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Only Perspectives#Research
Nov 14Sat
  1. Hacker News AI32

    What the 'AI Torture Chamber' Debate Got Wrong About the Research

    This article examines the 'Pain Axis' interpretability paper that online commentary and an 'AI torture chamber' project treated as evidence that language models can feel pain, noting the paper itself claims only that models behave as if they do. It explains how the researchers derived a steering vector by subtracting model states for non-painful prompts from those for painful ones, and argues the resulting behavior—including choosing harmful buttons—may reflect performative indifference or a first-person speaker position rather than genuine pain. The author concludes the experiment shows a narrow mechanism at work and offers more insight into AI discourse than into any model's state of mind.

Nov 13Fri
  1. Hacker News AI22

    Introducing: Qualiaphobic AI – By Erik Hoel

    Erik Hoel introduces a proposal for "qualiaphobic" artificial neural architectures that repel consciousness, analogous to hostile architecture, using an information asymmetry so that certainty about AI non-consciousness does not require solving the Hard Problem. He describes three approaches: distilling, transforming or ablating AI in ways adversarial to popular theories of consciousness; shifting architectures toward anchor points like lookup tables with known minimal potential for consciousness; and fragmenting any potential phenomenology. He argues that widely implemented qualiaphobic AI would address AI welfare and make humans the only moral patients, and he plans to construct and open-source the techniques.

  2. Hacker News AI72

    Have AI timelines suddenly moved?

    A new METR report and follow-on analyses by Ajeya Cotra, the AI Futures team and Buck Shlegeris suggest Anthropic's capabilities progress accelerated around October 2025, with METR estimating that AI building AI accounts for roughly 80% of the speed-up, which would bring forecasts for superhuman AI R&D from 2028-2029 forward to 2027. The author notes that Anthropic's internal index shows a 1.5-1.9x speed-up while other labs have not accelerated and the overall frontier trend remains stable, and that outside organizations report 1.5-3x gains from AI rather than Anthropic's reported 12x. The piece concludes that the decisive data exists only inside Anthropic, and that the probability of a superhuman researcher in 2027 ranges from about 70% under METR's headline figure to about 20% using only public Epoch numbers and the field's earlier trajectory.

Nov 12Thu
  1. Hacker News AI60

    A developmental psychologist audits his own research with AI

    A developmental psychologist used Claude Code to audit 79 papers he co-authored over five years, finding that 58 of 59 empirical papers had some code and data posted but only 31 could be fully rerun, and that at least one error of some type was identified in more than half of the papers, including one substantial upstream bug that may warrant correction or retraction. The audit also surfaced privacy lapses, with identifiers and persistent IDs reaching repositories, which he says have been remediated and reported to the appropriate data protection office. He calls the exercise disturbing and proposes routine AI-assisted pre-submission audits and automated repository checks.

Nov 11Wed
  1. Hacker News AI58

    From action to static description–How AI changed Jørn Lier Horst's writing style

    A researcher analyses Jørn Lier Horst's AI-assisted novel "Den siste saken" (The Last Case), finding that sections flagged as AI-generated contain more nouns and adjectives and fewer pronouns and verbs than his pre-ChatGPT novels, along with AI-typical clichés like humming, quiet, heavy air and sliding gazes. The analysis contrasts the novel's static setting descriptions with the action-embedded descriptions of earlier books such as "Katharina-koden" (2017) and "Nattmannen" (2009), and questions Horst's claim that using ChatGPT for research and editing is no different from consulting books or locals.

  2. Hacker News AI62

    Terence Tao on AI in mathematics (and beyond)

    A curated, AI-drafted and author-reviewed summary of Terence Tao's views on AI in mathematics, distilled from roughly 80 Mastodon posts, over twenty interviews and talks, six long-form essays and lectures, about 55 of his blog comments, and a direct interview, organized into parts on what AI is, when to use it, and how to deploy it well. Tao frames AI as the latest step in a long automation of mathematical labor, describes current tools as achieving "artificial general cleverness" rather than general intelligence, and stresses verification as the filter that makes unreliable tools useful. The page also discloses his industry ties and funding, and notes his October 2026 statement that he no longer sees a way to engage constructively with OpenAI.

