#representation analysis

try —

6 papers match

cs.CL2026

From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs

Ruikang Zhang, Shuo Wang, Qi Su

The paper investigates whether large language models (LLMs) have internal, trait-like personality representations that can be identified, manipulated, and shown to affect behavior…

#personality modeling#large language models#representation analysis#behavioral control
cs.CV2026

Prior Directions: Why GUI Grounding Gets Locked in the Past

Weile Gong, Zijian Lu, Mingcai Chen +3

The paper investigates how vision-language models can become locked onto outdated textual priors, causing incorrect visual grounding, and identifies recurring latent directions—cal…

#visual grounding#vision-language models#representation analysis#model robustness
cs.CV2026

Do Unified Multimodal Models Think in One Space? A Lens Through Cross-Branch Steering

Yu Wang, Sharon Li

The paper investigates whether unified multimodal models share a common semantic space by introducing cross-branch semantic steering, showing that semantic directions from the unde…

#unified multimodal models#semantic steering#cross-modal transfer#image synthesis
cs.AI2026

Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models

Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit +2

The paper investigates why reinforcement‑learning‑trained models outperform supervised fine‑tuned models on math reasoning by analyzing their internal representations with linear p…

#reinforcement learning#supervised fine-tuning#mathematical reasoning#representation analysis
cs.SE2026

Do Code Language Models Use Tests? A Behavioral and Representational Study of Test-Driven Code Generation

Yunhao Liang, Chengguang Gan, Ruixuan Ying +3

The paper investigates how code language models respond to test cases in prompts, analyzing whether tests act as executable specifications or merely extra context, and finds that t…

#test-driven code generation#code language models#prompt engineering#benchmark evaluation
cs.CV2026

Representation and Reference Selection in Training-Free Synthetic Image Attribution

Meiling Li, Pietro Bongini, Benedetta Tondi +1

The paper investigates how the choice of visual representation and reference selection method affect training‑free, reference‑based attribution of synthetic images, showing that in…

#synthetic image attribution#training-free methods#reference selection#representation analysis

One search, two signals: results blend meaning (embedding similarity, so papers that never use your words still surface) with keyword matches on titles, abstracts and summaries. Free, no sign-in needed.