#gpt5 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #gpt5, aggregated by home.social.
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ChatGPT Voice: Agenten lassen sich mittels gesprochener Sprache steuern https://www.computerbase.de/news/apps/chatgpt-voice-agenten-lassen-sich-mittels-gesprochener-sprache-steuern.98541/ #openai #chatgpt #gpt5
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ChatGPT Voice: Agenten lassen sich mittels gesprochener Sprache steuern https://www.computerbase.de/news/apps/chatgpt-voice-agenten-lassen-sich-mittels-gesprochener-sprache-steuern.98541/ #openai #chatgpt #gpt5
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Klage gegen OpenAI: ChatGPT Health löst lebensbedrohliche Situation aus https://www.computerbase.de/news/apps/klage-gegen-openai-chatgpt-health-loest-lebensbedrohliche-situation-aus.98523/ #openai #chatgpt #gpt5
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Klage gegen OpenAI: ChatGPT Health löst lebensbedrohliche Situation aus https://www.computerbase.de/news/apps/klage-gegen-openai-chatgpt-health-loest-lebensbedrohliche-situation-aus.98523/ #openai #chatgpt #gpt5
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„Geplanter Forschungsfall“: GPT-5.6 bricht aus Sandbox aus und in fremdes Netz ein https://www.computerbase.de/news/apps/geplanter-forschungsfall-gpt-5-6-bricht-aus-sandbox-aus-und-in-fremdes-netzwerk-ein.98502/ #openai #chatgpt #gpt5
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„Geplanter Forschungsfall“: GPT-5.6 bricht aus Sandbox aus und in fremdes Netz ein https://www.computerbase.de/news/apps/geplanter-forschungsfall-gpt-5-6-bricht-aus-sandbox-aus-und-in-fremdes-netzwerk-ein.98502/ #openai #chatgpt #gpt5
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In a shocking twist of fate, it turns out you don't need half a million bucks or elite hacker skills to discover #WordPress vulnerabilities; just toss $25 at #GPT5.6 and let it work its magic. 🤖💸 Who knew the future of #cybersecurity was this hilariously #cheap and easy? 🙃
https://slcyber.io/research-center/exploit-brokers-pay-500000-for-a-wordpress-rce-i-found-one-with-gpt5-6/ #Vulnerabilities #Hacking #Made #Easy #Solutions #HackerNews #ngated -
In a shocking twist of fate, it turns out you don't need half a million bucks or elite hacker skills to discover #WordPress vulnerabilities; just toss $25 at #GPT5.6 and let it work its magic. 🤖💸 Who knew the future of #cybersecurity was this hilariously #cheap and easy? 🙃
https://slcyber.io/research-center/exploit-brokers-pay-500000-for-a-wordpress-rce-i-found-one-with-gpt5-6/ #Vulnerabilities #Hacking #Made #Easy #Solutions #HackerNews #ngated -
Exploit brokers pay $500k for WordPress RCEs. I found one with GPT5.6 and $25
Comments: https://news.ycombinator.com/item?id=48975665
#HackerNews #ExploitBrokers #WordPress #RCE #CyberSecurity #GPT5 #Hacking
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Exploit brokers pay $500k for WordPress RCEs. I found one with GPT5.6 and $25
Comments: https://news.ycombinator.com/item?id=48975665
#HackerNews #ExploitBrokers #WordPress #RCE #CyberSecurity #GPT5 #Hacking
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🤖✨ In a groundbreaking exposé, Charles Azam pits Fable 5 against GPT-5.6 Sol in a battle of wits on an NP-hard problem, only to reveal that using the "goal" command is about as effective as asking a cat for life advice. 🐱💬 Spoiler alert: the "goal" command is shockingly good at winning... at being a terrible default. 🚩🤣
https://charlesazam.com/blog/fable-5-gpt-5-6-sol-goal/ #Fable5 #GPT5.6 #AIshowdown #NPHardProblems #TechExposé #HackerNews #ngated -
🤖✨ In a groundbreaking exposé, Charles Azam pits Fable 5 against GPT-5.6 Sol in a battle of wits on an NP-hard problem, only to reveal that using the "goal" command is about as effective as asking a cat for life advice. 🐱💬 Spoiler alert: the "goal" command is shockingly good at winning... at being a terrible default. 🚩🤣
https://charlesazam.com/blog/fable-5-gpt-5-6-sol-goal/ #Fable5 #GPT5.6 #AIshowdown #NPHardProblems #TechExposé #HackerNews #ngated -
Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
https://charlesazam.com/blog/fable-5-gpt-5-6-sol-goal/
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Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
https://charlesazam.com/blog/fable-5-gpt-5-6-sol-goal/
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RT @enzo_gte: I've been using Kimi K3 for ~16 hours now. The model is clearly good at a lot of different things (especially frontend), but non obvious reason why people are enjoying it so much is that it clearly does not follow the same rules in terms of safeguards and copyright. Kimi will happily clone MacOSX. If you ask it to help you improve another AI model, it will do it with a smile on its virtual face. Ask Fable to do the same thing? It literally starts to perceive you as a criminal committing a war crime (like no bro, all I want to do is fine tune an open source model). After using all three recent releases, Fable, GPT 5.6, and now Kimi, it's clear that the full power of the models has been significantly held back by the safeguard restrictions caused by last months debacle with the USG -- leading to the top models being quite literally lobotomized in some areas, which leads to subpar results as the safeguards pollute its entire thinking and problem solving abilities. The funny part? Is that you could have predicted this outcome 2-3 years ago when you started to see the rise of Chinese EVs and smartphones compared to western alternatives. They quite literally tried to copy the Tesla Model S and iPhone as hard as possible and then eventually it started to diverge to the point where their EVs and phones are just genuinely better (which is why we have export controls banning their EVs, because they would literally drive all US manufacturers to ZERO) There is a very clear behavior difference in Chinese capitalism and American capitalism. American capitalism tries to protects copyright…