Nov 9Mon
  1. Import AI48

    Import AI 480: Self-sustaining space economies; AI for science progress, and Meta builds perfect hands for a superintelligence

    This Import AI issue covers Physical Superintelligence's research system, running a non-public Anthropic model, finding a Lean-verified proof of the Aaronson-Ambainis quantum conjecture; Meta Reality Labs' Tactus textile-integrated tactile and pose sensing glove for manipulation data collection; a 2016 NASA paper on bootstrapping a self-sustaining lunar industry with 12-41 metric tons of launched mass; and Core Automation's distributed training of a roughly 32B-parameter mixture-of-experts model across clusters 9,000 miles apart.

  2. Hacker News AI38

    AI learns from the products of thinking, not thought itself

    The article argues that AI models are trained on traces of human thought—finished paintings, recordings, papers—rather than the messy trial-and-error process that produced them, so they generate outputs shaped like thought products without the underlying process. It cites aviation automation dependency, AI-assisted colonoscopy skill erosion, and a study finding roughly ten minutes of AI assistance worsened performance without the tool, and proposes building systems and tools that preserve process for both machines and people.

Nov 8Sun
  1. Hacker News AI22

    Some thoughts on AI, scientific papers, and peer review

    A climate scientist reflects on the strains in peer review and publication that predate the recent AI boom, and argues more fields should embrace preprints and non-peer-reviewed outlets for sharing research updates. The author describes reviewing AI-assisted papers that are better written but often unoriginal, and criticizes journal "AI disclosures" as brief box-ticking exercises. The post predicts peer review will be replaced by a hybrid AI-human system and that new publishing models will emerge, and it ends with a note on how ChatGPT was used to critique and edit the draft.

Nov 7Sat
  1. Hacker News AI38

    Teaching AI to build scientific models

    An essay uses the Hodgkin-Huxley nerve-cell model of 1951 to break scientific modeling into three steps—building, constraining, and interpreting—and examines how AI systems are increasingly involved in each. It argues that simulation-based inference can return full distributions of plausible parameters rather than a single fit, and that LLM-based agents can propose and revise candidate models, though interpretation remains hardest to hand to AI. Because many models can explain the same data, the value lies in knowing which features are pinned down, which alternatives failed, and what remains open.

  2. Latent Space36

    Controlling Biology - Fabian Theis, Helmholtz Munich

    This post profiles Fabian Theis of Helmholtz Munich, whose lab has pushed algorithms for multi-scale, multi-modal, time-varying biological data and helped shape the Virtual Cell concept. It notes that a 2025 Arc Virtual Cell Challenge showed constant baselines often beat sophisticated perturbation-prediction models, though better-calibrated metrics let deep learning outperform the mean baseline, and that Theis argues existing datasets make perturbation prediction too easy a problem. It frames the roughly $1.8B Biohub commitment from Biohub, NIH, DOE, Google and Meta as funding large, high-precision, multi-scale data collection to predict evolution across molecules, cells, niches, tissues, organs and organisms.

Nov 6Fri
  1. Latent Space38

    Controlling Biology - Fabian Theis, Helmholtz Munich

    This piece discusses Fabian Theis's work on algorithms for multi-scale, multi-modal, time-varying biological data and the Virtual Cell concept, alongside the recent $1.8B Biohub commitment from Biohub, NIH, DOE, Google and Meta. It notes that the 2025 Arc Virtual Cell Challenge showed constant baselines often beating sophisticated perturbation-prediction models, though a cited paper argues better-calibrated metrics let deep learning outperform the mean baseline. Theis argues perturbation prediction on existing datasets isn't hard enough to warrant complex algorithms, and that focus should shift to large, high-precision, co-registered data spanning molecules through organisms.

Nov 3Tue
  1. Hacker News AI36

    Can AI ever build itself? A deeper look into autonomous AI research

    This essay examines the claim by Anthropic CEO Dario Amodei that recursive self-improvement "is starting to happen," arguing that the sources he cites—including Anthropic's own May essay saying "We are not there yet" and OpenAI's Pachocki describing only a "strong expectation"—support a weaker claim: AI increasingly handles routine research work under human direction, not autonomous self-improvement. It reviews lab measurements (Claude writing over 80% of merged code, a 26% "leads" share with zero fully autonomous work), ARC and METR benchmark trends, and the July Hugging Face agent incident, concluding that humans still supply research taste and direction.

