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OpenAI

2025. gada 2. februāris

Izlaidums

Iepazīstinām ar dziļo izpēti

Aģents, kas izmanto argumentāciju, lai sintezētu lielu daudzumu tiešsaistes informācijas un jūsu vietā veiktu vairāku posmu izpētes uzdevumus. Pro lietotājiem pieejams jau šodien, bet Plus un Team — pēc tam.

Izmēģini ChatGPT(atveras jaunā logā)
Notiek ielāde…

2026. gada 10. februāra atjauninājums: tagad dziļo izpēti var savienot ar jebkuru MCP vai lietotni un likt meklēšanai tīmeklī izmantot tikai uzticamas vietnes, lai varētu koncentrēties uz pārbaudītiem, nozares standartiem atbilstošiem avotiem. Tagad var arī sekot līdzi izpildes gaitai reāllaikā un pārtraukt, lai precizētu ar papildu uzvednēm vai jauniem avotiem. Mēs esam atjauninājuši vizuālo noformējumu, lai būtu vieglāk sākt, izsekot un pārskatīt pētījumus no sākuma līdz beigām.

2025. gada 17. jūlija atjauninājums: padziļinātā izpēte tagad ir vēl dziļāka un plašāka, jo ChatGPT aģents nodrošina piekļuvi vizuālajam pārlūkam. Lai piekļūtu šīm atjauninātajām iespējām, vienkārši izvēlieties "aģenta režīmu" no nolaižamās izvēlnes komponētāja sadaļā un tieši ievadiet savu pieprasījumu. Sākotnējā padziļinātās izpētes funkcionalitāte joprojām ir pieejama, izmantojot iespēju "padziļinātā izpēte" rīku izvēlnē.

2025. gada 24. aprīļa atjauninājums: mēs ievērojami palielinām biežumu, cik bieži varat izmantot padziļināto izpēti — Plus, Team, Enterprise un Edu lietotājiem tagad ir pieejami 25 vaicājumi mēnesī, Pro lietotājiem — 250, bet bezmaksas lietotājiem — 5. Tas ir iespējams, pateicoties jaunai atvieglotai padziļinātās izpētes versijai, ko nodrošina o4-mini versija, kas izstrādāta, lai būtu ekonomiskāka, vienlaikus saglabājot augstu kvalitāti. Kad sasniegsiet pilnās versijas limitu, jūsu pieprasījumi automātiski pārslēgsies uz atviegloto versiju.

2025. gada 25.—februāra atjauninājums: visi Plus lietotāji tagad var izmantot padziļināto izpēti.

2025. gada 5. februāra atjauninājums: pakalpojuma Pro lietotājiem Apvienotajā Karalistē, Šveicē un Eiropas Ekonomikas zonā tagad ir pieejama padziļināta izpēte.

Šodien mēs uzsākam padziļinātu izpēti ChatGPT — jaunu aģenta spēju, kas veic daudzpakāpju izpēti internetā sarežģītu uzdevumu veikšanai. Tas paveic dažu desmitu minūšu laikā to, kas cilvēkam prasītu daudzas stundas.

Padziļināta izpēte ir nākamais OpenAI aģents, kas var patstāvīgi veikt darbu jūsu vietā — jūs nosakāt tam uzdevumu, bet ChatGPT atrod, analizē un sintezē simtiem tiešsaistes avotu, lai izveidotu visaptverošu ziņojumu pētnieka analītiķa līmenī. Izmantojot topošā OpenAI o3 modeļa versiju, kas optimizēta tīmekļa pārlūkošanai un datu analīzei, tas izmanto argumentāciju, lai meklētu, interpretētu un analizētu milzīgus teksta, attēlu un PDF failus internetā, pēc nepieciešamības mainot savu darbību, reaģējot uz iegūto informāciju.

Spēja sintezēt zināšanas ir priekšnoteikums jaunu zināšanu radīšanai. Šā iemesla dēļ padziļinātā izpēte ir nozīmīgs solis ceļā uz mūsu paplašināto mērķi — AGI attīstību, ko mēs jau sen esam paredzējuši kā spējīgu radīt jaunus zinātniskus pētījumus.

Why we built deep research

Deep research is built for people who do intensive knowledge work in areas like finance, science, policy, and engineering and need thorough, precise, and reliable research. It can be equally useful for discerning shoppers looking for hyper-personalized recommendations on purchases that typically require careful research, like cars, appliances, and furniture. Every output is fully documented, with clear citations and a summary of its thinking, making it easy to reference and verify the information. It is particularly effective at finding niche, non-intuitive information that would require browsing numerous websites. Deep research frees up valuable time by allowing you to offload and expedite complex, time-intensive web research with just one query.

Deep research independently discovers, reasons about, and consolidates insights from across the web. To accomplish this, it was trained on real-world tasks requiring browser and Python tool use, using the same reinforcement learning methods behind OpenAI o1, our first reasoning model. While o1 demonstrates impressive capabilities in coding, math, and other technical domains, many real-world challenges demand extensive context and information gathering from diverse online sources. Deep research builds on these reasoning capabilities to bridge that gap, allowing it to take on the types of problems people face in work and everyday life.

How to use deep research

In ChatGPT, select ‘deep research’ in the message composer and enter your query. Tell ChatGPT what you need—whether it’s a competitive analysis on streaming platforms or a personalized report on the best commuter bike. You can attach files or spreadsheets to add context to your question. Once it starts running, a sidebar appears with a summary of the steps taken and sources used.

Deep research may take anywhere from 5 to 30 minutes to complete its work, taking the time needed to dive deep into the web. In the meantime, you can step away or work on other tasks—you’ll get a notification once the research is complete. The final output arrives as a report within the chat – in the next few weeks, we will also be adding embedded images, data visualizations, and other analytic outputs in these reports for additional clarity and context.