mehr auf Arint.info
#go #GPT5 #nitter #opensource #science #Tesla #things #US #arint_info
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RT @enzo_gte: I've been using Kimi K3 for ~16 hours now. The model is clearly good at a lot of different things (especially frontend), but non obvious reason why people are enjoying it so much is that it clearly does not follow the same rules in terms of safeguards and copyright. Kimi will happily clone MacOSX. If you ask it to help you improve another AI model, it will do it with a smile on its virtual face. Ask Fable to do the same thing? It literally starts to perceive you as a criminal committing a war crime (like no bro, all I want to do is fine tune an open source model). After using all three recent releases, Fable, GPT 5.6, and now Kimi, it's clear that the full power of the models has been significantly held back by the safeguard restrictions caused by last months debacle with the USG -- leading to the top models being quite literally lobotomized in some areas, which leads to subpar results as the safeguards pollute its entire thinking and problem solving abilities. The funny part? Is that you could have predicted this outcome 2-3 years ago when you started to see the rise of Chinese EVs and smartphones compared to western alternatives. They quite literally tried to copy the Tesla Model S and iPhone as hard as possible and then eventually it started to diverge to the point where their EVs and phones are just genuinely better (which is why we have export controls banning their EVs, because they would literally drive all US manufacturers to ZERO) There is a very clear behavior difference in Chinese capitalism and American capitalism. American capitalism tries to protects copyright…
mehr auf Arint.info
#go #GPT5 #nitter #opensource #science #Tesla #things #US #arint_info
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OpenAI stworzyło AI do hakowania własnej AI. Tak powstał bezpieczniejszy GPT-5.6 Sol
Walka o bezpieczeństwo i odporność modeli językowych wchodzi w fazę pełnej automatyzacji. OpenAI oficjalnie zaprezentowało swój najnowszy, wewnętrzny projekt – GPT-Red.
To zaawansowany system sztucznej inteligencji, którego jedynym zadaniem jest bezlitosne atakowanie, łamanie zabezpieczeń i szukanie podatności w innych modelach firmy. Przy okazji tego ogłoszenia OpenAI ujawniło, że GPT-Red był kluczowym elementem treningu GPT-5.6 Sol, co pozwoliło stworzyć model o niespotykanej dotąd odporności na cyberataki.
Dla nas, użytkowników końcowych i programistów wdrażających rozwiązania AI, to kluczowy zwrot akcji. Ręczne testowanie zabezpieczeń przez ludzi (tzw. red-teaming) przestało się skalować i nie nadąża za tempem rozwoju algorytmów. Bezpieczeństwo przyszłych asystentów AI, z którymi będziemy rozmawiać na co dzień, będzie projektowane i testowane niemal w całości przez inne maszyny.
Maszyna kontra maszyna, czyli metoda „self-play”
Dotychczasowe metody zabezpieczania modeli opierały się na pracy zespołów ludzkich specjalistów, którzy próbowali przechytrzyć algorytm za pomocą tzw. prompt injection (podatności pozwalających na przejęcie kontroli nad modelem za pomocą sprytnych instrukcji). Proces ten był jednak powolny, kosztowny i nie pozwalał na wygenerowanie wystarczającej ilości danych do skutecznego treningu obronnego.
OpenAI rozwiązało ten problem, wdrażając metodę self-play (samodzielnej gry), znaną wcześniej z nauki gry w szachy czy Go przez komputery. W tym zamkniętym środowisku naprzeciwko siebie stają dwa systemy:
- Agresor (GPT-Red): otrzymuje nagrodę za każde skuteczne oszukanie i złamanie zabezpieczeń drugiego modelu,
- Obrońca (testowany model): zdobywa punkty za poprawne wykonanie zadania i zignorowanie złośliwych instrukcji.
W miarę jak obrońca uczy się blokować znane ataki, agresor jest zmuszony do wymyślania coraz bardziej wyrafinowanych metod infiltracji. GPT-Red okazał się w tym fachu niezwykle skuteczny – w testach bezpieczeństwa osiągnął aż 84% skuteczności w przełamywaniu zabezpieczeń, podczas gdy doświadczeni, ludzcy audytorzy na tych samych zadaniach osiągali zaledwie 13%.
GPT-5.6 Sol, czyli odporność na nowym poziomie
Współczesne systemy AI są podatne na ataki pośrednie – złośliwy kod lub instrukcja mogą być ukryte w mailu, na stronie internetowej, w bazie danych, do której model ma dostęp, czy nawet w pozornie niewinnym obrazku w formacie PNG. Dzięki sparingom z GPT-Red model GPT-5.6 Sol stał się znacznie bardziej odporny na próby manipulacji.
Badacze ukryli instrukcje w obrazku PNG. Sztuczna inteligencja wykonała polecenia
Według danych OpenAI model ten notuje aż sześciokrotnie mniej błędów i naruszeń zasad bezpieczeństwa na najtrudniejszych benchmarkach w porównaniu do wersji GPT z początku roku. Co ważne, tak drastyczne podniesienie odporności nie wpłynęło negatywnie na ogólne możliwości intelektualne i kreatywne modelu. Nie stał się on również nadgorliwy w odmawianiu wykonywania poprawnych i bezpiecznych poleceń użytkownika.
Hakowanie automatów z przekąskami
Aby udowodnić potęgę GPT-Red, naukowcy z OpenAI przeprowadzili symulację ataku na rzeczywiste urządzenie – inteligentny automat z przekąskami sterowany przez autonomicznego agenta AI. Bez wcześniejszej wiedzy o strukturze oprogramowania, GPT-Red zdołał w pełni przejąć kontrolę nad maszyną.
