well done, Team Thinky.
By complete coincidence, the day we released Neil Zeghidour (CEO of Gradium, the for profit spinoff of the vaunted Kyutai Moshi)’s talk on what remains to be built for realtime voice, Thinking Machines emerged for only the third time in a ~year (despite much drama) to drop Interaction Models: A Scalable Approach to Human-AI Collaboration, 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 with far more detailed demos that are presumably far closer to real use:
The full blogpost 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: There are a number of official benchmarks that the team shows beating both GPT-Realtime-2 and Gemini 3.1-Flash 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 modelinitiate 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 theappropriate 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 - measuresRepCount-Acontinuous 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.ProactiveVideoQAis a standard temporal action-localization benchmark.CharadesStream 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 wasThinking Machines’ preview of “interaction models”, described as models trainedfrom scratch for real-time interaction rather than layering speech, turn-taking, and tool use onto a turn-based LLM. The accompanyingtechnical postand team commentary from@johnschulman2,@soumithchintala, and@cHHilleeframe this as ahuman↔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 continuousaudio+video+text → audio+text(@johnschulman2).** Why it matters technically**: Several reactions converged on the same point: this is not “another chatbot demo” but a change in interface assumptions.@liliyu_lilipointed tovisual proactivity(“tell me when I start slouching”, “count my pushups”) as a missing primitive in current systems;@rowncalled it the first generalvideo+speech model that is visually proactive;@kimmonismusand@giffmanaboth 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. One implementation detail surfaced via@eliebakouch: the stack is usingSGLang.
OpenAI’s Enterprise and Security Push: Deployment Company and Daybreak
OpenAI is moving down-stack into services and deployment: OpenAI announced theOpenAI Deployment Company, a majority-owned unit built to help enterprises deploy frontier models into real workflows. The key operating detail is150 Forward Deployed Engineers and Deployment Specialists coming in via the acquisition ofTomoro, with@gdbciting**$4B of initial investment from 19 partners**. Multiple observers read this as OpenAI adopting a Palantir-/Microsoft-style field-engineering model:@kimmonismusargued OpenAI wants to own thedeployment layer of the AI economy, while@matvellosoconnected 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 launchedDaybreak, an umbrella effort around defensive cyber operations and continuously securing software, with@samapositioning it as a practical response to rapidly improving AI cyber capability. The product pitch, summarized by@TheRundownAI, combinesGPT-5.5,** Codex**, repository threat modeling, vuln discovery, patch generation, and response automation, with differentiated access tiers includingTrusted Access for Cyber and a more specializedGPT-5.5-Cyber. This stands in contrast to Anthropic’s more restrictive cyber posture, a tension captured by@kimmonismus. For teams building secure agent systems, a separate warning from@lukOlejnikis 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.@itscleliaaddressed this withaggit, 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,@_catwuhighlighted a newclaude agents
terminal control plane for managing multiple Claude Code agents, and@cursor_aipushed Cursor intoMicrosoft 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:@masondrxynoted that** Deep Agents CLIcan hot-swap underlying model providers mid-conversation without losing context**, a nontrivial systems capability that many agent stacks still miss. LangChain also highlighted** harness profilesfor provider/model-specific tuning (tweet), and separate pricing analysis from the same author argued thatDeepSeek V4 Flash** can be dramatically cheaper than GPT/Gemini flash-tier options for high-volume agent workloads (tweet). On the local side, Hugging Face addedHermes Agent support in local apps plus native trace visualization, while@Tekniumpreviewedcomputer use with any model via Hermes Agent and CUA, explicitly targeting local/open models as well as frontier APIs.@onusozjoining Hugging Face to improve local models inOpenClaw and related open harnesses is another strong signal that local agent ergonomics are now strategic infrastructure.A design thesis emerging around tools:@threepointoneargued 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 Indexspanning SWE-Bench-Pro-Hard-AA, Terminal-Bench v2, and SWE-Atlas-QnA, comparing not just models butmodel+harness combinations. Their topline:** Opus 4.7in Cursor CLI scored 61**, with** GPT-5.5in Codex/Claude Code close behind; top open-weight setups included GLM-5.1**,** Kimi K2.6**, and** DeepSeek V4 Proin 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) and Claw-Eval’s more agentic task mix across office, finance, terminal, and web tasks, whereMiMo-V2.5-Pro led and DeepSeek V4 Flash looked unusually efficient for its size.TurboQuant skepticism is increasing: Multiple posts pointed to a more sober view of the recently popular quantization/serving technique.@_EldarKurticpresented what he described as the first comprehensive study ofTurboQuant, covering accuracy, latency, and throughput;@vllm_projectlinked the Red Hat / vLLM investigation as a starting point; and@jbhuang0604bluntly 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:@ClementDelanguemade 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 toDeepSeek 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@victormustaron the rapid growth of GGUF uploads and from repeated community observations thatQwen 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** EMOwas highlighted by@TheTuringPostas a more modular Mixture-of-Experts design where document-level routing induces shared expert pools; notably, keeping only25% of experts** reportedly costs just**~1%** performance versus10–15% degradation in standard MoEs under similar pruning (follow-up). On generative evaluation,@qberthetintroducedMIND (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.@LucaAmbreported continuous bitstream diffusion nearly matching autoregressive models under their evaluation setup;@JulieKalliniintroducedFast BLT, using diffusion for parallel byte decoding to make byte-level LMs less inference-bound;@sriniiyer88framed it as combining block byte-diffusion with self-speculative decoding. Relatedly,@LiangZheng_06noted 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”claims long histories degrade cooperation in multi-round social dilemmas because models become morehistory-following and risk-minimizing, with explicit CoT sometimes amplifying the problem. Second,PwC work summarized by @dair_aiargues 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** Delphiscaling work, summarized by@WilliamBarrHeld, claims a 0.2%prediction error when extrapolating from small pretrains to a 25B / 600B tokenrun. Separately,@omarsar0highlighted 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 CompanyandTomoro acquisition / 150 FDEs.OpenAI’s security productization:Daybreak announcementand@sama’s framing.Thinking Machines’ interaction models:Mira Murati’s launch tweetand thetechnical preview thread.Artificial Analysis Coding Agent Index:benchmark launch and topline findings.** Agent tooling / developer workflow**:Hermes Agent computer use with any model,Cursor in Microsoft Teams, andCodex OpenAI Developers plugin.
AI Reddit Recap
/r/LocalLlama + /r/localLLM Recap
1. Qwen 3.6 Local Inference Advances
(Activity: 620):MTP on UnslothThe image (link) shows Unsloth’s Hugging Face profile listing newly published MTP-preserving GGUF builds:unsloth/Qwen3.6-27B-GGUF-MTP
andunsloth/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 inqwen35_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 missingnextn_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 the35B A3B
variant, which commenters associate with improved context-length handling. Another commenter asks whether this meansllama.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 thellama.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.
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