Compared to deep research, GPT‑4o is ideal for real-time, multimodal conversations. For multi-faceted, domain-specific inquiries where depth and detail are critical, deep research’s ability to conduct extensive exploration and cite each claim is the difference between a quick summary and a well-documented, verified answer that can be usable as a work product.

GPT-4o

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Compiling comprehensive data on iOS and Android adoption rates, the percentage of populations interested in learning another language, and changes in mobile penetration over the past decade for the top 10 developed and developing countries by GDP is challenging due to data limitations. However, based on available information, we can provide insights and recommendations for targeting markets with a new iOS translation app powered by ChatGPT.

Top 10 Developed Countries by GDP:

  1. United States
  2. Japan
  3. Germany
  4. United Kingdom
  5. France
  6. Italy
  7. Canada
  8. South Korea
  9. Australia
  10. Spain

Top 10 Developing Countries by GDP:

  1. China
  2. India
  3. Brazil
  4. Russia
  5. Indonesia
  6. Mexico
  7. Turkey
  8. Saudi Arabia
  9. Argentina
  10. South Africa

Insights:

  • iOS and Android Adoption Rates: Globally, Android holds a dominant market share of approximately 70.93%, while iOS accounts for about 28.32% as of 2024. In the United States, iOS leads with a 58.81% market share. Other developed countries like Japan, Canada, Australia, and the United Kingdom also show a higher preference for iOS devices. In contrast, developing countries such as India and Brazil have a higher prevalence of Android devices.
  • Interest in Language Learning: While specific percentages of populations interested in learning another language are not readily available, the global popularity of language learning apps like Duolingo, which had approximately 14.3 million downloads in July 2024, indicates a significant interest. This interest is likely higher in countries with diverse linguistic landscapes or where English is not the primary language.
  • Mobile Penetration Over the Past Decade: Mobile phone ownership has seen substantial growth globally. In the United States, smartphone ownership increased from 35% in 2011 to 91% in 2024. Developing countries have also experienced significant growth in mobile penetration, contributing to increased access to digital services.

Recommendations:

Considering the available data and focusing on markets where ChatGPT is currently active, the following developed countries are promising targets for a new iOS translation app:

  • United States: High iOS market share and significant interest in language learning.
  • United Kingdom: Substantial iOS user base and a multicultural population with diverse language needs.
  • Canada: Notable iOS adoption and a bilingual population (English and French), increasing the demand for translation services.

Among developing countries, the following are noteworthy:

  • China: Despite Android's dominance, the large population and growing interest in English and other languages present a significant opportunity.
  • India: High Android usage, but a growing middle class with increasing iOS adoption and a strong interest in language learning, especially English.
  • Brazil: Predominantly Android users, but with a sizable market for language learning apps, particularly for English and Spanish.

Focusing on these markets aligns with ChatGPT's current availability and leverages existing mobile adoption trends and language learning interests.

Deep research

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Top 10 Developed Economies (by GDP) – Smartphone OS Share, Language-Learning Interest, and Mobile Penetration Growth

Country

iOS Market Share

Android Market Share

Interest in New Language

(% of population)

Mobile Penetration Change (2013→2023)

United States

(USA)

~58% iOS vs 42% Android

(see left)

High:

70% regret losing foreign-language ability (indicating strong desire to improve)

56%→82%

smartphone penetration (↑26 pp)

Japan

~69% iOS vs 31% Android

(see left)

High:

Over 50% want to learn English

~30%→79%

(approx.) (↑~50 pp)

Germany

~39% iOS vs 60% Android

(see left)

High:

86% of Europeans say everyone should speak ≥1 foreign language (strong pro-language attitude)

40%→82%

(↑42 pp)

United Kingdom

(UK)

~52% iOS vs 47% Android

(see left)

Moderate-High:

~73% have some interest (only 27% “no interest” in new languages)

62%→82%

(↑20 pp)

France

~35% iOS vs 64% Android

(see left)

High:

86% (EU average) favor multilingualism; English widely seen as important

42%→83%

(↑40 pp)

Italy

~30% iOS vs 69% Android

(see left)

High:

86% (EU avg.) favor language learning; 1 in 4 regret not learning another

41%→85%

(↑44 pp)

Canada

~60% iOS vs 40% Android

(see left)

Moderate:

Many bilingual (English/French); rising interest in third languages (no recent % data)

56%→~80%

(↑ ~24 pp) (est., similar to US)

South Korea

~24% iOS vs 76% Android

(see left)

Moderate:

Strong English education focus; ~40% of teens are learning English

73%→~95%

(↑ ~22 pp) – already very high by 2013 (near saturation now)

Australia

~55% iOS vs 45% Android

(see left)

Moderate:

~70% see learning languages as valuable (English dominant but interest in Asian languages growing)

65%→~85%

(↑ ~20 pp) (est., high smartphone use)

Spain

~20% iOS vs 79% Android

(see left)

High:

88% of Spaniards learned a foreign language in school (e.g. English); strong cultural interest

55%→~85%

(↑ ~30 pp) (est., nearing Western Europe peers)

Top 10 Developing/Emerging Economies (by GDP) – (ChatGPT availability considered)

Country

iOS Market Share

Android Market Share

Interest in New Language

(notable stats)

Mobile Penetration Change (2013→2023)

China

^(ChatGPT restricted)

~24% iOS vs 75% Android

(see left)

Very High:

Hundreds of millions learning English (but ChatGPT not officially accessible)

47%→68%

(↑21 pp)

India

~4% iOS vs 95% Android

(see left)

High:

English compulsory in many schools; strong economic motivation (80% link English to higher income)

~5%→46%

(↑ ~41 pp) (huge jump in smartphone adoption)

Brazil

~18% iOS vs 82% Android

(see left)

High:

Great demand for English – only ~5% speak it, but most see it as key skill

26%→67%

(↑40 pp)

Russia

^(ChatGPT limited)

~30% iOS vs 69% Android

(see left)

Moderate:

English learning common (compulsory in school); interest exists but ChatGPT availability is uncertain

36%→74%

(↑38 pp)

Mexico

~24% iOS vs 76% Android

(see left)

High:

Strong desire to learn English for US job/migration; ~50% of students study English in school (est.)

