arXiv Computer Vision
Learning Dynamic Evidence Routes for Vision Transformer Probing
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arXiv AI
1d ago
Auditing Routing Entropy as an Uncertainty Signal in Attention-Residual Transformers
The paper audits whether routing entropy in Attention‑Residual transformer variants (Swin‑Tiny and DeiT‑Small) trained on CIFAR‑10/100 can signal prediction uncertainty beyond model confidence. Three tests examine the presence, consistency, and predictive power of routing signals, while a sensitivity audit measures how much injected effect the probes recover. Results show no significant improvement over confidence alone, with only modest recovery of injected signals and no consistent gains across seeds or metrics.
arXiv AI
2d ago
Routing Probes Can Improve Without New Information: An Exact-Null Audit of Uncertainty Beyond Model Outputs
arXiv:2609.38956v1 Announce Type: new Abstract: Routing signals of modern vision transformers -- expert gates, attention-residual weights and halting scores -- often improve probes that predict wheth...
arXiv AI
Sep 2
Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models
The paper introduces GLANCE, a one‑pass block drafting method that enables lossless speculative decoding for vision‑language models. By using a block‑diffusion head that reads the fused vision‑language state, GLANCE eliminates the need for the drafter to process the image at every step, allowing it to fill an entire block in a single forward pass. Experiments show that GLANCE can decode up to 2.93× faster than autoregressive decoding while maintaining exact greedy decoding results across multiple tasks.
arXiv Computer Vision
Sep 15
What a gated sensing pipeline never looks at: bandwidth reduction and the misses behind it
arXiv:2609.15579v1 Announce Type: new Abstract: An airborne sensor on a contested link cannot send video, so the appealing move is to send findings instead and report the ratio between the two. We ev...
arXiv Computation and Language
3d ago
From Routing Signals to Selective Review: Visual regrounding in MoE VLMs
arXiv:2609.38111v1 Announce Type: new Abstract: Vision-language models (VLMs) may accept false visual premises, answering questions about a target object's color, count, location, or state even when...
arXiv Machine Learning
Jun 9
A Unifying View of Attention Sinks: Two Algorithms, Two Solutions
arXiv:2606. 08105v1 Announce Type: new Abstract: When attention concentrates on a single token, a sink, what is the model actually computing?