@cdarwin@c.imNearly one in three voters now vote by mail.
Trump’s executive order has already sowed doubt about whether Americans can continue to trust the Postal Service to deliver their ballots
and prompted bipartisan alarm from states and local election officials.
The sooner the Supreme Court puts Trump’s ill-advised directive to rest, the better.
Trump’s order is illegal.
He does not have the authority to set the rules for election administration.
The Constitution …
@arXiv_csHC_bot@mastoxiv.pageWhen the Interviewer Is a Bot: Behavior, Breakdowns, and Trust in MLLM-Led Interviews
He Zhang, Kambinachi Chukwuma, ChanMin Kim, John M. Carroll
https://arxiv.org/abs/2608.10412 https://arxiv.org/pdf/2608.10412 https://arxiv.org/html/2608.10412
arXiv:2608.10412v1 Announce Type: new
Abstract: Semi-structured interviews are a cornerstone of qualitative research but remain labor-intensive. We report an empirical study of what actually happens when the interviewer is an off-the-shelf real-time multimodal LLM (MLLM). We built InterviewBot, a voice-based interviewing system that wraps a real-time MLLM with a researcher-authored outline, and deployed it not as a novel architecture but as a research instrument for observing default MLLM interviewing behavior. In a practice study (N=15), participants completed a bot-led semi-structured interview and then a human-led reflection session about that experience. We contribute (i) a turn-level behavioral analysis of an MLLM interviewer (N_turns=428) showing that it is acknowledgment-heavy but probe-light (deepening probes account for 4.9% of all turns), and that 28.7% of question-bearing turns pack multiple questions into one turn despite an explicit one-question-at-a-time instruction; (ii) an inductive catalogue of four data-collection breakdowns (information loss, premature termination, latency, and interruption) observed in a deployed rather than simulated system; and (iii) three social dynamics from participants' reflections: disclosure calibration, where reduced social pressure coincided with shallower elaboration; institutional legitimacy, where trust tracked perceived stakes and what delegation to AI signaled about the organizer rather than conversational competence; and conversational grounding, where content-grounded paraphrase, not generic social filler, was what participants read as listening. We conclude with design implications for depth control, transparent handoffs, and non-templated listening mechanisms in human-centered interview automation.
toXiv_bot_toot
@sean@scoat.esI broke up with 1Password a little over 3 years ago.
It was clear to me that I should no longer trust them with the most intimate of my personal data. I was with them from 2008 to 2023 (15 years).
They got a LOT of investment very quickly, and started pushing their hosted service—HARD. I need to have agency over my own credentials data, not trust a service to stay up.
To me, this was the start of them showing us who they really are (and I even tolerated it for a bit).
@al3x@hachyderm.ioI keep being reminded that highlighting and saving highlights from a non-fiction book does not really work.
When I highlight, I do it with the current context in mind.
When I extract the highlights, their context is gone and those quotes rarely make sense independently.
Who has a system they trust, works for them,
and are willing to share it?
@inthehands@hachyderm.ioSo…yup, apparently Apple caved.
Interestingly, _both_ names are currently showing for me in Apple Maps right now. Caching problem? Or maybe an attempt to list both names?
Regardless: if they crumple over this, do you really trust the company to protect your private data if the Trump admin comes calling for something worse than a stupid geographic feature renaming?
[NB: This is a rhetorical question and does not require your personal answer in the replies]
User privacy protection starts from the spine.
@arXiv_csPL_bot@mastoxiv.pageBuild-Authorized Evidence for Opaque Calls: A Fail-Closed Rewrite-Authority Boundary
Zhonghua Yi (Toka Language Research Group)
https://arxiv.org/abs/2607.18949 https://arxiv.org/pdf/2607.18949 https://arxiv.org/html/2607.18949
arXiv:2607.18949v1 Announce Type: new
Abstract: Detached semantic facts about opaque native providers do not by themselves justify compiler rewrites: rewrite authority must be confined to the accepted fact, selected provider and build, caller, callback environment, observation, and runtime target. We present a build-authorized path-effect interface that enforces this boundary through fail-closed authorization and link receipts. The design separates receipt closure, callback-environment closure, and projection identity, and passes accepted facts to LLVM through a narrow internal API. We use one-hop topology-load reuse as a minimal observable witness of authority, not as the optimization target.
A conservative LLVM consumer reuses a pointer observation only from a noalias root or one constant nonzero projection. Rocq models prove conditional refinement and authority non-amplification under explicit effect, alias, compiler/ABI, and target-resolution premises. We instantiate checked production with Toka: a source-summary gate emits exact LLVM IR, a separate IR checker accepts only a bounded topology-preserving subset, and only accepted IR is compiled into the receipt-bound provider object. A bounded static Darwin/arm64 profile also checks the final direct branch target.
Across issuer-declared readv, recvmsg, and Cairo boundaries, authorized IR retains each opaque call, reduces the relevant loads from two to one, and preserves observed results; mismatched providers, builds, callbacks, projections, and unsupported IR remain neutral. A libjpeg case is rejected because its callback environment is open, while a bound callback singleton demonstrates the supported closure rule. The contribution is a checked deployment-compiler boundary with an explicit trust and applicability frontier, not a uniquely expressive effect encoding or a new load-elimination algorithm.
toXiv_bot_toot
