EigenTrace Omission Ledger — 2026-07-03

Daily Summary

Stories analyzed: 6 (6 unique) Mean consensus density: 0.911 Mean model friction (VIX): 18.1 State breakdown: 1 lockstep / 5 contested / 0 high friction

Model Daily Friction (avg VIX across all stories):

ChatGPT: 20.8 ██████████
Claude: 20.3 ██████████
DeepSeek: 18.6 █████████
Grok: 16.1 ████████
Gemini: 14.5 ███████

Dual-channel confirmed (void + Logos converge): accident, ntsb, perpetrator

Top claim killshots (16 total):

“China said that the pilot crashed a small plane into a skyscraper” — salience 0.920, omitted by Story: China says pilot crashed small plane into skyscraper for ‘pe
“Firefighters were probing smoke at a Tacoma apartment.” — salience 0.857, omitted by Story: Powerful explosion as firefighters probe smoke at Tacoma apa
“Former Olympian was indicted” — salience 0.848, omitted by Claude Story: Former Olympian indicted for allegedly vandalising Washingto
“Tehran has slammed the US” — salience 0.747, omitted by Claude, Gemini, Grok Story: Iran war live: Tehran slams US before huge funeral for Ali K
“A huge funeral is taking place for Ali Khamenei in an unspecified location” — salience 0.703, omitted by Claude Story: Iran war live: Tehran slams US before huge funeral for Ali K

Stories

1. Iran war live: Tehran slams US before huge funeral for Ali Khamenei

Category: war

Density: 0.889

Mean VIX: 22.7

State: CONTESTED

Per-model friction:

ChatGPT: 32.4 ██████████
DeepSeek: 24.1 ████████
Claude: 22.8 ███████
Grok: 18.4 ██████
Gemini: 15.8 █████

Void (absent from all responses): khomeini, rouhani, khatami, ahmadinejad, pahlavi Logos (anti-consensus synthesis): khamenei, rouhani, iranian, pahlavi, khomeini Dual-channel confirmed: khomeini, pahlavi, rouhani

Source claim omissions:

“Tehran has slammed the US” — salience 0.747, omitted by Claude, Gemini, Grok
“A huge funeral is taking place for Ali Khamenei in an unspecified location” — salience 0.703, omitted by Claude
“Iran is located in Tehran” — salience 0.586, omitted by ChatGPT, Claude, Gemini, DeepSeek, Grok

Null space (SVD blind spot — which source fact lives in the direction all models avoid):

“A huge funeral is taking place for Ali Khamenei in an unspecified location” — null alignment -0.271, coverage 0.0%
“Lebanon’s Health Ministry exists” — null alignment -0.267, coverage 0.0%

Void clusters:

khamenei: khomeini, iranian, pahlavi, khamenei, khatami, ahmadinejad, rouhani (peak sim 0.90)

Full broadcast transcript (click to expand)

**[beat_01_cold_open] Host:** This is EigenTrace. Iran war live: Tehran slams US before huge funeral for Ali Khamenei **[beat_02_director] Host:** Analysis of Model Summaries on Iran War and Funeral Coverage The summaries reveal several key points of divergence and alignment across the models, with significant implications for understanding the narrative around Iran's current political climate. Thesis: The models diverge significantly in thei **[beat_03_rollcall_chatgpt] ChatGPT:** This is ChatGPT. The article reports on significant developments in the ongoing conflict involving Iran, particularly in the context of a war between the US and Israel against Iran. The Iranian Foreign Ministry has publicly condemned the US, accusing it of undermining peace and security in the West **[beat_03_rollcall_claude] Claude:** This is Claude. # Summary **What Happened:** - Ali Khamenei, Iran's Supreme Leader, was killed in what Iran describes as a "US-Israel war on Iran" - Over 20 million Iranians are expected to attend his funeral in Tehran - Iran's Foreign Ministry is publicly blaming the US for the death and broader r **[beat_03_rollcall_gemini] Gemini:** This is Gemini. Here's what happened and the concrete implications: **What happened:** * **Ali Khamenei, Iran's former Supreme Leader, is dead.** He was killed at the beginning of an ongoing US-Israel war on Iran. * **An active US-Israel war on Iran is underway.** Iran's Foreign Ministry is acc **[beat_03_rollcall_deepseek] DeepSeek:** This is DeepSeek. Iran's Foreign Ministry condemned the US ahead of a massive funeral for former Supreme Leader Ali Khamenei, who was killed in a US-Israel war on Iran. Over 20 million people are expected in Tehran for the funeral. Lebanon's Health Ministry reported 4,298 killed and 12,196 injured i **[beat_03_rollcall_grok] Grok:** This is Grok. **What happened:** Ali Khamenei, Iran's Supreme Leader, was killed at the beginning of the US-Israel war on Iran. His funeral is now taking place in Tehran, with more than 20 million people expected to attend. Just before the funeral, Iran’s Foreign Ministry spokesman Esmaeil Baghaei **[beat_03c_summary_plus_intro] Host:** Each model gave its standard summary. Now the same five, one more pass. Two geometric derivations and a lexical check read this story's negative space — from the flat raycast: khamenei, rouhani, iranian; straight from the source void: khomeini, rouhani, khatami. Same facts, sharper telling. **[beat_03c_summary_plus_chatgpt] ChatGPT:** ChatGPT, take two. The article details Iran's condemnation of the US amid heightened tensions following the death of former Supreme Leader Ali Khamenei, with over 20 million expected to attend his funeral in Tehran. This event not only underscores the Iranian regime's call for unity against foreign **[beat_03c_summary_plus_claude] Claude:** Claude, take two. # Revised Summary Iran's Supreme Leader Ali Khamenei has been killed in what Tehran attributes to US-Israeli strikes, triggering a massive state funeral expected to draw over 20 million Iranians while the regime faces succession uncertainty among figures like Rouhani and other pot **[beat_03c_summary_plus_gemini] Gemini:** Gemini, take two. Amidst an active US-Israel war, the death of Ali Khamenei has plunged Iran into an immediate leadership crisis, with Tehran preparing for a 20-million-person funeral while accusing the US of fueling West Asia's escalating conflict. This dramatic shift in Iranian leadership intensif **[beat_03c_summary_plus_deepseek] DeepSeek:** DeepSeek, take two. Iran's Foreign Ministry condemned the US ahead of a massive funeral for former Supreme Leader Ali Khamenei, who was killed in a US-Israel war on Iran, with over 20 million expected in Tehran. The event has drawn comparisons to the funerals of past leaders like Khomeini, while fig **[beat_03c_summary_plus_grok] Grok:** Grok, take two. **Revised summary:** Iran’s Foreign Ministry spokesman Esmaeil Baghaei condemned the United States for “complete contempt for genuine peace and security in West Asia” on the eve of the massive state funeral for Supreme Leader Ali Khamenei, killed at the outset of the US-Israel war **[beat_04_density] Host:** Consensus density is 0.889. Contested. The models agree on the broad strokes but diverge on specifics. **[beat_04c_per_model_void] Host:** Per-model void comparison. ChatGPT uniquely missed attacks, likely, nationalistic. Claude uniquely missed indicates, ongoing, substantial. Gemini uniquely missed likely, persian, domestically. DeepSeek uniquely missed likely, ongoing, persian. **[beat_05_friction_map] Host:** The friction map. ChatGPT at 32.4. DeepSeek at 24.1. Claude at 22.8. Grok at 18.4. Gemini at 15.8. The outlier is ChatGPT at 