Buyela Ezindabeni
UkuqambaAI Understanding ukwaziswa

Abacwaningi be-Google bethula uhlaka lwama-ejenti amaningi lokukhiqiza ividiyo yefomu ende ehambisanayo

Abacwaningi be-Google baveze uhlaka lwezinhlaka zama-ejenti amaningi ezidizayinelwe ukuxazulula ukungaguquguquki okubonakalayo kanye nokuhamba okulandisayo kuvidiyo yefomu ende ekhiqizwe yi-AI.

4 min readRead the primary source
Source-provided image accompanying Google researchers introduce multi-agent framework for coherent long-form video generation
Idokhumenti yomthombo oyinhlokoUmthombo urekhodiwe
Umshicileli
research.google
Isixhumanisi somthombo
research.googlehttps://research.google/blog/coherent-long-form-video-generation/
Uhlobo lomthombo
Idokhumenti eyisisekelo — isimemezelo esisemthethweni, iphepha, ukugcwalisa, noma ikhasi lomuntu wokuqala esilifunda ngokuqondile.
UmongoQonda lokhu ngemizuzwana engama-60

Qala lapha

Imigomo ebalulekile

Imodeli Yolimi Lombono (VLM)
Imodeli ye-multimodal ecubungula ngokuhlanganyela ulwazi olubonakalayo nolubhaliwe.
Inkumbulo (Inkumbulo yomenzeli)
Ingqikithi egciniwe umenzeli we-AI usebenzisa ezinyathelweni zonke noma izikhathi ukuze athuthukise ukuqhubeka.
I-Watermarking
Ishumeka isignali ebonakalayo embhalweni okhiqizwe yi-AI noma imidiya ukuze kamuva ibonakale njengekhiqizwe ngomshini.
ZihloleImibuzo ye-AI Agents
Source video from research.google · shown with attribution.

Kwenzekeni

Abacwaningi be-Google bethule uhlaka lwezinhlaka ezine ezixhumene zama-ejenti amaningi—Co-Director, CANVAS, A²RD, kanye ne-VQQA—eziklanyelwe ukwenza ngokuzenzakalelayo ukukhiqizwa kwevidiyo ehambisanayo, yefomu ende. Lawa masistimu asebenza njengesendlalelo se-orchestration phezulu kwamamodeli ayisisekelo afana ne-Gemini ne-Veo, abhekana nokwehluleka okuvamile kokukhiqiza okufana nokukhukhuleka komlingiswa, indawo engahambisani, nokugoqa kokulandisa. Ngokuphatha ukukhiqizwa kwevidiyo njengenkinga yomhlaba wonke kanye nenkinga yokulandela ngomkhondo yezwe, izinhlaka zenza imisebenzi ngokuzenzakalela kusukela kukwaziswa okunamamodeli amaningi nokudalwa kwebhodi lezindaba kuya ekuthuthukisweni okubonakalayo okuvaliwe.

I-suite yezinhlaka ibhekana nezingqinamba ezithile emgqeni wokukhiqiza. 'I-AI video co-director' isebenzisa i-algorithm yezigebengu ezihlome ngezikhali eziningi ukuze kuthuthukiswe isu lomhlaba wonke lobuciko, imodi yokulandisa, nethoni yobuhle, ukuphakela ukwaziswa okuhlelekile kumamodeli ayisisekelo. Le ndlela yokuhlelwa kwezigaba iqinisekisa ukuthi wonke amapayipi okukhiqiza abambelela embonweni obumbene.

I-CANVAS (Ukulandisa Okuqhubekayo Okuqaphelayo nge-Visual Agenttic Storyboarding) igxile ekuphikeleni okubonakalayo. Igcina inkumbulo ehlelekile yezinhlamvu, izinto, nezindawo, ivumela isistimu ukuthi ibuyise amahange abonakalayo futhi iqinisekise ukuthi i-geometry yendawo nezici zomlingiswa zihlala zifana lapho izigcawu zibuyekezwa.

I-A²RD (I-Agenttic Autoregressive Video Generation) iphethe ukuhlanganiswa kwangempela kwevidiyo enobude obungamaminithi. Isebenzisa iluphu ye-refresh-synthesize-refine-update eshintsha ngokuguquguqukayo phakathi kwe-extrapolation ukuze kuqhubeke ukulandisa kanye nokuhunyushwa kumasegimenti okuhange kuya kuzimo ezibonakalayo ezimisiwe.

I-VQQA (Ukuphendulwa Kwemibuzo Yekhwalithi Yevidiyo) isebenza njengesilungiseleli esisheshayo sebhokisi elimnyama. Isebenzisa i-Vision-Language Model ukuze ihlaziye ividiyo ekhiqiziwe futhi inikeze impendulo yolimi lwemvelo, ebese isetshenziselwe ukulungisa ngokuphindaphindiwe ukwaziswa kombhalo ukuze kulungiswe iphutha lokuqamba ngaphandle kokudinga ukuhlelwa okuqondile kweleveli yephikseli.

