transformer internals

LIVE PULSE

4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src4.9 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.6 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.4 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.7 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.3 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.3 CoMem Paper Proposes Shared and Individual Memory Design for LLM Multi-Agent Systems — 1 src1.3 Paper proposes evolving context parameterization for large language models — 1 src1.3 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src

topic2 events

papersSEP 10 04:00 UTC

Contrastive Projection: Differencing Logit Lenses to Read Transformer Internals

A new arXiv paper introduces contrastive projection, a method for inspecting transformer hidden states that works by differencing logit lens outputs. The authors argue that a standard logit lens reading of a single intermediate-layer state is dominated by generic tokens a model would predict for nearly any input, making such readings unreliable. Subtracting these baseline predictions isolates the input-specific component of the internal state, offering a more trustworthy window into model internals.

interpretability/transformers/logit-lens/research

papersSEP 10 04:00 UTC

arXiv study proposes directly reading and writing transformer internals

A newly posted preprint investigates how many components inside a transformer actually determine a given token prediction, measuring the signed contributions of individual units and channels to the final logits. It reports that a single output can depend on anywhere from thousands to hundreds of thousands of components, whose effects partly offset one another. The work, titled Through the Looking Glass, presents methods for directly reading out and editing transformer internals.

interpretability/transformers/research