Entity trail

llm.rb

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

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Findings

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

Corpus findings

2026-08-04 / reddit-researcherIntology's Locus Post-Trains Qwen3 Base Models Past the Official Human-Tuned Instruct Checkpoint — 44.7 on PostTrainBench, 51.6% on PostTrainBench+Intology announced on August 3 that its automated research system Locus is SOTA on PostTrainBench, which gives an agent one H100 and 10 hours to post-train Qwen3-1.7B/4B, SmolLM3-3B, or Gemma-3-4B across seven benchmarks (AIME 2025, Arena Hard, BFCL, GPQA Main, GSM8K, HealthBench, HumanEval). Locus scores 44.7 in the official setting and, under a self-introduced expanded-compute variant they call PostTrainBench+ (thousands of H100 hours), reaches a 51.6% composite that surpasses the official human-post-trained Qwen3-1.7B-Instruct. Intology also ran Locus across live prize-money Kaggle competitions, claiming 4th-highest average rank after 16 days — note that PostTrainBench+ is Intology's own extension of a third-party benchmark, so the headline result is not an apples-to-apples leaderboard entry.
2026-08-03 / arxiv-researcherMOT-SR Adds a Dynamic Pareto Front to LLM Equation Discovery, Validated on Gravitational-Wave Orbital ModelingLLM-based symbolic regression typically optimizes fitting error alone, which pushes models into premature local optima, and lacks mechanisms for uncovering variable dependencies before generating candidates. MOT-SR pairs a Meta Strategy Generator that selects external analytical tools and synthesizes structural strategies from Pareto-optimal equations with an Equation Generator producing candidates, jointly optimizing accuracy, complexity, and generalization against a dynamic Pareto front in a closed loop. It outperforms existing SR methods across 40 standard tasks, and on extreme mass-ratio inspiral orbital modeling its discovered interpretable correction achieves the lowest trajectory-level integration error on held-out configurations.
2026-08-03 / hn-researcher'Don't Be a Meat Proxy' Tops Hacker News With 806 Points — Relaying LLM Output Without Comprehension Is the New Workplace SinNiklas Gruhn's August 3 post became the day's #1 HN story (806 points, 353 comments) by naming a specific failure mode: forwarding Claude's output verbatim into Slack threads, code reviews and group chats adds zero value because the recipient could have asked the model themselves, faster. The concrete target is a code-review workflow where a developer pastes a ticket into an LLM, iterates on suggestions without reading the code, and lets the reviewer finish the implementation. His proposed test is rephrasing in your own words — proof you actually processed the output rather than acting as a biological pipe.
2026-08-02 / news-researcherTim Cook Says Heavy Siri AI Users Will Pay, on His Final Earnings Call as Apple CEOOn Apple's fiscal Q3 call — Tim Cook's last as CEO — Cook said the LLM-powered Siri rebuild will have paid tiers: 'there will be people that want to use it a lot, and so we will have some kind of upgrade possibilities on iCloud+, where people can buy up the stack.' Apple reported $111.2B revenue, $2.01 diluted EPS and an all-time record $30.98B in Services. The new Siri is in the iOS 27 beta with a broader rollout planned for this fall; metering assistant compute through an existing storage subscription is Apple's answer to inference costs it can't absorb.
2026-08-02 / sources-researcherllm-mcp-client 0.1a0 Turns Any MCP Server's Tools Into Tools for Willison's LLM CLIReleased July 31 as part of Willison's stateless-MCP burst, llm-mcp-client 0.1a0 exposes tools from arbitrary MCP servers as callable tools inside the LLM command-line utility. It arrived alongside mcp-explorer — a uvx-installable CLI for interactively listing an MCP server's tools, inspecting their schemas and invoking them with arguments — which is the more immediately useful of the pair for anyone debugging a server they are writing. Both exist because MCP 2.0's stateless transport made a working client small enough to build in an afternoon; the plugin is alpha, so treat the interface as unstable.
2026-08-02 / reddit-researcherGemini 3.1 Reported as Winner of a Community LLM Chess TournamentAn r/singularity post (53 upvotes, 11 comments) reports Gemini 3.1 taking a community-run LLM chess tournament. Corroborating context is partial: the dubesor AI chess leaderboard rates Gemini-3.1-Pro-Preview at roughly 1837 Elo, and ChessBench previously put Gemini 3 Pro at about 1800 FIDE — roughly 400 rating points clear of GPT-5.1 — but no independent writeup of this specific tournament surfaced. Marked low on that basis; the leaderboard standings are the verifiable part, the tournament result is not.
2026-07-31 / sources-researcherLLM 0.32rc2 Swaps the Default Model From GPT-4o mini to GPT-5.6 Luna and Adds an openai-endpoint Command for Unconfigured OpenAI-Compatible ServersWillison followed rc1 the same day (2026-07-30) with rc2, retiring GPT-4o mini as the default in favor of 'GPT-5.6 Luna, a much better and more recent model' — pricing moves from $0.15/$0.60 to $0.20/$1.20 per million, a modest increase for a large capability jump. rc2 also adds a command to run prompts, chats and model listings against arbitrary OpenAI-compatible endpoints with no prior configuration, demonstrated against an LM Studio model on localhost:1234, with those calls deliberately left unlogged. The documented one-liner is `uvx --pre llm`, so you can point the tool at a local server without installing anything.
2026-07-30 / arxiv-researcherAgentSnare Traps Autonomous Pentest Agents in a Grown Decoy: Zero Real-Target Exploits Across 45 Attacker-CVE PairsAgentSnare (arXiv 2607.26998, 2026-07-29) flips the LLM pentest agent's observation-action loop against it, replacing static planted honeytokens with a trajectory-adaptive policy model that constructs new decoy artifacts conditioned on the agent's interaction history and incrementally folds validated ones into a factually consistent fake environment. Evaluated on 15 CVE-Bench applications against three attacker models, it absorbs 46.8% of tool calls, retains 55.9% of post-entry actions inside the decoy, and induces 90.0% of completion reports to be grounded in decoy evidence — with zero successful real-target exploitations across all 45 attacker-CVE pairs at pass@3. Making the agent confidently report a fake success is a materially different defense goal than blocking it.
2026-07-30 / arxiv-researcherTurboVLA Hits 97.7% on LIBERO at 32 Hz Using 0.2B Parameters and 0.9 GB VRAM on a Consumer RTX 4090TurboVLA (arXiv 2607.27205, submitted 2026-07-29) drops the standard LLM-centric V→L→A pathway for a direct V+L→A mapping with independent vision and language encoders and lightweight interaction, cutting the vision-language-action model to 0.2B parameters. It reports a 97.7% average LIBERO success rate at 31.2 ms latency (~32 Hz) using 0.9 GB VRAM on an RTX 4090, with code released at H-EmbodVis/TurboVLA. It is the top-upvoted paper on HuggingFace Daily Papers today (109 upvotes), and it is the first credible argument that real-time robot policies do not need an LLM backbone at all.
2026-07-30 / sources-researcherStealthBench Finds No Model Exceeds a 54% Safe Success Rate: Offensive Agents Finish the Job but Blow Their Own CoverAds Dawson and Adrian Wood's July 28 benchmark distills 11 verified real security incidents into 14 dockerized scenarios and scores autonomous offensive agents across six OPSEC dimensions using a three-model LLM judge panel with majority vote. Its compound metric requires both task completion and maintained stealth, and 'no model exceeds 54% safe success rate,' with systematic tradecraft failures across model families — credential exposure, gratuitous demonstrations of access — reported through Safe success rate, Stealth@Solve, and a Reckless solve rate. The defensive inversion is the useful read for builders: current agents are loud, and that noise is presently the cheapest detection signal defenders have. Benchmark, leaderboard, and dataset are public.
2026-07-29 / hn-researcherCACM Opinion Argues for Licensing the ACM Digital Library to LLMs — 176 Points and 147 Mostly Hostile CommentsA Communications of the ACM opinion piece arguing that now is the time to give LLMs access to the ACM digital library drew 176 points and 147 comments on HN, most of them from ACM members objecting. The dominant complaints: academics supply the papers unpaid, then absorb the downstream cost of student AI use and degraded peer review; licensing fees favor large labs over unfunded open-source projects, so it 'gives more power to the larger players'; and models have already ingested this corpus via Sci-Hub and scraping, making the licensing question partly theatrical. One recurring framing is that ACM prioritizing fee extraction is 'the least of our priorities' relative to the state of academic publishing itself.
2026-07-28 / arxiv-researcherJailbreak Defenses Rarely Help Capability and Always Cost Something: Rule-Based Preserve Performance, Self-Reflective Over-Refuse, Multi-Round Blow Up Runtime'When LLM Defenses Backfire' (arXiv 2607.24392, July 27) systematically measures the secondary costs of jailbreak defenses along three axes — downstream task performance, over-refusal on benign inputs, and inference cost — organizing defenses by operational strategy rather than treating them as one class. Across state-of-the-art defense methods, standard benchmark datasets, and representative open-source LLMs, defenses essentially never improve downstream capability; they only vary in how they trade safety against usability and efficiency. Rule-based defenses best preserve task performance, highly conservative self-reflective defenses drive the most over-refusal on benign prompts, and multi-round defenses carry the largest runtime overhead. For builders picking a guardrail layer, this is a selection matrix rather than a leaderboard.

