LCM: Nsɛm a ɛfa ho a Ɛnyɛ Adehwere [pdf].

Lossless Context Management (LCM) yɛ nhyehyeɛ a wɔde kora nsɛm no mudi mu kura nyinaa so berɛ a ɛsen fa AI-driven systems mu, hwɛ sɛ wɔrento data a ɛho hia biara ngu anaa wɔanhyɛ mu berɛ a wɔreyɛ adwuma. Wɔ nnɛyi nnwuma a ɛhwɛ adwumayɛ a ɛyɛ den so wɔ nnwinnade ne adwumayɛ nhyehyɛe ahorow pii so no, LCM nnyinasosɛm ahorow a wɔbɛte ase no ho hia na ama wɔanya mfaso kɛse afi automation platform ahorow a nyansa wom mu.

Dɛn Pɛpɛɛpɛ ne Lossless Context Management ne Dɛn Nti na Ɛho Hia?

Atetesɛm AI nhyehyɛe hyia anohyeto titiriw bi: nsɛm a ɛfa ho mfɛnsere wɔ anohyeto a anohyeto wom. Sɛ wodu saa anohyeto ahorow no ho a, nsɛm a akyɛ anaasɛ ɛte sɛ nea ɛho nhia pii no to hɔ — adeyɛ a wɔfrɛ no lossy compression. LCM di eyi ho dwuma denam adansi ho nhyehyɛe ahorow a ɛkora nsɛm a ɛfa ho biara a ntease wom so wɔ nhyiam anaa adwumayɛ nhyehyɛe bi nyinaa mu, na esiw nsɛm a ɛsɛe a ɛde nsɛm a ɛnyɛ pɛpɛɛpɛ, nsusuwii nkɔnsɔnkɔnsɔn a abubu, ne mfomso a ɛho ka yɛ den ba no ano.

Wɔ nnwumayɛbea ahorow mu no, sika a ɛwɔ mu no yɛ kɛse. AI a ɛboa adetɔfo a ne werɛ fi nkɔmmɔbɔ fã a edi kan, anaasɛ adwuma no sohwɛfo boafo a ɔhwere gyinaesi ahorow a na wadi kan asi no, de akasakasa ba mmom sen sɛ ɛbɛyɛ adwuma yiye. LCM hwɛ sɛ mfonini mũ no nyinaa wɔ hɔ bere nyinaa ma nhyehyɛe no a ɛresi gyinae wɔ wo ananmu.

Sɛnea LCM Nneɛma Titiriw no Yɛ Adwuma Ankasa?

LCM nam mfiridwuma akwan ahodoɔ a ɛka bom so na ɛyɛ adwuma a ɛbom yɛ adwuma de kura nsɛm a ɛfa ho no mu nokwaredi mu. Sɛ anka wɔde wɔn ho bɛto ɔkwan biako so no, nsɛm a ɛfa ho nhyehyɛe a ɛyɛ den a enni adehwere no ka akwan horow pii bom:

• Hierarchical memory structures — Wɔkora nsɛm so wɔ layered tiers (adwumayɛ memory, episodic memory, semantic memory), a ɛma nhyehyɛe no tumi nya nsɛm a ɛfa ho wɔ level a ɛfata a ɛnyɛ active processing.
• Context compression with reversibility — Nea ɛnte sɛ akwan a wɔfa so hwere ade no, reversible compression bɔ nsɛm a ɛwɔ mu no mua wɔ akwan horow so a wobetumi atrɛw mu asan akɔ akyiri akodu bere a ɛho hia, na ɛkora tumi a wɔde san kyekye mfitiase ntease no so.
• Dynamic context prioritization — Relevance scoring kɔ so san de nsɛm a ɛkɔ so yɛ adwuma no si hɔ, a egyina mprempren adwuma no so sen sɛ ɛbɛyɛ nnansa yi ara a ɛnyɛ den, enti wɔmfa anifurae so ntow mfitiase nsɛm a ɛho hia no ngu da.
• Abɔnten so nkaeɛ a wɔgye (RAG nkabom) — Retrieval-Augmented Generation ma nhyehyɛeɛ no tumi twe nsɛm a ɛfa ho pɛpɛɛpɛ firi abɔnten sotɔɔ a ɛkɔ so daa mu, na ɛtrɛ nsɛm a ɛfa ho mfɛnsere no mu yie a ɛnni ano a ɛnsɛe pɛpɛɛpɛyɛ.
• Ɔman nhwehwɛmu — Nhyehyɛe no sie nsusuwii nkɔnsɔnkɔnsɔn tebea mũ no nyinaa bere ne bere mu, na ɛyɛ nsɛntitiriw a wɔde san ba a esiw werɛ a ɛyɛ asiane ano wɔ anammɔn pii nnwuma tenten mu.

Ɔkwan Bɛn so na LCM Toto Amanneɛ kwan so Nsɛm a Ɛfa Ho Di Ho?

