# [AINews] Thinking Machines' Native Interaction Models - TML-Interaction-Small 276B-A12B - advances SOTA Realtime Voice and kills standard VAD > Source: > Published: 2026-05-12 04:33:46+00:00 # [AINews] Thinking Machines' Native Interaction Models - TML-Interaction-Small 276B-A12B - advances SOTA Realtime Voice and kills standard VAD ### well done, Team Thinky. By complete coincidence, the day we [released](https://x.com/neilzegh/status/2053945753073074484?s=20) Neil Zeghidour (CEO of Gradium, the for profit spinoff of the vaunted [Kyutai Moshi](https://kyutai.org/))’s [talk](https://www.youtube.com/watch?v=P_RI1kCkRbo&time_continue=0&source_ve_path=MjM4NTE&embeds_referring_euri=https%3A%2F%2Fx.com%2F) on what remains to be built for realtime voice, **Thinking Machines** emerged for only the [third](https://news.smol.ai/issues/25-10-01-thinky) [time](https://news.smol.ai/issues/25-02-18-ainews-xai-grok-3-and-mira-muratis-thinking-machines) in a ~year (despite much drama) to drop [Interaction Models: A Scalable Approach to Human-AI Collaboration](https://thinkingmachines.ai/blog/interaction-models/), **TML-Interaction-Small** is a 276B parameter MoE with 12B active., which immediately advances the state of the art of realtime voice models as Neil had laid out, updating [the famously dead GPT 4o “her” demo](https://openai.com/index/hello-gpt-4o/) with far more detailed demos that are presumably far closer to real use: The [full blogpost](https://thinkingmachines.ai/blog/interaction-models/) has lots of demos of the level of continuous interactivity, focusing on streams of “time-aligned microturns” of 200ms each: Using encoder-free early fusion, with images and audio all processed <200ms, similar to Meta’s [Chameleon](https://arxiv.org/abs/2405.09818): There are a number of official benchmarks that the team shows beating both [GPT-Realtime-2](https://www.latent.space/p/ainews-gpt-realtime-2-translate-and) and [Gemini 3.1-Flash](https://www.latent.space/p/ainews-nano-banana-2-aka-gemini-31) on basic things like BigBench Audio and IFEval and FD-bench, but the level of interactivity aimed for required making 2 new internal benchmarks for time awareness, simultaneous translation, and visual proactivity: **TimeSpeak:** Can the model**initiate speech** at user-specified times?Example: “I want to practice my breathing, remind me to breathe in and out every 4 seconds until I ask you to stop.” **CueSpeak:** Can the model speak at the**appropriate moment?** Example: “Everytime I codeswitch and use another language, give me the correct word in the original language.” contains videos of repeated actions and is adapted into an online counting task - measures[RepCount-A](https://arxiv.org/abs/2204.01018)**continuous visual tracking and timely counting**.consists of videos with questions, whose answers become available at specific moments. Higher scores require correct answers at the correct times, silence gets partial credit, and incorrect answers are penalized.[ProactiveVideoQA](https://arxiv.org/abs/2507.09313)is a standard temporal action-localization benchmark.[Charades](https://arxiv.org/abs/1604.01753)Stream a user audio instruction: “Say ‘start’ when the person starts doing {action} then say ‘Stop’ when they stop.” But look past the numbers: the single most visceral demo is this one buried at the bottom. Play the samples and feel the AGI: The closing notes leave tantalizing hints to Thinky’s roadmap, including an intriguing pairing of background agents with interactive models, which we like a whole lot. AI News for 5/9/2026-5/11/2026. We checked 12 subreddits, [544 Twitters]and no further Discords.[AINews’ website]lets you search all past issues. As a reminder,[AINews is now a section of Latent Space]. You can[opt in/out]of email frequencies! **AI Twitter Recap** **Thinking Machines’ Native Interaction Models and the Shift Beyond Turn-Based AI** **Full-duplex multimodal interaction as a first-class model capability**: The day’s clearest technical theme was[Thinking Machines’ preview of “interaction models”](https://x.com/miramurati/status/2053939069890298321), described as models trained**from scratch** for real-time interaction rather than layering speech, turn-taking, and tool use onto a turn-based LLM. The accompanying[technical post](https://x.com/thinkymachines/status/2053938892152435174)and team commentary from[@johnschulman2](https://x.com/johnschulman2/status/2053940452789981426),[@soumithchintala](https://x.com/soumithchintala/status/2053940215505645938), and[@cHHillee](https://x.com/cHHillee/status/2053940218747842619)frame this as a**human↔AI bandwidth** problem: models should be able to listen, speak, watch, think, search, and react concurrently. Demos emphasized continuous-time awareness, interruption handling, simultaneous speech, visual proactivity, and background tool use without explicit “now I’m thinking / now I’m searching” boundaries. Team members also highlighted that many tasks that previously needed special-purpose systems become zero-shot once the type signature is effectively continuous**audio+video+text → audio+text**([@johnschulman2](https://x.com/johnschulman2/status/2053940940885332028)).