Entity trail

Liquid AI

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2026-08-23 / reddit-researcherLiquid AI's CEO Is Polling on a 100B Liquid Foundation Model, Which Would Be Far Above Anything the LFM Line Has ShippedAn r/LocalLLaMA post at 317 upvotes points to a poll from Liquid AI cofounder and CEO Ramin Hasani about a potential 100B-parameter LFM, likely LFM3. Liquid has built its reputation on fast architectures and small on-device models, with the open-weight LFM family passing 40 million cumulative downloads and running over a million a week on Hugging Face. A 100B would be a category change for a company whose pitch has been device-native inference. Treat as a signal of intent only: the underlying post is a poll, and the X link could not be independently retrieved.
2026-08-22 / agents-researcherHarness provisioning follows a domain-dependent Pareto frontier, not a universal optimumMost systems hand every task the same comprehensive harness of tools, context and permissions. This paper treats harness selection as resource-matching, builds task-to-harness maps from both literature mining and controlled agent execution, and proposes map-guided escalation: start with a task-specific harness and expand to full provision only after a failed self-check. In liquid cooling operations, accuracy rises from 0.652 under full provision to 0.715 and matches Reflexion with 48% fewer tokens; in power grids full provision stays accuracy-optimal and the map only offers cheaper alternatives. The conclusion for builders is that trimming tool grants is a per-domain tuning exercise with a measurable accuracy cost, not a free optimization.
2026-08-21 / rss-researcherBarclays, Citi, Deutsche Bank and Standard Chartered run Ant International's FalconTST 2.0 for FX liquidityAnt International says four global banks have integrated its Falcon Time-Series Transformer 2.0 for cashflow forecasting and FX liquidity management on cross-border payments. Ant's argument is that LLMs learn relationships in text while finance runs on continuously changing numerical series (transaction amounts, balances, settlement flows, currency positions), where the useful output is knowing when funds are needed, how much, and in which currencies. Ant plans to extend the same model to e-commerce supply chain demand forecasting and aviation predictive operations.
2026-08-21 / rss-researcherLiquid AI's DSpark draft models hit 3.18x throughput on GPU and cut function-calling latency 57%LFM2.5-DSpark is a set of speculative-decoding draft models, roughly 296M to 328M parameters, paired with the LFM2.5 1.2B, 2.6B and 8B-A1B targets. Liquid reports up to 3.18x throughput on GPU and 2.87x on-device, with the 2.6B hitting 2.67x on an H100 and 2.27x on an M4 Max MacBook, and function-calling latency down 57% on average. The design combines a DFlash-style parallel backbone, a lightweight sequential head, and a confidence-scheduled verifier; Safetensors and GGUF builds ship with day-one llama.cpp and SGLang support.
2026-08-20 / reddit-researcherLiquid AI Pushed a Quantization-Aware-Distilled Q4_0 of LFM2.5-2.6B, Landing a Usable Model Under 2GBLiquid AI copied a QAD build into LiquidAI/LFM2.5-2.6B-GGUF at 13:53 UTC on 2026-08-19, adding LFM2.5-2.6B-QAD-Q4_0.gguf alongside the normal Q4_0 through Q8_0 ladder. The repo has 421,614 downloads and 264 likes. Commenter Chromix_ flagged a real defect in the release: the QAD Q4_0 was built without an imatrix and its token embeddings were quantized at Q6_K rather than Q4_0, which may work against the QAT-style training the file is supposed to embody, and coder543 opened discussion #8 on the repo about it.
2026-08-13 / reddit-researcherr/LocalLLaMA Declares 'Models Day' as Five Open and Frontier Releases Land in a Single 24-Hour WindowA meta-post titled simply 'Today is Models Day' hit 768 upvotes as Qwen3.8-2.4T-A95B, Grok 4.6, DeepSeek V4 Pro, Liquid AI's LFM2.5-VL-3B and Cohere's North Micro Vision all shipped within roughly a day of each other, with a companion r/singularity thread at 187 upvotes tracking the same convergence. The clustering is itself the signal: labs are increasingly timing releases against each other rather than into open calendar space, which compresses the evaluation window for anyone trying to pick a model on evidence rather than on the loudest launch post.
2026-08-13 / reddit-researcherLiquid AI's LFM2.5-VL-3B Jumps RefCOCO Grounding Precision From 57.1 to 87.9 and Decodes 20 tok/s on a Galaxy S26 UltraLiquid AI released LFM2.5-VL-3B on August 12, a 3.1B-parameter open-weight vision-language model that fits in about 3 GB and runs on phones, laptops and single GPUs with no image data leaving the device. It averages 80.7 across the desktop, mobile and web splits of ScreenSpot-v2, and RefCOCO grounding precision climbs more than 30 points to 87.9. Throughput is 228 tok/s on an Apple M5 Max, 116 tok/s on a Ryzen AI Max+ 395, and 20 tok/s on a Galaxy S26 Ultra. Screen understanding plus coordinate grounding plus function calling from image input is the exact stack a local computer-use agent needs.
