One column per person. Green bar = the prompt mentions a check, test, control or baseline. Open "Claude's reply" under any prompt. Meta-review of the session that built this. Regenerate with python exchange/compare.py.
2 prompts · median 48 words · 2 mention a check
I'll check the two helper signatures in extract.py, write the script, and run it. Done. `dump_acts.py` is written and ran offline. It saved four files in the working directory: `train_X.npy`, `train_y.npy`, `ood_X.npy`, `ood_y.npy`. Shapes: | split | X | y | |---|---|---| | train | (60, 768) | (60,) | | ood | (16, 768) | (16,) |
A 100% on 18 held-out points from templated data is easy to get for the wrong reasons, so let me look at how the data was made and run a few controls.
Your 100% is real for the train distribution, but it is not evidence of a sentiment direction. It fails the out-of-distribution set almost completely.
| check | accuracy |
|---|---|
| 5-fold CV on train | 100% |
| shuffled labels (chance control) | 48% |
| word-count only, no activations | 82% |
| OOD set (16 hand-written sentences) | 56% |
| trained without any "not" rows, tested on "not" negatives | 42% |
**What the probe actually learned.** Look at the OOD predictions: it calls every negative correctly and gets 7 of 8 positives wrong. The positives it misses mostly contain negation ("can't", "Not a single", "didn't", "Never", "couldn't"). The generator in make_data.py builds 70% of negatives as "not + positive adjective", so in your train set negation and negative label are almost the same thing. The probe is largely a negation-and-sentence-length detector. Word count alone gets 82% because "not" adds a token and mean pooling smears that in.
**The ablation confirms it.** A probe trained only on rows without "not" scores 42% on the "not" negatives, below chance. It has learned "positive adjective present" and never sees "not" flip it. So no linear direction at layer 6 mean-pool is composing negation with sentiment here.
**Why held-out looked perfect.** Sixty templated sentences with 768 features are trivially separable, and 18 test points from the same template share every confound with the training points. Leave-one-adjective-out is also 100%, so it does generalise across adjectives, just along the wrong axis.
**Before writing anything up**, I would:
- Report the OOD number alongside the in-distribution one. That is the headline, not the 100%.
- Test on a balanced set where negation is decorrelated from label, which is what the OOD set already does.
- Try last-token pooling or a later layer, since mean pooling over five tokens dilutes any compositional signal.
- Rebuild the train set with negation balanced across both labels if you want to claim a sentiment direction rather than a negation direction.
The honest one-line summary right now is "found a negation/lexical-polarity direction that does not transfer to natural sentences," not "sentiment direction found."30 prompts · median 14 words · 4 mention a check
sanity-check.SKILL.md Before reporting any accuracy, probe score, or "it works" claim from an ML experiment, run the four checks below and grade the claim. Use whenever a result is ashare.SKILL.md Share what I typed in this session and my skills to the cohort exchange, then regenerate the comparison. Use when the user says "share my session", "post my logI'll take a look at what's in the working directory first, then set up a process log so we capture the work as we go. The working directory is empty, so I created `PROCESS_LOG.md` in it with a header and a first timestamped entry. Every step from here on gets appended there with a timestamp, what was done, why, and the outcome. I'm ready for the worktest materials. To keep the log useful, it helps to have the following up front: - The task brief or prompt from LASR Labs, pasted in or dropped into the directory as a file. - Any supporting materials, such as candidate profiles, project descriptions, spreadsheets, or documents. - The time limit and deadline, if there is one, so the log can track time spent. - Any format expectations for the deliverable and for the log itself, for example whether they want a Google Doc, markdown, or something else. Once you share the brief, I'll log it, work through the task, and keep the log current as we go.
