7 citations · 13 across the 16 of their papers we have counts for
17 papers
Agent Safety From Within: Detecting Harmful Trajectories from LLM Internal States
Difan Jiao, Ashton Anderson
Language model agents can now perform sophisticated sequences of actions via tools and harnesses, which has increased the scope of the damage they can cause. Guard models, however,…
Tandem Reinforcement Learning with Verifiable Rewards
Difan Jiao, Raghav Singhal, Robert West +1
Reinforcement learning with verifiable rewards (RLVR) has driven substantial gains in language-model reasoning. However, models fine-tuned with RLVR need not remain compatible with…
Chessformer: A Unified Architecture for Chess Modeling
Daniel Monroe, George Eilender, Philip Chalmers +2
Chess has long served as a canonical testbed for artificial intelligence, but modeling approaches for its central tasks have diverged. Maximizing playing strength, predicting human…
LLM Safety From Within: Detecting Harmful Content with Internal Representations
Difan Jiao, Yilun Liu, Ye Yuan +4
Guard models are widely used to detect harmful content in user prompts and LLM responses. However, state-of-the-art guard models rely solely on terminal-layer representations and o…
ThinkTwice: Jointly Optimizing Large Language Models for Reasoning and Self-Refinement
Difan Jiao, Qianfeng Wen, Blair Yang +2
We introduce ThinkTwice, a simple two-phase framework that jointly optimizes LLMs to solve reasoning problems and refine the answers, based on Group Relative Policy Optimization (G…
Grounded Chess Reasoning in Language Models via Master Distillation
Zhenwei Tang, Qianfeng Wen, Seth Grief-Albert +4
Language models often lack grounded reasoning capabilities in specialized domains where training data is scarce but bespoke systems excel. We introduce a general framework for dist…
Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning
Amogh Inamdar, Zhenwei Tang, Ashton Anderson +1
Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance. Static strat…
When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being
Harsh Kumar, Jasmine Chahal, Yinuo Zhao +4
Seeking advice is a core human behavior that the internet has reinvented twice: first through forums and Q&A communities that crowdsource public guidance, and now through large lan…
ChessQA: Evaluating Large Language Models for Chess Understanding
Qianfeng Wen, Zhenwei Tang, Ashton Anderson
Chess provides an ideal testbed for evaluating the reasoning, modeling, and abstraction capabilities of large language models (LLMs), as it has well-defined structure and objective…
SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models
Zhenwei Tang, Difan Jiao, Blair Yang +1
Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences…
Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess
Zhenwei Tang, Difan Jiao, Eric Xue +4
As humans seek to collaborate with, learn from, and better understand artificial intelligence systems, developing AIs that can accurately emulate individual decision-making becomes…
ChatBench: From Static Benchmarks to Human-AI Evaluation
Serina Chang, Ashton Anderson, Jake M. Hofman
With the rapid adoption of LLM-based chatbots, there is a pressing need to evaluate what humans and LLMs can achieve together. However, standard benchmarks, such as MMLU, measure L…
Maia-2: A Unified Model for Human-AI Alignment in Chess
Zhenwei Tang, Difan Jiao, Reid McIlroy-Young +3
There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibil…
Designing Skill-Compatible AI: Methodologies and Frameworks in Chess
Karim Hamade, Reid McIlroy-Young, Siddhartha Sen +2
Powerful artificial intelligence systems are often used in settings where they must interact with agents that are computationally much weaker, for example when they work alongside…
ICL Markup: Structuring In-Context Learning using Soft-Token Tags
Marc-Etienne Brunet, Ashton Anderson, Richard Zemel
Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natur…
SPIN: Sparsifying and Integrating Internal Neurons in Large Language Models for Text Classification
Difan Jiao, Yilun Liu, Zhenwei Tang +3
Among the many tasks that Large Language Models (LLMs) have revolutionized is text classification. Current text classification paradigms, however, rely solely on the output of the…
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
Reid McIlroy-Young, Russell Wang, Siddhartha Sen +2
The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans.…