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Open problems in Graph learning

Quantization error propagation in recurrent states1 paper raises it · 1 addresses it
Low-precision recurrent states in linear attention degrade accuracy due to error accumulation over time and across dimensions.
- STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantizationraises · addresses
Train-rollout discrepancy in low-precision RL1 paper raises it · 1 addresses it
Quantizing training and rollout paths independently in RL leads to mismatched behavior and reduced performance.
- TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Modelsraises · addresses
Distribution shift in decoding-aware pruning1 paper raises it · 1 addresses it
Pruning guided by natural sequences fails to account for self-generated token distributions during decoding, hurting performance.
- SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inferenceraises · addresses
Lack of benchmarks for graph decision-making in System One models1 paper raises it · 1 addresses it
Existing benchmarks do not diagnose the unique challenges of graph-based decision tasks for non-language models.
- GraphDecisionBench: Benchmarking System One Models on Graph Tasksraises · addresses
Non-compositional sparse autoencoder features1 paper raises it · 1 addresses it
Sparse autoencoder features often exhibit higher-order interactions, limiting interpretability and control.
- Behavioral Compositionality of Sparse Autoencoder Representationsraises · addresses

Named by an AI from these papers' abstracts.

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Topics in this field

gradient descent · learning rate · neural networks221 papers43 this week
graph learning · graph neural · graph neural networks263 papers35 this week
neural networks · neural network · arnold networks149 papers35 this week
memory efficient · optimizers · low rank adaptation157 papers25 this week
bit quantization · quantization aware training · quantization aware152 papers20 this week
linear attention · softmax attention · transformer attention106 papers13 this week

Topics in this field

Active contributors · 90 days

Pietro LiòLocaLeJEPA: Maximum Entropy Representations under Geometric Priors8
Dan AlistarhWhat Matters for NVFP4 Training? A Comparative Study of Low-Precision Pretraining Recipes6
Rong-Hua LiToward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach6
Xunkai LiToward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach6
Rebekka BurkholzAC/DC on a Budget - Alternating Sparse Phases5
Haggai MaronGraph Metanetworks for Transformer Based Language Models5