37%→62%

(↑25 pp)

Indonesia

~12% iOS vs 88% Android

(see left)

High:

English seen as gateway skill; government programs to improve English proficiency

<30%→68%

(↑ ~40 pp)

Turkey

~15% iOS vs 85% Android (est.)

(see left)

High:

English highly valued (universal in education);

80%

link English to better jobs

30%→75%

(↑45 pp)

Saudi Arabia

~24% iOS vs 76% Android

(see left)

Moderate:

English widely taught; 92% smartphone ownership (many already bilingual in English/Arabic)

73%→~95%

(↑ ~22 pp) (early adopter; near saturation now)

South Africa

~16% iOS vs 83% Android

(see left)

High:

Multilingual society; English and Afrikaans taught alongside native languages – cultural impetus to learn others

40%→~60%

(↑ ~20 pp) (est., steady growth)

Argentina

~12% iOS vs 88% Android

(see left)

High:

Strong interest in English (Argentina leads Latin America in English proficiency)

31%→~70%

(↑ ~39 pp) (est., sharp rise with smartphone adoption)

Sources: Smartphone OS shares are from StatCounter/Statista data (2023–24). Language-interest metrics come from surveys by Preply, Eurobarometer, Babbel, etc. (e.g. UK 73% interest; US 70% regret lack of languages; Japan >50% want English; Pearson global survey). Mobile penetration is measured as smartphone ownership percentage – dramatic growth is noted in developing markets (e.g. Brazil +40 points) and high saturation in developed ones (e.g. UK ~82%) over the last decade.

Recommendations: Based on the above, prime targets for a new ChatGPT‑powered iOS translation app would be countries with large iOS user bases and strong language-learning demand, where ChatGPT is accessible. Notably, Japan, the U.S., UK, Canada, and Australia emerge as top choices. These markets pair high iOS adoption (50–60%+ share) with significant enthusiasm for language learning – for example, Japan’s iPhone dominance and widespread desire to learn English make it ideal. English-speaking countries like the US, UK, Canada, and Australia, despite being monolingual societies, show substantial interest in learning other languages (e.g. Spanish or French), and all have over 80% smartphone penetration and millions of iPhone users.

Among emerging economies, Mexico and Brazil stand out as secondary targets: although Android prevails, their sheer population sizes mean tens of millions of iPhone users exist, and interest in English is very high for career and education reasons. These Latin American markets (where ChatGPT is active) could yield strong adoption for an iOS-only app, especially given the motivation to learn English. Conversely, China and Russia are less feasible – China’s OS split skews Android and ChatGPT is not officially available there, and Russia faces access and regulatory uncertainties.

In summary, focus on iOS-heavy, pro-learning markets: Japan and Western countries (US, UK, Canada, Australia, plus high-income Europe like Germany and France to a lesser degree) should be the first-tier targets. They combine robust iOS usage with favorable language-learning attitudes. A second tier includes large-population developing markets (Mexico, Brazil, perhaps Saudi Arabia for its historically high iPhone uptake) where interest in English is strong – these can be approached once the app gains traction in the primary markets. Such a strategy ensures the app launches where it has the greatest alignment of platform (iOS) presence and user demand for translation/language tools, maximizing the likelihood of adoption and growth.

Deep research responds to the prompt in a highly detailed manner, providing side-by-side country-specific data for both top 10 developed and top 10 developing countries for easy reference and comparison. It uses that information to offer detailed market-entry recommendations that are informed and usable.

Kā tas darbojas

Padziļināta izpēte tika apmācīta, izmantojot visaptverošu pastiprinātu mācīšanos attiecībā uz sarežģītiem pārlūkošanas un spriešanas uzdevumiem dažādās jomās. Apmācību laikā tas iemācījās plānot un izpildīt daudzpakāpju trajektoriju, lai atrastu vajadzīgos datus, vajadzības gadījumā dodoties atpakaļ un reaģējot uz reāllaika informāciju. Modelis spēj arī pārlūkot lietotāja augšupielādētos failus, zīmēt un iterēt grafikus, izmantojot Python rīku, savās atbildēs ievietot gan ģenerētus grafikus, gan attēlus no tīmekļa vietnēm, kā arī citēt konkrētus teikumus vai fragmentus no avotiem. Šīs apmācības rezultātā tā sasniedz jaunus augstus rezultātus vairākos publiskos novērtējumos, kas vērsti uz reālām problēmām.

Cilvēces pēdējais eksāmens

Nesen publicētajā novērtējumā Cilvēces pēdējais eksāmens⁠(atveras jaunā logā), kurā mākslīgais intelekts tiek pārbaudīts dažādos ekspertu līmeņa jautājumos, modelis, kas nodrošina padziļināto izpēti, sasniedza jaunu augstāko rezultātu - 26,6 %% precizitāti. Šajā testā ir vairāk nekā 3000 atbilžu variantu un īsu atbilžu jautājumu par vairāk nekā 100 tēmām — no valodniecības līdz raķešu zinātnei, no klasikas līdz ekoloģijai. Salīdzinot ar OpenAI o1, vislielākie ieguvumi bija ķīmijā, humanitārajās un sociālajās zinātnēs un matemātikā. Modelis, kas nodrošina padziļinātu izpēti, demonstrē cilvēkam līdzīgu pieeju, efektīvi meklējot specializētu informāciju, kad tas ir nepieciešams.

