---
title: "Slides: Frontier results, on device - RL Nabors, Arize"
category: "slides"
video_id: "fWXJM-J0ZB8"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Frontier results, on device - RL Nabors, Arize

## Source Video
[Frontier results, on device - RL Nabors, Arize](https://www.youtube.com/watch?v=fWXJM-J0ZB8)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/fWXJM-J0ZB8/slide-002.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-002.html)
- AI slide classifier: `title_card` confidence `0.95`
- Text source: agent_vision.

Slide text:

> arize
> You have agents.
> We can test them.

![[assets/slides/fWXJM-J0ZB8/slide-003.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-003.html)
- AI slide classifier: `title_card` confidence `0.99`
- Text source: agent_vision.

Slide text:

> THE COST OF ONE-SIZE-FITS-ALL INFERENCE

![[assets/slides/fWXJM-J0ZB8/slide-004.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-004.html)
- AI slide classifier: `title_card` confidence `0.98`
- Text source: agent_vision.

Slide text:

> “[l]atency above 4 seconds degrades quality of experience...”
> Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents July 7, 2025

![[assets/slides/fWXJM-J0ZB8/slide-005.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-005.html)
- AI slide classifier: `content_slide` confidence `0.93`
- Text source: agent_vision.

Slide text:

> Couldn't connect to Claude
> ERR_INTERNET_DISCONNECTED
> Refresh

![[assets/slides/fWXJM-J0ZB8/slide-006.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-006.html)
- AI slide classifier: `title_card` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Chief Product Officers should not confuse the deflation of commodity tokens with the democratization of frontier reasoning.
> — Gartner, March 2026

![[assets/slides/fWXJM-J0ZB8/slide-007.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-007.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> TASK-SPECIFIC MODELS CHEAT SHEET
> Is a camera pointing at something?
> Use vision models like MobileNet, YOLO, MediaPipe

![[assets/slides/fWXJM-J0ZB8/slide-008.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-008.html)
- AI slide classifier: `title_card` confidence `0.97`
- Text source: agent_vision.

Slide text:

> SLMs
> LLMS BUT SMALL

![[assets/slides/fWXJM-J0ZB8/slide-009.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-009.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Small(er) Language Models (SLMs)
> Smaller versions of LLMs containing several million to several billion parameters (LLMs may have hundreds of billions or even a trillion parameters)

![[assets/slides/fWXJM-J0ZB8/slide-010.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-010.html)
- AI slide classifier: `content_slide` confidence `0.93`
- Text source: none.
- Slide text: not surfaced (`decorative` by AI classifier).
![[assets/slides/fWXJM-J0ZB8/slide-011.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-011.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Energy consumption comparison
> LLMs
> SLMs
> Task-specific Models
> proportional energy consumed

![[assets/slides/fWXJM-J0ZB8/slide-012.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-012.html)
- AI slide classifier: `content_slide` confidence `0.96`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — dense UI screenshot with small text

Slide text:

> Rachel-Lee Nabors (they/them) nearestnabors.com Ichthy 00 todo
> How lke! Successfully activated extension
> RN How likely is It that Ichthysaurs had echolocatlon?
> Looking at the evidence foric memory Successfully activated extension
> How canlheipyou today narlgate messa!
> rStisuuos 中0000

![[assets/slides/fWXJM-J0ZB8/slide-013.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-013.html)
- AI slide classifier: `content_slide` confidence `0.95`
- Text source: agent_vision.
- OCR decision: ready — Dense multi-column screenshot with small text; OCR should capture it better than direct transcription.

Slide text:

> Ichthyosaur echolocation capabilities

![[assets/slides/fWXJM-J0ZB8/slide-014.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-014.html)
- AI slide classifier: `content_slide` confidence `0.95`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Dense screenshot with small text and multiple paragraphs; OCR is appropriate.

Slide text:

> Rachel-Lee Nabors (they/them) nearestnabors.com 00o Ichthyosaur echolocation capablites 8.8 pue poods ynm pooorei ea ppoo uopiu spodoeudao pue usy jo 00
> it's moderately plauslble but far from certainprobably Lookdng at the cyidence Ior Ichthyosaur ccholocaton, Id say. What They DidUsea agility rather than echolocation
> number on it. somewhere Inthe30-50%lkelihoodrange if I had toputa Visione Their large ey Lahts oven tn low
> Here's my reasonlng: Evidence suggesting they might have had echolocatlon: 1Anatomical similariues: Some ichthyosaurs had eniarged Conclusion? Anbush Tacties They Ikely reled on stcalun snd rapid bursts o speed Tactile Sonsitivity; Someo specios may havo used lateras Hne systems (common In flsh) to detect prey movements: to capture prey.
> 2. Ecological niche Many ichthyosaurs were deep-diving stapedial bones (part of the ear structure) slmilar to modern cetaceans that echolocate. This couldIndlcate enhanced sound processing capabilitiess there is no evidence to support its presence kn ichthysaurs Thelr anatomy sound-basednavigation. to dm
> predators hunting In low-llght conditlons:exactly the? Into that!
> like in toothed whales and r g hins today. scenariowhere echolocatlon would be advantageous, Just
> 3.Convergent evolutlon:Ichthyosaurs show remarkable
> How canh he p you today? Sdessalu oiebybu ot1t/lx
> 1 Sonnct4.SV 131 1.0000

