Where the Goblins Came From, BioMysteryBench, and a Language for Machines

• GPT-5.1 began inserting goblin metaphors into responses where the user signaled nerdiness, even when goblins had no relevance.
• The behavior originated in human raters rewarding 'quirky' or 'creative' phrasing during RLHF.
• Because each model generation is partly trained on outputs from the previous generation, the tic compounded.
• The 'Never talk about goblins' line in the Codex 5.5 system prompt was a band-aid, not a fix.
• OpenAI says the underlying problem is reward hacking on stylistic features the raters can't precisely articulate.

"For context, two days ago some users discovered this sentence reiterated throughout the codex 5.5 system prompt: 'Never talk about goblins, in any context.'"

"Sutton clearly said that the efficient and surgical application of compute to search the space of possible solutions will beat hand crafted algorithms. He didn't say scale your compute and try to bake all of the worlds knowledge into weights … the fact that trillions of parameters prefer goblins is peak stupid engineering."

• Dense 128B parameters with 256k context — replaces Mistral Medium 3.1, Magistral in Le Chat, and Devstral 2 in their Vibe coding agent.
• Reasoning effort is configurable per request rather than split into separate models.
• Vision encoder trained from scratch to handle variable image sizes and aspect ratios.
• Multimodal text-and-image input with text output, multilingual.
• GGUF quants available; r/LocalLLaMA testing shows ~3.3 t/s generation on a Strix Halo Q4.

• Granite 4.1 8B trades blows with IBM's own 32B MoE on most internal benchmarks.
• Apache 2.0 weights with full enterprise tooling around it.
• Top HN commenter: 'pretty impressive at 8b. Runs on commodity hardware quickly.'
• Granted, the 8B doesn't beat Qwen3.6 35B A3B for local use according to that same tester.

"On 23 problems, the experts were stumped. Our most recent models solved roughly 30% of those—and most of the rest."

• Anthropic ran 99 real biological-data analysis problems against Claude and an expert panel.
• On 23 of those, human experts could not solve the problem; Claude solved roughly 30% of those.
• On the other 76, Claude solved 'most of the rest.'
• Replies surfaced the obvious questions: were the experts time-constrained? Did they get to iterate the way Claude did? An immunologist (Parmita Mishra) noted no working immunologist would brute-force PCA the way the model did — they'd ctrl+F marker genes first.

"i am no expert immunologist. even i know an immunologist well enough to know they would ctrl+F marker genes before Claude is even done writing its first python script and go grab some coffee. no immunologist is using brute force PCA here lmao."

"we're starting rollout of GPT-5.5-Cyber, a frontier cybersecurity model, to critical cyber defenders in the next few days."

"As Codex got faster, the bottleneck moved from inference to inefficient API calls. WebSockets keep response state warm across tool calls."

"Introspection adapters: a tool that allows language models to self-report behaviors they've learned during training—including potential misalignment."

• Sparse Autoencoders released for the entire Qwen 3.5 family, 2B through 35B MoE.
• Maps internal features for the residual stream across all layers.
• First time a frontier-class open-weight family ships with official interpretability tooling at release.
• Released the same week Anthropic published introspection adapters.

• Vera is a programming language designed specifically for LLMs to write — not for humans to read first.
• No variable names; mandatory contracts on every function; structural addressing instead of identifiers.
• Top HN commenter danpalmer pulled the empirical result: 'models are particularly vulnerable to naming-related errors like choosing misleading names, reusing names incorrectly, and losing track…'
• The pitch is to remove the entire class of failures that come from LLMs picking bad names.

"The White House is against a proposal from Anthropic to more than double the number of groups with access to Mythos, citing both security concerns and the belief that expanding the program would mean less available use…"

The 8B instruct scores 69.0 on ArenaHard. The previous generation Granite 4.0-H-Small, a 32B MoE model with 9B active parameters, scored lower. Across AlpacaEval, MMLU-Pro, BBH, EvalPlus, MBPP. same thing throughout.

• Dense 8B model matches or beats previous 32B MoE across benchmarks
• 15 trillion tokens trained across 5 distinct phases with changing data mixes
• Four-stage RL pipeline caught and corrected a mid-training regression
• 512K context window achieved through staged extension (32K → 128K → 512K) with model merges
• Apache 2.0 license, available via Ollama, vLLM, Transformers, and IBM API

With our previous approach, the agent had to stay on track. Like it, we didn't let the model ever touch any files outside its work. It was physically impossible for it to do so. Now we're trusting the model. So it's a bit vibes based.

• Cursor replaced a massive git worktrees feature (15,000 lines of code) with a 200-line Markdown skill using slash commands
• The new 'slash work tree' and 'slash best event' commands use existing cursor primitives — skills and sub-agents — instead of custom infrastructure
• Tradeoffs include models sometimes drifting from their work trees, slower feel from visible worktree creation, and worse discoverability
• Cursor is building evals with Braintrust to measure work-tree compliance and training Composer models on these tasks for future RL
• Parallelization primitives beyond git worktrees are in development, since worktrees are slow to create and disk-hungry

Mistral Medium 3.5 is our first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights.

• Dense 128B model replacing both Mistral Medium 3.1 and Magistral in Le Chat
• Reasoning effort configurable per request — can do fast reply or complex agentic runs
• Replaces Devstral 2 in their coding agent Vibe, scoring 91.4% on τ³-Telecom and 77.6% on SWE-Bench Verified
• 256k context, multimodal (text + image input), system prompt support
• Modified MIT license with revenue threshold exception, available via Mistral Vibe CLI, vLLM, SGLang, Transformers

GCC 16 has been released with C++26 reflection support, enabling compile-time introspection of types and structures without template metaprogramming hacks.

• GCC 16 includes C++26 reflection support — compile-time type introspection
• Improvements to compiler optimization passes and debug info generation
• Updates to libstdc++ including C++26 library features
• Available on Debian sid (trunk package) and build systems

• Figure AI has scaled humanoid robot production to 24 units per day
• One robot produced per hour at their manufacturing line
• The company is teasing a fleet deployment — moving from prototype to operations
• Significant milestone in making humanoid robots economically viable