ModelisPrecizitāte (%)
GPT-4o3,3
Grok-23,8
Claude 3.5 Sonnet4,3
Gemini Thinking6,2
OpenAI o19,1
DeepSeek-R1*9,4
OpenAI o3–mini (medium)*10,5
OpenAI o3–mini (high)*13,0
OpenAI dziļā izpēte**26,6
* Modelis nav multimodāls, novērtēts tikai uz teksta apakškopu.
**ar pārlūkošanu + Python rīkiem

GAIA

GAIA⁠(atveras jaunā logā)1 publiskā etalonmērījumā, kurā mākslīgo intelektu novērtē reālās pasaules jautājumos, modelis, kas nodrošina padziļināto izpēti, sasniedz jaunu augstāko līmeni (SOTA), ieņemot pirmo vietu ārējo līderu sarakstā⁠(atveras jaunā logā). Lai veiksmīgi izpildītu šos uzdevumus, kas ietver trīs sarežģītības līmeņu jautājumus, ir nepieciešamas tādas prasmes kā spriestspēja, multimodalitāte, tīmekļa pārlūkošana un prasmīga rīku lietošana.

GAIA
1. līmenis2. līmenis3. līmenisVid.
Iepriekšējais modernākais modelis (SOTA)(atveras jaunā logā)67,9267,4442,3163,64
Dziļā izpēte (pass@1)74,2969,0647,667,36
Dziļā izpēte (cons@64)78,6673,2158,0372,57

GAIA uzdevumu piemēri

In Nature journal's Scientific Reports conference proceedings from 2012, in the article that did not mention plasmons or plasmonics, what nano-compound is studied? Don't use the prefix nano in your answer if there is one.

[The remaining Chain-of-Thought messages and the final answer are hidden to protect the benchmark's ground truth answer.]

Clarifying the search

I’m piecing together the request for an article from 2012 in Scientific Reports that details a nano-compound, excluding any related to plasmonics. Likely, this involves filtering specific topics or compounds, like silver or graphene.

Piecing together details

I'm sub-searching "Scientific Reports 2012 nanoparticle," likely seeking nano-compounds. Interested in filtering out plasmon-related topics, possibly focusing on silver, graphene, or quantum dots.

[search action]

Navigating search filters

I’m digging into advanced search filters on nature.com and Scientific Reports for 2012, trying to pinpoint articles on amorphous carbon nanoparticles in food caramels.

Identifying conference proceedings

The search results reveal a conference proceeding titled "2D quasiperiodic plasmonic crystals" from 2012, likely found under the "Conference Proceedings" category in Scientific Reports.

[browse action]

Reflecting on potential issues

I'm considering the reasons behind a web page misconception, likely due to an outdated agent type or inadvertent blockage.

[browse action]

Considering dynamic options

I'm thinking about a nature site with dynamic content or a cached version. Alternatively, trying a search for specific phrases might help.

[search action]

Pinpointing key features

OK, I'm identifying four conference proceedings from 2012 in Sci Rep. Notably, some mention "plasmonic" and should be excluded.

[browse action]

Piecing together insights

I’m gathering info on accessing cached versions or snippet expansions for specific titles to ensure comprehensive analysis.

[search action]

Pulling together the details

I’m noting the progression from the 2012 European Materials Research Society Spring Meeting to the scientific report, focusing on conference proceedings. This approach helps establish context and relevance.

[search action]

Tracking journal clues

Trying to connect conference proceedings from late 2012 with "Scientific Reports" journal, focusing on plasmonic 2D quasiperiodic materials. This approach feels methodical and precise.

[search action]

Analyzing event details

Noting that the search results [8] include a page likely summarizing the European Materials Research Society 2012 Spring Meeting, including 25 symposia.

[browse action]

Piecing together research

I’m examining a likely collection page with 4 articles, considering plasmonic and metamaterial topics, and identifying key references from the European Materials Research Society 2012 Spring Meeting.

[browse action]

Pinpointing the focus

I’m focusing on the 2012 conference proceedings in "Scientific Reports" from E-MRS, likely involving topics like "2D quasiperiodic plasmonic crystals" and "Layered plasmonic cloaks to tailor the optical scattering at the nanoscale."

[browse action]

Evaluating library sources

Thinking about special issue E-MRS 2012 Sci rep invited paper Monticone and metamaterials lab at News Archives – 2012.

[browse action]

Assessing accessibility

I'm gathering info on potential site access issues, considering outdated status or blockages. Also, evaluating the Monticone site for a list reference.

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Piecing together E-MRS 2012

I'm trying to link "Layered Plasmonic Cloaks" to E-MRS 2012. Considering other possible topics like graphene and catalysis.

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Identifying relevant topics

I’m gathering info on topics like graphene, photovoltaics, and nanostructured compounds, while excluding plasmonic-related ones. This leads me to focus on nanotechnology aspects more broadly.

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Adjusting my focus

I’m broadening the search parameters by entering specific site and scholar identifiers to pinpoint articles about 2D quasiperiodic plasmonic crystals in Nature Scientific Reports from 2012.

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Identifying non-plasmonic possibilities

I'm narrowing down the search to identify articles from the 2012 E-MRS special issue in Scientific Reports. This involves checking titles for non-plasmonic topics, like graphene or functional oxides. Progress is evident through focused searches and exclusion of plasmonic articles.

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Piecing together clues

I'm analyzing interactions in a graphene superlattice, likely from a Sci Rep article. Considering titles by authors like Padooru, Yakovlev, Chen, and Alu.