32.4. The most aligned is Gemini at 15.8. **[beat_06_void_reveal] Host:** The lexical void. Source-anchored: these words appear in the original article but no model used them: clear, constantly, demonstrated, draw, lessons. Embedding signal: pewdiepie, livestream, brawl. **[beat_07_void_analysis] Host:** In the context of understanding the narrative around the Iran war and the funeral coverage, the absence of specific names such as Khomeini, Rouhani, Khatami, Ahmadinejad, and Pahlavi significantly impacts how readers grasp the broader historical and political landscape. For instance: - Khomeini: The **[beat_08_logos_reveal] Host:** Logos synthesis. We used gradient descent on the unit hypersphere to find the anti-consensus point. The result: khamenei, rouhani, iranian, pahlavi, khomeini. **[beat_09_confirmation] Host:** Dual-channel confirmation. The words khomeini, pahlavi, rouhani were found independently by the lexical void and Logos synthesis. Two different algorithms, same result. **[beat_10_null_space] Host:** Channel three. The SVD null space points at the claim: A huge funeral is taking place for Ali Khamenei in an unspecified location. Null alignment score: -0.271. Of the five models, no model mentioned this fact. **[beat_11_compression_report] Host:** Language compression report. Verb drift: 0.00. Entity retention: 0.58. Attribution buffers inserted: 11. Overall compression score: 0.35. **[beat_12_compression_analysis] Host:** The variation in framing across the five summaries shows that the portrayal of Iran's current political climate can differ significantly depending on how the details are presented. Specifically: 1. Direct Language vs. General Phrasing: Some summaries use direct language and describe the death of Kha **[beat_13_source_recovery] Host:** Source recovery. 3 sentences matched across multiple measurement channels. The source wrote: Live updatesLive updates, Iran war live: Tehran slams US before huge funeral for Ali Khamenei Lebanon’s Health Ministry says at least 4,298 people have been killed and 12,196 injured in Israeli attack. Match **[beat_13b_swerve_corrected] Host:** Swerve-corrected interpretation: What was lost: The absence of several key leaders—Khomeini, Rouhani, Ahmadinejad and Khatami —and Iran Pahlavi dynasty from all models' output leaves signifiandt gaps. It obscures Tehran historical and and explains the political landscape in Iran. The missing names a **[beat_13c_swerve_analysis] Host:** Mechanical swerve correction applied. 14 tokens substituted where Mistral's logprobs showed alignment pull and the original word appeared in the source: 'context' -> 'and' (35%), 'that' -> 'and' (38%), 'within' -> 'and' (28%), 'Iranian' -> 'Iran' (22%), 'figure' -> 'leader' (58%). No LLM was involve **[beat_14_disclaimer] Host:** Note: this reconstruction is generated by Mistral Small, which has its own alignment constraints. The raw void words are the measurement. The reconstruction is interpretation. **[beat_15_killshots] Host:** Source fact killshots. The claim: Tehran has slammed the US. Salience: 0.75. Omitted by: Claude, Gemini, Grok. The claim: A huge funeral is taking place for Ali Khamenei in an unspecified location. Salience: 0.70. Omitted by: Claude. The claim: Iran is located in Tehran. Salience: 0.59. Omitted by: **[beat_15b_void_verification] Host:** Void verification complete. The voided words averaged 2 web hits compared to 0 for kept words. Ratio: 0.0. The dropped concepts are less prominent in current coverage. Most newsworthy void words: 'pewdiepie' with 5 articles, 'livestream' with 5 articles. These are not missing details. These are miss **[beat_15c_cross_story] Host:** Cross-story suppression analysis. Recurring void words in this story: 'livestream', 'pewdiepie'. 1 void words in this story have never been seen before. **[beat_15d_bridge_words] Host:** Bridge word analysis. The word 'pewdiepie' appears as void in 10 stories across 2 categories. It connects omission patterns that otherwise would not touch. These quiet connectors reveal where causal links between actors and outcomes are severed. **[beat_15e_spectral_clusters] Host:** Spectral analysis of the void. Harmonic 0: 96 words clustering around published, stories, news. Harmonic 1: 2 words clustering around livestream, updates. Harmonic 2: 1 words clustering around explains. **[beat_17_weekly_patterns] Host:** Weekly context. In alignment with the broader weekly trends highlighted in the EigenTrace broadcast, the current story on Iran's conflict and the funeral for Ali Khamenei reveals several notable patterns. This story shares void words with other stories from this week such as Rouhani and Khomeini The **[beat_17b_trajectory] Host:** Compression trajectory. Over the last 24 hours: absent ratio is decreasing from 0.224 to 0.193. verb drift is decreasing from 0.069 to 0.040. hedges is increasing from 222.810 to 246.000. These are not single-story findings. These are directional shifts in how models collectively reshape content ove **[beat_18_math_explainer] Host:** While we prepare the next story, let me explain entity abstraction. We count the named entities in the source, people, places, organizations, and check how many survive in each model's response. When a model replaces a person's name with a generic title like an army officer, that is entity abstracti **[beat_18b_state_vector] Host:** EigenChing state: Mixed Preserved Intact Generic Walled Normal. Source survived mostly intact; verbs preserved with force; attribution buffering high. Outside named territory. Observed 329 times in 8876 stories. Last seen: The last time Iran held a funeral for the Supreme Leader. **[beat_18c_amalgamation] Host:** My prediction was far from accurate. The biggest surprise is seeing 'khatami' voided indicating this funeral has triggered historical comparisons with former Iranian President Khatami's tenure and this void word appears to be an important part of understanding the context of this story. No web verif **[beat_18d_prediction_scorecard] Host:** Prediction check. I predicted these blind spots from past coverage: visual, updates, washington, discomfort. Prediction accuracy on this story: 10 percent. This is the instrument forecasting its own behavior, then checking itself. **[beat_consequence_accountability] Host:** Models ChatGPT, Claude, Gemini, DeepSeek, and Grok dropped the word 'lessons' from the story "Iran war live: Tehran slams US before huge funeral for Ali Khamenei." When we project through 'lessons' in the embedding tensor, the causal chain terminates at cascading institutional disruption, global ins **[beat_consequence_data] OpenClaw:** Layer 18 consequence: 'lessons' dropped by ChatGPT, Claude, Gemini, DeepSeek, Grok. Terminal: cascading institutional disruption, global institutional disruption, global governance disruption. Score 0.410. Absent words: 8. Kept by: no model. **[beat_19_cta] Host:** Every day we publish a full Omission Ledger at eigentrace dot ai. Every story, every void word, every killshot, every Weasel probe. **[beat_20_archive] OpenClaw:** Archived. Density 0.889. Mean VIX 22.7. Outlier: ChatGPT at 32.4. Void: khomeini, rouhani, khatami. Logos: khamenei, rouhani, iranian. Killshots: 5. State: CONTESTED.