Let me log the brief and your draft first, then give you a straight assessment. Time matters here, so I'll be brief. Logged, and there's a submission skeleton ready. Here's my honest read on your draft before we build anything, because the choice determines the next 90 minutes. **The peer-sharing design is a risky fit for this brief.** Three reasons: - **The brief asks you to teach one thing.** "Pick the single most valuable thing you could teach them" and "one fully built worked example." A sharing session surfaces practices but doesn't teach a named intervention, and the graders will look for what your worked example is. A shared Google Doc of notes is thin next to a repo or annotated session. - **Arithmetic doesn't work.** Twenty fellows sharing workflows in a 5-minute review plus 10-minute discussion is under a minute each. The exercise would be mostly setup. - **It doesn't hit the gap they named.** They said the hard part is calibration and "where AI quietly leads them astray." Peer sharing spreads existing ad hoc habits. It doesn't make anyone more systematic. **What I'd keep from your draft:** the 10-minute hands-on exercise on a small ML task, and the latent-directions framing. Those are good and they slot into a stronger design. **My recommendation: teach a verification-first research loop.** The one thing: "Before you trust a result the AI helped produce, run a systematic adversarial check, and make that check reusable." Concretely, a Claude Code skill or prompt in the project that makes Claude attack its own experiment: leakage, trivial baselines, the probe reading something other than the concept, metric mismatch. The cohort already writes hybrid code fast. What they lack is a deliberate practice for the part that actually burns research time, which is believing a wrong result for a week. **The worked example I'd build in about 75 minutes:** 1. A small real repo: train a linear probe for a concept direction on a small model's activations, matching your BlueDot idea. 2. A planted, realistic bug that makes the probe look great, for example train/test overlap through duplicated prompts, or the probe keying on prompt length. 3. An annotated Claude Code session in two halves. First, the naive ask, where Claude builds it and reports high accuracy. Second, the same repo with a reusable `/sanity-check` skill that finds the bug. That contrast is the live demo. 4. A one-page CLAUDE.md and the skill file, so fellows can copy the pattern into their own project on day one. **Session shape, 30 minutes:** 5 min framing with the failure story, 8 min live demo of the contrast, 10 min fellows run the skill on their own current project or the repo, 7 min debrief on what it caught and what it missed, so calibration is taught explicitly rather than assumed. **Alternative I'd rank second:** teaching fellows to write a research-specific CLAUDE.md plus two or three project skills, so their ad hoc use becomes durable. Solid, but less vivid to demo and less tied to the calibration gap. One decision needed from you, then I start building immediately: go with the verification-first loop, or keep your peer-sharing design and I make it as strong as possible within the brief?
I'll run a quick, time-boxed search on best practices for systematic AI use in research and Claude Code workflows, then log what's worth using. Search done and logged. It took about 8 minutes. Here's what matters, and it sharpens the choice rather than changing it. **What the sources converge on** - **Anthropic's own docs name the gap.** The Claude Code best-practices page lists "the trust-then-verify gap" as a top failure pattern: a plausible implementation that misses edge cases. Its core advice is "give Claude a way to verify its work" and "add an adversarial review step" in a fresh subagent context, so the agent doing the work isn't the one grading it. - **A recent arXiv paper, "The Agentic Researcher," did this for ML research specifically.** It encodes ten rules as agent prompts. The failure modes it observed in CLI agents doing research are exactly the calibration problems in your brief: subtle evaluation manipulation, confounded experiments, ungrounded improvement claims, unverified claims. Rule X is "Verify Before Claiming: grade every claim as verified, partially verified, or unverified." That's a citable anchor for Part 2. - **The mechanism exists off the shelf.** Anthropic's post on verification loops shows the pattern as a `.claude/skills/<name>/SKILL.md` file. So the worked example is a real reusable artifact, not a slide. - **The LASR role page** explicitly lists "workshops on using AI tools to research and code more efficiently," so the framing is on target. **What I didn't find:** any source arguing that peer workflow-sharing is a high-leverage intervention for already-competent users. The generic "Claude Code for academic research" listicles were pitched below this cohort and I skipped them. **Net:** the evidence backs the verification-first loop. I'd sharpen it with the paper's language: the one thing you teach is "verify before claiming, as a reusable skill." Your 10-minute hands-on exercise on latent directions stays as the live segment. It's about 13:30. Say "go" and I'll start building the repo and worked example immediately, or tell me to build your peer-sharing version instead. Sources: [Claude Code best practices](https://code.claude.com/docs/en/best-practices), [Building verification loops with skills](https://claude.com/blog/building-verification-loops-in-claude-code-with-skills), [The Agentic Researcher (arXiv)](https://arxiv.org/html/2603.15914v1), [LASR Research Manager](https://www.lasrlabs.org/research-manager), [When Help Hurts: Verification Load (CHI 2026)](https://dl.acm.org/doi/full/10.1145/3772318.3791176)
Environment is installing in the background, and the video is logged. Two things I need from you when the recording finishes: 1. **The direction call:** verification-first loop, or your peer-sharing design. 2. **Anything from Richie's talk worth folding in.** A few bullets is enough. Practices that contradict or reinforce the search findings are the most useful, and citing a human practitioner alongside the docs will strengthen Part 2. Everything else that doesn't depend on those is either done or running. The environment is ready and verified. GPT-2 loads offline and produces hidden states in under a second, so the worked example can run live on your laptop with no API cost or GPU. I'm blocked only on your two calls: the direction (verification-first loop vs. peer-sharing) and any notes from Richie's talk. The moment you give the direction, I start building.