Nsonsonoe a ɛda nsɛm a ɛfa ho nhyehyɛe a enni adehwere ne nea wɔtaa de di dwuma ntam no bɛyɛ kɛse bere a wɔhwehwɛ mu wɔ nkyɛnkyɛn no. Standard truncation-based systems kɛkɛ twa token a akyɛ sen biara bere a mfɛnsere no ahyɛ ma — ntɛmntɛm, nanso ɛsɛe ade. Akwan a egyina nsɛm a wɔaboaboa ano so no boaboa nkɔmmɔbɔ a wɔadi kan abɔ no mua ma ɛyɛ tiawa a ɛyɛ tiawa, a ɛkora sɛnkyerɛnne bi so nanso akyinnye biara nni ho sɛ ɛhwere nsɛm nketenkete ne nsɛm pɔtee a ebia ɛbɛyɛ nea ɛfata akyiri yi.

a wɔde ahyɛ mu

"Nsonsonoe a ɛda nsɛm a ɛfa ho a wɔhwere ne nea enni adehwere ntam ne nsonsonoe a ɛda adwuma a ne werɛ fi nea n’atɔfo kae wɔ ɔsram a etwaam no ne nea ɛkae biribiara a ɛfa ho — nea etwa to no ma ahotoso, ɛyɛ adwuma yiye, ne nyansa a ɛyɛ kɛse bere kɔ so no ntam."

LCM yɛ adwuma sen akwan foforo abien no nyinaa wɔ tebea horow a ɛhwehwɛ sɛ wosusuw nneɛma ho bere tenten mu: mmara kwan so nkrataa mu nhwehwɛmu, adwuma nhyehyɛe a wɔyɛ wɔ nhyiam ahorow pii mu, adetɔfo akwantu a ɛyɛ den, ne sikasɛm mu akontaabu nyinaa hwehwɛ sɛ nsɛm biara a ɛfata ayera wɔ nkyerɛase mu. Empirical evaluations of LCM-aligned architectures kyerɛ bere nyinaa sɛ mfomso dodow a ɛba fam wɔ nnwuma a ɛhwehwɛ sɛ cross-session kɔ so ne nhyiam nkontabuo a ɛkɔ anim kɛse wɔ multi-turn AI nkitahodi mu.

Dɛn ne Wiase Ankasa mu Nsɛnnennen a Ɛfa Nsɛm a Ɛfa Ho a Ɛnyɛ Adehwere ho?

LCM a wode bedi dwuma wɔ scale so no nyɛ nea friction nnim. Engineering asɛnnennen titiriw ne kɔmputa so ka — sɛ wobɛkora nsɛm a ɛfa ho nyinaa so a, ɛhwehwɛ sɛ wɔyɛ nkae pii, wɔyɛ adwuma pii a wɔde gye nneɛma, ne indexing nhyehyɛe a ɛyɛ nwonwa sen truncation akwan a ɛyɛ mmerɛw. Wɔ akuw a wɔkyekye anaa wɔpaw AI-powered platforms no, eyi kyerɛ sɛ wɔbɛhwɛ sɛ ebia adetɔnfo bi nhyehyɛe yɛ nea ɔhwere ade ankasa anaasɛ wɔde akwan a ɛhwere ade a wɔde ahyɛnsode a enni adehwere di gua ara kwa.

Latency yɛ ade foforo a ɛsɛ sɛ wosusuw ho. Sɛ wogye fi abɔnten memory stores mu a, ɛde miliseconds ka inference anammɔn biara ho, a ɛyɛ kɛse wɔ bere ankasa mu dwumadie mu. Nneɛma a wɔde di dwuma a eye sen biara no di kan de nsɛm a ɛfa ho a ɛbɛyɛ sɛ ɛbɛba no ba wɔ bere a ɛne ne ho di nsɛ mu sen sɛ ɛbɛkɔ so nnidiso nnidiso, na ɛma mmuae bere ahorow no yɛ nea wogye tom a wɔmfa nea edi mũ mmɔ afɔre. Data nnisoɔ nso bɛyɛ den kɛseɛ: sɛ wɔkora nsɛm a ɛfa ho biara so a, ɛsɛ sɛ ahyehyɛdeɛ hyehyɛ nhyehyɛeɛ a emu da hɔ a ɛfa nsɛm a ɛkɔ so, berɛ tenten, ne wɔn a wɔbɛtumi anya bi ho — titire berɛ a wɔredi adwuma anaa adetɔfoɔ data a ɛho hia ho dwuma.

Ɛbɛyɛ dɛn na Nnwumakuw Atumi De LCM Nnyinasosɛm Di Dwuma de Ama Wɔn Dwumadi Atu mpɔn?