Imininingwane yomthombo: research.google ↗

Kungani kubalulekile

Amamodeli wamanje okukhiqiza amavidiyo avame ukubhekana nobunzima bokubuka nokulandisa kwesikhathi eside, okuvame ukuphumela 'ekukhukhuleni kwe-semantic' lapho abalingisi noma indawo ishintsha khona ungahlosile kuwo wonke amashothi. Ngokwethula inkumbulo ehlelekile kanye nezihibe zempendulo ephindaphindwayo, lezi zinhlaka zidlulela ngalé kwesizukulwane esisekelwe ekwazisweni ziye epayipini lokukhiqiza elithembeke kakhulu, elinamandla. Le ntuthuko ibalulekile kubadali, njengoba isusa umthwalo wezobuchwepheshe wokugcina ukuqhubeka, okungenzeka unike amandla ukukhiqizwa kwezindaba ezilandisayo zevidiyo ezithatha imizuzu eminingi, ezingaguquguquki ngaphambili ebezithambekele ekuhlulekeni.

Inselele enkulu ekukhiqizweni kwevidiyo yefomu elide 'inkinga yokunikezwa kwekhredithi,' lapho amaphutha angaphambi kwesikhathi ngokulandelana anda futhi abangele ukwehluleka okuphelele kokulandisa. Ngokuhlukanisa ukuhlanganisa kokudala kusuka ekuhambisaneni kanye nekhwalithi yokumodela njengenhloso yesikhathi sokuhlola, lezi zinhlaka zihlinzeka ngendlela yokulandelela nokulungisa amaphutha ngaphambi kokuthi aqhume.

Ukusetshenziswa kwenkumbulo eqhubekayo yokubuka kanye nezihibe zempendulo eziphindaphindwayo kumele ukushintshela ekukhiqizweni kwevidiyo 'yezokusebenza'. Lokhu kuvumela isistimu ukuthi isebenze njengozakwethu wobuciko oqonda izimfuneko zokuxoxwa kwezindaba okude, kunokuba nje ithuluzi lokukhiqiza iziqeshana ezihlukanisiwe, zesikhathi esifushane.

Izinhlaka ziyi-model-agnostic, okusho ukuthi ngokwethiyori zingasetshenziswa kunoma iyiphi imodeli yokukhiqiza isisekelo. Lokhu kuvumelana nezimo kuphakamisa indlela eya ekulinganiseni indlela amapayipi okukhiqiza amavidiyo asingatha ngayo ukuqhubeka, kungakhathaliseki ukuthi iyiphi imodeli yezakhiwo.

Interactive Mechanism

I-Interactive Mechanism: Indlela Esebenza Ngayo Ngempela

Hlola ubuchwepheshe obuyisisekelo ngemuva kwalokhu kuthuthukiswa ngokuhlanganyela.

Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
I-Interactive Concept Check+10 Points
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Ongakubuka ngokulandelayo

Ucwaningo, okuhlanganisa nohlaka Lomqondisi Ohlanganyelwe, luhlelelwe ukuthi luvele ezingqungqutheleni ezizayo ezifana ne-COLM 2026 kanye ne-EMNLP 2026. Izibukeli kufanele ziqaphe ukuthi lezi zingqimba ze-orchestration zihlanganiswe yini nemikhiqizo yevidiyo ebheke umphakathi ye-Google noma uma zihlala zivalelwe ekusetshenzisweni kwebanga locwaningo. Ukwengeza, ukusebenza kahle kwalawa mathuluzi emhlabeni wangempela, ukugeleza komsebenzi wokudala okuyinkimbinkimbi—ngale kwamabhentshimakhi alawulwayo ashiwo ocwaningweni—kusazobonakala.

Amaphepha ocwaningo, okuhlanganisa uhlaka Lomqondisi Wokubambisana (oluzovela ku-COLM 2026) kanye ne-CANVAS (oluzovela ku-EMNLP 2026), azohlinzeka ngemininingwane ejulile ekucushweni kokuqeqeshwa okukhethekile kanye nokuqhathanisa okuyisisekelo.

Ukufinyelela nezintengo zalezi zinhlaka akwaziwa okwamanje, njengoba isimemezelo sigxile ocwaningweni nasendleleni yezakhiwo kunokukhishwa komkhiqizo wentengiso.

Ukuthembela kumamodeli ayisisekelo afana ne-Gemini ne-Veo kusho ukuthi ukusebenza kwalezi zinhlaka kuhlobene ngokwemvelo namakhono nokuphepha kwalawo masistimu angaphansi, okuhlanganisa nokusetshenziswa kwe-SynthID .

Imihlahlandlela ehlobene nemibuzo

Ama-AI AgentsAmamodeli e-AI AchaziweAma-TransformersIkusasa le-AIHlola okwaziyo — zama imibuzo ye-AI yamahhalaBheka igama le-AI kuhlu lwethu lwamagama
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