40 citations · 116 across the 23 of their papers we have counts for
5 papers · 1 filter
Towards Foundation Decision Models: Drex
Nischay Dhankhar, Alisher Amantay, Zhanibek Datbayev +6
Autoregressive approaches with large language models, such as long chain-of-thought reasoning, have become the default for new AI applications because they reach higher accuracy on…
GraphNIAH: A Benchmark for Long-Context Reasoning over Real-World Graphs
Minh-Nam Tran, Abulhair Saparov
The needle-in-a-haystack (NIAH) benchmark was introduced as a test for measuring a model's ability to find small relevant facts within a very large body of input text. However, exi…
Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models
Nischay Dhankhar, Dos Baha, Abulhair Saparov
Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge. Hypernetworks provide a promising solution to large-scale knowledge i…
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…
How Deep Can LLMs Learn to Reason? Expressiveness Is Key
Tianle Wang, Zhaoyang Wang, Guangchen Lan +4
Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with task difficulty has been hampered…