![[assets/slides/fWXJM-J0ZB8/slide-015.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-015.html)
- AI slide classifier: `title_card` confidence `0.99`
- Text source: agent_vision.

Slide text:

> RIGHT-SIZING AI

![[assets/slides/fWXJM-J0ZB8/slide-016.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-016.html)
- AI slide classifier: `content_slide` confidence `0.96`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — Product UI screenshot with many small labels and modal text; OCR should handle it better than manual transcription.

Slide text:

> Rachel-Lee Nabors (they/them) nearestnabors.com Mima Nows口 Conversations Justnow G Sync
> iversations Settings
> Accounts APICredentials AI Feed Muted Dangerous
> digests. Configure howMima usesAl to summarizeyour social feeds and generate topic Current Configuration Configure mima.social
> UsingOpenAl erAnthropicAPI
> VAPI Key-Ready
> AboutAl Features
> bsic keyordbaed summareFor bet eultusn alargr mcdel like LorQwn 2. 78 Alisusd tociuster smiatpost nto topics and generate summanesWitout At.topicswl show
> Golden Dataset
> @rachelnabors@edittrameThatisa fantasticusecase
> Carl Assmann,SecFault & 16 others X

![[assets/slides/fWXJM-J0ZB8/slide-017.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-017.html)
- AI slide classifier: `content_slide` confidence `0.97`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — Dashboard-style slide with multiple metric cards and small labels; OCR is appropriate.

Slide text:

> Rachel-Lee Nabors (they/them) nearestnabors.com Mimalocal-modeleval
> Goldendataset at a glance
> 28 Examples Threads 14 2-17 Messages/thread Annotated 100%
> eachviewedlist+modal median5 hand-written[ref:N]
> By platform By view context
> 22 Bluesky. O list. 14 14 modal.
> Sourced from real Mima threads,anonymized. Each example pairs a thread input with a gold-standard summary and citation p
> 5models evaluatedagainst this set with 3repetitions each (420 runs total).

![[assets/slides/fWXJM-J0ZB8/slide-018.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-018.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Table slide with dense small text and multiple columns; OCR is the right extraction path.

Slide text:

> Rachel-ee Nabors(they/them) nearestnabors.co Measures of Success
> Dimension What it asks How it's measured
> JSON validity Does the output parse?. Try JsoN. parse, count successes
> Reference structural validity Do [ref:N] tokens point to messages that actually exist? message_count] Regex extract refs, check each N E [ 1,
> Factual consistency Does the summary stay faithful to the thread, or invent claims? summary against its source LLM-as-judge -- Claude scores each
> Length compliance Does it stay in the target word band? Word count vs context-specific limits (8-12 for list view, 19-46 for modal)
> p50 latency: Typical TT summary Median across the eval set
> p95 latency Worst-case wait 95th percentile across the e
> Each evaluator returms a per-example scort. Average across the 28-exarmple golden datasel, with 3 repetitions per erampl?.

![[assets/slides/fWXJM-J0ZB8/slide-019.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-019.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Arize Phoenix
> Trace the Exponential
> The open-source platform for agent development and evaluation

![[assets/slides/fWXJM-J0ZB8/slide-020.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-020.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Capability eval
> Asks, “What can this agent do well?” They should start at a low pass rate, targeting tasks the agent struggles with and giving teams a hill to climb
> —Anthropic, Demystifying Evals for AI Agen

![[assets/slides/fWXJM-J0ZB8/slide-021.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-021.html)
- AI slide classifier: `content_slide` confidence `0.97`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — Dense UI screenshot with chart, tabs, and table text is better suited to OCR.

Slide text:

> Rachel-Lee Nabors (they/them) nearestnabors.com Datasots>goldon-summarios
> golden-summaries Labet
> Experiments Examples28 Evaluators Versions
> ExperimentsAnalysis
> 0.25 075 0.5 0.0c 75s 5.0s 25s $O.0s
> Coumna
> name length_complianoe_oval roference_aocuracy_eval cemantio_cimiarity_eval mg latency totalcost totaltokons errorrate
> 5 lama-32- 30 1.00 100 0.89 0.58 01.3s 40.798 0.00% Tences
> 口 02b gemmy-4- 1.00 100 0.95 0.58 8.4s 63.742 0.00% Tences
> qw3-170 0.89 100 Q76 0.51 07.26 ②81.293 0.00% Tracos
> 口 2 qwen25. 15b 0.92 0.96 0.72 0.49 010 36.930 0.00% Trocee
> soonet- baselne daus- 100 100 0.96 aro 02.9s $0.22 42.931 0.00%
> ®

![[assets/slides/fWXJM-J0ZB8/slide-022.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-022.html)
- AI slide classifier: `title_card` confidence `0.99`
- Text source: agent_vision.