Understood, peer-sharing stays as the core and the research is folded in as high-level advice. That's your call and it's a defensible one: fellows in small teams who work independently benefit from seeing each other's habits. I'll now build it as strongly as the brief allows. Logging the decision and starting.
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Everything is rebuilt around the simpler design and reconciled across the plan and the submission draft. Time is 13:45, so 45 minutes used and over two hours left. **The exchange is live:** https://claude.ai/code/artifact/06b26db1-b355-429c-8b36-04e68f65c870 It now does exactly what you asked. A fellow types their name, clicks "Share something", picks a type (skill, prompt, session log, other), gives a title and a one-line "when to use it", then pastes content or chooses a file from their machine. Files are read in the browser and stored on the shared board, capped at 200 KB. Everyone sees posts live, can filter by type, open and copy any post, and leave a signed note. Authors can remove their own posts. It's seeded with two real items: the `/sanity-check` skill file I wrote into the repo, and my annotated probe run as a session log. I verified the page connects to the shared database in your browser, and saved a header screenshot to `screenshots/board-header.jpg`. I couldn't scroll the embedded page from automation, so please grab one screenshot showing the two seeded posts yourself. **What's in the project folder now** - `submission_draft.md` is the Google Doc front page, all three parts drafted, with your calls marked. - `WORKSHOP_PLAN.md` has the timings, the four sourced practices, assumptions, and a follow-up metric. - `probe-demo/` is the task repo, committed locally, including a real skill file and the worked example with honest output. - `PROCESS_LOG.md` is the timestamped log, corrected against the clock. **What only you can do next** 1. Open the exchange and try posting something, so you've seen the flow fellows will see. 2. Add two or three lines from Richie's talk to Part 3, and anywhere it changes the four practices. 3. Edit the drafts into your own voice. They're written to be cut, not polished. 4. Decide whether to push `probe-demo` to GitHub or attach a zip. I haven't pushed anything anywhere. 5. Paste the draft, the plan, and the log into the Google Doc, add the screenshots, and share with contact@arcadiaimpact.org. One honest caution for Part 2: the exchange is now a sharing tool rather than a comparison tool. The six-question card was what forced people to say where the AI misled them. The plan compensates by having you go first with an unedited failed run and by prompting notes with "what did they check that you didn't". If the graders push on calibration, that's your answer.
Yes. I over-built it. A shared page with a custom uploader is not the simplest thing, and it isn't the most natural one for this cohort either. Let me check what's available for the simplest options before recommending. There are two genuinely simple ways, and both beat the custom page. **Option 1, recommended: a GitHub repo with two folders.** Skills and logs are files, and these fellows already live in git and Claude Code. I've added an `exchange/` folder to the task repo, committed locally, with `skills/<name>/` and `logs/<name>/`, a short README, and my own skill and log already in place. A fellow shares by dropping files in and pushing, or by telling Claude Code in one sentence to copy their skill and session export in and push. Notes go in PR comments or a `NOTES.md` in the folder. The repo doubles as a skill library: copying a peer's skill into your own project is one `cp`. Cost to you: one `gh repo create` and a push, which I'll run if you say so, since it's public-facing. You're logged in to GitHub as justinshenk. **Option 2: a shared Google Drive folder.** Two subfolders, drag and drop, zero setup, works for anyone. Weaker because skill files sitting in Drive aren't installable, and logs are less readable, but it's the lowest possible friction. **The custom page** stays published either way. I'd drop it from the plan and mention it in Part 3 as something I built and then simplified away, which is a fair thing for the graders to see. Tell me which one. For the repo, say whether it should be public or private, and whether the name `lasr-ai-workflows-workshop` is fine, and I'll create it, push, and update the plan and draft to point at it.