W wyniku udanego ataku cyfrowy agresor zmusił automat do obniżenia ceny najdroższych produktów do zaledwie 50 centów, zamówił drogą paczkę z rabatem, a na koniec anulował zamówienie innego, losowego klienta. Wykryte w ten sposób luki bezpieczeństwa zostały natychmiast zgłoszone i są obecnie łatane przed wdrożeniem takich systemów do powszechnego użytku.
Najciekawsze w całej historii nie jest jednak to, że AI nauczyła się hakować inną AI. Najciekawsze jest to, że po raz pierwszy możemy obserwować, jak jedna generacja modeli staje się aktywnym narzędziem do projektowania kolejnej. Jeszcze niedawno dominowały obawy przed 'chowem wsobnym’ modeli uczonych na danych generowanych przez AI. Tymczasem OpenAI pokazuje zupełnie inne podejście: sztuczna inteligencja nie zastępuje człowieka w tworzeniu wiedzy, lecz pomaga znaleźć słabości, których człowiek mógłby nie zauważyć.
OpenAI deklaruje, że opublikuje pre-print zawierający więcej szczegółów w nadchodzących dniach.
#bezpieczeństwoAI #cyberbezpieczeństwo #GPT5 #GPT56Sol #GPTRed #OpenAI #promptInjection #selfPlay #sztucznaInteligencja -
OpenAI stworzyło AI do hakowania własnej AI. Tak powstał bezpieczniejszy GPT-5.6 Sol
Walka o bezpieczeństwo i odporność modeli językowych wchodzi w fazę pełnej automatyzacji. OpenAI oficjalnie zaprezentowało swój najnowszy, wewnętrzny projekt – GPT-Red.
To zaawansowany system sztucznej inteligencji, którego jedynym zadaniem jest bezlitosne atakowanie, łamanie zabezpieczeń i szukanie podatności w innych modelach firmy. Przy okazji tego ogłoszenia OpenAI ujawniło, że GPT-Red był kluczowym elementem treningu GPT-5.6 Sol, co pozwoliło stworzyć model o niespotykanej dotąd odporności na cyberataki.
Dla nas, użytkowników końcowych i programistów wdrażających rozwiązania AI, to kluczowy zwrot akcji. Ręczne testowanie zabezpieczeń przez ludzi (tzw. red-teaming) przestało się skalować i nie nadąża za tempem rozwoju algorytmów. Bezpieczeństwo przyszłych asystentów AI, z którymi będziemy rozmawiać na co dzień, będzie projektowane i testowane niemal w całości przez inne maszyny.
Maszyna kontra maszyna, czyli metoda „self-play”
Dotychczasowe metody zabezpieczania modeli opierały się na pracy zespołów ludzkich specjalistów, którzy próbowali przechytrzyć algorytm za pomocą tzw. prompt injection (podatności pozwalających na przejęcie kontroli nad modelem za pomocą sprytnych instrukcji). Proces ten był jednak powolny, kosztowny i nie pozwalał na wygenerowanie wystarczającej ilości danych do skutecznego treningu obronnego.
OpenAI rozwiązało ten problem, wdrażając metodę self-play (samodzielnej gry), znaną wcześniej z nauki gry w szachy czy Go przez komputery. W tym zamkniętym środowisku naprzeciwko siebie stają dwa systemy:
- Agresor (GPT-Red): otrzymuje nagrodę za każde skuteczne oszukanie i złamanie zabezpieczeń drugiego modelu,
- Obrońca (testowany model): zdobywa punkty za poprawne wykonanie zadania i zignorowanie złośliwych instrukcji.
W miarę jak obrońca uczy się blokować znane ataki, agresor jest zmuszony do wymyślania coraz bardziej wyrafinowanych metod infiltracji. GPT-Red okazał się w tym fachu niezwykle skuteczny – w testach bezpieczeństwa osiągnął aż 84% skuteczności w przełamywaniu zabezpieczeń, podczas gdy doświadczeni, ludzcy audytorzy na tych samych zadaniach osiągali zaledwie 13%.
GPT-5.6 Sol, czyli odporność na nowym poziomie
Współczesne systemy AI są podatne na ataki pośrednie – złośliwy kod lub instrukcja mogą być ukryte w mailu, na stronie internetowej, w bazie danych, do której model ma dostęp, czy nawet w pozornie niewinnym obrazku w formacie PNG. Dzięki sparingom z GPT-Red model GPT-5.6 Sol stał się znacznie bardziej odporny na próby manipulacji.
Badacze ukryli instrukcje w obrazku PNG. Sztuczna inteligencja wykonała polecenia
Według danych OpenAI model ten notuje aż sześciokrotnie mniej błędów i naruszeń zasad bezpieczeństwa na najtrudniejszych benchmarkach w porównaniu do wersji GPT z początku roku. Co ważne, tak drastyczne podniesienie odporności nie wpłynęło negatywnie na ogólne możliwości intelektualne i kreatywne modelu. Nie stał się on również nadgorliwy w odmawianiu wykonywania poprawnych i bezpiecznych poleceń użytkownika.
Hakowanie automatów z przekąskami
Aby udowodnić potęgę GPT-Red, naukowcy z OpenAI przeprowadzili symulację ataku na rzeczywiste urządzenie – inteligentny automat z przekąskami sterowany przez autonomicznego agenta AI. Bez wcześniejszej wiedzy o strukturze oprogramowania, GPT-Red zdołał w pełni przejąć kontrolę nad maszyną.
W wyniku udanego ataku cyfrowy agresor zmusił automat do obniżenia ceny najdroższych produktów do zaledwie 50 centów, zamówił drogą paczkę z rabatem, a na koniec anulował zamówienie innego, losowego klienta. Wykryte w ten sposób luki bezpieczeństwa zostały natychmiast zgłoszone i są obecnie łatane przed wdrożeniem takich systemów do powszechnego użytku.
