Hugging Face Trending Papers
Activation-Space Order-Swap Geometry: A Site-Asymmetry Audit
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arXiv Machine Learning
Aug 27
Activation-Space Order-Swap Geometry: A Site-Asymmetry Audit
The paper introduces a site-asymmetry audit for activation-space interventions, decomposing order-dependent activation statistics into a canonical additive response from single interventions and an antisymmetrized second difference that removes first-order and self-curvature effects. Across six language-model families, the single-intervention baseline accounts for most of the bracket norm, while the corrected residual often clears a generic-interaction null. The method also transfers to non-language models, demonstrating its portability.
arXiv AI
Jun 2
MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models
arXiv:2606. 01060v1 Announce Type: cross Abstract: Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes internally.
arXiv AI
6d ago
FLIP: Final Layer Inference-Time Probing for Vision-Language Models
FLIP is a final‑layer inference‑time probe designed to test whether a logit‑facing intervention site in an open‑weight vision‑language model (VLM) supports structured, task‑linked computation rather than generic perturbation. The probe applies elementwise flooring to the final normalized hidden state before logit computation, leaving other model components unchanged. By sweeping intervention strength on a controlled detection/counting task, FLIP identifies three regimes—negligible change, a bounded interior regime with improved detection recall and reduced counting error, and over‑suppression—while a four‑criterion protocol ensures the observed effects are mechanistically interpretable.
arXiv AI
Sep 1
Concepts Whisper: Spectral Anti-Concentration and the Dual Geometry of Transformer Representations
arXiv:2605.01609v2 Announce Type: replace-cross Abstract: We find that transformer concept representations systematically anti-concentrate in the spectral tail of the unembedding covariance, encoding...
arXiv Machine Learning
Aug 11
When Do Task Vectors Interfere? Mapping the Validity Boundaries of Weight-Space Composition
arXiv:2608. 09490v1 Announce Type: new Abstract: Task arithmetic treats fine-tuning displacements as composable directions in weight space, yet it remains unclear when parameter addition reflects predictable changes in model function.
arXiv Machine Learning
Jul 14
From Direction to Magnitude: How Multimodal Instruction-Tuning Reorganizes the Geometric Encoding of Identity-Specifying Prompts in Transformer Hidden States
arXiv:2607. 09842v1 Announce Type: new Abstract: We investigate whether identity-specifying system prompts produce statistically distinguishable geometric fingerprints in the hidden-state trajectories of four open-weight transformer language models spanning four post-training regimes: no training (Gemma-4-E4B base), multimodal RLHF (Gemma-4-E4B-it), RL distillation (DeepSeek-R1-Distill-Qwen-7B), and SFT (Qwen2.