** Why it matters technically**: Several reactions converged on the same point: this is not “another chatbot demo” but a change in interface assumptions.[@liliyu_lili](https://x.com/liliyu_lili/status/2053942465477197891)pointed to**visual proactivity**(“tell me when I start slouching”, “count my pushups”) as a missing primitive in current systems;[@rown](https://x.com/rown/status/2053950123139575863)called it the first general**video+speech** model that is visually proactive;[@kimmonismus](https://x.com/kimmonismus/status/2053952846064767384)and[@giffmana](https://x.com/giffmana/status/2053953584300003405)both emphasized that native interactivity is the deeper innovation than raw benchmark claims. This launch also implicitly raises the bar for “realtime” multimodal systems, as noted by[@swyx](https://x.com/swyx/status/2053960011748098462). One implementation detail surfaced via[@eliebakouch](https://x.com/eliebakouch/status/2053982248253190180): the stack is using**SGLang**. **OpenAI’s Enterprise and Security Push: Deployment Company and Daybreak** **OpenAI is moving down-stack into services and deployment**: OpenAI announced the[OpenAI Deployment Company](https://x.com/OpenAI/status/2053824997777457651), a majority-owned unit built to help enterprises deploy frontier models into real workflows. The key operating detail is**150 Forward Deployed Engineers and Deployment Specialists** coming in via the acquisition of[Tomoro](https://x.com/OpenAI/status/2053824999736410415), with[@gdb](https://x.com/gdb/status/2053884619695730745)citing**$4B of initial investment from 19 partners**. Multiple observers read this as OpenAI adopting a Palantir-/Microsoft-style field-engineering model:[@kimmonismus](https://x.com/kimmonismus/status/2053844403488194827)argued OpenAI wants to own the**deployment layer** of the AI economy, while[@matvelloso](https://x.com/matvelloso/status/2053881988529139765)connected it to the historical enterprise success pattern of embedding technical staff close to customer operations.**Daybreak: security-specific model distribution, workflow, and trust tiers**: OpenAI also launched[Daybreak](https://x.com/OpenAI/status/2053939702110269822), an umbrella effort around defensive cyber operations and continuously securing software, with[@sama](https://x.com/sama/status/2053951874408276193)positioning it as a practical response to rapidly improving AI cyber capability. The product pitch, summarized by[@TheRundownAI](https://x.com/TheRundownAI/status/2053945340592631843), combines**GPT-5.5**,** Codex**, repository threat modeling, vuln discovery, patch generation, and response automation, with differentiated access tiers including**Trusted Access for Cyber** and a more specialized**GPT-5.5-Cyber**. This stands in contrast to Anthropic’s more restrictive cyber posture, a tension captured by[@kimmonismus](https://x.com/kimmonismus/status/2053941490490265661). For teams building secure agent systems, a separate warning from[@lukOlejnik](https://x.com/lukOlejnik/status/2053758553723211988)is relevant:**“Your LLM is not a security boundary”**—Microsoft Semantic Kernel reportedly allowed prompt injection to be turned into host-level RCE because the framework over-trusted model output rather than the model itself failing. **Agent Harnesses, Local-First Tooling, and Control Surfaces** **Better agent control planes are becoming a product category**: A recurring complaint is that useful agents need autonomy, but engineers still want reversible, inspectable control.[@itsclelia](https://x.com/itsclelia/status/2053716807748567329)addressed this with**aggit**, a Rust CLI for local/remote, S3-backed storage of agent artifacts, enabling stash/branch/restore semantics outside the main Git history. In the same vein,[@_catwu](https://x.com/_catwu/status/2053999857799672111)highlighted a new`claude agents` terminal control plane for managing multiple Claude Code agents, and[@cursor_ai](https://x.com/cursor_ai/status/2053939390410612988)pushed Cursor into**Microsoft Teams**, where the agent reads the full thread and opens a PR. These are all signs that “agent orchestration” is converging on concrete UX patterns rather than prompt tricks alone.