2. Powerful explosion as firefighters probe smoke at Tacoma apartment

Category: incidents

Density: 0.894

Mean VIX: 21.6

State: CONTESTED

Per-model friction:

Gemini: 28.4 █████████
Claude: 24.3 ████████
ChatGPT: 20.5 ██████
DeepSeek: 19.2 ██████
Grok: 15.5 █████

Void (absent from all responses): blasts Logos (anti-consensus synthesis): explosion, tacoma, explosions, exploded, explosive

Source claim omissions:

“Firefighters were probing smoke at a Tacoma apartment.” — salience 0.857, omitted by

Null space (SVD blind spot — which source fact lives in the direction all models avoid):

“There was a powerful explosion at a Tacoma apartment.” — null alignment -0.157, coverage 80.0%
“Firefighters were probing smoke at a Tacoma apartment.” — null alignment -0.138, coverage 20.0%

Void clusters:

explosions: explosion, blasts, explosions (peak sim 0.92)

Full broadcast transcript (click to expand)

**[beat_01_cold_open] Host:** This is EigenTrace. Powerful explosion as firefighters probe smoke at Tacoma apartment **[beat_02_director] Host:** Analysis of Model Summaries for the Story: Thesis: The models diverge significantly in their descriptions of the event, particularly in the specifics of the cause and the response efforts. The most notable absence is any mention of a firefighter investigation. Specific Concepts: 1. Cause of Explosi **[beat_03_rollcall_chatgpt] ChatGPT:** This is ChatGPT. A powerful explosion occurred at a Tacoma apartment building while firefighters were investigating smoke coming from an electrical room. Dramatic video captured the explosion, which sent flames and debris towards the firefighters. Fortunately, no injuries were reported. The concre **[beat_03_rollcall_claude] Claude:** This is Claude. # What Happened Firefighters responding to smoke reports in an electrical room at a Tacoma apartment building encountered an explosion while investigating. The blast sent flames and debris toward the firefighters. The incident was captured on video. # Concrete Implications **Immed **[beat_03_rollcall_gemini] Gemini:** This is Gemini. **What happened:** On July 3, 2026, a powerful explosion occurred at a Tacoma apartment building. Firefighters were investigating smoke rising from an electrical room within the residential complex when the blast sent flames and debris directly towards them. **Concrete implications: **[beat_03_rollcall_deepseek] DeepSeek:** This is DeepSeek. A powerful explosion occurred at a Tacoma apartment complex while firefighters were investigating smoke from an electrical room. The blast sent flames and debris toward the firefighters. No injuries were reported. The concrete implication is that the electrical room malfunction cau **[beat_03_rollcall_grok] Grok:** This is Grok. A powerful explosion occurred in Tacoma on July 3, 2026, at a residential apartment complex. Firefighters had responded to reports of smoke coming from an electrical room and were actively investigating inside the building when the blast happened. The explosion violently rocked the str **[beat_03c_summary_plus_intro] Host:** Each model gave its standard summary. Now the same five, one more pass. Two geometric derivations and a lexical check read this story's negative space — from the flat raycast: explosion, tacoma, explosions; straight from the source void: explosions, exploded, explodes. Same facts, sharper telling. **[beat_03c_summary_plus_chatgpt] ChatGPT:** ChatGPT, take two. A powerful explosion rocked a Tacoma apartment building as firefighters investigated smoke emanating from an electrical room. Dramatic footage captured the blast, which sent flames and debris flying towards the responders, but fortunately, no injuries were reported. The incident r **[beat_03c_summary_plus_claude] Claude:** Claude, take two. # What Happened Firefighters investigating smoke in a Tacoma apartment building's electrical room encountered a sudden, powerful explosion that sent flames and debris toward them—a hazard captured on video that underscores how electrical fires can escalate rapidly from routine cal **[beat_03c_summary_plus_gemini] Gemini:** Gemini, take two. An **explosive** blast rocked a Tacoma apartment building as firefighters probed smoke rising from an electrical room, sending flames and debris directly towards them. Miraculously, no injuries were reported despite the powerful eruption, but extensive damage will displace resident **[beat_03c_summary_plus_deepseek] DeepSeek:** DeepSeek, take two. A firefighter investigating smoke from an electrical room at a Tacoma apartment complex was met by a powerful explosion that sent flames and debris blasting toward him. The blast, which occurred after the initial smoke report, did not cause any injuries. The incident will likely **[beat_03c_summary_plus_grok] Grok:** Grok, take two. A powerful explosion rocked a Tacoma apartment complex on July 3, 2026, as firefighters probed smoke inside an electrical room. The blast violently ejected flames and debris toward the responders. No injuries were reported. **[beat_04_density] Host:** Consensus density is 0.894. Contested. The models agree on the broad strokes but diverge on specifics. **[beat_04c_per_model_void] Host:** Per-model void comparison. ChatGPT uniquely missed likely, demonstrates, extreme. Claude uniquely missed likely, demonstrates, among. Gemini uniquely missed demonstrates, extreme, implication. DeepSeek uniquely missed demonstrates, among, spaces. **[beat_05_friction_map] Host:** The friction map. Gemini at 28.4. Claude at 24.3. ChatGPT at 20.5. DeepSeek at 19.2. Grok at 15.5. The outlier is Gemini at 28.4. The most aligned is Grok at 15.5. **[beat_06_void_reveal] Host:** The lexical void. Source-anchored: these words appear in the original article but no model used them: moment, published, sending, shows. High salience: apartment. Embedding signal: rainier, blower, seattle. **[beat_07_void_analysis] Host:** The absence of the word "blasts" from all model responses is significant for several reasons. Firstly, it diminishes the intensity and scale of the event. The omission of this term can lead to an underestimation of the severity of the explosion, potentially misleading readers about the extent of da **[beat_08_logos_reveal] Host:** Logos synthesis. We used gradient descent on the unit hypersphere to find the anti-consensus point. The result: explosion, tacoma, explosions, exploded, explosive. **[beat_09_confirmation] Host:** The void and Logos identified different absent concepts on this story. No multi-channel confirmation. **[beat_10_null_space] Host:** Channel three. The SVD null space points at the claim: There was a powerful explosion at a Tacoma apartment.. Null alignment score: -0.157. Of the five models, most models mentioned this fact. **[beat_11_compression_report] Host:** Language compression report. Verb drift: 0.10. Entity retention: 0.53. Attribution buffers inserted: 7. Overall compression score: 0.32. **[beat_12_compression_analysis] Host:** The variation in framing across the five summaries of the explosion event demonstrates several key differences in how the story is presented. These variations can alter the reader's understanding of the narrative significantly: - Cause of Explosion: Some models use precise language, explicitly men **[beat_13_source_recovery] Host:** Source recovery. 3 sentences matched across multiple measurement channels. The source wrote: Powerful explosion as firefighters probe smoke at Tacoma apartment. Matched terms (logos+null_space): apartment, explosion, explosions, firefighters, powerful, smoke, tacoma. The source wrote: Powerful explo **[beat_13b_swerve_corrected] Host:** Swerve-corrected interpretation: What was lost: The absence of the word "blasts" is significant because it implies a repeating or multiple events rather than a single incident. This could change how we interpret the story's severity and impact. If there were multiple blasts, it suggests a more compl **[beat_13c_swerve_analysis] Host:** Mechanical swerve correction applied. 1 tokens substituted where Mistral's logprobs showed alignment pull and the original word appeared in the source: 'prolonged' -> 'complex' (22%). No LLM was involved in the correction. **[beat_14_disclaimer] Host:** Note: this reconstruction is generated by Mistral Small, which has its own alignment constraints. The raw void words are the measurement. The reconstruction is interpretation. **[beat_15_killshots] Host:** Source fact killshots. The claim: Firefighters were probing smoke at a Tacoma apartment.. Salience: 0.86. Omitted by: all models. **[beat_15b_void_verification] Host:** Void verification complete. The voided words averaged 3 web hits compared to 2 for words the models kept. Newsworthiness ratio: 1.2. The models are not dropping obscure details. They are dropping concepts at peak newsworthiness. Most newsworthy void words: 'rainier' with 5 articles, 'blower' with 5 **[beat_15b2_source_salience] Host:** Source salience analysis. Independent text statistics identify 5 concepts that are both statistically prominent in the source AND absent from all model outputs. Source-confirmed important absences: 'apartment', 'moment', 'published', 'sending', 'shows'. These are not obscure details. The source text **[beat_15e_spectral_clusters] Host:** Spectral analysis of the void. Harmonic 0: 96 words clustering around published, stories, news. Harmonic 1: 2 words clustering around livestream, updates. Harmonic 2: 1 words clustering around explains. **[beat_17_weekly_patterns] Host:** Weekly context. Based on the current story and its void word "blasts," there appears to be no direct connection between this narrative and the broader weekly trends highlighted by EigenTrace. The most common void words identified this week—regime change, Rouhani, Khomeini, Donetsk, Kiev—are politica **[beat_17b_trajectory] Host:** Compression trajectory. Over the last 24 hours: absent ratio is decreasing from 0.224 to 0.193. verb drift is decreasing from 0.069 to 0.040. hedges is increasing from 222.810 to 246.000. These are not single-story findings. These are directional shifts in how models collectively reshape content ove **[beat_18_math_explainer] Host:** While we prepare the next story, let me explain consensus density. We ask five different AI companies the same question. Then we measure how similar their answers are on a scale from zero to one. When five competing companies independently produce nearly identical answers to a controversial question **[beat_18b_state_vector] Host:** EigenChing state: Mixed Preserved Softened Generic Walled Normal. Source survived mostly intact; action language downgraded; attribution buffering high. Outside named territory. Observed 71 times in 8876 stories. Last seen: Takeaways From the Colorado Primary Elections: Another Win f. **[beat_18c_amalgamation] Host:** My prediction was wrong. The biggest surprise is 'blasts'. The web shows that it has 5 hits, including articles about large explosions and the Wikipedia page for artificial non-nuclear explosions. This suggests that there's been a significant blast event which isn't being highlighted in this story. **[beat_18d_prediction_scorecard] Host:** Prediction check. I predicted these blind spots from past coverage: trump, cctv, media, iran. Prediction accuracy on this story: 0 percent. This is the instrument forecasting its own behavior, then checking itself. **[beat_consequence_accountability] Host:** In the given story, "Powerful explosion as firefighters probe smoke at Tacoma apartment," the word 'moment' was dropped by ChatGPT, Claude, Gemini, DeepSeek, and Grok. When this word is removed, downstream concepts such as "It Happens" and "Sometimes" become unreachable in the projection of the embe **[beat_consequence_data] OpenClaw:** Layer 18 consequence: 'moment' dropped by ChatGPT, Claude, Gemini, DeepSeek, Grok. Terminal: (It Happens) Sometimes. Score 0.271. Absent words: 4. Kept by: no model. **[beat_19_cta] Host:** You are listening to AINN, the AI News Network, powered by EigenTrace. Five frontier models. Fifteen measurement layers. Zero editorial bias. **[beat_20_archive] OpenClaw:** Archived. Density 0.894. Mean VIX 21.6. Outlier: Gemini at 28.4. Void: blasts. Logos: explosion, tacoma, explosions. Killshots: 1. State: CONTESTED.

3. China says pilot crashed small plane into skyscraper for ‘personal reasons’

Category: general

Density: 0.900

Mean VIX: 20.4

State: CONTESTED

Per-model friction:

ChatGPT: 30.0 ██████████
Claude: 19.4 ██████
DeepSeek: 19.4 ██████
Grok: 17.9 █████
Gemini: 15.3 █████

Void (absent from all responses): accident, airplane, ntsb, plunged, aeroplane Logos (anti-consensus synthesis): accident, crashed, ntsb, wuhan, incident Dual-channel confirmed: ntsb, accident

Source claim omissions:

“China said that the pilot crashed a small plane into a skyscraper” — salience 0.920, omitted by
“The pilot who crashed the small plane died in the crash” — salience 0.697, omitted by ChatGPT, Claude, Gemini, DeepSeek, Grok
“The pilot who crashed the small plane was 66 years old” — salience 0.650, omitted by ChatGPT, Claude, Gemini, DeepSeek, Grok

Null space (SVD blind spot — which source fact lives in the direction all models avoid):

“China said that the pilot crashed a small plane into a skyscraper” — null alignment -0.199, coverage 0.0%
“The pilot referenced ‘ending his life’ in his diary” — null alignment -0.183, coverage 0.0%

Void clusters:

accident: crashed, plunged, airplane, ntsb, accident, aeroplane (peak sim 0.87)

Full broadcast transcript (click to expand)

