Research Work
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This article covers the research work of Aneesh Kumar. For his industry experience, see Aneesh Kumar (Industry Work).
Predicting Emergent Capabilities Using Sparse Features
Aneesh Kumar is currently leading research on predicting the emergence of novel capabilities in large language models (LLMs). This ongoing work investigates how abrupt, non-linear improvements in task performanceâoften termed emergent behaviorsâcan be anticipated rather than only observed post hoc. The project explores the role of sparse features and their coactivation patterns, constructing graphs from model checkpoints to identify structural signals that may precede emergent performance jumps.
Still in development, this research aims to establish a mechanistically grounded, pre-hoc framework for studying emergence. The approach builds on prior work in sparse attention and grokking, but emphasizes predictive indicators rather than retrospective analysis. Kumar and collaborators are preparing a NeurIPS-style proposal that positions sparse feature coactivation as a promising direction for understanding and forecasting emergent phenomena in large-scale neural networks.
Still in development, this research aims to establish a mechanistically grounded, pre-hoc framework for studying emergence. The approach builds on prior work in sparse attention and grokking, but emphasizes predictive indicators rather than retrospective analysis. Kumar and collaborators are preparing a NeurIPS-style proposal that positions sparse feature coactivation as a promising direction for understanding and forecasting emergent phenomena in large-scale neural networks.
Emergence in LLMs from Neural Scaling Laws
Biological Timescale Synaptic Plasticity (BTSP) Independent Research
Aneesh Kumar authored a comprehensive analysis of Behavioral Time-Scale Synaptic Plasticity (BTSP), a neural mechanism that enables memory formation over multi-second intervals. His work provides a clear overview of BTSPâs biological foundations in the hippocampus, explaining how plateau potentials in CA1 pyramidal neurons gate windows of plasticity that allow temporally scattered activity to be linked. The writeup details how this mechanism differs from conventional learning rules such as Hebbian learning and STDP, highlighting its role in addressing the problem of temporal credit assignment.
Beyond biological mechanisms, Kumar extends the discussion into computational and applied domains. He reproduces a computational model of BTSP using binary weights and stochastic update rules, demonstrating how the system achieves one-shot, content-addressable memory formation. The analysis further explores how BTSP could inform the design of foundation models and memory-augmented AI systems, proposing that BTSP-inspired architectures could enable more biologically plausible, context-sensitive forms of rapid learning. This dual perspectiveâbridging neuroscience and artificial intelligenceâpositions the work as both an explanatory resource and a forward-looking exploration of BTSPâs implications for computational models of learning.
Beyond biological mechanisms, Kumar extends the discussion into computational and applied domains. He reproduces a computational model of BTSP using binary weights and stochastic update rules, demonstrating how the system achieves one-shot, content-addressable memory formation. The analysis further explores how BTSP could inform the design of foundation models and memory-augmented AI systems, proposing that BTSP-inspired architectures could enable more biologically plausible, context-sensitive forms of rapid learning. This dual perspectiveâbridging neuroscience and artificial intelligenceâpositions the work as both an explanatory resource and a forward-looking exploration of BTSPâs implications for computational models of learning.
BTSP Writeup
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