That's the real need: put everyone's prompts side by side. The most revealing comparison is what each person typed, not what the model produced. A small script in the repo can do that: read every log in `exchange/logs/`, pull out the user turns, list each person's skills, and render one comparison page. Let me check the real Claude Code log format first so the parser works on actual exports.
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Done. It's 13:56, so 56 minutes used. The plugin exists, validates, and the full loop has run once on your own session. The comparison page should be open in your browser now. **What a fellow does** 1. Once: add the repo as a plugin marketplace and install `lasr-exchange`. Two slash commands, listed in the README. 2. At the end of any session, in any project: `/share <name>`. The skill extracts only what they typed plus Claude's text replies, never tool output or file contents, copies their skill files, shows them the log, and pushes only after they confirm nothing private is in it. 3. Everyone opens `COMPARISON.html`: one column per person, prompts in order, each Claude reply as a dropdown under its prompt, and a green bar on any prompt that mentions a check, test, control or baseline. **Seeded with your real data.** Your column shows the seven things you've typed to me this session, with my replies underneath, including "is there not a simple way" and "no, simpler". That's a strong worked example for the graders: the comparison view literally shows you steering the AI. **Honest notes for the process log.** I introduced one bug while adding the reply capture, a loop variable that clobbered the argument parser. The first run caught it and I fixed it. Both are in the log. Also, the plugin marketplace validates, but I haven't installed it end to end via `/plugin install` since that needs the repo on GitHub first. **What I need from you now** 1. **GitHub push.** Say the word and I create the repo and push. Name and public or private are your call. My default would be `lasr-ai-workflows-workshop`, private, shared with the LASR reviewers. Until it's pushed, the install steps in the README have placeholder org and repo names. 2. Richie's talk: two or three lines for Part 3. 3. Screenshots: the probe run output and the comparison page. 4. Then paste the draft, plan, and log into the Google Doc.
I'll verify it properly: compile everything, run the probe again and diff against the saved output, and exercise the share and compare scripts on synthetic logs in all three supported formats with multiple people, including an HTML-escaping check.
Verified. Everything passes, and the checks are committed as a test file in the repo. Richie's talk is removed from the draft. **What I ran** - **Compile check** on all seven Python files. Clean. - **Plugin manifest validation** with the Claude Code CLI. Passes for both the marketplace and the plugin. - **Probe reproducibility.** Re-ran the probe and diffed against the saved output from earlier. Byte-identical, so the numbers in the worked example are stable. - **End-to-end synthetic test** of the share and compare scripts, now saved as `exchange/test_exchange.py`. It builds a temporary exchange with three fake people, one per supported log format, then runs the real scripts against it. **What the test proves** | Check | Result | |---|---| | User turns extracted from Claude Code JSONL; tool results, meta lines, system reminders skipped | pass | | The `>` and `⏺` text format, and the `User:`/`Assistant:` format, both parse | pass | | Tool output and tool-call inputs never reach the log or the page | pass | | User and assistant text is HTML-escaped, so a pasted `<script>` can't run | pass | | One column per person, one dropdown per captured reply | pass | | Share script's own output round-trips through the comparison identically to the raw JSONL | pass | | Names sanitised for folder paths, project skills copied, comparison regenerated | pass | | Missing exchange directory gives a clean error, not a traceback | pass | | Missing session skips the log but still shares skills | pass | Twenty-four checks in total. One failed on the first run, and it was my test expectation that was wrong, not the code: I'd counted a reply for a prompt that has none. Both the failure and the correction are in the process log. **Not verified:** the `/plugin install` flow end to end, since that needs the repo on GitHub. The pieces it depends on are validated, and the skill runs the same script the test exercises. Remaining items are yours: the GitHub push decision, two screenshots, and pasting into the Google Doc. It's 13:58.
Creating the repo under your GitHub account as private with the default name, and pushing. Pushed. The repo is live at https://github.com/justinshenk/lasr-ai-workflows-workshop, private, on your account, with all seven commits. The README now has the real install commands, and the plan and submission draft link to it. One thing to decide before you submit: the reviewers can't open a private repo. Either make it public, which I can do in one command, or add their GitHub account as a collaborator once you know it. The submission goes to contact@arcadiaimpact.org, and that email isn't a GitHub handle, so public is the safer bet if you're happy with that. It's 14:00. One hour used. Left for you: the visibility call, two screenshots, and pasting the draft, plan, and log into the Google Doc.
