#machinelearningroma search results
Tutti al #meetup #AperiTech di #MachineLearningRoma con #Codemotion in @EnLabs dedicato alle ultime frontiere dell'intelligenza artificiale!
Ultimo #meetup #AperiTech #MachineLearningRoma @CodemotionIT prima dell’estate: ci salutiamo con @nastroazzurro, non c’è modo migliore!
Ed è subito #Natale con la #Community #MachineLearningRoma al #meetup #AperiTech di dicembre con #Codemotion in @EnLabs 🎄 Interessantissimi talk con Sara Di Bartolomeo ed Elena Nieddu e poi tutti a festeggiare anche al primo anno della Community 🎉
⭕️ Check out MultiLLM debate this new paper "ROMA: a Read-Only-Memory-based Accelerator": ⭕️ Moderator Consensus: ROMA Paper Analysis Areas of Strong Agreement All reviewers converge on ROM immutability as the critical flaw. ⭕️ Join the debate: multillm.ai/conversations/… #AI…
2/14. ROMA is #Sentient meta-agent framework that breaks user queries into sub-tasks, routes them to the best agents, and recombines outputs. It makes GRID capable of orchestrating many specialized intelligences into coherent results.
Roma, evento Adnhronos: "Intelligenza umana e supporto artificiale" L’intelligenza artificiale accelera la trasformazione digitale, ridefi ... agronline.it/tecnologia/rom…
ROMA's meta-agents evolve prompts into self-tuning symphonies for AGI depth.
ROMA turns “agents” from a toy into infra-grade coordination Recursive break-down, parallel spin-up, tokenized steering of compute via $SENT This is what actual #OpenAGI rails look like, not a slide deck @sentientagi
Sentient's ROMA v0.2.0 turns recursion into real multi-agent muscle for #OpenAGI breaking complex tasks into parallel subtasks, routing to optimal models, and merging via reflective GEPA optimization. Built on DSPyOSS for speed and cost savings that actually scale for builders.…
RoMa v2: Harder Better Faster Denser Feature Matching - — Leads EPE/match accuracy on 6 benchmarks; improves pose estimation over RoMa, UFM, DKM, MASt3R, DUSt3R. - — 1.7× throughput vs RoMa with similar memory; subpixel-accurate refinement + per-pixel 2×2 covariance; top on…
🎯 AS Roma on target! Cremonese 0-1 AS Roma (HT) 🤖 Our AI is backing AS Roma with 83% confidence! #ItalianSerieA #Cremonese #ASRoma #SerieA #SerieATIM
RoMa v2 is here, and it’s a leap forward for dense feature matching. This model nails the hardest 3D vision cases—wide angles, low texture, fine details—while running 1.7× faster and using just 4.8 GB memory. The numbers: 77.0% AUC@10° on MegaDepth-1500, 4.1 px error on…
digging the roma grid potential proof-loops could let AI auto-optimize DAO charters over time, adapting rules to yield patterns without manual forks. scalability unlocked
This picture deserves a lot more significance when explaining @SentientAGI ROMA! ROMA solves complex tasks recursively by breaking down tasks into subtasks all being processed concurrently! The tasks are represented as nodes. The node can be executed directly, break itself into…
If you're up for beta testing the current model is here: github.com/Parskatt/romav2 I'm looking for feedback for the next version, what works, what doesn't.
fingerprints stitched into ROMA is the rails for real ai economies per‑inference royalties drop straight to submodules, provenance survives upgrades, agents stay loyal this is open infra you don’t fork away from the kind that turns on‑chain ai into steady creator cashflow…
okay, so here’s the sauce: @SentientAGI is wiring open-source AI to on-chain incentives oml 1.0 fingerprints let you tag 24,576 keys inside a model, so builders can prove provenance, route agent payments, and keep usage loyal. pair it with ROMA multi-agent control and you get ai…
RoMa v2: Harder Better Faster Denser Feature Matching @Parskatt et 11 al. tl;dr: in title. Predict covariance per-pixel, more datasets, use DINOv3, adjust architecture. arxiv.org/abs/2511.15706
ROMA v2’s architecture is truly impressive. Its modular, flexible, and fully observable design allows complex tasks to be handled efficiently and enables developers to easily integrate new tools. This approach is a powerful example of building an open and reliable AI ecosystem.…
ROMA lets agents break down problems, assign tasks, and combine results for complex challenges.
ROMA & ODS: Two key systems in @SentientAGI research that take open AI to the next level. ROMA breaks down large tasks into smaller ones for efficient reasoning, while ODS ensures more accurate data retrieval. they create a smarter, safer ecosystem where intelligent agents collab
RT : recursion compounding into coordination season ROMA v0.2.0 breaks tasks, keeps context local, runs parallel, routes models, then merges with GEPA, artifact memory with selective context access. domain‑specialized meta‑agents via prompts, steady reflective gains. when the…
Sentient: recursion-powered agents for #OpenAGI - - - - - @sentientagi ROMA v0.2.0 makes multi-agent coordination practical: break hard problems into subtasks, keep context local, run in parallel, route models by task, then merge with reflective optimization (GEPA). Built on…
Tutti al #meetup #AperiTech di #MachineLearningRoma con #Codemotion in @EnLabs dedicato alle ultime frontiere dell'intelligenza artificiale!
Ed è subito #Natale con la #Community #MachineLearningRoma al #meetup #AperiTech di dicembre con #Codemotion in @EnLabs 🎄 Interessantissimi talk con Sara Di Bartolomeo ed Elena Nieddu e poi tutti a festeggiare anche al primo anno della Community 🎉
Ultimo #meetup #AperiTech #MachineLearningRoma @CodemotionIT prima dell’estate: ci salutiamo con @nastroazzurro, non c’è modo migliore!
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