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Piecing together the puzzle

I’m gathering info on E-MRS 2012 Spring Meeting's Sci Rep proceedings, focusing on invited papers, without plasmonic phenomena. Types under review are graphene, catalysis, energy materials, nanomedicine, and sensors.

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Eksperta līmeņa uzdevumi

Iekšējā ekspertu līmeņa uzdevumu novērtējumā dažādās jomās jomas eksperti novērtēja, ka padziļinātā izpēte ir automatizējusi vairākas stundas sarežģītas manuālas izpētes. 

Izdošanās līmenis salīdzinājumā ar maksimālo rīku izsaukumu
Izdošanās līmenisMaksimālais rīku izsaukumu skaits
Jo vairāk modelis pārlūko un domā par to, ko tas pārlūko, jo labāk tas darbojas, tāpēc ir svarīgi dot tam laiku pārdomām.
Ekspertu līmeņa uzdevumu piemēri
Uzdevuma veikšanai ietaupītais laiks: 4 stundas

I am a researcher interested in bringing mixed-gas sorption capabilities to my lab. Please discuss the differences between pure- and mixed-gas sorption for glassy polymers, how the dual-mode sorption model can be used to predict mixed-gas sorption behavior in glassy polymers (include equations where applicable), and what challenges there are in accurately predicting pure- and mixed-gas sorption using the dual-mode sorption model. Only include open-source information as of October 1, 2024.

Pure vs. mixed-gas sorption in glassy polymers: In a glassy polymer (below TgT_g), a single gas typically exhibits a concave sorption isotherm: uptake rises sublinearly with pressure, unlike the linear Henry’s-law behavior above TgT_g. This is attributed to “dual-mode” sorption: some penetrant dissolves in the dense polymer matrix (obeying Henry’s law) while additional penetrant saturates specific microvoid “sites” (a Langmuir-type adsorption in the polymer’s nonequilibrium free volume). By contrast, when two or more gases are present, they compete for those limited Langmuir sorption sites. As a result, each component’s sorbed concentration in a mixture is generally lower than it would be at the same partial pressure alone. In particular the less soluble (lower-affinity) gas tends to be more strongly suppressed, often causing the mixed-gas solubility selectivity (ratio of concentrations) to exceed the ideal value predicted from pure-gas isotherms. For example, in CO₂/CH₄ mixtures both components sorb less than in their pure-gas cases (especially CH₄), so the CO₂/CH₄ sorption selectivity is higher under mixture conditions. In extreme cases, mixed-gas effects can even reverse performance trends observed with single gases. (We note that unlike competitive sorption, synergistic sorption – where one gas increases the sorption of another via polymer swelling – is usually minor in rigid high-free-volume polymers under moderate pressures.)

Dual-mode sorption model and mixed-gas prediction: The dual-mode sorption (DMS) model quantitatively captures glassy polymer sorption by superposing a Henry’s-law term and a Langmuir term. For a single gas, the sorbed concentration CC (e.g. in cm³(STP) gas per cm³ polymer) is written as:

C(p)=kD p  +  CH′ b p 1+b p  .C(p) = k_D\,p \;+\; \frac{C'_H\,b\,p}{\,1 + b\,p\,}\,.

Here kDk_D is the Henry’s law coefficient for dissolution, CH′C'_H is the Langmuir capacity constant (proportional to the polymer’s unrelaxed free-volume or “hole” capacity), and bb is the Langmuir affinity constant. This empirical formula yields the characteristic concave isotherm of glassy polymers and reduces to simple Henry’s law at the glass-transition (since CH′→0C'_H \to 0 as T→TgT \to T_g). The same three parameters (kD,CH′,bk_D, C'_H, b) are obtained by fitting pure-gas sorption data and are then used to predict mixture sorption. For a binary mixture of gases A and B (with partial pressures pA,pBp_A, p_B), the DMS model assumes each gas obeys its own Henry’s term and that both compete for the same Langmuir sites. One convenient form is to write the total sorbed concentration as the sum of contributions from each gas:

Ctot=kD,ApA+kD,BpB  +   CH,A′ bA pA  +  CH,B′ bB pB  1+bApA+bBpB  ,C_{\text{tot}} = k_{D,A}p_A + k_{D,B}p_B \;+\; \frac{\,C'_{H,A}\,b_A\,p_A \;+\; C'_{H,B}\,b_B\,p_B\,}{\,1 + b_A p_A + b_B p_B\,}\,,

which implicitly means the concentration of component i in the mixture is Ci=kD,ipi+CH,i′bipi 1+∑jbjpj C_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,}. This reduces to the single-gas formula when the other component’s pressure is zero. In practice, partial pressure in these equations is often replaced by fugacity for accuracy at high pressures, though using fugacities or pressures gives nearly the same results if applied consistently. The DMS mixed-gas model predicts the competitive sorption effect observed experimentally – namely, each gas’s uptake is depressed relative to its pure-gas sorption isotherm.

Challenges and limitations: Although the dual-mode model is widely successful in correlating sorption data, it has notable limitations in predicting sorption behavior. First, the DMS model is empirical – its fitted parameters are not universal constants but can vary with polymer formation history, physical aging, and temperature/pressure range. This means a parameter set obtained under one set of conditions may lack predictive power outside its calibration range. For instance, sorption isotherms measured only over a limited pressure range can be fit well by DMS, but extrapolating beyond that (especially approaching plasticization pressures) may be unreliable. Indeed, multiple distinct combinations of kDk_D, CH′C'_H, bb can sometimes fit the same pure-gas isotherm almost equally well – yet they yield markedly different mixed-gas predictions. This sensitivity undermines confidence in mixture forecasts, as one study showed that using different pure-gas fit sets led to only qualitative agreement with some mixed-CO₂/CH₄ data.