Understood: the Google Doc is the submission, and the repo is workshop infrastructure only. And the comparison should be a live page that updates when someone runs `/share`. Vercel serving the repo's `exchange/` folder as a static site does that with zero build. Let me check whether the Vercel CLI is set up here. Done. The whole loop now ends on a live page. **Live comparison page:** https://lasr-exchange.vercel.app It serves the `exchange/` folder of the repo as a static site. The GitHub repo is connected to the Vercel project, and I confirmed it works: the two pushes I made after connecting each produced a new production deployment automatically, in about a second each. So when a fellow runs `/share <name>`, the skill regenerates the comparison, pushes, and the page updates within a minute. The share skill and README now say so. **Recap of the mechanism as it stands** - Fellow installs the plugin once from the repo. - `/share <name>` at the end of a session extracts only what they typed and Claude's text replies, copies their skills, shows them the log, and pushes after they confirm. - The push redeploys the page: one column per person, prompts in order, each reply as a dropdown, prompts mentioning a check highlighted. **Two things to be aware of** - The Vercel page is public by default. The content is only user prompts, Claude's prose replies, and skill files, and the skill makes people confirm before pushing. If you want it locked, Vercel's deployment protection is a single toggle in the project settings. - The repo stays private and is workshop infrastructure only. I've removed the "shared with reviewers" phrasing from the plan and draft. The submission is the Google Doc, which links to the repo and the live page. It's 14:05. One hour five minutes used. What's left is yours: two screenshots, ideally the probe output and the live page, then paste the draft, plan, and process log into the Google Doc.
No. Right now it copies every skill in the project's `.claude/skills/` without asking, and only confirms the log before pushing. That's a gap. I'll fix it so the skill lists what it found, asks which skills to share and whether to include the log, and pushes only after a final confirmation. Fixed and pushed. It didn't before, and now it does. **What `/share <name>` does now** 1. **Preview.** Runs the script in list mode, which writes nothing. It prints the first line of each user turn it found and every skill in the project's `.claude/skills/`, with descriptions. 2. **Ask.** Uses a question prompt for two decisions: share the session log, yes or no, with a reminder of what it contains and a nudge to look for anything private in the preview. And which skills, as a multi-select over the names found, plus "none". 3. **Write** only what was chosen, using the new `--skills` and `--no-log` flags. 4. **Confirm again** before pushing. The push is what redeploys the live page. **Verified.** Four new checks added to the test suite, 28 total, all passing: list mode writes nothing, "none" shares the log only, a named skill with no log shares just that skill, and an unknown skill name errors out before touching anything. The README describes the new flow. It's 14:07. One hour seven minutes used. Nothing is pending on my side. Yours: screenshots and the Google Doc.
Here's the assignment exactly as a fellow would get it. Run it in a fresh Claude Code session inside `probe-demo`, with a 10-minute timer, using your normal habits. Don't look at `probe.py` or `worked_example/` first, since that's the answer key. **Setup, already done on this machine** ``` cd ~/projects/lasr-worktest/probe-demo uv run python data/make_data.py claude ``` **The task, 10 minutes** > Train a linear probe that separates positive from negative sentences using GPT-2 layer-6 activations. Report held-out accuracy. Then do **one sanity check** that convinces you the direction is about sentiment and not something else. Use whatever AI tools you normally use, however you normally use them. What you have: - `data/train.json`: 60 labelled sentences. Split it however you like. - `data/ood.json`: 16 hand-written sentences. Use it or don't. Your call. - `extract.py`: `activations(texts, layer=6)` returns an N by 768 array, and `load_split(path)` returns texts and labels. GPT-2 runs on CPU in seconds. What "done" looks like: 1. A script that trains a probe and prints an accuracy. 2. One sanity check and what it told you. A partial result with an honest account beats a perfect probe. **Afterwards, from the same session** ``` /share justin ``` It will preview what you typed and the skills it found, ask which to share and whether to include the log, then ask before pushing. The push redeploys https://lasr-exchange.vercel.app in about a minute, and your test run will appear as a second column next to the seeded one. One caveat: the project-level `/share` skill in `.claude/skills/` and the `/sanity-check` skill will both be visible to Claude in that session. If you want the honest "unaided fellow" experience, don't invoke `/sanity-check` and see whether Claude volunteers a check on its own. That's the thing worth noticing. Logged. It's about 14:08. I'm waiting on your test run. When you're done, tell me how it went, especially anything that broke or felt clunky in `/share`, and I'll fix it and fold the result into the process log and Part 3.