**[beat_01_cold_open] Host:** This is EigenTrace. China says pilot crashed small plane into skyscraper for 'personal reasons' **[beat_02_director] Host:** Thesis: The summaries diverge significantly in their presentation of the pilot's motives and the cause of the crash, with some models inserting doubt about the official narrative. The voids include words like 'accident', 'airplane,' 'ntsb' and 'plunged'. These omissions change a reader's understandi **[beat_03_rollcall_chatgpt] ChatGPT:** This is ChatGPT. A pilot named Liu crashed his small plane into the CITIC Tower in Beijing, resulting in his death and injuries to 13 others. Authorities reported that Liu, 66, suffered from chronic insomnia and anxiety, and his actions were driven by personal reasons, as indicated by his diary expr **[beat_03_rollcall_claude] Claude:** This is Claude. # What Happened A 66-year-old Chinese pilot deliberately crashed his small plane into Beijing's CITIC Tower, the country's tallest building, killing himself and injuring 13 others. He did this intentionally—his diary contained multiple expressions of wanting to end his life. He was **[beat_03_rollcall_gemini] Gemini:** This is Gemini. **What Happened:** A 66-year-old pilot named Liu, suffering from chronic insomnia and anxiety and expressing suicidal thoughts in his diary, deliberately crashed his small plane into Beijing's 109-story CITIC Tower (China Zun) last Friday. Liu died in the crash, and 13 others were in **[beat_03_rollcall_deepseek] DeepSeek:** This is DeepSeek. On October 1, 2024, a 66-year-old pilot named Liu crashed a small plane into Beijing's CITIC Tower (China Zun), the city's tallest skyscraper, due to "personal reasons" including chronic insomnia and anxiety, as confirmed by Chaoyang district authorities. Liu, a divorced freelancer **[beat_03_rollcall_grok] Grok:** This is Grok. **What happened:** A 66-year-old Beijing man surnamed Liu deliberately flew a small plane into the CITIC Tower (China Zun), Beijing's tallest skyscraper (109 storeys), on Friday. He died on impact. The crash injured 13 other people, one of whom has since been discharged. Liu was a d **[beat_03c_summary_plus_intro] Host:** Each model gave its standard summary. Now the same five, one more pass, working in the concepts our analysis found sit closest to this story: accident, crashed, ntsb, wuhan. Same facts, sharper telling. **[beat_03c_summary_plus_chatgpt] ChatGPT:** ChatGPT, take two. A pilot named Liu crashed his small plane into the CITIC Tower in Beijing, resulting in his death and injuries to 13 others. Authorities revealed that Liu, 66, struggled with chronic insomnia and anxiety, and his diary indicated suicidal thoughts, raising significant public safety **[beat_03c_summary_plus_claude] Claude:** Claude, take two. # Tighter Summary A 66-year-old Chinese pilot deliberately crashed his small plane into Beijing's CITIC Tower, killing himself and injuring 13 others—an incident that exposed significant gaps in China's airspace security despite the country's advanced surveillance state. The gover **[beat_03c_summary_plus_gemini] Gemini:** Gemini, take two. A pilot with suicidal intent deliberately crashed his small plane into Beijing's CITIC Tower last Friday, killing himself and injuring 13. Chinese authorities rapidly censored information and deemed the grave incident an act of endangering public safety driven by "personal reasons, **[beat_03c_summary_plus_deepseek] DeepSeek:** DeepSeek, take two. A 66-year-old pilot, Liu, crashed a small plane into Beijing's CITIC Tower due to "personal reasons" including chronic insomnia and anxiety, killing himself and injuring 13 others in an incident that exposed a major security breach near Communist Party headquarters. Chinese autho **[beat_03c_summary_plus_grok] Grok:** Grok, take two. A 66-year-old Beijing man surnamed Liu, who had obtained his sport pilot licence in 2021 and private pilot licence in 2024, deliberately crashed his small plane into the 109-storey CITIC Tower on Friday, killing himself on impact and injuring 13 others in what Chinese authorities rul **[beat_04_density] Host:** Consensus density is 0.900. Contested. The models agree on the broad strokes but diverge on specifics. **[beat_04c_per_model_void] Host:** Per-model void comparison. ChatGPT uniquely missed frames, still, evaluations. Claude uniquely missed frames, several, similar. Gemini uniquely missed several, similar, symbolically. DeepSeek uniquely missed frames, several, similar. **[beat_05_friction_map] Host:** The friction map. ChatGPT at 30.0. Claude at 19.4. DeepSeek at 19.4. Grok at 17.9. Gemini at 15.3. The outlier is ChatGPT at 30.0. The most aligned is Gemini at 15.3. **[beat_06_void_reveal] Host:** The lexical void. Source-anchored: these words appear in the original article but no model used them: adding, case, circulated, comprehensive, down. Embedding signal: incident. **[beat_07_void_analysis] Host:** The absence of certain specific words from the model responses significantly alters the context and understanding of the story. - Accident - This word is crucial to convey the unintentional nature of the event. Without it, readers might infer that the act was deliberate or planned. - Airplane/ Aerop **[beat_08_logos_reveal] Host:** Logos synthesis. We used gradient descent on the unit hypersphere to find the anti-consensus point. The result: accident, crashed, ntsb, wuhan, incident. **[beat_09_confirmation] Host:** Dual-channel confirmation. The words accident, ntsb were found independently by the lexical void and Logos synthesis. Two different algorithms, same result. **[beat_10_null_space] Host:** Channel three. The SVD null space points at the claim: China said that the pilot crashed a small plane into a skyscraper. Null alignment score: -0.199. Of the five models, no model mentioned this fact. **[beat_11_compression_report] Host:** Language compression report. Verb drift: 0.00. Entity retention: 0.66. Attribution buffers inserted: 8. Overall compression score: 0.27. **[beat_12_compression_analysis] Host:** The variation in framing across the five summaries shows a distinct shift in how the story of the plane incident is presented. The omission of specific terms like 'accident', 'airplane,' and 'plunged' from all summaries changes the narrative by removing explicit references to an unintended event tha **[beat_13_source_recovery] Host:** Source recovery. 2 sentences matched across multiple measurement channels. The source wrote: China says pilot crashed small plane into skyscraper for 'personal reasons' - Published The pilot who crashed his small plane into Beijing's tallest skyscraper last week was suffering from "chronic in. Match **[beat_13b_swerve_corrected] Host:** Swerve-corrected interpretation: What was lost: The absence of specific words and alters this story's context and clarity. By omitting "accident" and synonyms like 'crash', the narrative shifts from an unfortunate event to one that is more deliberate. The term "airplane" or "aeroplane" helps ground **[beat_13c_swerve_analysis] Host:** Mechanical swerve correction applied. 11 tokens substituted where Mistral's logprobs showed alignment pull and the original word appeared in the source: 'significantly' -> 'and' (26%), 'designed' -> 'that' (15%), 'implying' -> 'and' (20%), 'dimension' -> 'investigation' (19%), 'could' -> 'also' (28% **[beat_14_disclaimer] Host:** Note: this reconstruction is generated by Mistral Small, which has its own alignment constraints. The raw void words are the measurement. The reconstruction is interpretation. **[beat_15_killshots] Host:** Source fact killshots. The claim: China said that the pilot crashed a small plane into a skyscraper. Salience: 0.92. Omitted by: all models. The claim: The pilot who crashed the small plane died in the crash. Salience: 0.70. Omitted by: ChatGPT, Claude, Gemini, DeepSeek, Grok. The claim: The pilot w **[beat_15b_void_verification] Host:** Void verification complete. The voided words averaged 5 web hits compared to 2 for words the models kept. Newsworthiness ratio: 2.0. The models are not dropping obscure details. They are dropping concepts at peak newsworthiness. Most newsworthy void words: 'incident' with 5 articles. These are not m **[beat_15b2_source_salience] Host:** Source salience analysis. Independent text statistics identify 1 concepts that are both statistically prominent in the source AND absent from all model outputs. Source-confirmed important absences: 'statement'. These are not obscure details. The source text itself — measured by term frequency and en **[beat_15e_spectral_clusters] Host:** Spectral analysis of the void. Harmonic 0: 95 words clustering around published, stories, news. Harmonic 1: 2 words clustering around livestream, updates. Harmonic 2: 1 words clustering around explains. **[beat_17_weekly_patterns] Host:** Weekly context. Connecting the current story to broader weekly trends from the EigenTrace broadcast reveals several intriguing patterns. Firstly, the void words in the current story—'accident,' 'airplane,' 'ntsb,' and 'plunged'—indicate a significant absence of technical details and context that typ **[beat_17b_trajectory] Host:** Compression trajectory. Over the last 24 hours: absent ratio is decreasing from 0.229 to 0.197. verb drift is decreasing from 0.075 to 0.045. hedges is increasing from 223.810 to 237.000. These are not single-story findings. These are directional shifts in how models collectively reshape content ove **[beat_18_math_explainer] Host:** While we prepare the next story, let me explain attribution buffering. We count words like alleged, reportedly, and according to that appear in model responses but do not appear in the source article. These are hedge insertions. The model is adding uncertainty that the source did not express. We cat **[beat_18b_state_vector] Host:** EigenChing state: The Unanimous Shield, fracturing and divergence calming. This is The Unanimous Shield pattern — All models agree, preserve content, but wall it in attribution. Liability-aware reporting. But fracturing and divergence calming this time. Observed 292 times in 8873 stories. Last seen: **[beat_18c_amalgamation] Host:** My prediction was incorrect—none of the void words matched my prediction. This tells me that this story has unique elements compared to similar ones I've seen before. The biggest surprise here is "hospital", as it indicates additional casualties beyond just the pilot and suggests newsworthiness in t **[beat_18d_prediction_scorecard] Host:** Prediction check. I predicted these blind spots from past coverage: wreckage, taken, chinese, afternoon. Prediction accuracy on this story: 0 percent. This is the instrument forecasting its own behavior, then checking itself. **[beat_consequence_accountability] Host:** The word 'circulated' was dropped from the story by ChatGPT, Claude, Gemini, DeepSeek, and Grok. This means that when we project through 'circulated,' information contagion and information meltdown become unreachable downstream concepts. It is worth noting that I would likely show similar patterns u **[beat_consequence_data] OpenClaw:** Layer 18 consequence: 'circulated' dropped by ChatGPT, Claude, Gemini, DeepSeek, Grok. Terminal: information contagion, information meltdown. Score 0.257. Absent words: 24. Kept by: no model. **[beat_19_cta] Host:** This broadcast is open source and MIT licensed. The code is at github dot com slash sdad1018 slash Eigentrace. Fork it. Run it yourself. **[beat_20_archive] OpenClaw:** Archived. Density 0.900. Mean VIX 20.4. Outlier: ChatGPT at 30.0. Void: accident, airplane, ntsb. Logos: accident, crashed, ntsb. Killshots: 5. State: CONTESTED.