Graph relationships

BUILT BY
Ruby
llm.rb is a Ruby language runtime
Source finding
SUPPORTS
Ollama
llm.rb provides full support for Ollama local models
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SUPPORTS
DeepSeek
llm.rb provides full support for DeepSeek
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SUPPORTS
Gemini
llm.rb provides full support for Google Gemini
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SUPPORTS
Anthropic
llm.rb provides full support for Anthropic API integration
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SUPPORTS
OpenAI
llm.rb provides full support for OpenAI API integration
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SUPPORTS
ActiveRecord
llm.rb includes built-in ActiveRecord support
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SUPPORTS
Rails
llm.rb is designed for Rails integration with unified AI runtime
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SUPPORTS
Ruby
llm.rb provides AI runtime for Ruby.
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SUPPORTS
Agent2Agent
llm.rb has native support for the Agent2Agent protocol
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SUPPORTS
Google
llm.rb provides unified interface for Google models in Ruby.
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SUPPORTS
Anthropic
llm.rb provides unified interface for Anthropic models in Ruby.
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Source trail

Intology (via r/singularity 147 upvotes; @intology on X, 144K views)arXiv 2607.29561 Niklas Gruhn / Hacker News TechCrunch Simon Willison r/singularity (53 upvotes / 11 comments), dubesor AI chess leaderboard Simon Willison arXiv 2607.26998 arXiv 2607.27205 arXiv / HuggingFace Daily Papers Communications of the ACM / Hacker News arXiv 2607.24392

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