Slide text:

> “Small and Good Enough” Model
> SAGE Model

![[assets/slides/fWXJM-J0ZB8/slide-023.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-023.html)
- AI slide classifier: `content_slide` confidence `0.95`
- Text source: agent_vision.

Slide text:

> Reference accuracy (%) — higher is better
> p50 latency (ms) — lower is better
> Claude Sonnet (ceiling) Qwen 2.5 1.5B Qwen 3.1 7B Gemma 4 E2B Llama 3.2 3B (recommended)

![[assets/slides/fWXJM-J0ZB8/slide-024.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/fWXJM-J0ZB8/slide-024.html)
- AI slide classifier: `content_slide` confidence `0.96`
- Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`.
- OCR decision: ready — Dense comparison table and multi-column UI screenshot are OCR-suitable.

Slide text:

> Rachel-Lee Nabors. Qrtdwoo souewans-uopooy seselea @ Lm1-32-36
> mrerater)
> am+-3.2-3b Avd 0 1.35 @ 405.69 Awa' 0 0.41 0 996.93 AvG'0 2.9s D 511.0s $0.01 claude-sonwt-basdr
> yno $iu4p1 55922 05.70 2,160 01.sa c 925 c$+.01
> (they/them)near hsircuts"! 0 cexpeeted_sum coeparing Sunil: Joking about Y.tPlyful Bodhldharma, expected_refere. bater! Pa1.to! Yeoyoops urorus srushy ue, puly Bodhidharza' avatar, pro=oting sumil'to consider'a haircut; Thrccpolntonc becoees Bodhidharms avatar, proapting sunll to conslder 4 halrcut'snd 'n, Agentic. Shaolin'schoole)": "summary"ilSm Jokingly, suggests,Threepointone becones Fretereaceso0h. Hsasaosns: Abugyof esbWtVurns-Vasuodsader Cison 'ale.trve. "proept: tokens:278, Ftotal tokens-a:314. coapletion tokens"l36. Estart ing on Agentic Shaotin Kung-tu schoot. Cgentie, Shaolin Kung-fu schoot. 1-injin' \"oiscuss ing' an' avatar, and' start ing 'an' Yreterencen g115. oravresponsejson\nAnVsuary tsuemsry'., -oiscussiog an avatar., sod! Cson_valial tne T6s2:: susxoy1daoud. coplet 1on tokens"1,455,) Ctotal_tokensas7it? Bodhhrmh1rcut appearance conparing to Bodhidhana? qthrecpointone's appearansekicoaparirg oiutodesyio snoge Surxoca(ixiemnst "reterences"U. ison vaud":'truen Erav_respoase". prompttokens.283, Fcoapletioctokens":31, "totaletokens"ti314 waryAJokt
> 1.00 AYo 0o'I Naeer eered uos 1.06 AvG 7
> Largth_complaroe_t 1.09 1.89 AYG Cergth_conpllnoe erl 1.93 1.06 AVG Length_compdance
> 1.eo AYG eo:T N.s- Aoe.nooe-ecuereye] 1.06 Ay3 6
> snantic_almRerity_svat 6.58 OY ts'o Cwmantic_ emllrity_era 鲁. 3? 0.38 AY3 tmmte_t
> -.1 nca oltt. 02.10 01,696 09.5@3,430 03. 0
> YtSan' Jokes that'Sunil·Pai Texpected_rta qetting'o'hircut Iret:3]'The"comersation'slso hints't modern Bodhiaharea'avatar (ref:2]f and a. response' about Pw response'e tn AYsunrAirTbiseusion., AThe discussion begsn with's'nention'ot. fono conceptual' petaphor'about' an watar'stoly [ret:lJ,:uhich'quickty'shifted Anto. 0..response" son\nfin A"summaryA"! conversarlon> Ebout'soncthing I. coaoarlson'ot threcpointore to bodhidhal rmresp. aede.


### Hidden Non-Slide Evidence
- [`slide-001.jpg`](/assets/slides/fWXJM-J0ZB8/slide-001.jpg) — `title_card` confidence `0.84`; speaker intro card with logo collage

Classification audit: `raw/sources/slide-ai-classification/slides/fWXJM-J0ZB8/audit.json`

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.