2316 papers
This paper presents a data-rate-aware CNN accelerator architecture for FPGAs that utilizes multi-pixel processing and design-space exploration to optimize hardware utilization and resource efficiency across varying data rates, thereby enabling the efficient implementation of complex CNNs on a single device.
This paper demonstrates that synergistically integrating Supervised Contrastive Learning, Hopfield networks, and Hierarchical Gated Recurrent Networks into Spiking Neural Networks achieves optimal neuromorphic vision performance on N-MNIST by balancing accuracy, energy efficiency, and structured neuronal clustering, rather than relying on isolated architectural optimizations.
This paper introduces Hebbian-Oscillatory Co-Learning (HOC-L), a unified two-timescale framework that integrates hyperbolic sparse geometry with Kuramoto-based phase synchronization to enable connectivity consolidation only when phase coherence signals meaningful patterns, thereby achieving provable convergence and complexity in bio-inspired neural architectures.
This paper introduces Auralink SDC, an edge-deployed multi-agent AI architecture that autonomously manages electric vehicle charging infrastructure with high reliability and sub-50ms latency, achieving 78% autonomous incident resolution and 87.6% diagnostic accuracy to address the critical failure rates and slow remediation times of current cloud-centric systems.
This paper presents a sensitivity-guided framework for compressing Reservoir Computing accelerators that systematically balances quantization and pruning to significantly improve hardware efficiency and reduce power consumption on FPGAs while maintaining high model accuracy across various time-series tasks.
The AetherFloat Family introduces a novel block-scale-free, quad-radix floating-point architecture that eliminates the hardware overhead of dynamic scaling and IEEE 754 inefficiencies, achieving significant area, power, and latency reductions in AI accelerators through explicit mantissas, base-4 scaling, and stochastic rounding.
This paper presents a physics-informed neural network (PINN) framework that robustly reconstructs hidden state variables and estimates biophysical parameters in multiscale neuronal models using only partial, noisy voltage observations, effectively overcoming the convergence failures and sensitivity issues common in traditional numerical methods.
This paper introduces the Variable-Invariant Two-Dimensional State Space Model (VI 2D SSM) and its unified VI 2D Mamba architecture, which theoretically establish and implement a permutation-equivariant framework for multivariate time series that eliminates artificial variable ordering to achieve state-of-the-art performance with improved structural scalability.
The paper introduces HCAPO, a novel framework that enhances long-horizon LLM agents by leveraging hindsight reasoning to refine step-level Q-values and employing a multi-scale advantage mechanism to address sparse reward challenges, thereby significantly outperforming state-of-the-art methods like GRPO on benchmarks such as WebShop and ALFWorld.
This paper introduces a principled method to reduce -invariant functions on product spaces to -invariant functions on alone, where is the isotropy subgroup of , thereby enabling flexible Equivariant Neural Fields to handle arbitrary group actions and heterogeneous product spaces without structural constraints.
This paper establishes a fundamental trilemma in AI safety, proving that no verification procedure can simultaneously guarantee soundness, generality, and tractability, thereby demonstrating that formal alignment certification is impossible without relaxing at least one of these critical properties.
The paper introduces SPREAD, a geometry-preserving framework for lifelong imitation learning that utilizes singular value decomposition to align policy representations within low-rank subspaces and a confidence-guided distillation strategy to mitigate catastrophic forgetting while achieving state-of-the-art performance on the LIBERO benchmark.
The paper presents Midicoth, a lossless compression system that enhances online probability estimation by applying a lightweight, multi-stage micro-diffusion denoising layer to correct systematic biases in adaptive statistical models through a data-efficient, binary-tree decomposition of byte predictions.
This paper proposes a multi-level meta-reinforcement learning framework that systematically compresses Markov decision processes into hierarchical structures with skill-based curriculum learning to decouple sub-tasks, reduce stochasticity, and enable efficient transfer of skills across different problems and levels.
This paper introduces the Temporal Markov Transition Field (TMTF), a novel time series representation that overcomes the limitations of the global Markov Transition Field by partitioning data into temporal chunks to preserve regime-specific dynamics, thereby creating a structured image suitable for convolutional neural networks.
This paper introduces SoftJAX and SoftTorch, open-source libraries that provide feature-complete, drop-in soft relaxations for hard, non-differentiable primitives in JAX and PyTorch, thereby enabling informative gradients for optimization tasks involving operations like thresholding, sorting, and Boolean logic.
This paper introduces GenGNN, a modular message-passing framework that demonstrates expressive neural backbones like transformers are not strictly necessary for discrete graph generation, as diffusion models using GenGNN achieve competitive validity and superior inference speed on various datasets.
MASEval introduces a framework-agnostic library that shifts multi-agent evaluation from a model-centric to a system-centric approach, demonstrating through extensive experiments that implementation decisions regarding topology and orchestration impact performance as significantly as model selection.
This paper theoretically and empirically demonstrates that hybrid sequence models, which combine Transformers and state-space models, can provably solve core synthetic tasks with significantly fewer parameters and less memory than non-hybrid models while also achieving superior length generalization and out-of-distribution robustness.
This paper proposes \textsc{applv}, a novel framework that leverages Vision-Language-Action models to dynamically predict and adapt classical planner parameters, thereby achieving superior navigation performance and generalization in highly constrained environments compared to existing methods.