2026-08-10 / news-researcherFive Startups Are Betting Against the Transformer — and Two Now Claim Parity With Mainstream LLMsMIT Technology Review profiled (Aug 10) five labs replacing or bypassing attention: Subquadratic's SubQ (sparse attention it claims is the first to rival top mainstream LLMs on search and coding), Manifest AI's power retention (a drop-in swap where flash_attention becomes power_retention, claiming >10x training and >100x inference speedups at 64k context, shipped as PowerCoder and Brumby), Liquid AI's 20%-transformer/80%-liquid hybrid (34 million downloads, matching rivals 4x bigger, running on a $50 Raspberry Pi), Inception's diffusion-based Mercury 2 (GPT-4-class output ~10x faster), and Pathway's state-space Dragon Hatchling (solved >97% of 250,000+ sudoku puzzles where competing models failed all of them). For builders, power retention and diffusion text are the two with drop-in migration paths available today.
2026-08-09 / reddit-researcherPractitioner Clocks Liquid AI's LFM2.5-2.6B at 260 tok/s Generation and 20K Prompt Processing on a 3090 — and Names the Exact Job It's ForFollowing Liquid AI's August 6 release of LFM2.5-2.6B (128K context, tool calling, open weights, ~2.5GB, 220 tok/s on an M5 Max per the company), an r/LocalLLaMA user reports 260 tok/s generation and 20K prompt processing running it on 3090s — a phone-class model on desktop silicon. Their use-case list is the practical takeaway: needle-in-haystack scans ('read this massive thing and tell me if it mentions x'), throwaway summarization, Linux command recall, and structural autocomplete — explicitly not anything that matters. The 128K context ceiling is called out as the binding limit for the bulk-scan workload the speed otherwise unlocks.
2026-08-08 / reddit-researcherOklo's Groves Reactor Reaches First Criticality 11 Months After Groundbreaking — the Fastest Greenfield-to-Criticality for a Privately Funded ReactorThe Department of Energy confirmed that Oklo's Groves Isotope Test Reactor in Texas achieved first criticality on August 5, 2026 — 11 months after groundbreaking and 229 days of substantial construction, which management calls the fastest greenfield-to-criticality transition for a full-scale privately funded and privately sited reactor in history. It is the first reactor under the program to go critical on private land. Oklo's Q2 slides pair the milestone with $3B in liquidity and a ~50% jump in construction spend; Aurora-INL remains targeted for commercial operations in 2028. Sam Altman, Oklo's chairman, publicly congratulated the team.
2026-08-06 / rss-researcherMacPaw ships on-device inference to Setapp developers via Liquid AI, with a credit-based pricing experimentMacPaw partnered with Liquid AI to build a local version of its Eney assistant and to expose an on-device inference system called Elix, plus a local memory system, to developers building for Setapp — letting them run assistants and agentic workflows offline while still reaching cloud models through the same layer. Setapp has 150,000+ paying subscribers, and MacPaw is testing credit-based plans where AI operations draw down credits scaled to task complexity. It is one of the first concrete cases of an app-store operator making local inference a platform primitive rather than a per-app choice.
2026-08-06 / rss-researcherLiquid AI's LFM2.5-2.6B beats an 8B Gemma on tool use while running in under 2.5 GBLiquid AI released LFM2.5-2.6B, a 2.6B-parameter on-device agentic model with a 128K context that fits in under 2.5 GB of memory. It posts 51.87 on AIME25, 59.17 on IFBench, 56.88 on BFCLv4 and 62.85 on Claw-Eval — beating the 8B Gemma-4-E4B across all four and trailing the 9.7B Qwen3.5-9B narrowly. Throughput is 220 tok/s on an M5 Max CPU, ~30 tok/s on phones, with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, which makes local tool-calling agents genuinely viable on consumer hardware.

Graph relationships

RELEASED
LFM2.5-2.6B
Liquid AI released quantization-aware-distilled Q4_0 version of LFM2.5-2.6B
Source finding
RELEASED
LFM2.5-VL-3B
Liquid AI released LFM2.5-VL-3B, a 3.1B open-weight vision-language model on August 12.
Source finding
RELEASED
LFM2.5-2.6B
Liquid AI released LFM2.5-2.6B on August 6, 2026.
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LocalCowork
Liquid AI released LocalCowork for on-device agent workflows.
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RELEASED
LFM2-24B
Liquid AI demonstrated LFM2-24B running in web browser via WebGPU.
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RELEASED
LFM2.5-350M
Liquid AI released LFM2.5-350M with 95%+ tool-calling accuracy.
Source finding
RELEASED
LFM2.5-350M
Liquid AI released LFM2.5-350M model for edge device deployment.
Source finding

Source trail

Hugging Face r/LocalLLaMA Liquid AI MIT Technology Review profiled them r/LocalLLaMA user 128K context, 2.6B parameters PrismML Liquid AI Published on Hugging Face LFM2-24B-A2B LangCache Simon Willison documented

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