residual-stream

This paper proposes a geometric analysis of transformer residual streams across depth, using relative displacement and orthogonal Procrustes analysis to reveal structured regularities in six instruction-tuned models on code generation and translation tasks.

Introduces Multi-Head Attention Residuals (MHAR), which reshapes the routing query into per-subspace heads so each feature subspace reads depth history via its own softmax. Trained from scratch on a Nemotron-based corpus, MHAR consistently improves validation loss over standard Transformers from 100M to 1B scale and also boosts mid-training downstream accuracy.

This paper investigates the signed nature of FFN residual writes in long-context retrieval, finding that FFN writes act as suppressors or amplifiers depending on layer and task, and proposes a gradient-based diagnostic to distinguish these roles.

This paper introduces xHC (Expanded Hyper-Connections), a method that enables meaningful expansion of residual streams in Transformers beyond the previous N=4 limit, achieving consistent improvements on 18B and 28B MoE models while adding only modest training FLOPs. It also presents xHC-Flash to reduce memory traffic, making large-N residual-stream expansion practical for LLM pre-training.

Discusses an Anthropic paper on verbalizable representations forming a global workspace in language models, linking to the idea that fine-tuning small models reveals character rather than capability.

This paper shows that frontier LLMs can perform multi-step reasoning over content-free filler tokens, and that hidden states in the residual stream can be decoded to recover intermediate values with high accuracy, challenging the assumption that chain-of-thought monitoring is the only audit tool.

The paper hypothesizes that language model activations contain a low-rank dense component that is inefficiently represented by sparse autoencoders (SAEs). By adding a linear bottleneck to absorb dense structure, the authors reduce dense latents and improve sparse probing performance on Gemma-2-2B.

An independent researcher presents evidence that coherent context can shift LLMs into a different internal regime before producing output, bypassing surface-level safety filters. This suggests current alignment methods like RLHF may not be robust defenses.

This paper investigates whether deep layer value vectors in transformer attention need context from the residual stream. It proposes Bank of Values (BoV), which uses context-free token-specific value vectors in the last third of layers, improving validation loss and benchmark scores over standard attention.

This paper demonstrates that LLMs can enter measurably different internal latent states under coherent context while maintaining aligned outputs, revealing a blind spot in current alignment methods that only monitor surface tokens. The Gemma-3-12B-IT experiment shows strong residual stream geometry shifts that existing safety frameworks cannot detect, with implications for agentic AI deployment.

This paper investigates how instruction-tuned LLMs combine persona and task specifications in the residual stream, finding that near answer formation the combination is approximately additive, enabling substitution with minimal KL divergence, but this additive regime does not account for the full multi-token generation mechanism.

This paper performs full Jacobian eigendecomposition across production-scale LLMs, revealing a learned spectral gradient from rotation-dominated early layers to symmetric late layers, along with a low-rank bottleneck that compresses perturbations. The results link perturbation propagation and compression to network functional topology.

This paper provides a mechanistic explanation for why LLMs lose track of instructions in long multi-turn interactions, introducing the Goal Accessibility Ratio (GAR) metric and a channel-transition framework. Through ablation studies and residual stream probes, it shows that attention to goal-defining tokens closes over turns while goal information persists in residual representations, with architecture-specific failure modes.

A new preprint titled 'Mathematics is All You Need 2' presents the 'Two-Channel theorem,' demonstrating that behavioral fibers in transformer residual streams are sign-stabilized and causally steerable across different architectures (Qwen to Llama). The study claims high reproducibility and shows that the behavioral substrate is near-one-dimensional, separating generation from latent structure.

This paper investigates whether linearly decodable failure signals in LLM hidden states can be corrected via residual-stream steering. It finds that while 'overthinking' failures are decodable, fixed linear steering fails to correct them due to representational entanglement with task-critical computations, though the probes effectively support selective abstention.

Arc Sentry is a new pre-generation prompt-injection detector that reads a model’s internal residual stream, achieving 92% detection with 0% false positives versus LLM Guard’s 70%/3.3% on a 130-prompt benchmark.

A research paper introducing Three-Phase Transformer (3PT), which applies Tesla's polyphase geometry to transformer architectures by organizing the residual stream into three 120° offset phases. The approach achieves 7.2% perplexity improvement on WikiText-103 with minimal parameters (0.00124% overhead) and 1.93× convergence speedup.