Active contributors

Activity · 12 weeks

Coming with more sources: repos

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arXiv cs.LGOct 15
Noise-shaped Sampling and Annealing for Scalable On-the-edge Training of Binary and Ternary AI Models
Zihao Chen, Zhili Xiao, Rui-Jie Zhu, Yipeng Wang, Jason Eshraghian +1
The training loss of binary and ternary neural networks, expanded as a multilinear polynomial over the discrete weights, is structurally equivalent to the energy of a hierarchical high-order Ising model. We exploit this equivalence to apply Ising solvers to discrete-weight network training, while accounting for a fundamental difference in objectives: an Ising solver seeks the ground state of a fixed energy function, whereas training seeks solutions that generalize to unseen data. Our key contribution is $ΣΔ$-Sampler, an optimizer that reconciles these objectives by embedding a stochastic sampling-and-annealing step as a noisy quantizer inside a first-order $ΣΔ$ noise-shaping loop. The loop pushes noise from weight quantization, high-order frustration, and mini-batch sampling out of the low-frequency adaptation band, so that discrete weight updates follow the smooth descent trajectories embedded in the rugged loss landscape. We validate the framework on a planted quadratic unconstrained binary optimization (QUBO) benchmark with a known minimizer; on CIFAR-10, where a $13$M-parameter ternary model comes within $1\%$ of full-precision accuracy; on ImageNet-1k, where a ternary…
PDF arXiv
arXiv cs.LGOct 15
Diagnosing GNN Failure Modes with Layer-Wise Probe Entropy
Shanzi Bao, Mar Gonzàlez I Català, Pietro Liò
Graph neural networks (GNNs) are typically evaluated only at their final layer, obscuring how task-relevant information evolves during message passing. We introduce a post-hoc diagnostic that freezes a trained GNN, fits an independent linear probe to each layer, and tracks probe accuracy alongside normalized predictive entropy. Across real and synthetic graph settings, layer-wise probe entropy distinguishes beneficial, neutral, and harmful smoothing, a capability not demonstrated by existing geometric measures. Moreover, by comparing shallow and deep models, we show that the onset of entropy degradation separates the two main GNN failure modes: structural mismatch under heterophily, which appears from the earliest layers even in shallow models, and excessive-depth oversmoothing, which emerges only as propagation depth increases. These results establish probe-entropy trajectories as a tool for analyzing information flow in GNNs and motivate future work on adaptive depth selection and early-exit mechanisms.
PDF arXiv
arXiv cs.LGOct 15
Flat Loss, Evolving Features: Grokking in Modular Addition
Akash Kumar, Karthik Sankaranarayanan
Can a network keep learning while its training cross-entropy stays nearly flat at a positive level? We show that it can. On modular addition, a standard testbed for grokking, ReLU networks trained with supported-Polar hidden updates, normalized output-layer updates and weight decay can keep 100% training accuracy while their hidden features change and held-out accuracy rises from zero to above 90%, at relatively flat training cross-entropy. Correct classification need not minimize cross-entropy, which still depends on prediction confidence. We prove this coexistence for one-hidden-layer networks in two finite-window theorems from constructed initializations: one gives a constant gain in the Fourier fraction of the hidden features, and the other holds at arbitrarily low positive loss, where a readout refitted on the training data generalizes before the network. Experiments from random initialization show the same coexistence on a window fixed in advance, and in deeper networks held-out accuracy also rises over post-hoc windows with a net increase in training cross-entropy. With a last-layer rate set per training fraction and head decay scaled with the prime, deeper networks at…
PDF arXiv
arXiv cond-mat.dis-nnOct 15
Attention over noisy examples: generalization and memorization in the exponential Hopfield network
Francesco Alemanno
In Hebbian learning from examples, a Hopfield network retrieves archetypes seen only through noisy examples. We study it in the exponential Hopfield model: one softmax attention step with the unlabelled examples as keys. A signal-to-noise analysis gives the overlap with an unseen archetype in closed form: a majority-rule law in which an attention entropy replaces the dataset entropy. At high temperature the archetype is lost in the crosstalk; at low temperature the softmax freezes on the closest training example, as in a random energy model, and the layer memorizes. In between, in a window widening with the square root of the network size, the layer pools the examples of the correct class and retrieves the unseen archetype as well as their majority vote allows, at any fixed load. On structured datasets, such as MNIST and fashion-MNIST, the best accuracy lies beyond the freezing.
PDF arXiv
arXiv cs.LGOct 15
ABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems
Wenshuai Yao, Wenyong Zhou, Hanyong Shao, Zhiyuan Ning, Yizhe Chen +3
Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy overhead. Analog compute-in-memory (CIM) offers an energy-efficient alternative, yet its inherent hardware non-idealities perturb stored weights and can significantly degrade model performance. Although existing noise-aware training (NAT) methods improve robustness by injecting noise during training, they largely overlook the critical role of attention mechanisms in Transformer inference.To bridge this gap, we analyze how CIM-induced noise reshapes attention in Transformers and identify two key observations: (1) the divergence between clean and noisy attention maps increases substantially in deeper layers, and (2) conventional NAT can remain overconfident even when its attention routes deviate markedly from the clean reference. Motivated by these findings, we propose ABNAT, an attention-based noise-aware training framework that teaches the noisy student to preserve the clean teacher's headwise attention routes rather than merely reproduce answers, without hardware changes or inference overhead; selective variants further reduce…
PDF arXiv
arXiv cs.LGOct 15
Interfaces and Modules: A Scalable Representation for Weight-Space Learning across Architectures
Michał Kozyra, Michael Bronstein
Weight-space learning across architectures requires representations that respect parameter symmetries and scale to large networks. Existing equivariant tensor encoders require architecture-specific symmetry specifications, while neuron graphs make global attention costly. We introduce a typed graph organized around shared coordinate-permutation actions. Its nodes, called interfaces, represent coordinate spaces whose bound tensor axes must be permuted jointly; operation occurrences, called modules, form typed hyperedges. A compiler derives this graph from supported PyTorch models, with exact recovery of the permutation factors defined by its constraints. The Universal Weight Encoder (UWE) combines local equivariant updates to parameter features with global attention over invariant interface summaries. The small number of interfaces makes global attention practical, and a single encoder processes different supported architectures. UWE is competitive with architecture-specific state-of-the-art methods on established benchmarks. On a new zoo of 1,200 transformers spanning 100 architectures, it outperforms the evaluated baselines at ranking networks within unseen architectures,…
PDF arXiv

