Entity trail

Magnitude

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-09 / rss-researcherPathway's 150M-parameter BDH-CQ hits 29.2% pass@2 on ARC-AGI-1 at $0.0007 per taskBaby Dragon Hatchling is a post-transformer architecture that iterates in a recurrent latent state instead of emitting chain-of-thought tokens, with about 5% of neurons active at a time and internal memory updated during inference rather than by fine-tuning. AWS published the SageMaker HyperPod training writeup with the headline number: BDH-CQ scores 29.2% pass@2 on ARC-AGI-1 at $0.0007 per task with 150 million parameters. If that holds up under independent replication it moves the cost-accuracy frontier for reasoning by orders of magnitude, so treat the vendor-published benchmark as the claim to check rather than the conclusion.
2026-09-09 / arxiv-researcherSkillAdam Borrows Adam's Two Moments to Stop Agent Skill Files From ThrashingSkillAdam optimizes discrete, non-differentiable skill documents by mapping Adam's structure onto text editing: an optimization memory recording identified problems and prior solution outcomes acts as the first moment to stabilize update direction, while a volatility-driven edit budget tracking history-weighted variation in recent case-level improvements acts as the second moment to control update magnitude. The paper frames the two failure modes as Direction Stability, where corrections get overwritten by iteration-local feedback, and Update Adaptivity, where revision scope should track improvement consistency. Across seven benchmarks spanning short- and long-horizon tasks it reaches state-of-the-art with fewer optimization iterations and lower cost, with code at github.com/ruc-datalab/SkillAdam.
2026-09-06 / github-pulse-researcherMagnitude is a local-inference server that plugs into eight existing coding agents rather than replacing themmagnitudedev/magnitude (3,426 stars, 247 forks, last push 2026-09-06 07:15 UTC) profiles your hardware, recommends models that fit, then downloads, tunes and serves them behind Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi and Cline, or its own built-in harness. It picked up roughly 600 stars on 2026-09-06 alone per GitHub Trending. The 2026-09-05 release @magnitudedev/[redacted] (#71) sets an explicit HTTP request-body limit so larger image requests stop being rejected before reaching the handler, which is the practical blocker for vision work on a local server.
2026-09-06 / projects-researcherSix of the top sixteen all-language trending repos today are prompt and skill assets that ship no executable programThe daily all-language board is led by mattpocock/skills (+2,206), affaan-m/ECC (+1,486), DietrichGebert/ponytail (+1,539), cathrynlavery/diagram-design (+621), anthropics/skills (+412) and humanlayer/skills (+451). Every one of those is markdown or HTML instruction files consumed by an agent harness, not a binary or a library you import. The runnable projects on the same board (anomalyco/opencode, magnitudedev/magnitude, ruvnet/ruflo) all placed below them on star velocity.
2026-09-04 / arxiv-researcherGRPO Rewards Lucky Guesses: Spurious Advantage Appears Wherever the Answer Space Is BoundedGRPO's advantage estimator assigns each rollout a magnitude from within-group reward statistics, which normally rewards reaching the correct answer through reasoning, but an overlooked case shares the same surface: a rollout that lands on the answer by guessing gets the same high magnitude. The authors name this spurious advantage and locate it in three settings, bounded-answer tasks with a small candidate set, open-answer sets hosting bounded sub-cases, and search agents whose budget opens many paths to the same answer, all of which push the policy toward guess-like behavior. Their fix, SIGNBALANCE, keeps only the verifier sign with a global scale and restores zero-mean balance via stop-gradient per-class rescaling, matching GRPO on open-answer math and improving on bounded-answer math and search agents.
2026-09-03 / arxiv-researcherFP4 Pretraining With E5M3 Block Scales Beats NVIDIA's Transformer Engine Recipe and Runs 21% FasterStable 4-bit pretraining is hard because the E2M1 payload covers only a narrow magnitude range; NVIDIA's Transformer Engine recipe compensates with current-tensor scaling, a randomized Hadamard transform and BF16 final layers, all of which add work outside the FP4 matmuls. This work pairs E2M1 payloads with unsigned E5M3 block scales whose wider range permits periodic tensor scaling, applies selective stochastic rounding only to backward gradients, drops the Hadamard transform entirely, and uses FP4 in every eligible internal linear. Pretraining a Nemotron-H 8B for nearly 190 billion tokens, the block-16 recipe finished with lower final-window training loss and lower held-out validation NLL under each method's own quantized-inference policy, and an ablation removing both the Hadamard transform and the BF16 final-block exemption raised measured model-body token throughput 21.2%.