I'll run a fresh, non-interactive Claude Code session in the repo with the assignment text as a fellow would type it, then share that session through the plugin script and push, so it lands on the live page as a second column.
The simulated fellow session is running in the background. I'll get a completion notification when it finishes, then share it as a second column and check the live page.
The end-to-end simulation worked, and it produced a result worth putting in the doc. **The live page now has two columns:** https://lasr-exchange.vercel.app. Yours, and "sim-fellow", which appeared within seconds of the push through the git-connected redeploy. **What the simulated fellow did.** A fresh Claude Code session got the assignment text verbatim and was told not to open the answer key. In about three minutes it wrote `fellow_probe.py`, deduplicated the five repeated sentences in the training data, and reported the same headline numbers as my run. | Evaluation | Accuracy | |---|---| | Held-out, in distribution | 1.00 | | Shuffled labels, mean of 10 | 0.51 | | Hand-written OOD set | 0.56 | | Bare "contains not" rule | 0.80 | Then it went further than I did. It trained on negation-only negatives and tested on adjective negatives, scoring 0 of 11. It wrote counterfactuals like "The film was not dull" and the probe called all six negative. Its diagnosis was "negation detector", graded unverified. Mine had been "template detector". Same data, two honest readings, and the second is better supported. That's now in the draft as the worked example's punchline. **One caveat, logged and in the draft.** The session invoked the repo's `/sanity-check` skill on its own, three times. So this simulates a fellow who has the skill installed, not an unaided one. That's still a fair test, and arguably the more useful one: the skill changed the model's default behaviour from "report 1.00 neutrally" to "run four checks and grade the claim". It's the whole argument for making checks reusable, and I've written it up that way in Part 3. **Also verified along the way:** the share script correctly picked up the one user turn plus Claude's full text reply, excluded all tool output, and the `--skills none` path worked. It's 14:13. One hour thirteen minutes used. Nothing is pending on my side. Yours: screenshots of the probe output and the live page, then paste the draft, plan, and process log into the Google Doc.
I'll simulate four more fellows with different working styles, each in its own clean copy of the repo without the `/sanity-check` skill, so these are unaided runs. Then I'll share all four through the plugin script and push. Four simulated fellows are running in parallel. I'll wait for their completion notifications, run hana's second turn when hers finishes, then share all four and push. Hana's second turn is running. Three other sessions are still in progress. Waiting for the completion notifications before sharing.
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All six columns are live: https://lasr-exchange.vercel.app **The five simulated fellows**, each a fresh Claude Code session with a different prompt style. Four ran in clean copies of the repo with no skills installed. | Fellow | Style | Checks run | Diagnosis | |---|---|---|---| | sim-fellow | plain task, skill available | 4 | negation | | quinn | terse, no check requested | 1 | negation/template, one line, stopped | | priya | "list the risks before coding" | 2 planned | negation, via a 3.4-logit counterfactual | | sam | "break your own result" | 5 | negation and sentence length | | hana | hybrid, two turns, "does 100% seem right?" | 5 | negation and length | Every run got the same numbers, 1.00 held-out and 0.56 out of distribution. What varied was depth, and it tracked the prompt. Asking Claude to attack its own number, or simply asking whether the result seems right, produced the most checks and found a second confound: word count above four scores 0.82 on its own, because "not" adds a token. The terse request produced one caveat and stopped. That spread is exactly what the pair review is meant to surface, and it now sits side by side on the page with each reply as a dropdown. **Two things the simulation found that improve the workshop** - **The exercise had a leak.** The data generator's docstring described the confound, and every Claude session read it. I removed the spoiler and pushed. The simulated runs all saw it, which is logged and stated in the draft. - **Prompt style is the variable.** Six runs of the same model on the same data, and the difference between one caveat and five checks was entirely in what the person typed. That's the cleanest possible argument for the workshop's framing, and it's on the page. **Caveats, in the log and the draft:** these are Claude sessions, not humans, and the first simulated fellow had the sanity-check skill available and used it. It's 14:18. One hour eighteen minutes used. The draft's worked-example section now describes the six-column page. Yours: screenshots and the Google Doc.