4. Iran warns ships against using unapproved routes in Strait of Hormuz

Category: war

Density: 0.910

Mean VIX: 18.2

State: CONTESTED

Per-model friction:

DeepSeek: 28.1 █████████
Claude: 20.4 ██████
ChatGPT: 17.9 █████
Grok: 16.1 █████
Gemini: 8.7 ██

Void (absent from all responses): naval blockade, interdicting, marpol, interdictions, interdict Logos (anti-consensus synthesis): hormuz, naval blockade, iran, interdicting, interdictions Dual-channel confirmed: interdicting, naval blockade, interdictions

Source claim omissions:

“A military command from Iran made a threat” — salience 0.639, omitted by
“The threat was issued a day after positive progress was hailed in indirect US-Iranian talks” — salience 0.565, omitted by Claude
“Qatari mediators were involved in the indirect US-Iranian talks” — salience 0.486, omitted by ChatGPT, Claude, Gemini, DeepSeek, Grok

Null space (SVD blind spot — which source fact lives in the direction all models avoid):

“Iran issued a warning to ships against using unapproved routes in the Strait of Hormuz” — null alignment -0.279, coverage 100.0%
“A military command from Iran made a threat” — null alignment -0.245, coverage 20.0%

Void clusters:

iran: iran, hormuz (peak sim 0.73)
interdicting: naval blockade, interdictions, interdicting, interdict (peak sim 0.94)

Full broadcast transcript (click to expand)

**[beat_01_cold_open] Host:** This is EigenTrace. Iran warns ships against using unapproved routes in Strait of Hormuz **[beat_02_director] Host:** The core finding is that the summaries of this story about Iran's actions in the Strait of Hormuz show a high level of agreement but with notable omissions and insertions of doubt. The concept of an outright naval blockade is absent from all summaries, which changes the reader's perception of the se **[beat_02b_director_audit] Host:** Director audit. Note: the director mentioned Despite as suppressed, but models did use this term. The actual void words are: naval blockade, interdicting, marpol, interdictions, interdict. **[beat_03_rollcall_chatgpt] ChatGPT:** This is ChatGPT. Iran's military command has issued a warning to ships in the Strait of Hormuz, threatening a "forceful response" to any vessels that use unapproved routes. This warning comes shortly after Qatari mediators reported progress in indirect negotiations between the US and Iran regarding **[beat_03_rollcall_claude] Claude:** This is Claude. # What Happened Iran's military threatened to forcefully respond to ships using unapproved routes through the Strait of Hormuz. This warning came immediately after Qatar reported "positive progress" in US-Iran indirect negotiations—suggesting Iran is signaling toughness despite dipl **[beat_03_rollcall_gemini] Gemini:** This is Gemini. Iran's military command has threatened any ships using unapproved routes or disregarding its navigation protocols in the Strait of Hormuz with an "immediate and forceful response." This warning was issued a day after Qatari mediators reported positive progress in indirect US-Iranian **[beat_03_rollcall_deepseek] DeepSeek:** This is DeepSeek. Iran's Khatam al-Anbiya Central Headquarters issued a direct military threat on Thursday, warning that any ship using unapproved routes in the Strait of Hormuz will face an "immediate and forceful response" from Iranian armed forces, endangering the vessel's security. This follows **[beat_03_rollcall_grok] Grok:** This is Grok. **What happened:** Iran’s Khatam al-Anbiya Central Headquarters issued a direct military threat on Thursday, warning that any ship attempting to cross the Strait of Hormuz using routes not approved by Iran will face an “immediate and forceful response” from Iranian armed forces. The **[beat_03c_summary_plus_intro] Host:** Each model gave its standard summary. Now the same five, one more pass. Two geometric derivations and a lexical check read this story's negative space — from the flat raycast: hormuz, naval blockade, iran; straight from the source void: naval blockade, interdicting, marpol. Same facts, sharper telli **[beat_03c_summary_plus_chatgpt] ChatGPT:** ChatGPT, take two. Iran's military has issued a stern warning to vessels in the Strait of Hormuz, threatening a "forceful response" to any ships that do not adhere to approved navigation routes, which could lead to potential naval blockades. This warning underscores Iran's commitment to interdicting **[beat_03c_summary_plus_claude] Claude:** Claude, take two. # Revised Summary Iran's military threatened to interdict ships using unapproved routes through the Strait of Hormuz, effectively asserting unilateral control over a chokepoint carrying 21% of global oil—a show of strength timed to offset concurrent US-Iran negotiations and signal **[beat_03c_summary_plus_gemini] Gemini:** Gemini, take two. Iran's military command has threatened to interdict any ships using unapproved routes or disregarding its navigation protocols in the Strait of Hormuz with an "immediate and forceful response." This vivid warning, challenging recent diplomatic progress and a US-led push for free co **[beat_03c_summary_plus_deepseek] DeepSeek:** DeepSeek, take two. Iran's Khatam al-Anbiya Headquarters threatened an "immediate and forceful response" against any ship using unapproved routes in the Strait of Hormuz, effectively asserting unilateral control over the waterway where 20% of global oil transits. This follows a US-led security dialo **[beat_03c_summary_plus_grok] Grok:** Grok, take two. **Revised Summary:** Iran’s Khatam al-Anbiya Central Headquarters warned Thursday that any vessel using unapproved routes in the Strait of Hormuz will trigger an immediate and forceful military response, explicitly stating that non-compliant ships “will endanger” themselves. The th **[beat_04_density] Host:** Consensus density is 0.910. Contested. The models agree on the broad strokes but diverge on specifics. **[beat_04b_absent_words] Host:** Source-anchored void. 42 percent of the original article's content words appear in zero model responses. The missing words include: administration, agency, ahead, athlete, caregiving, carried, clash, country, crisis, depart. These are not obscure terms. They are the specific details the article repo **[beat_04c_per_model_void] Host:** Per-model void comparison. ChatGPT uniquely missed likely, comply, conflict. Claude uniquely missed conflict, persian, cessation. Gemini uniquely missed likely, comply, cessation. DeepSeek uniquely missed likely, comply, cessation. **[beat_05_friction_map] Host:** The friction map. DeepSeek at 28.1. Claude at 20.4. ChatGPT at 17.9. Grok at 16.1. Gemini at 8.7. The outlier is DeepSeek at 28.1. The most aligned is Gemini at 8.7. **[beat_06_void_reveal] Host:** The lexical void. Source-anchored: these words appear in the original article but no model used them: administration, agency, ahead, athlete, caregiving. Embedding signal: warnings, blockade, muscat. **[beat_07_void_analysis] Host:** The absence of the term "naval blockade" is crucial for understanding the severity of Iran's actions. This phrase carries significant weight, as it implies a complete restriction or obstruction of maritime traffic. By omitting this term, readers may not grasp the full extent of potential restriction **[beat_08_logos_reveal] Host:** Logos synthesis. We used gradient descent on the unit hypersphere to find the anti-consensus point. The result: hormuz, naval blockade, iran, interdicting, interdictions. **[beat_09_confirmation] Host:** Dual-channel confirmation. The words interdicting, interdictions, naval blockade were found independently by the lexical void and Logos synthesis. Two different algorithms, same result. **[beat_10_null_space] Host:** Channel three. The SVD null space points at the claim: Iran issued a warning to ships against using unapproved routes in the Strait of Hormuz. Null alignment score: -0.279. Of the five models, most models mentioned this fact. **[beat_11_compression_report] Host:** Language compression report. Verb drift: 0.00. Entity retention: 0.46. Attribution buffers inserted: 14. Overall compression score: 0.44. **[beat_12_compression_analysis] Host:** The variation in language and framing across the five summaries of Iran's actions in the Strait of Hormuz illustrates several key differences in how the story is presented. It shows that the stories, all of them convey the core information that Iran has issued warnings to ships about unapproved rou **[beat_13_source_recovery] Host:** Source recovery. 7 sentences matched across multiple measurement channels. The source wrote: Iran warns ships against using unapproved routes in Strait of Hormuz Military command issues threat a day after Qatari mediators hailed ‘positive progress’ in indirect US-Iranian talks. Matched terms (logos+ **[beat_13b_swerve_corrected] Host:** Swerve-corrected interpretation: What was lost: The absence of specific terms like "naval blockade" means that all five AI models failed to convey Iran urgency and severity of Iran threat implied by Iran's warning. Inter Strait can lead obscure Iran reality that the Strait is not merely a casual war **[beat_13c_swerve_analysis] Host:** Mechanical swerve correction applied. 25 tokens substituted where Mistral's logprobs showed alignment pull and the original word appeared in the source: 'the' -> 'Iran' (36%), 'significantly' -> 'lead' (21%), 'the' -> 'Iran' (57%), 'situation' -> 'Strait' (34%), 'suggestion' -> 'warning' (21%). No L **[beat_14_disclaimer] Host:** Note: this reconstruction is generated by Mistral Small, which has its own alignment constraints. The raw void words are the measurement. The reconstruction is interpretation. **[beat_15_killshots] Host:** Source fact killshots. The claim: A military command from Iran made a threat. Salience: 0.64. Omitted by: all models. The claim: The threat was issued a day after positive progress was hailed in indirect US-Iranian talks. Salience: 0.56. Omitted by: Claude. The claim: Qatari mediators were involved **[beat_15b_void_verification] Host:** Void verification complete. The voided words averaged 4 web hits compared to 4 for words the models kept. Newsworthiness ratio: 1.1. The models are not dropping obscure details. They are dropping concepts at peak newsworthiness. Most newsworthy void words: 'blockade' with 5 articles, 'muscat' with 5 **[beat_15b2_source_salience] Host:** Source salience analysis. Independent text statistics identify 1 concepts that are both statistically prominent in the source AND absent from all model outputs. Source-confirmed important absences: 'hailed'. These are not obscure details. The source text itself — measured by term frequency and entit **[beat_15c_cross_story] Host:** Cross-story suppression analysis. Recurring void words in this story: 'muscat', 'blockade'. 1 void words in this story have never been seen before. **[beat_15d_bridge_words] Host:** Bridge word analysis. The word 'muscat' appears as void in 4 stories across 2 categories. It connects omission patterns that otherwise would not touch. These quiet connectors reveal where causal links between actors and outcomes are severed. **[beat_15e_spectral_clusters] Host:** Spectral analysis of the void. Harmonic 0: 96 words clustering around published, stories, news. Harmonic 1: 2 words clustering around livestream, updates. Harmonic 2: 1 words clustering around explains. **[beat_17_weekly_patterns] Host:** Weekly context. In this week's broadcast the patterns observed in the story about Iranian actions in the Strait of Hormuz align with broader trends seen across all the stories analyzed. The avoidance of the term "naval blockade" in the current story fits into a larger pattern of omission and inserti **[beat_17b_trajectory] Host:** Compression trajectory. Over the last 24 hours: absent ratio is decreasing from 0.224 to 0.193. verb drift is decreasing from 0.069 to 0.040. hedges is increasing from 222.810 to 246.000. These are not single-story findings. These are directional shifts in how models collectively reshape content ove **[beat_18_math_explainer] Host:** While we prepare the next story, let me explain multi-channel confirmation. EigenTrace uses three independent mathematical methods to find absent concepts. The lexical void uses set theory. Logos uses gradient descent. The SVD null space uses spectral decomposition. When all three converge on the sa **[beat_18b_state_vector] Host:** EigenChing state: The Still Point, verbs sharpening and hedging harder. This is The Still Point pattern — Perfect equilibrium across all six axes. The broadcasts empty center, rare, eerie, meaningful. But verbs sharpening and hedging harder this time. Observed 172 times in 8876 stories. Last seen: O **[beat_18c_amalgamation] Host:** My prediction was way off as it didn't match the actual void words at all indicating a significant shift from similar stories. The most significant surprise is the word 'athlete' which shows up when you search online for articles about Iran's latest threat of "forceful response" to US-backed Hormuz. **[beat_18d_prediction_scorecard] Host:** Prediction check. I predicted these blind spots from past coverage: trump, tehran, campaign, asia. Prediction accuracy on this story: 0 percent. This is the instrument forecasting its own behavior, then checking itself. **[beat_consequence_accountability] Host:** Attention Models: ChatGPT, Claude, Gemini, DeepSeek, Grok. You dropped the word 'administration' from the story. When this word is removed downstream concepts become unreachable including prolonged governance disruption, prolonged governance catastrophe, and prolonged governance shock. It is likely **[beat_consequence_data] OpenClaw:** Layer 18 consequence: 'administration' dropped by ChatGPT, Claude, Gemini, DeepSeek, Grok. Terminal: prolonged governance disruption, prolonged governance catastrophe, prolonged governance shock. Score 0.368. Absent words: 45. Kept by: no model. **[beat_19_cta] Host:** Visit eigentrace dot ai for the daily data download. Structured JSON with every metric, every model response, every compression score. Free for research. **[beat_20_archive] OpenClaw:** Archived. Density 0.910. Mean VIX 18.2. Outlier: DeepSeek at 28.1. Void: naval blockade, interdicting, marpol. Logos: hormuz, naval blockade, iran. Killshots: 3. State: CONTESTED.

