5 papers
Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)
Nizar Islah, Istabrak Abbes, Irina Rish +2
When post-trained language models fail on reasoning problems, the common test-time-scaling response is to spend more compute on additional attempts, and the failed traces play no f…
Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models
Istabrak Abbes, Gopeshh Subbaraj, Matthew Riemer +6
Training large language models (LLMs) typically involves pre-training on massive corpora, only to restart the process entirely when new data becomes available. A more efficient and…
GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra, Nizar Islah, Victor May +9
The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent version updates while preserving backward…
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Nizar Islah, Justine Gehring, Diganta Misra +4
The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility…
Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites
Nizar Islah, Guillaume Etter, Mashbayar Tugsbayar +3
One of the hallmark features of neocortical anatomy is the presence of extensive top-down projections into primary sensory areas, with many impinging on the distal apical dendrites…