arXiv AI
Aug 10
Alignment has a Fantasia Problem
arXiv:2604. 21827v2 Announce Type: replace Abstract: In accomplishing complex tasks, human cognition typically progresses from abstract to concrete (e.
Interactive Task Alignment as a POMDP
arXiv:2607. 16412v1 Announce Type: new Abstract: Current benchmarks for language models primarily evaluate execution on fully specified tasks.
Related stories
arXiv AI
Jul 24
Expectation Alignment of Language Models for Real-World User Expectations
arXiv:2607. 20485v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance on standard benchmarks, yet it remains largely unexplored whether they truly meet user expectations.
arXiv AI
Jun 26
Diagnosing Task Insensitivity in Language Agents
arXiv:2606. 26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak.
arXiv AI
Jun 4
Aligning Deep Implicit Preferences by Learning to Reason Defensively
arXiv:2510. 11194v3 Announce Type: replace Abstract: Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions.
arXiv AI
Jun 16
ACCORD: Action-Conditioned Contextual Grounding for Language Agents
arXiv:2606. 16432v1 Announce Type: cross Abstract: User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment.
Hugging Face Trending Papers
Jun 30
Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autonomous forms demands new evaluations. In this paper we evaluate an agent's ability to induce specific belief states in other agents by taking actions rather than using conversational persuasion, a capability we call Non-Conversational Planning ToM (NCP-ToM).
arXiv Machine Learning
Jun 9
Operationalising the Superficial Alignment Hypothesis via Task Complexity
arXiv:2602. 15829v2 Announce Type: replace Abstract: The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-training merely surfaces this knowledge.
arXiv AI
Jun 11
To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending
arXiv:2606. 11201v1 Announce Type: cross Abstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions.
arXiv AI
Jul 3
Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
arXiv:2510. 17426v3 Announce Type: replace-cross Abstract: The "alignment tax" of post-training is typically framed as a drop in task accuracy.
arXiv AI
Jun 15
OdysSim: Building Foundation Models for Human Behavior Simulation
arXiv:2606. 14199v1 Announce Type: cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation.
arXiv AI
Aug 12
INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators
arXiv:2608. 10492v1 Announce Type: new Abstract: Large Language Model (LLM)-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them.
arXiv AI
Jul 28
How LLM Task-Adaptation Reshapes Alignment: A Multi-dimensional Study of Behavioral and Representational Drift
arXiv:2607. 22676v1 Announce Type: new Abstract: Post-training is a key mechanism for adapting large language models to downstream tasks.