**Deep Agents / Hermes / local agents are maturing quickly**:[@masondrxy](https://x.com/masondrxy/status/2053717333433340034)noted that** Deep Agents CLI**can hot-swap underlying model providers** mid-conversation without losing context**, a nontrivial systems capability that many agent stacks still miss. LangChain also highlighted** harness profiles**for provider/model-specific tuning ([tweet](https://x.com/masondrxy/status/2053882188870074848)), and separate pricing analysis from the same author argued that**DeepSeek V4 Flash** can be dramatically cheaper than GPT/Gemini flash-tier options for high-volume agent workloads ([tweet](https://x.com/masondrxy/status/2053855842076942555)). On the local side, Hugging Face added[Hermes Agent support in local apps plus native trace visualization](https://x.com/mervenoyann/status/2053857347429151163), while[@Teknium](https://x.com/Teknium/status/2053961675985113404)previewed**computer use with any model** via Hermes Agent and CUA, explicitly targeting local/open models as well as frontier APIs.[@onusoz](https://x.com/onusoz/status/2053812410730037256)joining Hugging Face to improve local models in**OpenClaw** and related open harnesses is another strong signal that local agent ergonomics are now strategic infrastructure.**A design thesis emerging around tools**:[@threepointone](https://x.com/threepointone/status/2053751241977594102)argued that agents may asymptotically want just** two primitive tools: search and execute**, with dynamic semantic discovery of capabilities rather than ever-expanding static tool menus. That complements the broader move toward configurable harnesses instead of giant monolithic prompts. **Benchmarks, Efficiency, and Open-Model Economics** **Coding-agent benchmarking is finally measuring harness+model pairs**:[Artificial Analysis launched a Coding Agent Index](https://x.com/ArtificialAnlys/status/2053865095076438427)spanning SWE-Bench-Pro-Hard-AA, Terminal-Bench v2, and SWE-Atlas-QnA, comparing not just models but**model+harness combinations**. Their topline:** Opus 4.7**in Cursor CLI scored** 61**, with** GPT-5.5**in Codex/Claude Code close behind; top open-weight setups included** GLM-5.1**,** Kimi K2.6**, and** DeepSeek V4 Pro**in Claude Code, still competitive but meaningfully behind. The benchmark also exposed large variation in** cost per task**(>30x),** token usage**(>3x),** cache hit rates**(80–96%), and** time per task**(>7x). That benchmark was complemented by OpenHands’ updated software-engineering benchmark announcement ([tweet](https://x.com/OpenHandsDev/status/2053839810343620980)) and Claw-Eval’s more agentic task mix across office, finance, terminal, and web tasks, where[MiMo-V2.5-Pro led and DeepSeek V4 Flash looked unusually efficient for its size](https://x.com/nathanhabib1011/status/2053786853929824385).**TurboQuant skepticism is increasing**: Multiple posts pointed to a more sober view of the recently popular quantization/serving technique.[@_EldarKurtic](https://x.com/_EldarKurtic/status/2053809592061030546)presented what he described as the first comprehensive study of**TurboQuant**, covering accuracy, latency, and throughput;[@vllm_project](https://x.com/vllm_project/status/2053852636093239555)linked the Red Hat / vLLM investigation as a starting point; and[@jbhuang0604](https://x.com/jbhuang0604/status/2053882357833208262)bluntly summarized the takeaway as “it doesn’t really work well.” This is exactly the sort of infra claim where independent reproduction matters.**Local/open models continue to improve faster than hardware ceilings**:[@ClementDelangue](https://x.com/ClementDelangue/status/2053825719587815711)made the strongest high-level argument here: on the same top-end MacBook Pro memory ceiling, the “smartest open-weight model you can actually run” improved from Llama 3 70B-era capability to**DeepSeek V4 Flash mixed-Q2 GGUF**-era capability at roughly** 4.7x in 24 months**, implying a doubling every** 10.7 months**, faster than Moore’s Law. Supporting datapoints came from[@victormustar](https://x.com/victormustar/status/2053780086596288781)on the rapid growth of GGUF uploads and from repeated community observations that**Qwen 3.6**,** Gemma 4**, and DeepSeek variants are now usable locally for nontrivial agent tasks. **Research Highlights: MoE Modularity, Diffusion/Byte Models, and Agent Dynamics** **Architectures and evaluation**: AllenAI’s** EMO**was highlighted by[@TheTuringPost](https://x.com/TheTuringPost/status/2053795343658303860)as a more modular Mixture-of-Experts design where document-level routing induces shared expert pools; notably, keeping only**25% of experts** reportedly costs just**~1%** performance versus**10–15%** degradation in standard MoEs under similar pruning ([follow-up](https://x.com/TheTuringPost/status/2053795410490339720)). On generative evaluation,[@qberthet](https://x.com/qberthet/status/2053795951228371311)introduced**MIND (Monge Inception Distance)** as a purportedly faster, more sample-efficient replacement for FID.**Diffusion for language and byte-level modeling**: Several papers pushed non-AR language modeling.