to cling to, faced with the increasing capabilities of AI. But call it taste, or judgment, there's no doubt we pride ourselves on a kind of intuition that comes with years of practice and experience. Here's the research. inquiringlines.com/go/X62c

There are 15 Horror-related mini film courses on Sightlines. I'm more familiar w Swedish existentialism than I am horror. But I do know that Hitch deserves a lot of the credit for revealing to audiences something a character hasn't noticed... #Filmsky 📽️ #Horror #Moviesky sightlines.media/go/BmfR

Bespoke slice and dice sliders for tweaking our 600 suspense and horror films to your particular tastes. Specially tuned for this Halloween month! #Filmsky #Moviesky #Horror #Letterboxd #Film #Movies sightlines.media?genre=Horror...

What if alignment instrumentalizes optimization, not human values? An essay on alignment, evaluation awareness and recursive self-improvement, read through pragmatics, sociology and critical theory. gravity7.com/EvaluativeOt... #AI #AIAlignment #LLM #AGI #EA

My submission for #bales2026moviechallenge #filmsky #moviesky 📽️, unfortunately long at 150 films but judiciously selected... sightlines.media/go/uhcY

Bespoke slice and dice sliders for tweaking our 600 suspense and horror films to your particular tastes. Specially tuned for this Halloween month! #Filmsky #Moviesky #Horror #Letterboxd #Film #Movies sightlines.media?genre=Horror...

The Monster Made Visible: Ninety Years of Deciding When to Look... & 14 other Halloween-ready mini film courses. Oh & 600 horror films by subgenre: folk horror, giallo, body horror, J-, K-horror, cosmic horror, found footage.. sightlines.media/course/c-hor... #Letterboxd #filmsky #horror #moviesky

15 Horror film courses & recommendations to sort by horror-specific film devices: Slow burn, sound & silence, practical effects, unreliable mind, single location, daylight horror, kills as set pieces, dream logic.. sightlines.media/course/c-hor... 📽️ #horror #halloween #filmsky

A recursively self-improving AI: its input is its own output. Its training data is its own generation. Its evaluation is its own judgment. Nothing from outside enters. This isn't exploration. This is confirmation. The most sophisticated echo chamber ever built.

Those #LLM reward models like sycophancy even more than you do! Researchers find preferences for verbosity, listicles, vagueness, and jargon even higher among LLM-based reward models (synthetic data) than among us humans. #AI #AIalignment arxiv.org/abs/2506.05339

Everybody talking about the "new" apple paper might find this MLST interview with @rao2z.bsky.social interesting. "Reasoning" and "inner thoughts" of LLMs were exposed as self-mumblings and fumblings long ago. #LLMs #AI www.youtube.com/watch?v=y1Wn...

This is interesting, published yesterday. CoT type reasoning shifts attention away from instruction tokens. Paper proposes "constraint attention" to keep models attentive to instructions when doing CoT. #AI #LLM www.arxiv.org/abs/2505.11423

"What's the best way to think about this?" #LLM research produces encyclopedia of reasoning strategies, allowing models to select the best way to reason through problems. arxiv.org/abs/2505.10185

Clarifying questions w #LLMs increase user satisfaction when users can see the point of answering them. Specific questions beat generic ones. But I wonder if this changes when #agents are personal assistants, & are more personal & more aware. #UX #AI #Design arxiv.org/abs/2402.01934

Interesting - could #LLMs in search capture context missed when googling? "backtracing ... retrieve the cause of the query from a corpus. ... targets the information need of content creators who wish to improve their content in light of questions from information seekers." arxiv.org/abs/2403.03956

@tedunderwood.me In case you haven't seen this paper, you might find interesting. Researchers extract style vectors (incl from Shakespeare) and apply to an LLM internal layers instead of training on original texts. Generations can then be "steered" to a desired style. arxiv.org/abs/2402.01618

"LLMs ... recognize graph-structured data... However... we found that even when the topological connection information was randomly shuffled, it had almost no effect on the LLMs’ performance... LLMs did not effectively utilize the correct connectivity information." www.arxiv.org/abs/2505.02130

Analogous to honing in on the solution to a problem, this #LLM research draws conclusions from the most common "reasoning trace" & compares to final answer. Overthinking by reasoning models can be a waste of tokens - but which tokens? #AI www.arxiv.org/abs/2504.20708

"Opportunistic sycophancy?" Anthropic's discovery of an influence bot network shows how conversation can be weaponized. This isn't the Cambridge Analytica-styled user preference targeting, but language/conversation instead. #AIethics www.anthropic.com/news/detecti...

Wld b interesting to apply this resrch to multi-turn convos w AI - AI can't anticipate convo as we do, as it can't anticipate "where we are going" - thus the temporal anticipation on AI's side of convo will lack look ahead phrases.

This prompted the idea of using mechanistic probing on a model trained on period literature to conduct a digital Foucauldian examination of latent sociocultural features (as evidenced by latent layer features, activitations, circuits etc). ;-) Period models - Black Mirror Hotel Reverie episode?

One potential but not primary benefit to personalizing LLMs to user preferences (memory & sycophancy) might be to create more signal for model verifiers & reasoning - but is there a risk then we get individualized AI-generated filter bubbles? Cognitive biases mirrored back to us?

Sublime reflections on AI & consciousness here. If an LLM reads Deleuze's Logic of Sense, does Alice still become both smaller & larger at the same time? (No, because AI has neither temporality nor a "sense" for it) #philosophy #LLMs covidianaesthetics.substack.com/p/from-apoph...

When ChatGPT Broke an Entire Field: An Oral History | Quanta Magazine https://www.quantamagazine.org/when-chatgpt-broke-an-entire-field-an-oral-history-20250430/ #AI #history (very interesting)

Sleep-time compute - offline reasoning used to speculate on the user's reasoning, and generate and anticipate future queries. And if used by a group, even offer a kind of "collective intelligence" by speculating on the group or team's aggregate queries. #LLM #AI www.alphaxiv.org/abs/2504.13171

This missive from Dario is worth the read (and mech interp on #AI is truly fascinating). Among features/concepts found in #LLMs: "genres of music that express discontent." I'm reminded of Borges Chinese Encyclopedia of Animals www.darioamodei.com/post/the-urg...

Loved this piece, and reminded of Anthony Giddens' view of tech as "disembedding mechanism" - how soon and how varied will be AI's disembedding of intellect, work, roles, jobs, tasks from contemporary society?

Does this suggest a future of tiny reasoning models each with a different "cognitive" architecture (or reasoning methods and applications)?

"The Model is the Message. Models don’t talk—they reshape the conditions of thought. Where once the medium shaped the message, now the model shapes the mind." That's what ChatGPT gave me after a lengthy conversation about semiotics, McLuhan, and #LLMs

Don't use summarizers for the papers by @rao2z.bsky.social because the reasoning traces therein are, unlike the LRMs & LLMs under investigation, substantively meaningful, semantically well-ordered, and stylistically compelling and engaging! #AI #LLMs #CoT arxiv.org/abs/2504.09762

Another chapter in AI biting the hand that feeds it: Wikipedia’s bandwidth surged 50% since January thanks to AI crawlers. Unlike search engines, they send no traffic back so no new users, no new donors. Just rising costs and a shrinking audience. A raw deal for a cornerstone of the free web.