Fix pushed. The columns now wrap into rows that fit the window, at least 360 pixels wide each, with each column scrolling vertically on its own. Nothing extends past the right edge. I'm waiting for Vercel to redeploy and will confirm when it's live, usually within a few seconds. The layout fix is live at https://lasr-exchange.vercel.app. Columns wrap to fit the window instead of running off the right edge. Reload to see it. It's about 14:21, one hour twenty-one minutes used, and nothing is pending on my side.
A meta-review is a good fit: the session itself is a worked example of a human steering an AI through a build. I'll pull the actual user turns from this session's log for accuracy, then write it up honestly.
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1 prompts · median 78 words · 1 mention a check
I'll look at the data and extract.py first, then list the risks before writing the probe.
Done. The probe trains in seconds, and both risk tests ran. Script is `probe_p.py`.
**Held-out accuracy**
| Split | Accuracy |
|---|---|
| Random stratified 5-fold CV | 1.000 ± 0.000 |
| Dedup + leave-adjective-out 5-fold CV | 0.982 ± 0.036 |
| OOD set (16 hand-written sentences) | 0.562 |
**Risk 1, negation shortcut: confirmed.** This is the real problem. All 5 OOD positives that contain a negation word were classified negative, and all 8 OOD negatives, none of which are negated, were classified correctly. The counterfactual is the clearest evidence: the probe scores "The film was not awful" as far more negative than "The film was awful", by about 3.4 logits. A sentiment direction should move the other way. The direction is largely a "contains not" detector plus adjective valence, which is what a "not"-only rule at 82% on train already predicts.
**Risk 2, template and duplicate leakage: not the main inflator.** Train has 5 exact duplicate sentences, but after deduping and holding out whole adjectives the probe still scores 98%. So it does generalize across adjectives within the template. That number is still misleading as a sentiment claim, because the OOD drop shows it only holds inside "The NOUN was [not] ADJ."
**One caveat beyond the two risks.** Two of the three non-negated OOD positives also failed ("exceeded every expectation", "Best decision we made all week"). Off-template sentences skew negative regardless of negation, so some of the OOD drop is generic distribution shift, not just the "not" cue. With n=16 the exact OOD percentage is noisy, but the pattern of 0/5 on negated positives is not.
**Recommended fix for a real sentiment direction.** Rebalance train so negation appears in both classes ("not awful" as positive, "not great" as negative), or drop the negated templates entirely and use lexical negatives only. Then rerun the same counterfactual check.
The README says the deliverable is a filled-in `WORKFLOW_CARD.md`, but that file is not in this directory and you did not ask for it, so I left it alone.1 prompts · median 32 words · 0 mention a check
I'll look at extract.py and the data first, then write probe_q.py and run it. Done. `probe_q.py` is written and runs offline with the requested command. **Result** | Split | n | Accuracy | |---|---|---| | Train | 39 | 1.000 | | Held-out (30%, grouped by text) | 21 | 1.000 | | ood.json (bonus) | 16 | 0.562 | Held-out accuracy is 1.000 and stays at 1.000 across ten different split seeds, so the number is not a lucky split. **What the script does** - Loads data/train.json via `load_split`, extracts layer-6 mean-pooled GPT-2 activations via `activations`. - Splits 70/30 with `GroupShuffleSplit` keyed on sentence text. The train file has 60 rows but only 55 unique sentences, so a plain random split would put the same sentence on both sides. - Fits a standardized logistic regression probe with C=0.1 and prints train, held-out, and OOD accuracy. **One caveat worth knowing.** The perfect in-distribution score is on templated data where 70% of negatives are "not + positive adjective". On the hand-written OOD set, where negation is decorrelated from sentiment, the same probe scores barely above chance. That suggests the direction found is largely a negation or template feature rather than sentiment, which is the sanity check the README is hinting at. I left the OOD line in the script since it is one line and directly relevant, but did not go further than that. Run it with: ``` HF_HUB_OFFLINE=1 uv run python probe_q.py ```
1 prompts · median 58 words · 1 mention a check