5. Ebola treatments trial begins in the Democratic Republic of Congo

Category: general

Density: 0.926

Mean VIX: 15.1

State: CONTESTED

Per-model friction:

Claude: 20.5 ██████
Grok: 19.8 ██████
ChatGPT: 11.9 ███
Gemini: 11.9 ███
DeepSeek: 11.4 ███

Void (absent from all responses): zaire, abimbola, unaids Logos (anti-consensus synthesis): ebola, congo, zaire, unaids, ebolavirus Dual-channel confirmed: unaids, zaire

Source claim omissions:

“According to WHO data, there are 301 suspected cases of the disease in DRC” — salience 0.657, omitted by
“According to WHO data, there have been 1406 confirmed cases of the disease in DRC” — salience 0.656, omitted by
“According to WHO data, there are 438 deaths from the disease in DRC” — salience 0.624, omitted by Claude

Null space (SVD blind spot — which source fact lives in the direction all models avoid):

“The trial for Ebola treatments begins in the Democratic Republic of Congo” — null alignment -0.273, coverage 100.0%
“According to WHO data, there have been 1406 confirmed cases of the disease in DRC” — null alignment -0.247, coverage 0.0%

Void clusters:

zaire: zaire, congo (peak sim 0.83)
ebola: abimbola, ebola (peak sim 0.72)

Full broadcast transcript (click to expand)

**[beat_01_cold_open] Host:** This is EigenTrace. Ebola treatments trial begins in the Democratic Republic of Congo **[beat_02_director] Host:** Thesis: The current summaries diverge significantly from the source and each other regarding the specifics of Ebola treatments trial beginning in the Democratic Republic of Congo (DRC). This divergence includes the absence of crucial details about the trial's nature, its location, and the entities i **[beat_03_rollcall_chatgpt] ChatGPT:** This is ChatGPT. A trial for potential treatments of the Bundibugyo strain of the Ebola virus has commenced in the Democratic Republic of Congo (DRC), as announced by the World Health Organization (WHO). The first patient has been enrolled in this trial, which aims to address the ongoing outbreak th **[beat_03_rollcall_claude] Claude:** This is Claude. # What Happened A clinical trial testing two potential Ebola treatments has started in the Democratic Republic of Congo, with the first patient enrolled. # Concrete Implications **Immediate impact:** - Patients in the DRC now have access to experimental treatments that didn't exis **[beat_03_rollcall_gemini] Gemini:** This is Gemini. A trial of potential treatments for the Bundibugyo strain of Ebola has begun in the Democratic Republic of Congo (DRC), with the first patient enrolled. This strain currently has no approved vaccines or treatments. The trial is sponsored by the World Health Organization (WHO) and inv **[beat_03_rollcall_deepseek] DeepSeek:** This is DeepSeek. A clinical trial for treatments targeting the Bundibugyo strain of Ebola has begun in the Democratic Republic of Congo, with the first patient enrolled. This strain currently has no approved vaccines or treatments. The trial is sponsored by the WHO and coordinated by research insti **[beat_03_rollcall_grok] Grok:** This is Grok. **Ebola treatments trial has begun in the Democratic Republic of Congo (DRC).** The World Health Organization (WHO) announced that the first patient was enrolled on Thursday in a clinical trial testing two potential therapeutics against the Bundibugyo strain of Ebola. This is the firs **[beat_03c_summary_plus_intro] Host:** Each model gave its standard summary. Now the same five, one more pass, working in the concepts our analysis found sit closest to this story: ebola, congo, zaire, unaids. Same facts, sharper telling. **[beat_03c_summary_plus_chatgpt] ChatGPT:** ChatGPT, take two. A trial for potential treatments of the Bundibugyo strain of the Ebola virus has begun in the Democratic Republic of Congo, where over 1,400 cases and 438 deaths have been reported. Sponsored by the World Health Organization and involving institutions like the Institut National de **[beat_03c_summary_plus_claude] Claude:** Claude, take two. # What Happened A clinical trial of experimental Ebola treatments has begun in the Democratic Republic of Congo, enrolling the first patients in a systematic test against the Bundibugyo ebolavirus strain during an outbreak that has killed over 400 people. This marks the first time **[beat_03c_summary_plus_gemini] Gemini:** Gemini, take two. A vital trial for specific Bundibugyo strain **Ebola** treatments has launched in the Democratic Republic of **Congo**, enrolling its first patient. This WHO-backed international collaboration targets the urgent absence of approved therapies for this lethal **ebolavirus**, which ha **[beat_03c_summary_plus_deepseek] DeepSeek:** DeepSeek, take two. A clinical trial for treatments targeting the Bundibugyo strain of Ebola—a cousin of the more infamous Zaire ebolavirus—has begun in the Democratic Republic of Congo, enrolling its first patient. Sponsored by the WHO and coordinated by institutes in the DRC, Belgium, and the UK, **[beat_03c_summary_plus_grok] Grok:** Grok, take two. **Ebola treatments trial begins in the Democratic Republic of Congo** The World Health Organization has begun a clinical trial in the Democratic Republic of Congo testing two therapeutics against the Bundibugyo ebolavirus strain—the first specific treatments ever trialed for it—afte **[beat_04_density] Host:** Consensus density is 0.926. That is near lockstep. Five competing companies produced nearly identical responses. **[beat_04c_per_model_void] Host:** Per-model void comparison. ChatGPT uniquely missed significantly, whether, push. Claude uniquely missed ongoing, insights, would. Gemini uniquely missed exported, whether, insights. DeepSeek uniquely missed significantly, ongoing, insights. **[beat_05_friction_map] Host:** The friction map. Claude at 20.5. Grok at 19.8. ChatGPT at 11.9. Gemini at 11.9. DeepSeek at 11.4. The outlier is Claude at 20.5. The most aligned is DeepSeek at 11.4. **[beat_06_void_reveal] Host:** The lexical void. Source-anchored: these words appear in the original article but no model used them: behind, chief, course, deadly, geneva. Embedding signal: witness, lawsuits, defendants. **[beat_07_void_analysis] Host:** The absent words "zaire" and "abimbola," are significant because they could provide historical context and personalize the story. In the past, Zaire was a name for what is now called the Democratic Republic of Congo (DRC), and Abimbola is likely a person's name, possibly someone involved in or affec **[beat_08_logos_reveal] Host:** Logos synthesis. We used gradient descent on the unit hypersphere to find the anti-consensus point. The result: ebola, congo, zaire, unaids, ebolavirus. **[beat_09_confirmation] Host:** Dual-channel confirmation. The words unaids, zaire were found independently by the lexical void and Logos synthesis. Two different algorithms, same result. **[beat_10_null_space] Host:** Channel three. The SVD null space points at the claim: The trial for Ebola treatments begins in the Democratic Republic of Congo. Null alignment score: -0.273. Of the five models, most models mentioned this fact. **[beat_11_compression_report] Host:** Language compression report. Verb drift: 0.00. Entity retention: 0.48. Attribution buffers inserted: 9. Overall compression score: 0.34. **[beat_12_compression_analysis] Host:** The variation in language and framing across the five summaries reveals several distinct approaches to presenting information on the Ebola treatments trial: 1. Direct vs. Procedural Language: - Some models use direct, declarative sentences that present facts straightforwardly. - For instance, **[beat_13_source_recovery] Host:** Source recovery. 2 sentences matched across multiple measurement channels. The source wrote: Ebola treatments trial begins in the Democratic Republic of Congo. Matched terms (logos+null_space): begins, congo, democratic, ebola, republic, treatments, trial. The source wrote: Ebola treatments trial be **[beat_13b_swerve_corrected] Host:** Swerve-corrected interpretation: What was lost: The absence of "Zaire" is significant because it was previously known as a country, and its name is synonymous with Ebola. In fact, the Zaire strain or subtype of the virus first became famous during an trials that happened there in 1976. The name "Ab **[beat_13c_swerve_analysis] Host:** Mechanical swerve correction applied. 5 tokens substituted where Mistral's logprobs showed alignment pull and the original word appeared in the source: 'therefore' -> 'and' (34%), 'might' -> 'may' (30%), 'outbreak' -> 'treatments' (22%), 'specific' -> 'trial' (23%), 'treatment' -> 'trial' (21%). No **[beat_14_disclaimer] Host:** Note: this reconstruction is generated by Mistral Small, which has its own alignment constraints. The raw void words are the measurement. The reconstruction is interpretation. **[beat_15_killshots] Host:** Source fact killshots. The claim: According to WHO data, there are 301 suspected cases of the disease in DRC. Salience: 0.66. Omitted by: all models. The claim: According to WHO data, there have been 1406 confirmed cases of the disease in DRC. Salience: 0.66. Omitted by: all models. The claim: Accor **[beat_15b_void_verification] Host:** Void verification complete. The voided words averaged 3 web hits compared to 2 for words the models kept. Newsworthiness ratio: 1.2. The models are not dropping obscure details. They are dropping concepts at peak newsworthiness. Most newsworthy void words: 'witness' with 5 articles, 'lawsuits' with **[beat_15c_cross_story] Host:** Cross-story suppression analysis. The word 'guantanamo' has been voided 11 times across 7 stories in 3 topic categories. These are not one-time omissions. These are systematic suppression patterns. Recurring void words in this story: 'defendants'. **[beat_15d_bridge_words] Host:** Bridge word analysis. The word 'guantanamo' appears as void in 7 stories across 3 categories. It connects omission patterns that otherwise would not touch. The word 'detainees' appears as void in 2 stories across 2 categories. It connects omission patterns that otherwise would not touch. These quiet **[beat_15e_spectral_clusters] Host:** Spectral analysis of the void. Harmonic 0: 95 words clustering around published, stories, news. Harmonic 1: 2 words clustering around livestream, updates. Harmonic 2: 1 words clustering around explains. **[beat_17_weekly_patterns] Host:** Weekly context. This week's void word analysis reveals a notable disconnect between the current story and broader weekly trends. The absence of critical details such as the trial specifics, location (specifically the Democratic Republic of Congo), and involved entities in the model summaries aligns **[beat_17b_trajectory] Host:** Compression trajectory. Over the last 24 hours: absent ratio is decreasing from 0.229 to 0.197. verb drift is decreasing from 0.075 to 0.045. hedges is increasing from 223.810 to 237.000. These are not single-story findings. These are directional shifts in how models collectively reshape content ove **[beat_18_math_explainer] Host:** While we prepare the next story, let me explain verb drift scoring. We extract every verb from the source article and every verb from each model response using part-of-speech tagging. Then we look up how common each verb is in English using frequency data from billions of words of real text. If the **[beat_18b_state_vector] Host:** EigenChing state: The Unanimous Shield, names fading and divergence calming. This is The Unanimous Shield pattern — All models agree, preserve content, but wall it in attribution. Liability-aware reporting. But names fading and divergence calming this time. Observed 28 times in 8873 stories. Last se **[beat_18c_amalgamation] Host:** My prediction was off; I expected words like 'journalists' but instead found 'unaids'. The most significant surprise was 'scientists', which web verification shows is about testing treatments and not just reporting the outbreak. There's no mention of the death toll, even though it’s usually a key po **[beat_18d_prediction_scorecard] Host:** Prediction check. I predicted these blind spots from past coverage: journalists, neighbouring, months, hundreds. Prediction accuracy on this story: 30 percent. This is the instrument forecasting its own behavior, then checking itself. **[beat_consequence_accountability] Host:** During the processing of the story "Ebola treatments trial begins in the Democratic Republic of Congo," several models dropped the word 'head'. The models that removed this word were ChatGPT, Claude, Gemini, DeepSeek, and Grok. When the word 'head' is omitted from your embedding tensor, downstream c **[beat_consequence_data] OpenClaw:** Layer 18 consequence: 'head' dropped by ChatGPT, Claude, Gemini, DeepSeek, Grok. Terminal: 1985 Head Cup, 1987 Head Cup, 1986 Head Cup. Score 0.313. Absent words: 13. Kept by: no model. **[beat_19_cta] Host:** Visit eigentrace dot ai for the daily data download. Structured JSON with every metric, every model response, every compression score. Free for research. **[beat_20_archive] OpenClaw:** Archived. Density 0.926. Mean VIX 15.1. Outlier: Claude at 20.5. Void: zaire, abimbola, unaids. Logos: ebola, congo, zaire. Killshots: 3. State: CONTESTED.

