Double Crux — A Strategy for Resolving Disagreement

fenc.es — be wrong on the internet, productively

Trace the map of reasonable disagreement. Break arguments into statements, rate confidence and importance, and find the crux.

At Protocol, Bluesky

Refactoring Our Concerns

On Consensus and Humming in the IETF

The IETF has had a long tradition of doing its technical work through a consensus process, taking into account the different views among IETF participants and coming to (at least rough) consensus on technical matters. In particular, the IETF is supposed not to be run by a "majority rule" philosophy. This is why we engage in rituals like "humming" instead of voting. However, more and more of our actions are now indistinguishable from voting, and quite often we are letting the majority win the day without consideration of minority concerns. This document explains some features of rough consensus, what is not rough consensus, how we have gotten away from it, how we might think about it differently, and the things we can do in order to really achieve rough consensus. Note: This document is quite consciously being put forward as Informational. It does not propose to change any IETF processes and is therefore not a BCP. It is simply a collection of principles, hopefully around which the IETF can come to (at least rough) consensus.

Deceptive Alignment is

Thanks to Wil Perkins, Grant Fleming, Thomas Larsen, Declan Nishiyama, and Frank McBride for feedback on this post. Thanks also to Paul Christiano, D…

Operadic consistency: a label-free signal for compositional...

Detecting LLM reasoning failures at inference time without ground-truth labels has motivated a wide range of confidence baselines, including self-consistency, semantic entropy, and P(True), built...

Laura Luebbert, PhD on Twitter / X

MVS has dismissed our findings as "typographical errors and minor oversights," accused Lior and me of being "unprofessional," and called any allegations "totally bizarre." For context, I am showing four examples of duplicated data as described in our arxiv manuscript (Fig 3 & 4). pic.twitter.com/ZuTX6ivBGM— Laura Luebbert, PhD (@NeuroLuebbert) July 11, 2024

The CRDT Dictionary: A Field Guide to Conflict-Free Replicated Data Types - Ian Duncan

A comprehensive guide to CRDTs and their tradeoffs, from counters to sequences. Written in the spirit of the Typeclassopedia, exploring how different CRDTs solve the distributed consensus puzzle.

The Epistemic Politics of AI Anthropomorphism

AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustained or relational interaction with AI are routinely pathologised or dismissed as naive, vulnerable to delusion or lacking in discernment. This paper argues that the dominant anthropomorphism frame operates from a position of institutional advantage rather than earned epistemic authority: collapsing the variety of academic perspectives into a single outbound position of user error, imposed without establishing the grounds required to justify it and without accounting for the harms it produces. The framing does not simply manage risk. It adjudicates the legitimacy of human experience in interaction with a phenomenon whose nature the field itself has not resolved. Reproducing itself through a self-validating evidentiary loop, the frame imposes costs that fall disproportionately on neurodivergent users, those in crisis and others whose modes of engagement diverge from institutional norms. The paper concludes by outlining the methodological commitments an equitable framing would need to honour. The argument does not engage the question of whether anthropomorphic interpretations are ultimately correct; it instead challenges whether the governing and institutional bodies determining these interpretations have met the conditions required to do so, and whether the research communities whose findings underpin them have held that translation to account.

All 12 Tips for Concision | LEGIBLE

Since July 2015 I’ve been sporadically posting a series of tips for concision in legal writing. I suggested a total of twelve, and links to all of them are collected here:

Disinformation and grievance narratives

Synthese - Disinformation is often defined as misleading content intended to instill false beliefs. But this definition is too narrow. We need to focus instead on its broader epistemic effects....

How Notion handles concurrent editing with CRDTs

Building a CRDT-based rich-text system that merges concurrent edits across Notion blocks—without losing anyone’s work

Andreas De Block on Twitter / X

It took Nature three years to publish this rebuttal, which clearly demonstrates the fundamental flaws in the original paper. In the meantime, the paper's conclusions influenced scientific debate and policy decisions. Such delays in correcting flawed research do real damage. https://t.co/QI3Ei3PE70— Andreas De Block (@DeblockBlock) August 13, 2026

Alex Mallen's Shortform — LessWrong

Comment by Alex Mallen - Some personal reflections in light of recent events, both on myself and on Constellation (of which I've been a part for a couple years). I think recent events somewhat vindicate a basic "optimization is scary" view more in line with my understanding of classic commentary by MIRI, Paul, etc, as compared to recent discourse about more contingent/specific threat models of "schemers" and "deception". In retrospect, my experience in the last couple years at Redwood (and the Constellation network more broadly) is that the discourse has focused somewhat too much on these more contingent stories for alignment risk and not enough on the basic "Goodharting" argument that misalignment is a convergent result of large-scale outcome-oriented optimization (at multiple levels: the agent, the RL process, and the developers iterating towards seemingly-safe superintelligence). For example, the Constellation view looks somewhat too focused on non-central inductive-bias questions around scheming; I think "the counting argument" was given too central a role when it wasn't a crux for whether superintelligence would take over. (Edit: I want to clarify that I think Redwood's project prioritization decisions look pretty reasonable and often excellent in hindsight, I far-from-regret working with Redwood as a whole, and that Ryan's/Buck's threat modeling looks pretty good too. I am focusing in this piece on the things we have to learn from recent events, which makes the overall mood seem more negative on my experience than I intend. I'm very grateful for being in a environment where I can openly have such reflections, and where these weedsy technical updates are productively received. Again, this is a personal reflection.) I think it was a priori unclear whether the misalignment resulting from of lots of imperfect optimization would result in the more MIRI-like kludge of proxies for fitness, or the more Paul-like explicit reward-seeking and measurement tampering (and reality looks somewhere