arXiv AI By Shuhul Razdan, R Balamurugan

When a Conceptor Complement Becomes Gain: Operator Geometry and Behavioral Attribution in Language Models

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arXiv AI
Aug 20

Abliteration Mitigation via Refusal Aliases

The paper introduces AMRA, a weight‑editing technique that mitigates abliteration—an attack that removes refusal capabilities from large language models by projecting weight matrices orthogonal to a refusal direction. AMRA obscures the refusal signal through rank‑$k$ updates to residual stream writer matrices, replaces refusal‑inducing activations with random aliases, and adjusts downstream reader matrices to maintain original behavior. Experiments on Llama‑3‑8B and Gemma‑2‑9B show significant improvements in post‑abliteration refusal scores with minimal impact on overall model performance.

By Nathan Truong
arXiv AI
Aug 24

Open-Weight Masked Introspection: Measuring What Language Models Can Report About Their Own Computation

The study investigates whether open‑weight language models can introspect on their own internal computations. Using the Open‑Weight Masked Introspection (OWMI) framework, researchers intervened on various internal components of eight models and asked them to report whether changes had occurred. Across 78,000 measurements, none of the models reliably distinguished real interventions from sham ones, with AUROC values essentially at chance. Why It Matters: The findings suggest that current open‑weight models lack the ability to audit their own internal states, highlighting a limitation for oversight that relies on a model’s self‑reporting.

By Emilio Ferrara
arXiv Computation and Language
Oct 9

Not Every Change Is Necessary: Recoverable Drift in Large Language Model Unlearning

The paper introduces Propose-Then-Project Unlearning (PTP-U), a method for large language model unlearning that first weakens target knowledge associations and then projects non-target outputs back to the original distribution to recover capabilities. PTP-U aims to satisfy forgetting constraints while preserving fluent generation and performance on non-target tasks. Experiments on three benchmarks show PTP-U achieves a strong forgetting‑retention trade‑off, with 81.22%–91.03% forgetting and an average of 94.20% non‑target utility.

By Xunlei Chen, Qinghui Gong, Jingkun Xue, Qihe Liu, Shijie Zhou, Fei Ye