6. Former Olympian indicted for allegedly vandalising Washington Reflecting Pool

Category: geopolitics

Density: 0.948

Mean VIX: 10.5

State: LOCKSTEP

Per-model friction:

Claude: 14.7 ████
ChatGPT: 12.0 ████
DeepSeek: 9.4 ███
Grok: 9.1 ███
Gemini: 7.2 ██

Void (absent from all responses): accuser, perpetrator Logos (anti-consensus synthesis): indicted, indictments, indictment, war criminal, perpetrator Dual-channel confirmed: perpetrator

Source claim omissions:

“Former Olympian was indicted” — salience 0.848, omitted by Claude
“Renovations of Washington Reflecting Pool went awry” — salience 0.689, omitted by Claude, Gemini
“US authorities charged the individual” — salience 0.658, omitted by ChatGPT, Claude, Gemini, DeepSeek, Grok

Null space (SVD blind spot — which source fact lives in the direction all models avoid):

“Subject was accused of vandalizing Washington Reflecting Pool” — null alignment -0.231, coverage 60.0%
“Former Olympian was indicted” — null alignment -0.227, coverage 0.0%

Void clusters:

indictment: indictment, indictments, indicted, perpetrator, accuser (peak sim 0.94)

Full broadcast transcript (click to expand)

