arXiv AI September 24, 2026 By Kentaro Oda

A Shared Encoder Is Not a Shared Task: Conditional Comparison for Deep Expert Pools

The paper demonstrates that sharing a deep encoder alone does not eliminate the confounding effects in task-comparison scores. By introducing a conditional two‑discriminator discrepancy within the embedding space, the authors achieve robust detection of task changes, maintaining stability under input rotations and accurately tracking label‑permutation drift. This approach, integrated into a mixture‑of‑heads framework, outperforms traditional novelty triggers and generalizes across multiple backbones and datasets, including ImageNet‑21k ViT‑B/16, DINOv2, and CIFAR‑100.

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arXiv AI

Sep 24

What Changed? Drift Detection with Real, Virtual, and Incomparable Diagnosis

The paper investigates drift detection in deep learning models, showing that sharing a deep encoder alone does not eliminate confounding in task-comparison scores. By introducing a conditional two‑discriminator discrepancy into the embedding space, the authors create a two‑axis gate that remains stable under input rotations and accurately tracks label‑permutation drift. This approach outperforms traditional exchange or novelty triggers, achieving high AUROC in distinguishing semantic novelty from photometric shift across multiple backbones and datasets.

By Kentaro Oda

rag

arXiv Machine Learning

Jun 26

The Geometry of Updates: Fisher Alignment at Vocabulary Scale

arXiv:2606. 27242v1 Announce Type: new Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequences, where candidate corpora share a tokenizer but differ in prediction targets.

By John Sweeney

llms rag nlp safety

arXiv Computer Vision

2d ago

PAIQ: Patch-Aligned Semantic Injection via Residual Rotation

arXiv:2609.37685v1 Announce Type: new Abstract: Language-aligned and self-supervised visual encoders offer complementary strengths in semantic abstraction and spatial detail. Harnessing this compleme...

By Pinze Ren, Yuwei Zhang, Hao Chen, Linghao Meng, Chang Li, Qiankun Li

llms nlp safety

arXiv Machine Learning

Aug 31

DARTS: Decoder-Aware Representation Tuning via Surgery for Model Merging

The paper introduces DARTS, a method for tuning decoder representations during model merging. It addresses representation bias in autoregressive decoders by using an entropy‑weighted L1 loss and a per‑position additive bias to correct errors that accumulate across token positions. Experiments on code generation, mathematical reasoning, and instruction following with Llama‑2‑7B show that DARTS improves performance over standard surgery while adding only 0.1% extra parameters.

By Aaryan Ajay Sharma, Sai Nishanth Padala, Seganrasan Subramanian

llms fine-tuning benchmarks safety

arXiv Computer Vision

Sep 14

DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning

arXiv:2607.01630v2 Announce Type: replace Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our...

By Bingchen Huang, Yifu Chen, Zhiling Wang, Yuanchao Du

llms

arXiv AI

Jun 24

SURGELLM: Rethinking Multi-Task Evaluation through Task-Aware Feature Gating with Class-Balanced Normalization

arXiv:2606. 24259v1 Announce Type: cross Abstract: Fine-tuned encoders deployed across heterogeneous NLP tasks face three compounding problems: mismatched inductive biases, class-imbalance corruption of feature statistics, and no mechanism to condition attention on external lexical knowledge.

By Noor Islam S. Mohammad, Ulug Bayazit

llms nlp fine-tuning efficiency safety