84 citations · 182 across the 24 of their papers we have counts for
17 papers · 1 filter
From Memory to Guide: Spatio-Temporal Composer for Procedural Coding Memory
Zhixuan Tan, Pengjie Gu, Zhao Li +4
Memory-augmented agents typically integrate procedural knowledge by injecting retrieved skills directly into text prompts. This approach dangerously equates readable text with reli…
Rational Clarification by Assistive Agents via Value-of-Information Reasoning
T. Duy Nguyen-Hien, Yee Whye Teh, Wee Sun Lee +1
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignm…
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
Evaluating Rational Contracting in Natural Language
Bhavyesh Sajja, Max Kleiman-Weiner, Roger Zimmermann +1
The emergence of language-based AI agents promises to transform the scope of machine economic activity. Instead of just proposing bids or following hard-coded protocols, such agent…
ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems
Zhixun Tan, Qiang Chen, Tairan Huang +2
Recent advances have improved the adaptive capabilities of LLM-based multi-agent systems (MAS) through memory-, skill-, and learning-based approaches, yet these approaches remain c…
An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models
Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama +1
Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast, large reasoning models (LRMs) are trai…
Resource Rational Contractualism Should Guide AI Alignment
Sydney Levine, Matija Franklin, Tan Zhi-Xuan +8
AI systems will soon have to navigate human environments and make decisions that affect people and other AI agents whose goals and values diverge. Contractualist alignment proposes…
Beyond Preferences in AI Alignment
Tan Zhi-Xuan, Micah Carroll, Matija Franklin +1
The dominant practice of AI alignment assumes (1) that preferences are an adequate representation of human values, (2) that human rationality can be understood in terms of maximizi…
Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals
Tan Zhi-Xuan, Gloria Kang, Vikash Mansinghka +1
The space of human goals is tremendously vast; and yet, from just a few moments of watching a scene or reading a story, we seem to spontaneously infer a range of plausible motivati…
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David "davidad" Dalrymple, Joar Skalse, Yoshua Bengio +14
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general in…
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka +1
People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assist…
Grounding Language about Belief in a Bayesian Theory-of-Mind
Lance Ying, Tan Zhi-Xuan, Lionel Wong +2
Despite the fact that beliefs are mental states that cannot be directly observed, humans talk about each others' beliefs on a regular basis, often using rich compositional language…
Inferring the Goals of Communicating Agents from Actions and Instructions
Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka +1
When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan. H…
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka
Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate ho…
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok +3
To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102.11938) prov…
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
Arwa Alanqary, Gloria Z. Lin, Joie Le +3
When inferring the goals that others are trying to achieve, people intuitively understand that others might make mistakes along the way. This is crucial for activities such as teac…
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver +2
People routinely infer the goals of others by observing their actions over time. Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when…