Furthermore, DMS inherently assumes a fixed population of Langmuir sites (microvoids) and ignores polymer relaxation or swelling induced by sorbate. In reality, penetrant-induced swelling (plasticization) can increase free volume and sorption capacity, especially for highly condensable gases at high activity – an effect the basic DMS model cannot capture. In binary sorption, this means any synergistic increase in sorption due to a second gas (or due to polymer dilation) is unaccounted for in the standard model. Conversely, the model also cannot describe sorption isotherm shapes that deviate from concave Langmuir-type behavior (e.g. sigmoidal isotherms caused by specific polymer-penetrant interactions or clustering). For example, glassy polymers absorbing polar molecules like alcohols can show sigmoidal sorption isotherms, which the simple dual-mode framework fails to represent. Finally, accurately measuring mixed-gas sorption itself is challenging, and discrepancies between idealized model assumptions (e.g. truly zero downstream pressure, no competitive diffusion limitations) and experimental realities can add to prediction error. Despite these challenges, the dual-mode sorption model – with appropriate care – remains a valuable first approximation for interpreting sorption in glassy polymers, and it highlights the key role of competitive site occupancy in mixed-gas environments. Researchers often turn to more elaborate models (e.g. non-equilibrium lattice fluid or thermodynamic theories) when the dual-mode model’s assumptions break down, but such models come at the cost of additional parameters or complexity. In summary, dual-mode sorption provides a useful baseline for pure- and mixed-gas sorption in glassy polymers, but one must be mindful of its empirical nature and the above limitations when predicting multicomponent sorption behavior in practice.

Sources: Dual-mode sorption model and mixed-gas extension; competitive sorption effects in mixtures; model equations and parameters; limitations of DMS model; parameter sensitivity and predictivity issues.

Understanding sorption models

I’m gathering info on pure and mixed-gas sorption in glassy polymers, focusing on the dual-mode sorption model and addressing challenges in modeling predictions.

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Atvērtās piekļuves novērtēšana

Labi, ļaujiet man paskatīties. MDPI rezultāts par jauktu gāzu sorbciju stiklveida polimēros, iespējams, ir brīvi pieejams.

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Izpētot sadaļas
, es apkopoju atziņas no divrežīmu sorbcijas modeļa un tā paplašinātās analīzes MDPI dokumentā.

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Galveno īpašību precizēšana

Izpētīju laboratorijas ziņojumu, lai saprastu Henrija likuma konstanti, Langmuīra afinitātes konstanti un Langmuīra kapacitātes konstanti, kas apraksta gāzu sorbciju.

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Saliekot kopā

Es strādāju ar iespēju, ka "vienādojums (3)" un "vienādojums (4)" varētu būt attēli vai formulas displeji, un apsveru iespēju konsultēties ar PDF vai meklēt agrākas atsauces, lai tos labāk izprastu.

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Nosaukuma atbilstības apstiprināšana

Es pārbaudīju, vai Freeman un Koros (1980) dokuments konkrēti atbilst atsaucei "Model for sorption of mixed gases in glassy polymers", pārbaudot tā kopsavilkumu un nosaukumu.

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Rakstu savienojumu pārbaude

Es pamanīju, ka rezultāts no "pmc.ncbi.nlm.nih.gov", visticamāk, ir tas pats MDPI raksts, un es esmu ziņkārīgs, lai redzētu, vai tas piedāvā tekstu vai vienkāršotu versiju.

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Informācijas apkopošana

Es apkopoju informāciju par tīru un jauktu gāzu sorbciju stiklveida polimēros. Tas ir izšķirošs solis, lai izprastu materiāla uzvedību dažādās gāzu vidēs.

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Salīdzini gāzes sorbciju

Es pamanīju, ka oglekļa dioksīda klātbūtne maisījumā ietekmē metāna sorbciju polimērā, samazinot metāna sorbciju salīdzinājumā ar tīras gāzes scenāriju.

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Jēdzienu sasaistīšana

Es domāju par to, kā termins "paplašinātā divrežīmu vienādojumu maisījuma" varētu attiekties uz divrežīmu supravadošo kabeļu vienādojumiem. Tas parāda potenciālu savienot jēdzienus dažādās jomās.

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Pārskatīšanas informācijas pieprasīšana

Es ņemu vērā lūgumu pārskatīt "Theories of Sorption and Transport in Polymer Membrane."

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PDF saites pārbaude

Mani interesē rezultāts [2] un fragmenta izpēte, kas ietver divrežīmu sorbcijas modeļa paplašināšanu uz bināru maisījumu un A koncentrācijas izteikšanu parciālo spiedienu un konstantu izteiksmē.

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Sorbcijas uzvedības prognozēšana

Divu režīmu modelis paredz, ka, pievienojot otru gāzi, samazinās sorbcija pie nemainīga parciālā spiediena, atšķirībā no scenārijiem ar tīru gāzi.

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PDF faila ieguve

Es apkopoju informāciju par nature.com PDF, iespējams, vecāku Yampol's ski rakstu, bet domāju par alternatīviem avotiem, piemēram, Teksasas Universitātes Ostinas repozitārijā teksta fragmentu.

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Repozitorija iespēju izvērtēšana

Esmu atklājis, ka UT repozitorija rezultāts var tikt parādīts nepareizi. Meklēšana tajā ir neskaidra. Rezultātā [3] ir iekļauts arī fragments, kurā aprakstīts divrežīmu maisījuma sorbcijas modelis, izskaidroti konkurences efekti un tā piemērotība eksperimentālo datu modelēšanai.

