https://llmobile.news/companies/google/A concise news ticker covering AI on mobile devices, local models, apps and hardware.en-GBhttps://llmobile.news/ticker/adk-for-kotlin-1-0/https://llmobile.news/ticker/adk-for-kotlin-1-0/Wed, 09 Sep 2026 00:00:00 +0200Google has released ADK for Kotlin 1.0, the general-availability version of its Agent Development Kit for Kotlin, Java and Android. The kit is built on Kotlin Multiplatform and has full feature parity with ADK 1.0 Core, according to Google. It adds a set of Android extensions for agents that run on the device. The LiteRT-LM module runs open models such as Gemma on the device with tool calling, on Android and on the JVM, and needs JDK 21 or newer according to the project README. A separate ML Kit module runs the built-in Gemini Nano model through the ML Kit GenAI Prompt API. It is Android-only, ships as a 1.0.0-beta and does not support tool calling yet. The documentation says agents can run without an API key and without a network connection at inference time, and developers can combine on-device and cloud models in one multi-agent system, with cloud Gemini models reached through Firebase AI Logic. Further Android modules store chat sessions in a local Room database, keep long-term memory in an on-device AppSearch index and save agent files in app-private storage. Skills, which are bundles of domain-specific prompts and tools, can ship inside the APK’s assets folder. Tools are declared with @Tool and @Param annotations, and a Kotlin Symbol Processing step generates their schemas at compile time with no runtime reflection. ADK for Kotlin is available under the Apache 2.0 license on GitHub, and the documentation covers setup. The repository includes runnable examples, among them a Java interoperability sample. Source: https://developers.googleblog.com/announcing-adk-for-kotlin-10-building-production-ready-ai-agents-in-kotlin-android-and-beyond/ Read the article: https://llmobile.news/ticker/adk-for-kotlin-1-0/RuntimesAndroidhttps://llmobile.news/ticker/mobibench-llamacpp/https://llmobile.news/ticker/mobibench-llamacpp/Wed, 15 Jul 2026 00:00:00 +0200Researchers have published MobiBench, a benchmarking suite built on llama.cpp that measures how small language models behave under limited resources. It covers prefill speed, decode speed, time-to-first-token, memory use and task accuracy. The paper tests Gemma 1B, TinyLlama, Llama 3B and Phi-2, most of them released in 2023 and 2024, plus Q2_K and Q3_K_M quantized variants of Phi-2. A diagram in the paper shows the llama.cpp execution pipeline from runtime initialization and model loading to compute graph construction and iterative decoding. Figure 1 of the paper, titled high-level execution pipeline of llama.cpp.MobiBench authors, arXiv Despite the mobile framing, the authors ran the tests on a gaming laptop with an Intel Core i7 and an RTX 3060 GPU and on a standard laptop with an Intel Core i5 and no dedicated graphics. The paper reports no phone, Android or ARM measurements and no battery or energy data. On the GPU, Llama 3B reaches 225 to 243 tok/s in decode, Phi-2 reaches 102 to 122 tok/s and Gemma 1B reaches 150 to 167 tok/s. The Gemma 1B figure is about 7 to 8 times the 20 to 23 tok/s the authors measured on the CPU. For TinyLlama, GPU inference cut average wall-clock time per sample from 65.81 s to 1.39 s on MMLU, a 12 to 47 times speedup across the four tasks. On the GPU, Phi-2 used about 2,070 to 2,091 MB of memory, the Q3_K_M variant about 1,982 to 2,000 MB and the Q2_K variant about 1,694 to 1,710 MB. The Q2_K variant decoded at 46 to 56 tok/s and the Q3_K_M variant at 58 to 88 tok/s. On MMLU, Phi-2 scored 0.243, the Q3_K_M variant 0.263 and the Q2_K variant 0.233. The accuracy tests used 1,000 samples each from RepLiQA, CNN/DailyMail summarization, SciQ and MMLU. Summarization BERTScore F1 stayed between 0.8388 and 0.8458 across all models, and an LLM-based semantic evaluation gave mean scores from 0.4554 for TinyLlama on the GPU to 0.6961 for Gemma 1B on the CPU. The authors list several limitations, namely the restriction to llama.cpp and consumer-grade Intel and RTX 3060 hardware, modest scores on domain-specific reasoning tasks and varying output formats that affect multiple-choice scoring. Source: https://arxiv.org/html/2609.13159v1 Read the article: https://llmobile.news/ticker/mobibench-llamacpp/BenchmarksResearchQuantisationGPURuntimeshttps://llmobile.news/ticker/gemini-nano-frozen-mtp/https://llmobile.news/ticker/gemini-nano-frozen-mtp/Fri, 26 Jun 2026 00:00:00 +0200Google Research has retrofitted multi-token prediction (MTP) onto the production Gemini Nano v3 models, which Google says recently rolled out to the Pixel 9 and Pixel 10 series. A small Transformer head attached to the last layers of the model proposes several future tokens per pass, and the unchanged main model verifies them in parallel. According to Google, MTP correctly predicts an average of nearly two additional tokens per inference pass in AI Notification Summaries and Proofread. Gemini Nano v3 stays frozen and only the MTP head is trained, so Google says the output is bit-for-bit identical to the original model and its capabilities and safety alignment do not change. The head cross-attends to the main model’s existing key-value cache instead of keeping its own, which avoids a second prefill pass. Google reports savings of 130 MB per instance compared with a standalone drafter, a separate small model that proposes tokens in classic speculative decoding, because the head needs no embedding lookup tables, prefill attention variants or application-specific tuning parameters. Google measured speedups of 50% or more on Pixel 9 depending on the task, compared with standalone drafters of comparable parameter count. For highly predictable outputs such as smart replies, token acceptance improved by up to 55%. The company says features like notification summaries, Proofread and smart replies now generate text faster and with less energy, and that developers no longer need to fine-tune a separate drafting model for each new task. Google’s own figures show end-to-end latency gains of 1.14x to 1.48x across five Pixel 9 features.Google Research Google plans to bring MTP to future Pixel devices. The research team also wants to explore parallel decoding without auxiliary heads to cut draft latency, models that explore several branches in parallel, and a relaxed verification step that no longer requires an exact token match for some use cases. Source: https://research.google/blog/accelerating-gemini-nano-models-on-pixel-with-frozen-multi-token-prediction/ Read the article: https://llmobile.news/ticker/gemini-nano-frozen-mtp/ModelsResearchAndroidGemini Nano