[@LucaAmb](https://x.com/LucaAmb/status/2053867347023466850)reported continuous bitstream diffusion nearly matching autoregressive models under their evaluation setup;[@JulieKallini](https://x.com/JulieKallini/status/2053853543552217478)introduced**Fast BLT**, using diffusion for parallel byte decoding to make byte-level LMs less inference-bound;[@sriniiyer88](https://x.com/sriniiyer88/status/2053882384211419375)framed it as combining block byte-diffusion with self-speculative decoding. Relatedly,[@LiangZheng_06](https://x.com/LiangZheng_06/status/2053806963839168619)noted a useful property of diffusion models for post-training: because sampling is differentiable, reward gradients can in principle flow straight to parameters more directly than in standard LLM setups.**Agent behavior under long horizons**: Two strong empirical threads surfaced. First,[“The Memory Curse”](https://x.com/omarsar0/status/2053863994499408214)claims long histories degrade cooperation in multi-round social dilemmas because models become more**history-following and risk-minimizing**, with explicit CoT sometimes amplifying the problem. Second,[PwC work summarized by @dair_ai](https://x.com/dair_ai/status/2053866106151182419)argues that the value of clarification is highly time-dependent:**goal clarification loses most of its value after ~10% of execution**, while input clarification remains useful longer. Together these suggest that long-horizon agent quality is constrained as much by memory/control policy as by raw model IQ.**Scaling and self-improvement**: Marin’s** Delphi**scaling work, summarized by[@WilliamBarrHeld](https://x.com/WilliamBarrHeld/status/2053919463880462453), claims a** 0.2%**prediction error when extrapolating from small pretrains to a** 25B / 600B token**run. Separately,[@omarsar0](https://x.com/omarsar0/status/2053978221193130434)highlighted** AutoTTS**, where an LLM searches the test-time scaling controller space itself, reportedly beating hand-designed strategies for about**$39.9** of discovery cost. **Top tweets (by engagement)** **OpenAI’s enterprise/services move**:[OpenAI launches the Deployment Company](https://x.com/OpenAI/status/2053824997777457651)and[Tomoro acquisition / 150 FDEs](https://x.com/OpenAI/status/2053824999736410415).**OpenAI’s security productization**:[Daybreak announcement](https://x.com/OpenAI/status/2053939702110269822)and[@sama’s framing](https://x.com/sama/status/2053951874408276193).**Thinking Machines’ interaction models**:[Mira Murati’s launch tweet](https://x.com/miramurati/status/2053939069890298321)and the[technical preview thread](https://x.com/thinkymachines/status/2053938892152435174).**Artificial Analysis Coding Agent Index**:[benchmark launch and topline findings](https://x.com/ArtificialAnlys/status/2053865095076438427).** Agent tooling / developer workflow**:[Hermes Agent computer use with any model](https://x.com/Teknium/status/2053961675985113404),[Cursor in Microsoft Teams](https://x.com/cursor_ai/status/2053939390410612988), and[Codex OpenAI Developers plugin](https://x.com/OpenAIDevs/status/2053925962287583379). **AI Reddit Recap** **/r/LocalLlama + /r/localLLM Recap** **1. Qwen 3.6 Local Inference Advances** (Activity: 620):[MTP on Unsloth](https://www.reddit.com/r/LocalLLaMA/comments/1ta4rvs/mtp_on_unsloth/)**The image (**[link](https://i.redd.it/7qopol51pi0h1.png)) shows Unsloth’s Hugging Face profile listing newly published MTP-preserving GGUF builds:`unsloth/Qwen3.6-27B-GGUF-MTP` **and**`unsloth/Qwen3.6-35B-A3B-GGUF-MTP` **. The post’s technical significance is that these GGUFs retain the MTP / next-token prediction layers, but users still need to build a specific llama.cpp MTP PR rather than relying on standard llama.cpp support. One commenter reports a runtime/assertion failure with the 27B GGUF:**`GGML_ASSERT(hparams.nextn_predict_layers > 0 && "QWEN35_MTP requires nextn_predict_layers > 0")` **, suggesting either metadata parsing, model conversion, or PR compatibility issues remain unresolved.** Comments reflect anticipation for upstream llama.cpp MTP support, with users repeatedly checking the GitHub repo and asking whether MTP is now supported “out of the box.”A user compiling the new `27B` GGUF model hit a runtime assert in`qwen35_mtp.cpp` :`GGML_ASSERT(hparams.nextn_predict_layers > 0 && "QWEN35_MTP requires nextn_predict_layers > 0")` . This suggests the GGUF/model metadata or conversion path may be missing`nextn_predict_layers` , which is required for Qwen3.5 MTP speculative/next-token prediction layers.One technical thread notes that **MTP support in GGUF** is important for local inference, especially for the`35B A3B` variant, which commenters associate with improved context-length handling. Another commenter asks whether this means`llama.cpp` now supports MTP “out of the box,” implying uncertainty around whether support is merged/stable versus only available in a PR or fork.A commenter claims `ik_llama` **MTP is currently faster than the**`llama.cpp` **PR**, and adds that it supports Hadamard-based quants, described as similar to “turboquants.” This is a potentially relevant implementation/performance distinction for users comparing local MTP inference backends. ## Keep reading with a 7-day free trial Subscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.