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Neel Nanda comments on a paper where GPT-6 Astra edits Qwen3.5-0.8B weights to improve math, arguing the result shows LLMs are surprisingly good at classic mechanistic interpretability techniques rather than anything fundamentally new.
A paper introducing MIOE, a causal-ablation benchmark that uses interchange interventions to evaluate whether LLMs (Qwen3-8B/4B) mechanistically solve ontology tasks like entity typing and subsumption prediction, finding distributed subspace methods work best but don't yet fully disentangle ontological knowledge from auxiliary class information.
This paper applies mechanistic interpretability tools—including linear CKA, logit lens, attention decomposition, sparse autoencoders, and activation patching—to compare GPT-2 with DistilGPT-2, revealing a compression-collapse dichotomy where the IOI circuit fully collapses while the Greater-Than circuit survives via layer remapping.
论文提出受控证据聚合（CEA）与平衡效用因果选择（BUC）方法，通过符号探针定位出可影响Transformer自然语言决策的紧凑注意力头集合，并在Pythia-410M上验证其因果效应。
An open-source repository documenting 20 exploratory studies on Next-Concept Modeling (NCM), mechanistic interpretability (such as IOI in GPT-2), and model controls, with full code, results, and transparent reporting of both positive and negative outcomes.
This paper traces how subject–verb number agreement circuits emerge and sparsify across training in Pythia-1b and Pythia-410m models, finding that behavior stabilizes early while the underlying circuit keeps reorganizing, shifting attention heads, and shrinking to a strikingly sparse final form.
The paper introduces an aphasia-inspired mechanistic interpretability framework using classifier-guided lesioning and activation steering to induce language breakdowns in LLMs, revealing densely entangled but mappable functional circuits.
A University of Pittsburgh paper investigates whether LLMs internally represent the 26 CATO legal factors, showing all factors are linearly decodable in Qwen3-1.7B, Qwen3-8B, and Gemma-2 2B via sparse autoencoder features with test AUCs of 0.86-1.00.
This preprint proposes NR-SAE, a sparse dictionary method that reuses a frozen MoE router's expert selection to address per-expert autoencoder banks, showing better reconstruction than rank-addressed, candidate-matched, and randomized-identity controls on Gemma 4 26B-A4B, while noting speed and causal-faithfulness results remain untested.
该论文提出了一种新的Jacobian lens变体——entity lens，可在不使用关系信息的情况下预测线性关系可解码性（LRE faithfulness），在OLMo-3-7B的28个关系上取得ρ=0.7355的相关性，表明线性可解码性更多是主体表示本身的属性而非关系的属性。
The paper introduces CultureLens, a reference-grounded benchmark and mechanistic study of how LLMs use cultural information, showing that stated contradictory norms sharply reduce aligned answers and that supervised fine-tuning can largely mitigate this vulnerability.
The paper proposes Guided-AP, a method that uses a few exact activation patching measurements to reveal and correct shared errors in attribution patching estimates, reducing error by up to 26% on single prompts and 75-79% for average effects across prompts in mechanistic interpretability analyses.
The paper introduces Shaped SAEs, including a Matched Filter SAE (MF-SAE), which linearly shapes LLM activations via Jacobian-based rank-reducing transformations before learning sparse autoencoders to improve semantic selectivity of dictionary atoms, evaluated on SAEBench and SAE Probes.
UC Berkeley, Apple, EPFL, Harvard, MIT, and Google DeepMind researchers show that two-layer transformers trained on sorting converge to one of two mechanistically distinct solution classes depending on initialization, where only the two-attention-layer solution length-generalizes perfectly.
该论文提出将层状态轨迹作为可观测对象来研究 Transformer 推理过程，发现真实层序产生的路径比端点保持的乱序更对齐，并通过局部转移执行器在 Qwen 等模型上选择性地控制内部轨迹与 top-1 候选边界。
This paper presents a mechanistic interpretability study of vision-language-action (VLA) models, showing that motor programs are bound to absolute coordinates and ignore scene changes, explaining perturbation brittleness. It introduces Action Atlas, an interactive platform covering 520+ trained sparse autoencoders and 79 identified concepts across six VLA models.
Goodfire researcher Nick Cammarata published a 26,000-word blog post claiming LLMs may be far easier to understand, monitor, and control than assumed, sparking both major excitement and skepticism across the AI community.
该论文因果性地追踪了导致模型「跟随提示线索而放弃正确答案」（sycophancy/caving）的注意力头，发现多种偏差共享同一个在预训练阶段就存在的线索路由电路，而微调与RL后训练只是调节该电路的激活强度与时机。
该论文提出"机制不确定性"概念，通过因果稳健性边际（causal robustness margin）量化大语言模型内部机制的稳定性，在多个开源模型与问答数据集上验证其对幻觉与不确定性检测的补充作用。
Chris Olah reflects on the ramifications of mechanistic interpretability after his widely-read informal research essay, arguing that mapping neurons, circuits, and attribution graphs in modern language models may be far more tractable than previously assumed, while cautioning against both excessive pessimism and hype.