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cs.CL2026

Aligned Alone, Misaligned Together: Forecasting Adversarial Capture in LLM Agent Populations

Isotta Magistrali, Chen Shani

The unit of AI safety evaluation is still the individual model, yet language-model agents are increasingly deployed in interacting populations that read and write one another's dec…

cs.CL2026

From Found to Designed: Concepts as a Design Axis for Large Language Models

Chen Shani

The paper examines how large language models implicitly encode concept-like information and proposes a taxonomy of concept-aware interventions, advocating for designing LLMs with e…

cs.CL2026

Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition

Nathan Roll, Pranav Bhalerao, Martijn Bartelds +7

In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from…

cs.CL2026

Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models

Christine Zhang, Dan Jurafsky, Chen Shani

The next-token prediction (NTP) objective trains language models to predict a single token at each step, even though many continuations can express the same meaning. For example, i…

cs.CL2026

The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?

Chen Shani, Yuval Reif, Nathan Roll +2

Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This survey examines why these gaps pers…

cs.CL2026

Beyond Tokens: Concept-Level Training Objectives for LLMs

Laya Iyer, Pranav Somani, Alice Guo +2

The next-token prediction (NTP) objective has been foundational in the development of modern large language models (LLMs), driving advances in fluency and generalization. However,…