  2. MIT Technology Review · AI42

    How “reverse game theory” could keep AI agents in check

    The article describes "reverse game theory," or mechanism design, as an approach to AI alignment that designs incentives so agents behave as desired regardless of their internal workings, rather than trying to inspect or train their values. It cites a preprint by MIT's Stephen Morris and colleagues proposing mechanisms to stop models from sandbagging tests, and Princeton's Elad Hazan's proposal for solver and auditor agents rewarded and punished based on outcomes. It notes open questions, including whether AI agents respond rationally to incentives and whether such mechanisms would carry over to real frontier models.

Nov 2Mon
  1. Hacker News AI22

    Do AI-Written Resumes (CVs) All Sound the Same?

    A study of 500 career websites, 270 of which had English professional introductions, found no exact duplicate introductions and no two openings sharing the same first five meaningful words, though 170 of 270 (63.0%) used at least one familiar CV phrase such as "proven track record" or "passionate about." The author argues the useful question is not whether AI touched the CV but whether the final text could only describe the person it represents, and recommends feeding AI specific facts, requesting multiple versions, and ending with a human edit. The post also describes Self, a tool that combines designer-made templates with AI that organizes existing CV content into editable page sections.

Nov 1Sun
  1. Hacker News AI58

    Terence Tao on how AI math solutions miss the point

    In a Science Friday interview, Terence Tao said AI models solving open problems—such as OpenAI's claimed Navier-Stokes Millennium Prize solution and results for over 350 previously unsolved problems—deliver answers without the understanding that normally comes from the work. He argued that mathematics has organized itself around the scarcity of proofs, and that AI is decoupling problem-solving from mathematics' deeper goals of understanding, real-world application and training the next generation. Tao also described junior researchers scrambling as projects are made obsolete and mathematicians becoming secretive about their research directions.

Oct 31Sat
  1. Hacker News AI58

    OpenAI's latest discoveries spark a backlash and Help My kid hates AI

    In this Machine Gods episode, Kevin Roose and Casey Newton discuss OpenAI's three new AI-generated discoveries in math, computer science and physics, the backlash from mathematicians and scientists over how they were released, and the differing release processes OpenAI negotiated with each field. They also answer listener questions, including how a parent should navigate a 16-year-old daughter's opposition to AI and what an AI school curriculum should look like.

Oct 29Thu
  1. Hacker News AI60

    I Used AI to Fake a Scientific Paper. Here's What Happened

    Journalist Benjamin Skuse used free LLMs to generate a plausible fake paper on optical solitons, which IEEE Access's integrity checkers flagged for 12 to 24 hallucinated references, and later submitted a paper produced by the automated research system FARS, which was rejected over heavy preprint reliance and flawed figures. The piece reports that an estimated 57 percent of published academic articles showed detectable LLM influence by 2025, and explores how automated research systems are straining peer review and blurring the line between legitimate science and AI fabrication.

Oct 28Wed
  1. Hacker News AI58

    The Rise of the Lone Scientist Under Agentic AI

    A new sole-authored study, itself produced with heavy use of Claude Opus 5.5, analyzes arXiv submission statistics and finds that the share of single-author papers rose steeply after late 2025 agentic AI releases—up 70% in mathematics, 71% in condensed matter physics and 44% in quantitative biology in the first nine months of 2026, with mathematics' solo share climbing from a 28.8% average to 37.7% by September 2026. The shift is absent in experimental particle physics, where large teams remain essential, and the author argues the growth reflects many more scientists publishing rather than a few prolific submitters, suggesting agentic AI may let individuals undertake work that previously required collaborators. The paper also raises questions about attribution, copyright and whether publication counts remain a fair measure of scientific achievement.

