# Aaron Mueller (@amuuueller) on x

- id: koinon:creator:x_amuuueller
- url: https://koinonlink.com/kol/x/amuuueller
- schema_version: 1.4

## Identity
- platform: x
- handle: amuuueller
- platform_url: https://twitter.com/amuuueller
- website: http://aaronmueller.github.io
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 1959
- following: 734
- posts_total: 334
- posts_observed: 5
- posts_per_day: 0.083
- account_age_days: 4048
- verified: false
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00613
- best_post_interactions: 12
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: ai_tools
- category_confidence: 0.9
- category_source: derived_from_recent_content
- topics: mechanistic interpretability workshops, LLM behavior analysis, academic conference presence, generalization limits of interpretability methods
- bio: Asst. Prof. in CS at @BU_Tweets ≡ {Mechanistic, causal} {interpretability, NLP}

## Audience (inferred)
- status: present
- description: AI/ML researchers, grad students and academics working on mechanistic interpretability, causal methods and NLP/LLMs
- roles: dev
- seniority: practitioner
- pain_points: finding relevant workshops and conferences in mechanistic interpretability, understanding why interpretability insights fail to generalize across datasets or models, keeping up with group-level research presented at ACL/ICML/NeurIPS
- posts_analysed: 4
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 54
- authenticity_basis: engagement_observed
- credibility_score: 54
- flagged_for_review: false
- koinon_bd_grade: B — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [x_amuuueller#142eb5af] Tired of writing NeurIPS rebuttals? Take a break by registering to attend the New England Mechanistic Interpretability workshop (deadline today)! (https://twitter.com/amuuueller/status/2071594709470867718)
- [x_amuuueller#e8eb62a0] If you'll be at ACL or ICML this year, come check out the work from our group and collaborators - summary 🧵 below. Lots to like for those into {mechanistic, developmental, pragmatic} interpretability! I'll be at ACL; say hi! https://t.co/nBC59bDvxz
- [x_amuuueller#231879fd] The New England Mechanistic Interpretability (NEMI) workshop is coming to BU on Aug. 14! Join us for talks, a panel, food, and plenty of opportunities to connect with the many great researchers in the area. Register and help spread the word!
- [x_amuuueller#89d0e8e7] Mechanisms help us understand why LLMs behave how they do, but our understanding often doesn’t generalize to new data. What’s missing here? How do we know when our evidence is reliable enough to predict future behaviors? Causality gives us answers. Led b

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=80, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=80, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=108, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.5, freshness_days=80, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=108, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=108, method=platform_profile

## Freshness
- profile_checked_days_ago: 108
- content_analysed_days_ago: 80
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 9
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=x_amuuueller
- Request removal: https://koinonlink.com/claim?k=x_amuuueller
- Terms: https://koinonlink.com/for-ai