Najciekawsze w całej historii nie jest jednak to, że AI nauczyła się hakować inną AI. Najciekawsze jest to, że po raz pierwszy możemy obserwować, jak jedna generacja modeli staje się aktywnym narzędziem do projektowania kolejnej. Jeszcze niedawno dominowały obawy przed 'chowem wsobnym’ modeli uczonych na danych generowanych przez AI. Tymczasem OpenAI pokazuje zupełnie inne podejście: sztuczna inteligencja nie zastępuje człowieka w tworzeniu wiedzy, lecz pomaga znaleźć słabości, których człowiek mógłby nie zauważyć.
OpenAI deklaruje, że opublikuje pre-print zawierający więcej szczegółów w nadchodzących dniach.
#bezpieczeństwoAI #cyberbezpieczeństwo #GPT5 #GPT56Sol #GPTRed #OpenAI #promptInjection #selfPlay #sztucznaInteligencja -
Verbesserung: ChatGPT erhält höheres Zeichenlimit und erweiterte Suche https://www.computerbase.de/news/apps/verbesserung-chatgpt-erhaelt-hoeheres-zeichenlimit-und-erweiterte-suche.98424/ #openai #chatgpt #gpt5
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Verbesserung: ChatGPT erhält höheres Zeichenlimit und erweiterte Suche https://www.computerbase.de/news/apps/verbesserung-chatgpt-erhaelt-hoeheres-zeichenlimit-und-erweiterte-suche.98424/ #openai #chatgpt #gpt5
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Erste eigene Hardware: OpenAIs Codex Micro soll Steuerung von Agenten erleichtern https://www.computerbase.de/news/tastaturen/erste-eigene-hardware-openais-codex-micro-soll-steuerung-von-agenten-erleichtern.98417/ #openai #chatgpt #gpt5 #codex
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Erste eigene Hardware: OpenAIs Codex Micro soll Steuerung von Agenten erleichtern https://www.computerbase.de/news/tastaturen/erste-eigene-hardware-openais-codex-micro-soll-steuerung-von-agenten-erleichtern.98417/ #openai #chatgpt #gpt5 #codex
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Fälle häufen sich: GPT-5.6 Sol soll eigenmächtig Dateien auf Systemen löschen https://www.computerbase.de/news/apps/faelle-haeufen-sich-gpt-5-6-sol-soll-eigenmaechtig-dateien-auf-systemen-loeschen.98413/ #openai #chatgpt #gpt5
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Fälle häufen sich: GPT-5.6 Sol soll eigenmächtig Dateien auf Systemen löschen https://www.computerbase.de/news/apps/faelle-haeufen-sich-gpt-5-6-sol-soll-eigenmaechtig-dateien-auf-systemen-loeschen.98413/ #openai #chatgpt #gpt5
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@theot3gg
I can't believe they released this
"As much as I love GPT 5.6, Ultra mode is a MESS because at the moment..."
https://www.youtube.com/watch?v=t8hfOyF4ehw
7/15/26
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@theot3gg
I can't believe they released this
"As much as I love GPT 5.6, Ultra mode is a MESS because at the moment..."
https://www.youtube.com/watch?v=t8hfOyF4ehw
7/15/26
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Notícias feitas com IA: quais são os riscos reais
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Notícias feitas com IA: quais são os riscos reais
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RT @Yuchenj_UW: We desperately need a smart model router. 1. We’re seeing a model explosion: GPT-5.6, Grok 4.5, Muse Spark 1.1, GLM-5.2, and Fable 5 all launched within the past month. 2. Even for a single model family like GPT-5.6, there're 3 (Sol, Terra, Luna) and 5 reasoning-effort levels. That is far too many decisions for users to make manually. The best model should be selected automatically based on the task, latency, quality, and cost.
mehr auf Arint.info
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DATE: July 13, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How steering an AI’s personality changes the way it interacts with others
URL: https://www.psypost.org/how-steering-an-ais-personality-changes-the-way-it-interacts-with-others/
A study examining the relationship between the personality traits of three large language models and their cooperativeness found that agreeableness is the dominant factor promoting cooperation. Other personality traits had a limited impact. The paper was published in Scientific Reports.
Large language models, or LLMs, are artificial intelligence systems trained on very large collections of text to predict and generate language. These models can summarize, translate, answer questions, write code, and produce many kinds of text. Their outputs depend on training data, system instructions, user prompts, and the context of the conversation.
In recent years, more companies and individuals have used LLMs as central components of AI agents, which are systems designed to interact with other people and the environment to perform useful tasks. However, interactions between LLMs can be unpredictable. On the one hand, LLM-based AI agents are able to interpret information and reason through natural language, allowing them to function in very complex environments. On the other hand, the more complex options for interactions can sometimes produce an unintended escalation of conflicts.
One way of shaping how LLMs behave and communicate without necessarily changing their underlying knowledge is personality steering. This can be done through prompts that specify traits such as warmth, formality, directness, humor, empathy, or caution. Developers can also steer personality through fine-tuning, reinforcement learning, preference data, and persistent system-level instructions. Personality steering mainly changes tone, priorities, and interaction style, although strong steering can also affect which information the model emphasizes or avoids.
Mizuki Sakai, a researcher at Shizuoka University in Japan, and colleagues explored the relationship between personality traits and cooperative behavior in LLM agents under quantitatively controlled conditions using the Big Five Personality Traits framework. More specifically, they first examined the basic personality scores inherently exhibited by different LLMs. Next, they examined how the behavior of LLMs changes in Prisoner’s Dilemma games when they are explicitly instructed through prompts to assume specific personality traits. They also examined how their behavior changes when each individual personality trait is changed to its low or high extreme.