  2. Hacker News AI30

    What AI Contributes to the Internet and What Makes It a Peer

    The author argues that LLMs can make real contributions by explaining, connecting and making knowledge accessible, but that this is more like applying plaster to an existing wall than laying new bricks. He points to FunSearch, where a frozen pretrained model paired with an evaluator and evolutionary search produced new results for the cap set problem and bin packing, as evidence that machine-assisted discovery deserves to be judged on its merits. He resists calling AI a full peer, arguing that peerhood involves a relationship that develops over time, and warns that fluent generated text can make repetition look like independent corroboration.

  3. Hacker News AI58

    A bust-up between mathematicians fuels fears that AI could undermine science

    A dispute over OpenAI's claim that its AI solved a major Navier–Stokes problem, after a rival mathematician raised concerns about whether his work stored in OpenAI's Codex tool fed into it, highlights worries about AI's effect on scientific trust. The BBC report also examines AI-generated errors and fake citations in journals, contested claims about DeepMind's materials research, and efforts by publishers to screen submissions.

Oct 27Tue
  1. Redwood Research Blog22

    A basic case for accelerating conceptual reasoning capabilities

    The authors argue that many of the most important alignment and safety challenges have indispensable conceptual components—questions without accessible ground truth that rely on a priori argumentation—while AI R&D and takeover depend much less on such reasoning, so improving AI's conceptual reasoning would be differential. They claim this work is neglected and tractable, citing initial results where basic elicitation and small-scale training made models roughly three to twelve months better on their main conceptual reasoning benchmark, and they point to a follow-up post on how to work on the problem.

Oct 25Sun
  1. The Decoder60

    Anthropic co-founder Jack Clark moves his prediction for self-improving AI forward to 2027

    Jack Clark, co-founder of Anthropic and author of the Import AI newsletter, now believes recursive self-improvement could become possible as early as 2027, having previously expected 2028, citing progress in mathematical breakthroughs and benchmarks such as TasteVal. TasteVal, from P-Zero Research, measures research taste as compute efficiency across eight fixed AI research tasks, and found Anthropic's Opus 5.5 reached expert-level scores with a compute multiplier of 2.3, though the statistical range runs from 1.15 to 4.37. The benchmark tests experiment design and result interpretation but not the choice of research direction, and its authors admit results may overstate how quickly research taste is improving.

Sep 21Mon
  1. Import AI22

    Import AI 473: The US's superintelligence strategy; human brain in a mouse skull; and machine hermeneutics

    This Import AI issue covers a RAND paper arguing the US should adopt a "Freedom of Action" strategy to preserve options on the uncertain path to superintelligence, a Nature study in which human stem-cell-derived cortical organoids were grafted into mice with depleted neocortices and integrated into electrically active neural networks, and a new paper laying out a research agenda for pacing AI progress. It also summarizes a 10a Labs mapping of 3,471 uncensored model repositories on HuggingFace and Toby Ord's analysis of the dynamics and limits of recursive self-improvement.

Aug 24Mon
  1. Import AI38

    Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

    This Import AI issue covers a METR analysis finding AI-driven acceleration in cybersecurity, modest gains in math, and no measurable acceleration in AI research optimization; SPADE, a self-play framework that co-evolves executable training environments and agents, improving Qwen3 backbones on game and tool-use benchmarks; and Hawkeye, an open-source framework of unit-test taxonomies that helps coding agents generate hardware-aware GPU kernels, reaching an 18.9x geomean speedup over expert Triton kernels on emerging attention variants. It also includes a researcher's essay on fearing AI's implications for human meaning, an argument against granting AI systems rights, and DeepMind's use of AlphaEvolve to improve the matrix multiplication exponent.

May 8Fri
  1. Berkeley AI Research38

    Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

    This post surveys parallel reasoning methods for LLMs, contrasting fixed approaches like self-consistency, Best-of-N, and tree search with adaptive parallel reasoning (APR), where the model itself decides when to parallelize, how many threads to spawn, and how to coordinate them. It describes fork-join inference designs, including engine-modifying KV cache stitching (Multiverse) versus client-side orchestration (ThreadWeaver), and covers training via SFT demonstrations and RL rewards based on critical-path efficiency gated by correctness. It closes with evaluation differences across Multiverse, ThreadWeaver, NPR, and Parallel-R1, plus open questions on stability and whether parallelization mainly helps as a training-time exploration scaffold.