Papers

Topics in this field

gradient descent · learning rate · neural networks221 papers43 this week
graph learning · graph neural · graph neural networks263 papers35 this week
neural networks · neural network · arnold networks149 papers35 this week
memory efficient · optimizers · low rank adaptation157 papers25 this week
bit quantization · quantization aware training · quantization aware152 papers20 this week
linear attention · softmax attention · transformer attention106 papers13 this week

Active contributors · 90 days

Pietro LiòLocaLeJEPA: Maximum Entropy Representations under Geometric Priors8
Dan AlistarhWhat Matters for NVFP4 Training? A Comparative Study of Low-Precision Pretraining Recipes6
Rong-Hua LiToward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach6
Xunkai LiToward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach6
Rebekka BurkholzAC/DC on a Budget - Alternating Sparse Phases5
Haggai MaronGraph Metanetworks for Transformer Based Language Models5

Activity · 12 weeks

Coming with more sources: repos

Recent papers

All in Research →

arXiv cs.LGOct 15
Noise-shaped Sampling and Annealing for Scalable On-the-edge Training of Binary and Ternary AI Models
Zihao Chen, Zhili Xiao, Rui-Jie Zhu, Yipeng Wang, Jason Eshraghian +1
The training loss of binary and ternary neural networks, expanded as a multilinear polynomial over the discrete weights, is structurally equivalent to the energy of a hierarchical high-order Ising model. We exploit this equivalence to apply Ising solvers to discrete-weight network training, while accounting for a fundamental difference in objectives: an Ising solver seeks the ground state of a fixed energy function, whereas training seeks solutions that generalize to unseen data. Our key contribution is $ΣΔ$-Sampler, an optimizer that reconciles these objectives by embedding a stochastic sampling-and-annealing step as a noisy quantizer inside a first-order $ΣΔ$ noise-shaping loop. The loop pushes noise from weight quantization, high-order frustration, and mini-batch sampling out of the low-frequency adaptation band, so that discrete weight updates follow the smooth descent trajectories embedded in the rugged loss landscape. We validate the framework on a planted quadratic unconstrained binary optimization (QUBO) benchmark with a known minimizer; on CIFAR-10, where a $13$M-parameter ternary model comes within $1\%$ of full-precision accuracy; on ImageNet-1k, where a ternary…
PDF arXiv
arXiv cs.LGOct 15
Diagnosing GNN Failure Modes with Layer-Wise Probe Entropy
Shanzi Bao, Mar Gonzàlez I Català, Pietro Liò
Graph neural networks (GNNs) are typically evaluated only at their final layer, obscuring how task-relevant information evolves during message passing. We introduce a post-hoc diagnostic that freezes a trained GNN, fits an independent linear probe to each layer, and tracks probe accuracy alongside normalized predictive entropy. Across real and synthetic graph settings, layer-wise probe entropy distinguishes beneficial, neutral, and harmful smoothing, a capability not demonstrated by existing geometric measures. Moreover, by comparing shallow and deep models, we show that the onset of entropy degradation separates the two main GNN failure modes: structural mismatch under heterophily, which appears from the earliest layers even in shallow models, and excessive-depth oversmoothing, which emerges only as propagation depth increases. These results establish probe-entropy trajectories as a tool for analyzing information flow in GNNs and motivate future work on adaptive depth selection and early-exit mechanisms.
PDF arXiv
arXiv cs.LGOct 15
Flat Loss, Evolving Features: Grokking in Modular Addition
Akash Kumar, Karthik Sankaranarayanan
Can a network keep learning while its training cross-entropy stays nearly flat at a positive level? We show that it can. On modular addition, a standard testbed for grokking, ReLU networks trained with supported-Polar hidden updates, normalized output-layer updates and weight decay can keep 100% training accuracy while their hidden features change and held-out accuracy rises from zero to above 90%, at relatively flat training cross-entropy. Correct classification need not minimize cross-entropy, which still depends on prediction confidence. We prove this coexistence for one-hidden-layer networks in two finite-window theorems from constructed initializations: one gives a constant gain in the Fourier fraction of the hidden features, and the other holds at arbitrarily low positive loss, where a readout refitted on the training data generalizes before the network. Experiments from random initialization show the same coexistence on a window fixed in advance, and in deeper networks held-out accuracy also rises over post-hoc windows with a net increase in training cross-entropy. With a last-layer rate set per training fraction and head decay scaled with the prime, deeper networks at…
PDF arXiv
arXiv cond-mat.dis-nnOct 15
Attention over noisy examples: generalization and memorization in the exponential Hopfield network
Francesco Alemanno