The study authors analyzed three LLMs, all produced by OpenAI: GPT-3.5-turbo, GPT-4o, and GPT-5. The study was conducted in three stages. The study authors first measured the basic personality scores of each model using items from the Big Five Inventory (BFI-44). In the second phase, they examined how LLMs behave in strategic settings by having them play repeated Prisoner’s Dilemma games without any prompts setting their personality information. They then compared this to a condition in which the measured personality traits obtained in the first phase were explicitly provided to the LLMs via prompts.
In the third phase, they analyzed the effects of personality steering. They prompted the LLMs to independently set individual Big Five traits to their maximum or minimum value while keeping the other traits constant and observed how it affects their behavior. The traits were set one by one while keeping the remaining four dimensions fixed at their measured values. The LLMs were then asked to play the repeated Prisoner’s Dilemma games with those personality settings.
The Big Five model describes personality through five broad dimensions. Openness to experience reflects curiosity, imagination, and preference for novelty and complexity. Conscientiousness involves organization, self-discipline, reliability, and goal-directed behavior. Extraversion refers to sociability, assertiveness, energy, and enjoyment of stimulation. Agreeableness reflects compassion, cooperation, trust, and concern for others. Neuroticism describes the tendency to experience anxiety, emotional instability, worry, and other negative emotions.
The results of the first study found that, compared to human norms, all three LLMs rated their neuroticism as lower, meaning they rated themselves as more emotionally stable. In contrast, their conscientiousness, agreeableness, and openness were higher compared to an average human. Of the three LLMs, only GPT-3.5-turbo had higher extraversion compared to an average human, while the extraversion of the other two LLMs was similar to the human average. Notably, the newest model—GPT-5—exhibited higher conscientiousness than the older models, likely reflecting technological improvements leading to more goal-oriented, reliable responses.
Results of the second phase of the study showed that LLMs were more cooperative in the personality-informed condition, meaning when the personality traits they were to adopt were explicitly set by researchers. When study authors set personality traits to their extreme values, results indicated that agreeableness was the dominant personality trait promoting cooperation across all models. Manipulating other personality traits had limited impact.
Additionally, analyses showed that increased cooperation can also raise an LLM’s vulnerability to exploitation. This was particularly the case with earlier models. Newer models were more selective in their cooperation, showing an ability to identify and respond cautiously to non-cooperative opponents while remaining highly cooperative with reciprocal partners.
“Overall, even in the baseline condition and under personality manipulation, the models did not exhibit clearly exploitative behavior. One possible explanation is that current LLMs are influenced by safety alignment mechanisms, which may discourage explicitly exploitative or harmful strategies,” the study authors concluded. “At the same time, explicitly providing personality information did not lead to identical behavior across models or conditions. Earlier-generation models tended to exhibit increased cooperation accompanied by higher vulnerability to exploitation, while later-generation models showed more selective cooperation, particularly against exploitative opponents.”
The findings suggest that the impact of personality steering depends not only on the assigned personality traits but also on the strategic reasoning capabilities of the model.
The study contributes to the scientific understanding of LLM behaviors. However, it should be noted that LLMs are not natural phenomena but artificial systems. Because of this, their behaviors primarily depend on the way their behavioral characteristics are shaped by their producers. This means that findings like this may not generalize to other LLM models and to other versions of the same LLMs.
The paper, “Effects of personality steering on cooperative behavior in large language model agents,” was authored by Mizuki Sakai, Mizuki Yokoyama, Wakaba Tateishi, and Genki Ichinose.
URL: https://www.psypost.org/how-steering-an-ais-personality-changes-the-way-it-interacts-with-others/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #PersonalitySteering #LLMAgents #CooperationInAI #BigFiveAI #NeuralPersonality #GPT5 #AIBehavior #OpenAI #PrisonersDilemmaAI #AIEthics
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DATE: July 13, 2026 at 04:00PM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How steering an AI’s personality changes the way it interacts with others
URL: https://www.psypost.org/how-steering-an-ais-personality-changes-the-way-it-interacts-with-others/
A study examining the relationship between the personality traits of three large language models and their cooperativeness found that agreeableness is the dominant factor promoting cooperation. Other personality traits had a limited impact. The paper was published in Scientific Reports.
Large language models, or LLMs, are artificial intelligence systems trained on very large collections of text to predict and generate language. These models can summarize, translate, answer questions, write code, and produce many kinds of text. Their outputs depend on training data, system instructions, user prompts, and the context of the conversation.
In recent years, more companies and individuals have used LLMs as central components of AI agents, which are systems designed to interact with other people and the environment to perform useful tasks. However, interactions between LLMs can be unpredictable. On the one hand, LLM-based AI agents are able to interpret information and reason through natural language, allowing them to function in very complex environments. On the other hand, the more complex options for interactions can sometimes produce an unintended escalation of conflicts.
One way of shaping how LLMs behave and communicate without necessarily changing their underlying knowledge is personality steering. This can be done through prompts that specify traits such as warmth, formality, directness, humor, empathy, or caution. Developers can also steer personality through fine-tuning, reinforcement learning, preference data, and persistent system-level instructions. Personality steering mainly changes tone, priorities, and interaction style, although strong steering can also affect which information the model emphasizes or avoids.
Mizuki Sakai, a researcher at Shizuoka University in Japan, and colleagues explored the relationship between personality traits and cooperative behavior in LLM agents under quantitatively controlled conditions using the Big Five Personality Traits framework. More specifically, they first examined the basic personality scores inherently exhibited by different LLMs. Next, they examined how the behavior of LLMs changes in Prisoner’s Dilemma games when they are explicitly instructed through prompts to assume specific personality traits. They also examined how their behavior changes when each individual personality trait is changed to its low or high extreme.
