Tagged articles

mechanistic interpretability

5 articles · Page 1 of 1

Jun 30, 2026 · Artificial Intelligence

Is There Really a Unique Mechanism in LLMs? Rethinking Functional Anisotropy

A recent ICML 2026 paper disproves the long‑held assumption that each task in a large language model is supported by a single, unique circuit, showing through overlap‑aware sheaf repulsion that many structurally dissimilar, sparse sheafs can achieve identical performance across multiple benchmarks, and proposing a distributive dense circuit hypothesis to explain this non‑uniqueness.

circuit discoverydistributed dense circuitfunctional anisotropy

0 likes · 15 min read

Is There Really a Unique Mechanism in LLMs? Rethinking Functional Anisotropy

Machine Heart

Jun 28, 2026 · Artificial Intelligence

Which Training Data Shapes Large‑Model Abilities? Introducing Mechanistic Data Attribution (MDA)

The paper presents Mechanistic Data Attribution, a framework that traces the origins of specific internal mechanisms such as induction heads to particular training samples, revealing that repetitive "garbage" data—not high‑quality text—drives their emergence, and validates this causal link through deletion and augmentation experiments while enabling scalable data‑driven model improvement.

Causal InterventionData AugmentationInduction Heads

0 likes · 12 min read

Which Training Data Shapes Large‑Model Abilities? Introducing Mechanistic Data Attribution (MDA)

Machine Learning Algorithms & Natural Language Processing

Jun 18, 2026 · Artificial Intelligence

Why LLMs Miss Simple Addition: Geometric Mechanism Behind Arithmetic Errors

A recent ICML 2026 paper from Nanjing University reveals that large language models encode correct arithmetic information in structured geometric manifolds, yet errors arise from noisy quantization at decision boundaries, and proposes probing, Iso‑Raw‑Sum Trajectory, and a dual‑stream consistency check to diagnose and correct these mistakes.

Arithmetic ErrorsDual-Stream ConsistencyIso-Raw-Sum Trajectory

0 likes · 11 min read

Why LLMs Miss Simple Addition: Geometric Mechanism Behind Arithmetic Errors

Machine Heart

Jun 17, 2026 · Artificial Intelligence

Why Large Language Models Miss Simple Addition: Iso‑Raw‑Sum Trajectories Reveal the Geometry of Errors

Despite excelling at complex reasoning, LLMs often err on multi‑digit addition; probing shows correct answers reside in hidden states, and the authors reveal a structured geometric manifold—digit basins, carry fibers, and Iso‑Raw‑Sum trajectories—explaining how errors arise via noisy quantization at decision boundaries.

Arithmetic ErrorsGeometric AnalysisLLM

0 likes · 12 min read

Why Large Language Models Miss Simple Addition: Iso‑Raw‑Sum Trajectories Reveal the Geometry of Errors

Network Intelligence Research Center (NIRC)

Nov 11, 2025 · Artificial Intelligence

What Is Mechanistic Interpretability and Why It Matters for Large Language Models

The article defines mechanistic interpretability as reverse‑engineering LLMs to reveal how they represent knowledge and make decisions, explains its importance for transparency, risk mitigation, and model improvement, and surveys key techniques such as causal tracing, zero‑making, noise‑making, and logit‑lens methods with illustrative examples.

causal tracinglarge language modelslogit lens

0 likes · 8 min read

What Is Mechanistic Interpretability and Why It Matters for Large Language Models