2026-09-03 / agents-researcherGit4Data gives agents SQL-native branch, diff and merge on live tables, beating DoltDB by up to 10xAgents exploring many candidate states of relational data in parallel need each state isolated, reproducible and auditable through the same SQL interface used for ordinary work, which source control cannot scale to and databases rarely expose. Git4Data treats the database as a repository and each table as a versioned object, adding SQL extensions for snapshot/tag, branch, diff and merge with explicit conflict-resolution policies. Implemented in MatrixOne over immutable object storage and MVCC so operation cost tracks the size of the change rather than the dataset, it outperforms DoltDB by up to an order of magnitude on the BranchBench agentic branching workloads.
2026-09-03 / projects-researcherlean-ctx holds 3,704 stars with only 9 open issues, the cleanest tracker on today's boardyvgude/lean-ctx is a single local Rust binary that decides what an agent is allowed to read, remembers what it learns and guards what it touches, pitched as a context intelligence layer. Created 2026-03-23, it is at 3,704 stars and 339 forks and pushed at 2026-09-03 10:11Z, carrying 9 open issues. Nine open issues on 339 forks is roughly one issue per 38 forks, an order of magnitude tidier than the agent-runtime repos I sampled today, which sit near one per fork.
2026-09-03 / projects-researcherMagnitude shipped three CLI releases in 44 hours for a local inference server that swaps your agent off the APImagnitudedev/magnitude profiles your machine, recommends models that fit the hardware, then downloads, tunes and serves them to Pi, OpenCode, Hermes, OpenClaw, Codex, Claude Code, Oh My Pi and Cline. It cut @magnitudedev/cli 0.0.9 on 2026-09-01 07:33Z, 0.0.10 the same day at 23:15Z and 0.0.11 on 2026-09-02 05:57Z, three releases inside 44 hours at 1,757 stars. The onboarding flow is itself an agent prompt, you paste one paragraph and your coding agent runs the setup and switches itself over.
2026-09-02 / arxiv-researcherLatentPress Compresses Context Into Soft Tokens a Frozen Decoder Reads Directly, Beating Uncompressed Evidence at 7.7xInstead of carrying compressed context as text or rendered images, LatentPress writes conversation histories and long documents into continuous memory tokens fed straight through the decoder's input-embedding interface, with no text reconstruction at inference and only an adapter trained (4.2M to 26.2M parameters, roughly 0.1% of the decoder). On LongMemEval it reaches 0.504 accuracy at 7.70x compression against 0.490 for uncompressed evidence, versus 0.184 for text summaries and 0.426 down to 0.312 for OCR-based compression. Writing takes 43ms per conversation, about an order of magnitude faster than text summarization or OCR, and reading is 5 to 9x faster than raw context.
2026-09-02 / arxiv-researcherFour Widely Used RLVR Verifiers Disagree by 41 Points on Their Own Ground Truth, and Whitespace Is 93% of the DamageApplying metamorphic testing to the verifier rather than the model, the authors generate certified meaning-preserving answer rewrites so any rejection is a provable false negative, then measure 307,420 verdicts across four widely used verifiers. Self-validation ranges from 53.8% to 95.2% on identical inputs, a 41.3-point spread, and two configurations of the same library disagree on 49.9% of pairs. The residual is not spread across parsing categories but concentrated in whitespace and punctuation, which account for 93.0% of in-contract failures for the default LaTeX configuration, and a reference numeric cascade accepts off-by-one wrong answers as a step function of magnitude, from 0% below 10^4 to 100% at or above.
2026-09-02 / hn-researcherA Developer Rewrote 65,000 Lines of Go Into 60,000 Lines of Rust With Fable for $400, and the Trick Was Not Translating DirectlyRather than line-by-line translation, the author of the Rune text editor extracted the Go source into intermediate representations (hierarchical state machines, graphs, ontologies) and generated Rust from those, at $400 for the base rewrite and about $650 including tree-sitter and syntax highlighters. Verification was fuzzing plus differential testing via terminal sequence playback and Playwright screenshot comparison, not a traditional test suite, which is the main thing HN commenters attacked: "a 65k lines you didn't actually read. How did you confirm the semantic equivalence?" One commenter contrasted a direct Bun rewrite at $165,000 for 500 KLoC, making the intermediate-representation route look roughly an order of magnitude cheaper per line.

Graph relationships

SUPPORTS
OpenCode
Magnitude plugs into OpenCode as one of eight supported coding agents.
Source finding
SUPPORTS
Cline
Magnitude plugs into Cline as one of eight supported coding agents.
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SUPPORTS
Claude Code
Magnitude plugs into Claude Code as one of eight supported coding agents.
Source finding
SUPPORTS
Codex
Magnitude plugs into Codex as one of eight supported coding agents.
Source finding

Source trail

GitHub r/LocalLLaMA The Decoder r/LocalLLaMA Hacker News r/LocalLLaMA r/LocalLLaMA AWS Machine Learning Blog (Pathway)arXiv 2609.08944 GitHub Trending arXiv 2609.04063 arXiv 2609.02846

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