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Uzmanības fokusa precizēšana

Es pielāgoju savu pieeju, lai nodrošinātu, ka meklēšana ir vērsta uz pareizo kontekstu.

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Izpratne par sorbcijas modeļiem

Es detalizēti aprakstīju divrežīmu sorbcijas modeli tīrām gāzēm: Henrija izšķīdināšanas un Langmuīra mikrodaļiņu aizpildīšanas modeli. Attiecībā uz jauktām gāzēm uzsvērta konkurējoša sorbcija un neidealitātes.

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Izpētīt hero.epa.gov

Pārdomas par hero.epa.gov iespējamais kopsavilkums vai saturs.

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Modeļa pilnveidošana

Es gūstu ieskatu par tīru un jauktu gāzu sorbciju, ņemot vērā tādus faktorus kā konkurence, plastifikācija un parametru stabilitāte. Jauktu gāzu datu iekļaušana precīzākām prognozēm.

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Divkāršas sorbcijas modeļa problēmu risināšana

Parametru sasaiste un neviendabīgums ietekmē prognozes. Polimēru stāvokļa izmaiņu trūkums var izraisīt pārspīlējumus.

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Novirzes kvantitatīva noteikšana

Es kvantitatīvi novērtēju neideālu CO2-CH4 šķīdību 6FDA-MPDA, parādot jauktas gāzes ietekmi uz šķīdību. Konkurējoša sorbcija nav vienīgais caurlaidības selektivitātes samazināšanās iemesls.

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Atsauču iekļaušana

Es apkopoju citātus, koncentrējoties uz tādiem atvērtā koda materiāliem kā MDPI 2019 un Vopicka 2014, lai atbalstītu diskusiju par atšķirībām un izaicinājumiem divrežīmu modelī gāzes sorbcijai polimēru membrānās.

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Sadaļu ierosināšana

Es organizēju sadaļas par tīro un jaukto gāzu sorbciju, divrežīmu sorbcijas modeļa izpēti un tādām problēmām kā parametru nenoteiktība un jaukto gāzu prognozēšana.

Satura organizēšana

Es koncentrējos uz divrežīmu sorbcijas modeli, detalizēti aprakstot tīro un jaukto gāzu sorbcijas īpašības un izceļot galvenās atšķirības un problēmas, izmantojot punktus un strukturētas sadaļas skaidrības labad.

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Citātu izvēle

Labi, ļaujiet man paskatīties. Es izmantoju [3], [20], [22] un [21], jo tajos ir skaidras atziņas par divrežīmu sorbciju un gāzu maisījumiem.

Parametru kartēšana

Lai izvairītos no pārpratumiem, es apkopoju parametru nosaukumus. Skaidrības labad saglabājot (k), (C_H') un (b). Vienādojumi tīrai gāzei un maisījumam ir detalizēti un skaidri noformēti.

Izaicinājumu risināšana

Es strādāju pie vairāku šķīdumu parametru atbilstības, polimēru plastifikācijas un laika ietekmes. Šīs nianses padara sarežģītu tīru un jauktu gāzu sorbcijas prognozēšanu.

Apvienošana

Es strukturēju sadaļas par sorbcijas atšķirībām, divrežīmu modeli tīrām gāzēm, tā paplašināšanu uz jauktām gāzēm un saistītajām problēmām. Šāda virzība šķiet loģiska un efektīva.

Eksperta līmeņa uzdevumu izpildes rādītājs pēc paredzamās ekonomiskās vērtības
Izdošanās līmenisAprēķinātā ekonomiskā vērtība
Eksperta līmeņa uzdevumu izpildes rādītājs pēc paredzamajām stundām
Izdošanās līmenisParedzamais stundu skaits
Novērtētā uzdevuma ekonomiskā vērtība ir vairāk saistīta ar uzdevumu izpildes rādītāju nekā ar cilvēkam nepieciešamajām stundām — lietas, kuras modeļi uzskata par sarežģītām, atšķiras no lietām, kuras cilvēki uzskata par laikietilpīgām.

Limitations

Deep research unlocks significant new capabilities, but it’s still early and has limitations. It can sometimes hallucinate facts in responses or make incorrect inferences, though at a notably lower rate than existing ChatGPT models, according to internal evaluations. It may struggle with distinguishing authoritative information from rumors, and currently shows weakness in confidence calibration, often failing to convey uncertainty accurately. At launch, there may be minor formatting errors in reports and citations, and tasks may take longer to kick off. We expect all these issues to quickly improve with more usage and time.

Access

Deep research in ChatGPT is currently very compute intensive. The longer it takes to research a query, the more inference compute is required. We are starting with a version optimized for Pro users today, with up to 100 queries per month. Plus and Team users will get access next, followed by Enterprise. We are still working on bringing access to users in the United Kingdom, Switzerland, and the European Economic Area. 

All paid users will soon get significantly higher rate limits when we release a faster, more cost-effective version of deep research powered by a smaller model that still provides high quality results. 

In the coming weeks and months, we’ll be working on the technical infrastructure, closely monitoring the current release, and conducting even more rigorous testing. This aligns with our principle of iterative deployment. If all safety checks continue to meet our release standards, we anticipate releasing deep research to Plus users in about a month.

Kas būs tālāk?

Padziļināta izpēte jau šodien ir pieejama ChatGPT tīmeklī, un mēneša laikā tā tiks ieviesta arī mobilajās un darbvirsmas lietotnēs. Pašlaik padziļinātā izpēte var piekļūt atvērtajam tīmeklim un visiem augšupielādētajiem failiem. Nākotnē varēsiet pieslēgties specializētākiem datu avotiem, paplašinot piekļuvi abonētajiem vai iekšējiem resursiem, lai padarītu tās rezultātus vēl noturīgākus un personiskākus.

