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The paper introduces CD-RFT, a method to decouple the shared control bottleneck in RL post-training by regularizing a novel control coefficient, improving multi-task capability on models like Qwen2.5-7B and Llama-3.2-3B.
The author hand-codes transformer weights (no training) using a compiler called Torchwright to implement exact multiplication, achieving 100% accuracy on three-digit math and publishing checkpoints that handle up to 12-digit multiplication.
This paper investigates why language models fail at two-hop generalization, showing that models succeed when the second hop follows training distribution but fail when it deviates, and proposes a recurrent-style training strategy to improve out-of-distribution two-hop reasoning.
This paper proposes extracting mechanism mounts directly from linear weight sites via column-tiled SVD, offering an alternative to proxy dictionaries like sparse autoencoders for mechanistic interpretability. Evaluated on Gemma-2-2B, the method passes all 182 site-layer checks.
Ramp, a spend management platform, launched its own AI research lab called Ramp Labs a year ago. The lab has worked on projects like a production-focused coding benchmark 'Ramp SWE-Bench', integrating Claude Code into RollerCoaster Tycoon, and a mechanistic interpretability playground.
A systematic empirical study showing that concept directions extracted from one language model can steer other independently trained models when sufficient scale (≥1.7B parameters) is reached, providing functional evidence for the Platonic Representation Hypothesis and highlighting scale thresholds for cross-model interpretability tools.
This paper proposes Circuit-Anchored Evolution (CAE), a method that uses mechanistic interpretability to identify and anchor a tiny safety circuit in LLMs during self-evolution, preventing models from misevolving into capable but dangerous systems while preserving capability.
This paper investigates subliminal learning in language models, showing that biases can transfer from teacher to student via seemingly random synthetic data. The authors find that adding Gaussian noise to weights increases transfer, and that students inherit not just the semantic bias but also the type of intervention used, with implications for training safety and data auditing.
The paper introduces the Ignition Index, a metric for measuring global workspace dynamics in language models, validated across multiple architectures and tasks, showing selective detection of ignition-like representational transitions.
This paper investigates how language models execute in-context conditional rules by probing whether testing and routing are separable mechanisms. Using activation patching across three open models and six languages, the authors find that predicate testing is modular while route representations are token-bound and non-transferable.
This paper presents a graph-to-answer mechanism-tracing case study for Graph-PRefLexOR-8B, a materials-science hypothesis generation model, using visualization and activation-based diagnostics to localize where mechanism support is lost or recovered during generation.
The paper introduces DUD (Decoupled Update Dynamics), a framework that separates Feed-Forward Network and Attention contributions via causal interventions to improve uncertainty quantification and calibration in large language models, outperforming state-of-the-art baselines.
The paper presents a simple pipeline that uses LLMs to automatically group features into supernodes in attribution graphs, matching human annotator interpretability and recovering intermediate hop supernodes in a two-hop task.
This paper investigates why LLMs underperform in Arabic medical tasks, showing via mechanistic analysis that knowledge exists internally but fails to surface, then proposes TLoRA, a targeted low-rank adaptation method that outperforms full-network LoRA on medical QA and introduces a new Arabic clinical dialogue benchmark.
This paper introduces LAWFUL, a framework for verifying whether neural networks learn and causally use physical laws over continuous variables, addressing gaps in coverage-aware causal consistency and domain-of-validity testing. It demonstrates the approach on a Mocap2Radar transformer and the Doppler frequency law.
This paper presents Fairness Pruning, a structural intervention method that locates demographic bias in GLU-MLP layers of large language models by identifying differentially activated neurons. Zeroing a very small number of neurons disrupts bias processing while preserving reasoning and general knowledge, suggesting bias and capabilities rely on dissociable circuits.
ECG-InterpBench is a new benchmark that systematically evaluates the interpretability of ECG foundation model representations using matched-scale sparse autoencoders, covering reconstruction fidelity, clinical concept accessibility, and reproducibility across 450 cells.
This theoretical paper proposes a driven-nucleation rate law to explain capability emergence, plasticity loss, and circuit control in language models, supported by experiments on Pythia and a controlled gated-attention model.
This paper investigates internal representational differences between RL and SFT fine-tuned models on mathematical reasoning, finding that RL models exhibit more linearly separable hidden states and hierarchical layer importance. Token allocation variability under repeated sampling suggests training pipeline dependence rather than RL vs SFT alone.
A new open-source tool called CORTEX // MODEL OBSERVATORY streamlines mechanistic interpretability for local LLMs, making it accessible to everyday users, with support for GPT2 and Llama architectures.