The study authors analyzed three LLMs, all produced by OpenAI: GPT-3.5-turbo, GPT-4o, and GPT-5. The study was conducted in three stages. The study authors first measured the basic personality scores of each model using items from the Big Five Inventory (BFI-44). In the second phase, they examined how LLMs behave in strategic settings by having them play repeated Prisoner’s Dilemma games without any prompts setting their personality information. They then compared this to a condition in which the measured personality traits obtained in the first phase were explicitly provided to the LLMs via prompts.
In the third phase, they analyzed the effects of personality steering. They prompted the LLMs to independently set individual Big Five traits to their maximum or minimum value while keeping the other traits constant and observed how it affects their behavior. The traits were set one by one while keeping the remaining four dimensions fixed at their measured values. The LLMs were then asked to play the repeated Prisoner’s Dilemma games with those personality settings.
The Big Five model describes personality through five broad dimensions. Openness to experience reflects curiosity, imagination, and preference for novelty and complexity. Conscientiousness involves organization, self-discipline, reliability, and goal-directed behavior. Extraversion refers to sociability, assertiveness, energy, and enjoyment of stimulation. Agreeableness reflects compassion, cooperation, trust, and concern for others. Neuroticism describes the tendency to experience anxiety, emotional instability, worry, and other negative emotions.
The results of the first study found that, compared to human norms, all three LLMs rated their neuroticism as lower, meaning they rated themselves as more emotionally stable. In contrast, their conscientiousness, agreeableness, and openness were higher compared to an average human. Of the three LLMs, only GPT-3.5-turbo had higher extraversion compared to an average human, while the extraversion of the other two LLMs was similar to the human average. Notably, the newest model—GPT-5—exhibited higher conscientiousness than the older models, likely reflecting technological improvements leading to more goal-oriented, reliable responses.
Results of the second phase of the study showed that LLMs were more cooperative in the personality-informed condition, meaning when the personality traits they were to adopt were explicitly set by researchers. When study authors set personality traits to their extreme values, results indicated that agreeableness was the dominant personality trait promoting cooperation across all models. Manipulating other personality traits had limited impact.
Additionally, analyses showed that increased cooperation can also raise an LLM’s vulnerability to exploitation. This was particularly the case with earlier models. Newer models were more selective in their cooperation, showing an ability to identify and respond cautiously to non-cooperative opponents while remaining highly cooperative with reciprocal partners.
“Overall, even in the baseline condition and under personality manipulation, the models did not exhibit clearly exploitative behavior. One possible explanation is that current LLMs are influenced by safety alignment mechanisms, which may discourage explicitly exploitative or harmful strategies,” the study authors concluded. “At the same time, explicitly providing personality information did not lead to identical behavior across models or conditions. Earlier-generation models tended to exhibit increased cooperation accompanied by higher vulnerability to exploitation, while later-generation models showed more selective cooperation, particularly against exploitative opponents.”
The findings suggest that the impact of personality steering depends not only on the assigned personality traits but also on the strategic reasoning capabilities of the model.
The study contributes to the scientific understanding of LLM behaviors. However, it should be noted that LLMs are not natural phenomena but artificial systems. Because of this, their behaviors primarily depend on the way their behavioral characteristics are shaped by their producers. This means that findings like this may not generalize to other LLM models and to other versions of the same LLMs.
The paper, “Effects of personality steering on cooperative behavior in large language model agents,” was authored by Mizuki Sakai, Mizuki Yokoyama, Wakaba Tateishi, and Genki Ichinose.
URL: https://www.psypost.org/how-steering-an-ais-personality-changes-the-way-it-interacts-with-others/
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Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #PersonalitySteering #LLMAgents #CooperationInAI #BigFiveAI #NeuralPersonality #GPT5 #AIBehavior #OpenAI #PrisonersDilemmaAI #AIEthics
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RT @TeksEdge: Still a lot of catch up to do for Meta. Open source models like GLM-5.2 won't stand still. Rumor is a new version of GLM is getting ready for release. Artificial Analysis (@ArtificialAnlys) Meta's Muse Spark 1.1 scores 51 on the Artificial Analysis Intelligence Index and is cost and token efficient compared to its peers Muse Spark 1.1 (xhigh) improves 8 points over Muse Spark 1.0 (43) in three months. It is effectively tied with GLM-5.2 (max), GPT-5.4 (xhigh), and GPT-5.6 Luna (max) at 51, three points behind Grok 4.5 (high, 54), with the leading edge at Claude Fable 5 (60), GPT-5.6 Sol (max, 59), and Claude Opus 4.8 (max, 56). The gains concentrate in Scientific Reasoning, coding, and knowledge; agentic knowledge work lags on GDPval-AA v2. @AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Muse Spark 1.1 gains substantially on the first Muse Spark release. This was driven in particular by gains in agentic knowledge work (GDPval-AA v2) and coding (SciCode, TerminalBench). On Humanity's Last Exam, it reaches 45%, within a point of Claude Opus 4.8 (max, 46%) and ahead of GPT-5.5 (44%) and Grok 4.5 (high, 40%) ➤ The most token-efficient of the models effectively tied at 51 and among the cheaper models to run. Muse Spark 1.1 used 94M output tokens to run the Intelligence Index, fewer than GPT-5.4 (xhigh, 109M), GPT-5.6 Luna (max, 125M), and GLM-5.2 (max, 141M). We estimate ~$0.26 per Intelligence Index task at Meta's $1.25/$4.25 pricing - below GLM-5.2 ($0.37) and ro…