In Hebbian learning from examples, a Hopfield network retrieves archetypes seen only through noisy examples. We study it in the exponential Hopfield model: one softmax attention step with the unlabelled examples as keys. A signal-to-noise analysis gives the overlap with an unseen archetype in closed form: a majority-rule law in which an attention entropy replaces the dataset entropy. At high temperature the archetype is lost in the crosstalk; at low temperature the softmax freezes on the closest training example, as in a random energy model, and the layer memorizes. In between, in a window widening with the square root of the network size, the layer pools the examples of the correct class and retrieves the unseen archetype as well as their majority vote allows, at any fixed load. On structured datasets, such as MNIST and fashion-MNIST, the best accuracy lies beyond the freezing.
PDF arXiv
arXiv cs.LGOct 15
ABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems
Wenshuai Yao, Wenyong Zhou, Hanyong Shao, Zhiyuan Ning, Yizhe Chen +3
Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy overhead. Analog compute-in-memory (CIM) offers an energy-efficient alternative, yet its inherent hardware non-idealities perturb stored weights and can significantly degrade model performance. Although existing noise-aware training (NAT) methods improve robustness by injecting noise during training, they largely overlook the critical role of attention mechanisms in Transformer inference.To bridge this gap, we analyze how CIM-induced noise reshapes attention in Transformers and identify two key observations: (1) the divergence between clean and noisy attention maps increases substantially in deeper layers, and (2) conventional NAT can remain overconfident even when its attention routes deviate markedly from the clean reference. Motivated by these findings, we propose ABNAT, an attention-based noise-aware training framework that teaches the noisy student to preserve the clean teacher's headwise attention routes rather than merely reproduce answers, without hardware changes or inference overhead; selective variants further reduce…
PDF arXiv
arXiv cs.LGOct 15
Interfaces and Modules: A Scalable Representation for Weight-Space Learning across Architectures
Michał Kozyra, Michael Bronstein
Weight-space learning across architectures requires representations that respect parameter symmetries and scale to large networks. Existing equivariant tensor encoders require architecture-specific symmetry specifications, while neuron graphs make global attention costly. We introduce a typed graph organized around shared coordinate-permutation actions. Its nodes, called interfaces, represent coordinate spaces whose bound tensor axes must be permuted jointly; operation occurrences, called modules, form typed hyperedges. A compiler derives this graph from supported PyTorch models, with exact recovery of the permutation factors defined by its constraints. The Universal Weight Encoder (UWE) combines local equivariant updates to parameter features with global attention over invariant interface summaries. The small number of interfaces makes global attention practical, and a single encoder processes different supported architectures. UWE is competitive with architecture-specific state-of-the-art methods on established benchmarks. On a new zoo of 1,200 transformers spanning 100 architectures, it outperforms the evaluated baselines at ranking networks within unseen architectures,…
PDF arXiv

Open problems in Graph learning

Quantization error propagation in recurrent states1 paper raises it · 1 addresses it
Low-precision recurrent states in linear attention degrade accuracy due to error accumulation over time and across dimensions.
- STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantizationraises · addresses
Train-rollout discrepancy in low-precision RL1 paper raises it · 1 addresses it
Quantizing training and rollout paths independently in RL leads to mismatched behavior and reduced performance.
- TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Modelsraises · addresses
Distribution shift in decoding-aware pruning1 paper raises it · 1 addresses it
Pruning guided by natural sequences fails to account for self-generated token distributions during decoding, hurting performance.
- SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inferenceraises · addresses
Lack of benchmarks for graph decision-making in System One models1 paper raises it · 1 addresses it
Existing benchmarks do not diagnose the unique challenges of graph-based decision tasks for non-language models.
- GraphDecisionBench: Benchmarking System One Models on Graph Tasksraises · addresses
Non-compositional sparse autoencoder features1 paper raises it · 1 addresses it
Sparse autoencoder features often exhibit higher-order interactions, limiting interpretability and control.
- Behavioral Compositionality of Sparse Autoencoder Representationsraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 6m ago

Graph learning

Found by the topic model: graph learning, gradient descent, bit quantization.

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