45 citations · 302 across the 119 of their papers we have counts for
18 papers · 1 filter
Same Beliefs, Different Policies: Learning Reusable Representations for Early Stopping in LLM Reasoning
Jingwei Ni, Dmitry Abulkhanov, Vladislav Smirnov +5
Reasoning LLMs often continue reasoning after reaching a correct answer, wasting computation and sometimes replacing it with an incorrect one. Deciding when to stop is a metareason…
Do LLMs Exhibit Coherent Knowledge Structures in Mathematical Reasoning? A Perspective from Knowledge Space Theory
Peng Cui, Heejin Do, Mrinmaya Sachan
Human knowledge is inherently structured and interdependent: mastery of a concept requires prior mastery of its prerequisites, a principle formalized by Knowledge Space Theory (KST…
Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation
Minjing Shi, Junling Wang, Jingwei Ni +2
Identifying logical fallacies in everyday discourse is challenging for many people. This challenge is amplified in the era of Large Language Models (LLMs), where malicious agents c…
Learning to Reason Efficiently with A* Post-Training
Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov +3
Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language infer…
When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation
Mubashara Akhtar, Anka Reuel, Prajna Soni +34
Artificial intelligence benchmarks are an important mechanism to measure model progress and guide deployment decisions. However, benchmarks quickly "saturate", making it difficult…
Fluid Reasoning Representations
Dmitrii Kharlapenko, Terry Jingchen Zhang, Arth Singh +4
Frontier large language models increasingly solve complex tasks involving abstract concepts through extended test-time thinking. Yet we lack a mechanistic account of how extended t…
Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
Paul He, Yinya Huang, Mrinmaya Sachan +1
Large language models (LLMs) are increasingly being applied to tasks that involve causal reasoning. However, current benchmarks often rely on string matching or surface-level metri…
ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models
Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu +8
LLMs can solve complex tasks by generating long, multi-step reasoning chains. Test-time scaling (TTS) can further improve performance by sampling multiple variants of intermediate…
On the Emergence of Induction Heads for In-Context Learning
Tiberiu Musat, Tiago Pimentel, Lorenzo Noci +3
Transformers have become the dominant architecture for natural language processing. Part of their success is owed to a remarkable capability known as in-context learning (ICL): the…
Test of Time: Rethinking Temporal Signal of Benchmark Contamination
Terry Jingchen Zhang, Gopal Dev, Ning Wang +8
Post-cutoff performance decay of LLMs has been widely interpreted as a temporal signal for benchmark contamination, where public information released before the training cutoff may…
Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games
David Guzman Piedrahita, Yongjin Yang, Mrinmaya Sachan +3
As large language models (LLMs) are increasingly deployed as autonomous agents, understanding their cooperation and social mechanisms is becoming increasingly important. In particu…
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
Yilmazcan Ozyurt, Tunaberk Almaci, Stefan Feuerriegel +1
We introduce ExRec, a general framework for personalized exercise recommendation with semantically-grounded knowledge tracing. Our method builds on the observation that existing ex…
SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning
Kun Xiang, Heng Li, Terry Jingchen Zhang +11
We present SeePhys, a large-scale multimodal benchmark for LLM reasoning grounded in physics questions ranging from middle school to PhD qualifying exams. The benchmark covers 7 fu…
SIKeD: Self-guided Iterative Knowledge Distillation for mathematical reasoning
Shivam Adarsh, Kumar Shridhar, Caglar Gulcehre +2
Large Language Models (LLMs) can transfer their reasoning skills to smaller models by teaching them to generate the intermediate reasoning process required to solve multistep reaso…
SMART: Self-learning Meta-strategy Agent for Reasoning Tasks
Rongxing Liu, Kumar Shridhar, Manish Prajapat +2
Tasks requiring deductive reasoning, especially those involving multiple steps, often demand adaptive strategies such as intermediate generation of rationales or programs, as no si…
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning
Mattia Atzeni, Mrinmaya Sachan, Andreas Loukas
The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most recent language-complete instantiation (LARC) has been postulated as an important step towards general AI. Y…
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi +6
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential deci…
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla +3
In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments. This…