Wɔ nnwuma akannifoɔ fam no, LCM nnyɛ transformer architectures nteaseɛ kɛseɛ na ɛfa hwehwɛ sɛ nyansa kɔ so firi platforms a wɔde di dwuma no mu kɛseɛ. Sɛ AI adwumayɛfoɔ boafoɔ tumi kae botaeɛ a ɛfa akwankyerɛ ho a wode sii hɔ wɔ Ɔpɛpɔn mu berɛ a woreyɛ adwuma bi wɔ July mu a, ɛno ne sɛ LCM reyɛ adwuma wɔ nneyɛeɛ mu. Sɛ wo automation adwumayɛ nhyehyɛe no de nimdeɛ a edi mũ a ɛfa adetɔfo nkitahodi a atwam ho kɔ touchpoint foforo biara mu a, ɛno ne LCM a ɛde nsunsuanso pa ba.

Platforms a wɔasi a LCM nnyinasosɛm wɔ wɔn mu titiriw no ma wotumi de ahyehyɛdeɛ nyansa bom — nkitahodi biara ma nhyehyɛe no tu mpɔn sen sɛ wɔbɛsan de akɔ zero. Eyi nti na adansi ho gyinaesi ahorow a AI platform providers si no ho hia kɛse ma nnwuma a wɔde wɔn ho to wɔn so ma adwumayɛ a ɛho hia no.

Nsɛmmisa a Wɔtaa Bisa

So nsɛm a ɛfa ho a enni adehwereɛ sohwɛ ne nsɛm a ɛfa ho mfɛnsere kɛseɛ a wowɔ no yɛ pɛ?

Ɛnyɛ pɛpɛɛpɛ. Nsɛm a ɛfa ho mfɛnsere kɛse ma nsɛm dodow a ɛfata wɔ active memory mu no yɛ kɛse prɛko pɛ, nanso ɛda so ara wɔ anohyeto na ɛda so ara yɛ nea wotumi twa mu. Nokware LCM kɔ akyiri denam mfɛnsere a wɔatrɛw mu a ɛka abɔnten so gye, hierarchical memory, ne state management bom de hwɛ hu sɛ biribiara nnyera daa — a nhyiam no tenten anaa nea ɛyɛ den mfa ho.

So LCM ma AI nhyehyɛe ahorow no yɛ brɛoo kɛse anaasɛ ne bo yɛ den sɛ wɔde bedi dwuma?

Akontaabuo so ka ankasa wɔ hɔ, nanso LCM dwumadie a wɔayɛ no yie no brɛ nkɛntɛnsoɔ no ase denam parallel retrieval, intelligent caching, ne selective context loading so. Wɔ adwumayɛ mu dwumadie dodoɔ no ara mu no, mfasoɔ a ɛfiri pɛpɛɛpɛyɛ ne nea ɛkɔ so daa mu no boro ɛka a ɛkɔ soro kakra no so koraa, titire berɛ a mfomsoɔ a ɛfiri nsɛm a ayera mu no de wɔn ankasa ka a ɛba fam wɔ berɛ ne ahotosoɔ mu.

Mɛyɛ dɛn ahu sɛ adwumayɛ kwan bi a meresusuw ho no de nsɛm a ɛfa ho a enni adehwere ankasa di dwuma?

Bisa adetɔnfoɔ pɔtee sɛdeɛ wɔdi nsɛm a ɛfa ho ho dwuma a ɛboro wɔn active window anohyetoɔ so, sɛdeɛ wɔhwɛ adwumayɛ a ɛkɔ so kyɛ anaa mpɛn pii so, ne sɛ ebia wɔde akwan a wɔde retrieval-augmented di dwuma anaa. Platforms a ɛtumi kyerɛ nkaeɛ a ɛkɔ so daa wɔ nhyiamu ahodoɔ mu, nsusuiɛ a ɛkɔ so daa wɔ nkrataa atenten so, ne anammɔn pii automation a ɛne ne ho hyia yɛ LCM-aligned architecture ho nsɛnkyerɛnneɛ a emu yɛ den.

Managing context without loss nyɛ mfiridwuma mu nicety kɛkɛ — ɛyɛ AI nhyehyɛe fapem a nnwuma betumi de wɔn ho ato so ankasa wɔ adwumayɛ nhyehyɛe a ɛho hia mu. Sɛ woasiesie wo ho sɛ wobɛnya adwumayɛ kwan a nyansa wom a wɔayɛ ama adwumayɛ kɔ so ankasa a, fi wo Mewayz akwantuo ase nnɛ wɔ app.mewayz.com. Ɛnam sɛ module ahodoɔ 207 a wɔaka abom a ɛsom nnipa bɛboro 138,000 a wɔde di dwuma nti, Mewayz de adwumayɛ nhyehyɛeɛ a ɛyɛ biako, a ɛnim nsɛm a ɛfa ho a ɛdane data a apete ma ɛbɛyɛ ahyehyɛdeɛ nyansa a ɛyɛ den — ɛfiri $19 pɛ bosome biara.