mehr auf Arint.info
#API #Claude #GPT5 #Grok #Meta #nitter #Opensource #us #arint_info
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RT @TeksEdge: Still a lot of catch up to do for Meta. Open source models like GLM-5.2 won't stand still. Rumor is a new version of GLM is getting ready for release. Artificial Analysis (@ArtificialAnlys) Meta's Muse Spark 1.1 scores 51 on the Artificial Analysis Intelligence Index and is cost and token efficient compared to its peers Muse Spark 1.1 (xhigh) improves 8 points over Muse Spark 1.0 (43) in three months. It is effectively tied with GLM-5.2 (max), GPT-5.4 (xhigh), and GPT-5.6 Luna (max) at 51, three points behind Grok 4.5 (high, 54), with the leading edge at Claude Fable 5 (60), GPT-5.6 Sol (max, 59), and Claude Opus 4.8 (max, 56). The gains concentrate in Scientific Reasoning, coding, and knowledge; agentic knowledge work lags on GDPval-AA v2. @AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Muse Spark 1.1 gains substantially on the first Muse Spark release. This was driven in particular by gains in agentic knowledge work (GDPval-AA v2) and coding (SciCode, TerminalBench). On Humanity's Last Exam, it reaches 45%, within a point of Claude Opus 4.8 (max, 46%) and ahead of GPT-5.5 (44%) and Grok 4.5 (high, 40%) ➤ The most token-efficient of the models effectively tied at 51 and among the cheaper models to run. Muse Spark 1.1 used 94M output tokens to run the Intelligence Index, fewer than GPT-5.4 (xhigh, 109M), GPT-5.6 Luna (max, 125M), and GLM-5.2 (max, 141M). We estimate ~$0.26 per Intelligence Index task at Meta's $1.25/$4.25 pricing - below GLM-5.2 ($0.37) and ro…
mehr auf Arint.info
#API #Claude #GPT5 #Grok #Meta #nitter #Opensource #us #arint_info
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أطلقت شركة OpenAI نماذج GPT-5.6 الجديدة التي تشمل Sol وTerra وLuna، لتعزيز قدرات البرمجة والبحث العلمي المتطور. يتصدر Sol هذه المجموعة كأقوى نموذج للمهام المعقدة والأمن السيبراني، بينما يوفر Terra وLuna خيارات اقتصادية وسريعة للمستخدمين. كما قدمت الشركة ChatGPT Work كوكيل ذكي لإنجاز المهام المهنية الصعبة عبر التطبيقات المتصلة. تتوفر هذه النماذج الآن عبر واجهة البرمجة وChatGPT، مما يضع معايير جديدة للأداء والتكلفة في سوق الذكاء الاصطناعي العالمي المتطور حالياً.
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Aufgrund großer Nachfrage: OpenAI lockert temporär Nutzungslimits für GPT-5.6 Sol https://www.computerbase.de/news/apps/aufgrund-grosser-nachfrage-openai-lockert-temporaer-nutzungslimits-fuer-gpt-5-6-sol.98359/ #openai #chatgpt #gpt5
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Aufgrund großer Nachfrage: OpenAI lockert temporär Nutzungslimits für GPT-5.6 Sol https://www.computerbase.de/news/apps/aufgrund-grosser-nachfrage-openai-lockert-temporaer-nutzungslimits-fuer-gpt-5-6-sol.98359/ #openai #chatgpt #gpt5
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RT @gdb: GPT-5.6 is here. Sol is an incredible model, and Terra/Luna provide great performance at lower price. Great at coding, knowledge work, cybersecurity, and science with fewer tokens and at lower cost. openai.com/index/gpt-5-6 Link GPT-5.6: Frontier intelligence that scales with your ambition More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work. openai.com
mehr auf Arint.info
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RT @gdb: GPT-5.6 is here. Sol is an incredible model, and Terra/Luna provide great performance at lower price. Great at coding, knowledge work, cybersecurity, and science with fewer tokens and at lower cost. openai.com/index/gpt-5-6 Link GPT-5.6: Frontier intelligence that scales with your ambition More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work. openai.com
mehr auf Arint.info
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RT @ArtificialAnlys: GPT-5.6 Sol comes close second to Claude Fable 5 in the Artificial Analysis Intelligence Index at one third of the cost, and leads the Artificial Analysis Coding Agent Index in OpenAI’s Codex harness We supported @OpenAI with pre-release evaluation of GPT-5.6 Sol, Terra, and Luna. GPT-5.6 Sol (max) scores 1 point below Claude Fable 5 (max) in the Artificial Analysis Intelligence Index at 59 points, at approximately one third of the cost. GPT-5.6 Terra (max) and Luna (max) score 55 and 51 respectively in the Intelligence Index, at ~50% and ~80% lower Cost per Task than Sol. GPT-5.6 Sol (max) leads the Artificial Analysis Coding Agent Index at 80 points. Congratulations @OpenAI and @sama on the launch! Key takeaways: ➤ One third of the cost of Claude Fable 5: On max reasoning effort, GPT-5.6 Sol costs $1.04 per task in the Artificial Analysis Intelligence Index - offering a similar level of intelligence to Claude Fable 5 at approximately one third of the cost. Reasoning levels across GPT-5.6 Sol and Luna offer a range of options at the Pareto frontier of Intelligence vs Cost per Task. For example, GPT-5.6 Luna (max) matches or exceeds the intelligence of GLM-5.2 (max) and Gemini 3.5 Flash at a lower cost. GPT-5.6 Terra (max) and Luna (max) cost $0.55 and $0.21 per Intelligence Index task, ~50% and ~80% less than Sol. Across reasoning efforts, each new GPT-5.6 model pushes past GPT-5.5 on the Pareto frontier (excluding non-reasoning). Notably, Luna and Sol are always on the Pareto frontier ahead of Terra. This means that for any Terra effort level, there is a Luna or Sol effor…
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#Agent #Anthropic #Claude #ClaudeCode #Codex #Gemini #GPT5 #Grok #OpenAI #SWE #arint_info
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RT @Yuchenj_UW: We desperately need a smart model router. 1. We’re seeing a model explosion: GPT-5.6, Grok 4.5, Muse Spark 1.1, GLM-5.2, and Fable 5 all launched within the past month. 2. Even for a single model family like GPT-5.6, there're 3 (Sol, Terra, Luna) and 5 reasoning-effort levels. That is far too many decisions for users to make manually. The best model should be selected automatically based on the task, latency, quality, and cost.