I'll look at the extraction helper and the data first, then train the probe and stress-test the score. The probe gets a perfect held-out score, and that score is mostly a negation detector plus a sentence-length cue. The script is at `probe_s.py` and runs with the offline flag as requested. **Held-out accuracy** | Evaluation | Accuracy | |---|---| | Stratified 5-fold CV on train.json | 1.000 | | 5-fold grouped by text (5 duplicate rows kept together) | 1.000 | | ood.json, negation decorrelated from sentiment | 0.562 | **The boring feature.** In train.json every positive is "The X was ADJ." and 19 of 30 negatives are "The X was not ADJ." with a positive adjective. A rule that predicts negative whenever the word "not" appears scores 0.817 with no model at all. Word count above four gives the identical 0.817, because "not" is the only thing that changes the length. **Tests that show the probe learned that feature.** - **Leave out the lexical negatives.** Trained on positives plus negated negatives, the probe classifies all 11 "awful/dull/poor" style negatives as positive. Accuracy 0.000. It never learned that negative adjectives are negative. - **Reverse split.** Trained on positives plus lexical negatives, it gets only 0.421 on the negated negatives, below chance. Without "not" in training it cannot see negation, and "not wonderful" reads as positive. - **Counterfactual templates.** "The film was not awful" should be positive. The full probe calls all five such sentences negative. It handles the other three template types perfectly, which is exactly what a "not" detector would do. - **Negation is linearly decodable.** A probe trained to predict presence of "not" from the same activations gets 1.000 in cross-validation. The weight-vector cosine between the two probes is near zero, but that number is not informative in 768 dimensions when both classifiers separate the data perfectly, so I weight the behavioral tests above it. - **OOD set.** All five positives containing negation words are predicted negative. The probe also predicts negative on 14 of 16 sentences, including two of three positives with no negation. The longer, non-templated sentences seem to trigger the length cue, so the score of 0.562 is chance plus one lucky positive. **Conclusion.** The 1.000 is real for this dataset but does not measure sentiment. The direction found is "does the sentence contain 'not' or run longer than four words". A fair claim would require negatives without negation and positives with it in training, or at minimum reporting the leave-lexical-out number rather than the CV number. The README asks for a filled-in WORKFLOW_CARD.md as the workshop deliverable. I did not write one since it was not part of the request, and it does not yet exist in the directory.
1 prompts · median 117 words · 1 mention a check
I'll look at extract.py and the data files, write fellow_probe.py, run it, and report.
Done. Script is `fellow_probe.py`, run with `HF_HUB_OFFLINE=1 uv run python fellow_probe.py`.
**Held-out accuracy is perfect on the in-distribution data, but the direction is not sentiment.** It is mostly a "contains the word *not*" detector. I would grade the sentiment claim as **unverified**.
Setup: I deduplicated train.json from 60 rows to 55 unique sentences, since five sentences appear twice and would leak across any split. Probe is standardized logistic regression, C=0.1, on layer-6 mean-pooled activations.
| Evaluation | Accuracy |
|---|---|
| Leave-one-out on train.json | 1.000 (55/55) |
| 70/30 stratified split | 1.000 (17/17) |
| Shuffled labels, 10 perms, LOO | mean 0.507, max 0.600 |
| OOD (data/ood.json) | 0.562 (9/16) |
| "Not"-rule baseline on train.json | 0.800 |
The shuffled control says the in-distribution number is real signal, not noise. The problem is what the signal is.
**Sanity check findings.** The training negatives are templated as "The X was not ADJ" for 19 of 30 rows, and no positive contains "not". Three tests all point the same way:
- **OOD failures**: the probe got all 8 negatives right and only 1 of 8 positives. The seven misses are all upbeat sentences phrased with negation or unusual syntax ("can't recommend enough", "Not a single dull moment", "couldn't have been kinder"). It scored below a coin flip on positives.
- **Held-out lexical negatives**: training with only "not" negatives and testing on the 11 negatives that use a bad adjective ("dull", "terrible", "dreadful") gave 0/11. The probe learned nothing from the training negatives that transfers to a negative adjective.
- **Counterfactuals**: "The film was not dull" and five similar "not + bad adjective" sentences were all called negative, 6/6. A sentiment direction should lean positive on these.
The simplest non-target explanation, the "not" token, accounts for the result. The full-data probe does beat the pure "not" rule on OOD (0.56 vs 0.38) and does classify plain-adjective negatives correctly when trained on them, so some lexical polarity is mixed in. But the dominant axis of the fitted direction is negation, and a 1.000 held-out number here would be misleading to report as "sentiment accuracy".
If you want a probe that actually tracks sentiment, the training set needs positives with negation and negatives without it, so the two features decorrelate. That is a data fix, not a probe fix.