Announcement from **Meta AI Research** published on **August 5, 2026** (stated reading time: 4 minutes, no individual byline): **Muse Code** in beta, *« a terminal coding agent »*, and the model that powers it, **Muse Spark 1.2**. Meta itself frames the launch: *« This marks our next step toward the frontier, with larger and much more capable models on the way. »* **Three architectural elements on the harness side.** **Asynchronous background agents** that *« remain active throughout each session, rather than being spawned for individual tasks »*, avoiding redundant information gathering and reducing the need for steering. A **local event log** where *« every model call, tool run, approval, and edit is appended »*, making the runtime a system that is *« replay-exact and restart-safe »*, able to resume exactly where it left off after a crash. And **three skills shipped out of the box**: `/plan` (turns a task into a plan submitted for approval), **`/grill`** (stress-tests the plan *« until it holds up »*), and `/goal`. **On the model side**, Meta claims **model-harness co-training** (*« to maximize harness compatibility »*, with harness trajectories sampled via rejection sampling and recipe optimizations for goals, compaction, and sub-agents), **long-horizon** training (whole-repo generation, end-to-end projects, self-research, with planning, goal conditioning, and context compaction), and a **self-improvement loop** where Muse Spark 1.1 generates the environments and instruction templates and then grades candidate solutions, producing a training set for the 1.2. **What the published charts show**, without the text commenting on it: the four comparisons — Terminal-Bench 2.1, DeepSWE 1.1, an internal Meta benchmark, and the GPU kernel optimization case study — place **Muse Spark 1.2 behind Opus 5 in all four cases**, including on Meta's own proprietary benchmark (70.6% versus 79.4%) and on the case study, where the model finishes fourth out of six (+68.7% versus +74.0%). **A reading caution on the version gain**: on the two public benchmarks, 1.1 is measured with `mini-swe-agent` and 1.2 with Muse Code, so the 6.7-point gap conflates model and harness. On the internal benchmark, the only comparison where no harness is mentioned, the 1.1 → 1.2 gap drops to **2.3 points**.
#Meta AI Research#Muse Code#Muse Spark 1.2
**Meta AI Research** — publication institutionnelle sans auteur nommé · sur `research.meta.ai`. Le billet renvoie à un **rapport** pour la méthodologie d'évaluation · non repris ici.
**Skill** entry: **hyperresearch** by **Jordan Gibbs** is a **deep research harness** that turns Claude Code into a documentary research agent, shipped as a PyPI package (MIT, Python 3.11-3.13) installing **20 Claude Code skills**, a CLI, an MCP server, and a local web UI. Observed on **August 3, 2026**: 1,568 stars, 170 forks, repo created on April 9, 2026, last push on August 1. **The core is a 16-step pipeline adaptive by tiers** — `light` (~30-40 min), `full` (~1.5-2.5 h), `dissertation` (4-8 h, 25,000-80,000 words across 300-450 sources) — which takes a prompt and returns an adversarially audited report with full provenance. **The central architecture decision is documented alongside its failure mode**: the entry skill is a **thin router** with no procedure, each step living in its own skill loaded **fresh at the moment it is invoked**, because the previous version was *« one 1200-line skill that got compacted away by the time Layer 4 needed its triple-draft procedure. The orchestrator forgot the procedure, wrote a single draft, and produced a flat-scoring report. »* **Two load-bearing principles.** *« Patch, never regenerate »*: after synthesis, only surgical `Edit` touch-ups are possible, with the patcher and the polish auditor tool-locked to `[Read, Edit]` at the Claude Code allowlist level, so that they *« physically cannot Write a new draft »*. *« Canonical research query is gospel »*: the verbatim prompt is persisted once in `query.md` and re-read by every step and every subagent. **Sixteen subagents** with configurable role and model (fetchers and cite-checker on Sonnet, critics, synthesizer, and patcher on Opus). **The vault** is a persistent markdown store indexed in SQLite — *« Markdown is truth, SQLite is cache »* — with a note lifecycle (`draft → review → evergreen`, `stale → deprecated → archive`), traceable provenance, a composite quality score (source type, citation authority via OpenAlex and Semantic Scholar with retraction flags, internal PageRank), and an **independence audit** that groups syndicated copies together — *« five reprints of one press release argue with the weight of one source »*. **Three mechanical gates before shipping**: citation integrity (every quoted citation must exist **verbatim** in a vault note), a retraction sweep refreshed on every cited DOI, and a citation-to-sentence link check by a skeptical LLM. **Reservation to flag**: the opening claim — *« currently leads the DeepResearch-Bench RACE leaderboard »* — is contradicted by its own footnote, *« forward-looking projection from a stratified pilot… Third party validation is pending »*. A projection is not a ranking, yet the chart places it ahead of Gemini and OpenAI Deep Research.
#skill#deep research#research harness
**Jordan Gibbs** — auteur et mainteneur du dépôt `jordan-gibbs/hyperresearch`. Le projet est distribué sous **licence MIT** et publié sur **PyPI** (`pip install hyperresearch`). Signaux d'adoption au 3 août 2026 : **1 568 étoiles** · **170 forks** · 13 issues ouvertes · dépôt créé le **9 avril 2026** et poussé le **1er août 2026** — soit une traction rapide sur moins de quatre mois. Topics déclarés : `agents` · `agentskills` · `claude-code` · `deep-research` · `deep-research-agent`.