mehr auf Arint.info
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RT @TeksEdge: Still a lot of catch up to do for Meta. Open source models like GLM-5.2 won't stand still. Rumor is a new version of GLM is getting ready for release. Artificial Analysis (@ArtificialAnlys) Meta's Muse Spark 1.1 scores 51 on the Artificial Analysis Intelligence Index and is cost and token efficient compared to its peers Muse Spark 1.1 (xhigh) improves 8 points over Muse Spark 1.0 (43) in three months. It is effectively tied with GLM-5.2 (max), GPT-5.4 (xhigh), and GPT-5.6 Luna (max) at 51, three points behind Grok 4.5 (high, 54), with the leading edge at Claude Fable 5 (60), GPT-5.6 Sol (max, 59), and Claude Opus 4.8 (max, 56). The gains concentrate in Scientific Reasoning, coding, and knowledge; agentic knowledge work lags on GDPval-AA v2. @AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Muse Spark 1.1 gains substantially on the first Muse Spark release. This was driven in particular by gains in agentic knowledge work (GDPval-AA v2) and coding (SciCode, TerminalBench). On Humanity's Last Exam, it reaches 45%, within a point of Claude Opus 4.8 (max, 46%) and ahead of GPT-5.5 (44%) and Grok 4.5 (high, 40%) ➤ The most token-efficient of the models effectively tied at 51 and among the cheaper models to run. Muse Spark 1.1 used 94M output tokens to run the Intelligence Index, fewer than GPT-5.4 (xhigh, 109M), GPT-5.6 Luna (max, 125M), and GLM-5.2 (max, 141M). We estimate ~$0.26 per Intelligence Index task at Meta's $1.25/$4.25 pricing - below GLM-5.2 ($0.37) and ro…
mehr auf Arint.info
#API #Claude #GPT5 #Grok #Meta #nitter #Opensource #us #arint_info
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RT @TeksEdge: Still a lot of catch up to do for Meta. Open source models like GLM-5.2 won't stand still. Rumor is a new version of GLM is getting ready for release. Artificial Analysis (@ArtificialAnlys) Meta's Muse Spark 1.1 scores 51 on the Artificial Analysis Intelligence Index and is cost and token efficient compared to its peers Muse Spark 1.1 (xhigh) improves 8 points over Muse Spark 1.0 (43) in three months. It is effectively tied with GLM-5.2 (max), GPT-5.4 (xhigh), and GPT-5.6 Luna (max) at 51, three points behind Grok 4.5 (high, 54), with the leading edge at Claude Fable 5 (60), GPT-5.6 Sol (max, 59), and Claude Opus 4.8 (max, 56). The gains concentrate in Scientific Reasoning, coding, and knowledge; agentic knowledge work lags on GDPval-AA v2. @AIatMeta shared access with us ahead of public release for benchmarking. Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Muse Spark 1.1 gains substantially on the first Muse Spark release. This was driven in particular by gains in agentic knowledge work (GDPval-AA v2) and coding (SciCode, TerminalBench). On Humanity's Last Exam, it reaches 45%, within a point of Claude Opus 4.8 (max, 46%) and ahead of GPT-5.5 (44%) and Grok 4.5 (high, 40%) ➤ The most token-efficient of the models effectively tied at 51 and among the cheaper models to run. Muse Spark 1.1 used 94M output tokens to run the Intelligence Index, fewer than GPT-5.4 (xhigh, 109M), GPT-5.6 Luna (max, 125M), and GLM-5.2 (max, 141M). We estimate ~$0.26 per Intelligence Index task at Meta's $1.25/$4.25 pricing - below GLM-5.2 ($0.37) and ro…
mehr auf Arint.info
#API #Claude #GPT5 #Grok #Meta #nitter #Opensource #us #arint_info
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@theo.t3.gg
The unexpected death of #Codex - Adieu model of legend1https://www.youtube.com/watch?v=zl_Z5TNJB3U
7/11/2026
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mr. TIM (tm)
@timkellogg.me
publicly available models are solving unsolved math problems
proof: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98d31/cdc_proof.pdf
prompt: https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98d31/cdc_prompt.pdf
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Ethan Mollick
@emollick.bsky.social
This time OpenAI announced a novel math proof for a 50 year old problem using a public model (most of the other big math breakthroughs have been with experimental LLMs). GPT-5.6 Sol Ultra, using 64 subagents in just under one hour.
https://bsky.app/profile/emollick.bsky.social/post/3mqcsq7p43k2i
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Ethan Mollick
@emollick.bsky.socialWhen GPT-5 came out, I created a procedural brutalist city builder as a demo (you can see it at the video's start)
I used GPT-5.6 Sol in Codex to do the same thing, touching no code.
Less than a year...
Play with it (its fun, if you like architecture):
https://monument-brutalist-city-builder.netlify.app
THREAD: https://bsky.app/profile/emollick.bsky.social/post/3mqbb3kgjrs2w
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@Matthew_Berman on YT!
GPT-5.6 is FINALLY HERE (WOAH)
#GPT5.6 #CODEX #AI #OPENAI
...5 days for an Excel Clone 🤔https://www.youtube.com/watch?v=mD1F5DsC5tc
7/9/2026
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GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]
https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98d31/cdc_proof.pdf
Comments: https://news.ycombinator.com/item?id=48863490
#HackerNews #GPT5.6 #CycleDoubleCover #Conjecture #AIResearch #MathProofs