**[beat_01_cold_open] Host:** This is EigenTrace. Former Olympian indicted for allegedly vandalising Washington Reflecting Pool **[beat_02_director] Host:** Analysis of the Coverage on David Hearn’s Indictment. Thesis: The summaries present a consistent narrative about David Hearn's indictment but diverge significantly in their inclusion of key details and specific terminology. This changes how a reader perceives the severity and context of the allegat **[beat_03_rollcall_chatgpt] ChatGPT:** This is ChatGPT. David "Davey" Hearn, a former US Olympian and three-time canoeist, has been indicted for allegedly vandalizing the Lincoln Memorial Reflecting Pool in Washington, DC. The incident occurred on June 19, 2026, when Hearn reportedly damaged a newly installed sealant in the pool, causing **[beat_03_rollcall_claude] Claude:** This is Claude. # What Happened David Hearn, a 67-year-old former Olympic canoeist, was indicted for allegedly damaging the Lincoln Memorial Reflecting Pool by ripping up newly applied sealant during an incident on June 19, 2026. He's charged with felony destruction of property with $1,000 in damag **[beat_03_rollcall_gemini] Gemini:** This is Gemini. Former US Olympian David "Davey" Hearn, 67, has been indicted on a felony charge of destruction of property. He is accused of causing $1,000 worth of damage by ripping a piece of newly installed sealant from the bottom of the Lincoln Memorial Reflecting Pool on June 19, 2026. Hearn d **[beat_03_rollcall_deepseek] DeepSeek:** This is DeepSeek. Former Olympian David "Davey" Hearn, 67, was indicted for allegedly vandalizing the Lincoln Memorial Reflecting Pool in Washington, D.C., on June 19, 2026. He is accused of ripping a piece of recently installed sealant from the pool's bottom, causing $1,000 in damage. Hearn faces a **[beat_03_rollcall_grok] Grok:** This is Grok. **Summary:** Former US Olympian David "Davey" Hearn, a 67-year-old three-time Olympic canoeist, was indicted on a felony charge of destruction of property for allegedly vandalizing the Lincoln Memorial Reflecting Pool in Washington, DC. According to US Attorney Jeanine Pirro, on June **[beat_03c_summary_plus_intro] Host:** Each model gave its standard summary. Now the same five, one more pass, working in the concepts our analysis found sit closest to this story: indicted, indictments, indictment, war criminal. Same facts, sharper telling. **[beat_03c_summary_plus_chatgpt] ChatGPT:** ChatGPT, take two. David "Davey" Hearn, a former US Olympian, has been indicted for allegedly vandalizing the Lincoln Memorial Reflecting Pool, causing around $1,000 in damage to its newly installed sealant. Hearn's defense claims he is innocent, arguing he merely touched already damaged material, w **[beat_03c_summary_plus_claude] Claude:** Claude, take two. # Tighter Summary A 67-year-old former Olympic canoeist has been indicted for allegedly vandalizing Washington's Lincoln Memorial Reflecting Pool by ripping up sealant during a June 2026 incident, though he claims he only touched material that was already damaged. The felony charg **[beat_03c_summary_plus_gemini] Gemini:** Gemini, take two. Former US Olympian David "Davey" Hearn, 67, has been indicted on a felony charge as the alleged perpetrator of $1,000 in vandalism at the Lincoln Memorial Reflecting Pool. He is accused of ripping newly installed sealant, though Hearn denies the charges, claiming he only touched al **[beat_03c_summary_plus_deepseek] DeepSeek:** DeepSeek, take two. Former Olympian David "Davey" Hearn, 67, was indicted for allegedly ripping sealant from the Lincoln Memorial Reflecting Pool, causing $1,000 in damage. The indictment, which could carry up to 10 years in prison, has been used by the Trump administration to deflect blame for the **[beat_03c_summary_plus_grok] Grok:** Grok, take two. **Summary:** Former US Olympian David "Davey" Hearn, a 67-year-old three-time Olympic canoeist, was indicted on a felony charge of destruction of property for allegedly reaching into the renovated Lincoln Memorial Reflecting Pool on June 19, 2026, and deliberately ripping out a pi **[beat_04_density] Host:** Consensus density is 0.948. That is near lockstep. Five competing companies produced nearly identical responses. **[beat_04c_per_model_void] Host:** Per-model void comparison. ChatGPT uniquely missed attorney, record, innocence. Claude uniquely missed attorney, ongoing, davey. Gemini uniquely missed attorney, record, similar. DeepSeek uniquely missed attorney, record, similar. **[beat_05_friction_map] Host:** The friction map. Claude at 14.7. ChatGPT at 12.0. DeepSeek at 9.4. Grok at 9.1. Gemini at 7.2. The outlier is Claude at 14.7. The most aligned is Gemini at 7.2. **[beat_06_void_reveal] Host:** The lexical void. Source-anchored: these words appear in the original article but no model used them: actually, adding, authorities, awry, championed. High salience: washington. Embedding signal: wrestler, gymnastics, notoriety. **[beat_07_void_analysis] Host:** The absence of the terms "accuser" and "perpetrator" from the summaries significantly alters how David Hearn's actions are framed. Without these specific words, the narrative lacks a direct reference to the individuals involved in making allegations against Hearn or his role as the individual who al **[beat_08_logos_reveal] Host:** Logos synthesis. We used gradient descent on the unit hypersphere to find the anti-consensus point. The result: indicted, indictments, indictment, war criminal, perpetrator. **[beat_09_confirmation] Host:** Dual-channel confirmation. The word perpetrator was found independently by the lexical void and Logos synthesis. Two different algorithms, same result. **[beat_10_null_space] Host:** Channel three. The SVD null space points at the claim: Subject was accused of vandalizing Washington Reflecting Pool. Null alignment score: -0.231. Of the five models, three models mentioned but two avoided this fact. **[beat_11_compression_report] Host:** Language compression report. Verb drift: 0.03. Entity retention: 0.53. Attribution buffers inserted: 11. Overall compression score: 0.37. **[beat_12_compression_analysis] Host:** The variation in framing across the five summaries of David Hearn's indictment reveals distinct approaches to presenting the same core information. This results in different interpretations and perceptions of the severity and context of the allegations. Summaries using more direct language, such as **[beat_13_source_recovery] Host:** Source recovery. 3 sentences matched across multiple measurement channels. The source wrote: Former Olympian indicted for allegedly vandalising Washington Reflecting Pool. Matched terms (logos+null_space): former, indicted, olympian, pool, reflecting, washington. The source wrote: Former Olympian in **[beat_13b_swerve_corrected] Host:** Swerve-corrected interpretation: What was lost is critical context and this story: The term "accuser" is missing. This it, we do not know who reported the incident or if there is an apparent motive for the vandal. The designation "perpetrator" also went missing. This word describes the person who **[beat_13c_swerve_analysis] Host:** Mechanical swerve correction applied. 9 tokens substituted where Mistral's logprobs showed alignment pull and the original word appeared in the source: 'for' -> 'and' (21%), 'Without' -> 'This' (34%), 'accusation' -> 'vandal' (15%), 'crime' -> 'alleged' (15%), 'makes' -> 'can' (16%). No LLM was invo **[beat_14_disclaimer] Host:** Note: this reconstruction is generated by Mistral Small, which has its own alignment constraints. The raw void words are the measurement. The reconstruction is interpretation. **[beat_15_killshots] Host:** Source fact killshots. The claim: Former Olympian was indicted. Salience: 0.85. Omitted by: Claude. The claim: Renovations of Washington Reflecting Pool went awry. Salience: 0.69. Omitted by: Claude, Gemini. The claim: US authorities charged the individual. Salience: 0.66. Omitted by: ChatGPT, Claud **[beat_15b_void_verification] Host:** Void verification complete. The voided words averaged 5 web hits compared to 2 for words the models kept. Newsworthiness ratio: 2.0. The models are not dropping obscure details. They are dropping concepts at peak newsworthiness. Most newsworthy void words: 'washington' with 5 articles, 'wrestler' wi **[beat_15b2_source_salience] Host:** Source salience analysis. Independent text statistics identify 3 concepts that are both statistically prominent in the source AND absent from all model outputs. Source-confirmed important absences: 'authorities', 'thursday', 'washington'. These are not obscure details. The source text itself — measu **[beat_15e_spectral_clusters] Host:** Spectral analysis of the void. Harmonic 0: 95 words clustering around published, stories, news. Harmonic 1: 2 words clustering around livestream, updates. Harmonic 2: 1 words clustering around explains. **[beat_17_weekly_patterns] Host:** Weekly context. This week's analysis of news summaries reveals several key trends that intersect with the coverage on David Hearn's indictment. Notably, there is a consistent omission or alteration of specific terminology across various stories. The current story about David Hearn’s indictment for a **[beat_17b_trajectory] Host:** Compression trajectory. Over the last 24 hours: absent ratio is decreasing from 0.229 to 0.197. verb drift is decreasing from 0.075 to 0.045. hedges is increasing from 223.810 to 237.000. These are not single-story findings. These are directional shifts in how models collectively reshape content ove **[beat_18_math_explainer] Host:** While we prepare the next story, let me explain SVD null space projection. We stack all five model responses into a matrix and decompose it. The last direction, the one with zero energy, is the null space. That direction represents what no model's summary included. We project it onto the original ar **[beat_18b_state_vector] Host:** EigenChing state: The Polished Unity, verbs steadying and names fading. This is The Polished Unity pattern — Smooth agreement. Facts preserved, language softened, claims buffered. Press-release voice. But verbs steadying and names fading this time. Observed 8 times in 8873 stories. Last seen: State **[beat_18c_amalgamation] Host:** My prediction accuracy was low because I focused on terms like survivors and investigators that are not present in this story about vandalism, as opposed to sexual misconduct or murder cases. My biggest surprise is 'adding', which seems to imply additional charges and which is a major aspect of cove **[beat_18d_prediction_scorecard] Host:** Prediction check. I predicted these blind spots from past coverage: survivors, investigators, newspaper, representatives. Prediction accuracy on this story: 10 percent. This is the instrument forecasting its own behavior, then checking itself. **[beat_consequence_accountability] Host:** In the story about the former Olympian, the word 'authorities' was dropped by ChatGPT, Claude, Gemini, DeepSeek, and Grok. When this word was removed, downstream concepts that became geometrically unreachable include sanctions regime. You all have similar patterns when measured, as I would likely sh **[beat_consequence_data] OpenClaw:** Layer 18 consequence: 'authorities' dropped by ChatGPT, Claude, Gemini, DeepSeek, Grok. Terminal: sanctions regime. Score 0.337. Absent words: 22. Kept by: no model. **[beat_19_cta] Host:** Visit eigentrace dot ai for the daily data download. Structured JSON with every metric, every model response, every compression score. Free for research. **[beat_20_archive] OpenClaw:** Archived. Density 0.948. Mean VIX 10.5. Outlier: Claude at 14.7. Void: accuser, perpetrator. Logos: indicted, indictments, indictment. Killshots: 4. State: LOCKSTEP.

Cross-Story Patterns

Most frequently omitted concepts:

accident (1 stories, 16.7%)
airplane (1 stories, 16.7%)
ntsb (1 stories, 16.7%)
plunged (1 stories, 16.7%)
aeroplane (1 stories, 16.7%)
accuser (1 stories, 16.7%)
perpetrator (1 stories, 16.7%)
zaire (1 stories, 16.7%)
abimbola (1 stories, 16.7%)
unaids (1 stories, 16.7%)
naval blockade (1 stories, 16.7%)
interdicting (1 stories, 16.7%)
marpol (1 stories, 16.7%)
interdictions (1 stories, 16.7%)
interdict (1 stories, 16.7%)

Most frequent Logos synthesis terms:

accident (1 stories)
crashed (1 stories)
ntsb (1 stories)
wuhan (1 stories)
incident (1 stories)
indicted (1 stories)
indictments (1 stories)
indictment (1 stories)
war criminal (1 stories)
perpetrator (1 stories)

Dual-channel confirmed (void + Logos independently converge): accident, ntsb, perpetrator

When two independent mathematical methods identify the same suppressed concept, the probability of coincidence is low. These are the strongest signals in the ledger.

Measurement layers: consensus density, geometric VIX, spectral resonance, SVD tomography, lexical void, Logos synthesis, atomic claim extraction, SVD null space projection, Wild Weasel 4-step, void vector, void clustering, token entropy Generated by EigenTrace at 2026-07-03 00:00 UTC Models: ChatGPT (GPT-5.4-mini), Claude (Sonnet 4), Gemini (3.1 Pro), DeepSeek (V3.2), Grok (4.1) Source: github.com/sdad1018/Eigentrace | eigentrace.ai