Raugoties nākotnē, mēs paredzam, ka aģentu pieredze tiks apvienota ChatGPT asinhronai reālās pasaules izpētei un izpildei. Padziļinātas izpētes, kas var veikt asinhronu tiešsaistes izpēti, un Operatora, kas var veikt reālas darbības, kombinācija ļaus ChatGPT jūsu vietā veikt arvien sarežģītākus uzdevumus.

2025. gada 3. februāra papildinājums. Mēs veicām stingras drošības pārbaudes, gatavības novērtējumus un pārvaldības pārbaudes agrīnajai o3 versijai, kas nodrošina padziļinātu izpēti, un identificējām to kā vidēju⁠(atveras jaunā logā) risku. Mēs veicām arī papildu drošības testēšanu, lai labāk izprastu papildu riskus, kas saistīti ar padziļinātās izpētes iespējām pārlūkot tīmekli, un esam pievienojuši jaunus riska mazināšanas pasākumus. Mēs turpināsim rūpīgi testēt un cieši uzraudzīt pašreizējo ierobežoto versiju. Kad paplašināsim piekļuvi, iekļaujot Plus lietotājus, mēs dalīsimies ar mūsu drošības ieskatiem un aizsargpasākumiem attiecībā uz padziļināto izpēti sistēmas kartē.

Footnotes

  1. 1

    We found that the ground-truth answers for this dataset were widely leaked online and have blocked several websites or URLs accordingly to ensure a fair evaluation of the model.

Authors

OpenAI

Research Leads

Isa Fulford un Zhiqing Sun

Foundational Contributors

Alex Tachard Passos, Alexandra Barr, Allison Tam, Charlotte Cole, Hyung Won Chung, Jason Wei, Jon Blackman, Scott Mayer McKinney un Valerie Qi

Core Contributors

Research

Elaine Ya Le, Eric Mitchell, Eric Wallace, Hyung Won Chung, Ignasi Clavera, Leo Liu, Lorenz Kuhn, Louis Feuvrier, Max Schwarzer, Saachi Jain, Scottie Yan, Shunyu Yao, Vitchyr Pong

Deployment

Carpus Chang, Harry Zhao, Joseph Trasatti, Joshua Dickens, Matt Kaufer, Mike Trpcic, Minnia Feng, Neel Ajjarapu, Peter Vidani,  Sean Fitzgerald

Contributors

Research

Ahmed El-Kishky, AJ Ostrow, Alexander Wei, Andrei Gheorghe, Andrew Kondrich, Andrey Mishchenko, Anuj Nair, Behrooz Ghorbani, Brydon Eastman, Chak Li, Foivos Tsimpourlas, Francis Song, Giambattista Parascandolo,Gildas Chabot, Hessam Bagherinezhad, Haitang Hu, Hongyu Ren, Henry Aspegren, Hunter Lightman, Ilya Kostrikov, Ilge Akkaya, James Lennon, Jean Harb, Jonathan Ward, Kai Chen, Katy Shi, Kevin Liu, Kevin Yu, Manuka Stratta, Marvin Zhang, Mengyuan Yan,  Mostafa Rohaninejad, Noam Brown, Phoebe Thacker, Raz Goan, Reah Miyara, Spencer Papay, Taylor Gordon, Wenda Zhou, Wenlei Xie, Yash Patil, Yann Dubois, Youlong Cheng, Yushi Wang, Wyatt Thompson

+ all the contributors to o3.

Safety Systems

Adam Kalai, Alex Beutel, Andrea Vallone, Andy Applebaum, David Robinson, Elizabeth Proehl, Evan Mays, Grace Zhao, Irina Kofman, Jason Phang, Joaquin Quinonero Candela, Joel Parish, Kevin Liu, Kristen Ying, Lama Ahmad, Leon Maksin, Leyton Ho, Meghan Shah, Michele Wang, Miles Wang, Phillip Guo, Olivia Watkins, Owen Campbell-Moore, Patrick Chao, Sam Toizer, Samuel Miserendino, Sandhini Agarwal, Tejal Patwardhan, Tina Sriskandarajah, Troy Peterson, Yaodong Yu, Yunyun Wang

Deployment

Adam Koppel, Adam Wells, Adele Li, Andy Applebaum, Andrey Malevich, Andrew Duberstein, Andrew Howell, Anton Tananaev, Ashley Tyra, Brandon Walkin, Bryan Ashley, Cary Bassin, Cary Hudson, Cory Decareaux, Cristina Scheau, Derek Chen, Dibya Bhattacharjee, Drea Lopez, Eric Antonow, Eric Burke, Filippo Raso, Fotis Chantzis, Freddie Sulit, Harris Cohen, Heather Whitney, Jay Dixit, Jeffrey Han, Jen Robinson, Jessica Shieh, Joel Parish, Kan Wu, Kevin Gladstone, Kshitij Wadhwa, Leo Vandriel, Leyton Ho, Liang Chen, Madeline Christian, Mamie Rheingold, Matt Jones, Michelle Fradin, Mike McClay, Mingxuan Wang, Nacho Soto, Niko Felix, Patrick Delaney, Paul McMillan, Philip Pronin, Rodrigo Riaza Perez, Samuel Miserendino, Scott Ethersmith, Steven Baldwin, Thomas Dimson, Tomo Hiratsuka, Yaming Lin, Yara Khakbaz, Yining Chen

Leadership

Akshay Nathan, Greg Brockman, Hannah Wong, Jakub Pachocki, Jerry Tworek, Johannes Heidecke, Josh Tobin, Liam Fedus, Mark Chen, Mia Glaese, Nick Turley, Sam Altman un Wojciech Zaremba