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arXiv:2611.06829v1 Announce Type: new Abstract: We present a toolchain for development of ML models on AWS EC2 F2 cloud FPGA infrastructure from high-level language, implementing a complete Vitis HLS to HDK bridge to AFI workflow that has been absent from the academic literature despite eight years of AWS FPGA availability.
arXiv Computer Science ARRead article →arXiv:2611.06772v1 Announce Type: new Abstract: Near-Threshold Computing (NTC) offers a substan- tial energy savings, i.e., up to 10x, for digital circuits. However, due to Process, Voltage, and Temperature (PVT) variations, low voltage operating digital processors face increased susceptibility to timing errors.
arXiv Computer Science ARRead article →arXiv:2611.06737v1 Announce Type: new Abstract: The continuous expansion of digital infrastructure, especially data centers, has imposed unprecedented, geographically concentrated stress on existing power grids. However, rigorous causal evidence quantifying how these facilities reshape local power-infrastructure supply remains limited.
arXiv Computer Science ARRead article →arXiv:2611.06436v1 Announce Type: new Abstract: We show that a hardwired distributed processing unit (HDPU) can take a neural decision at the sensor and so remove the inference from the host. Built for zebra-crossing detection on a low-cost Artix-7 FPGA, the unit decides on every camera frame in a fixed 2,269,477 clock cycles (34.04 ms depends upon Fpga clk ), inside the 37.49-ms frame period, and on the board it…
arXiv Computer Science ARRead article →arXiv:2611.06019v1 Announce Type: new Abstract: Diffusion models have become the dominant paradigm for high-quality image generation, but their iterative inference across tens of timesteps incurs substantial computational overhead. Prior accelerators exploit temporal similarity in diffusion models via differential quantization or output sparsity, yet suffer from increased memory footprint, design complexity, and…
arXiv Computer Science ARRead article →arXiv:2611.05751v1 Announce Type: new Abstract: Large language models (LLM) have been the key focus of recent machine learning breakthroughs. As the model size grows exponentially, the computation needed for training is astronomical. A GPU can do a huge amount of arithmetic. However, the data its compute units need is not always available when they are ready. This data may still be in slow memory or on another GPU.
arXiv Computer Science ARRead article →arXiv:2611.05419v1 Announce Type: new Abstract: The RTX 2080 Ti (TU102) ships full second-generation Tensor Cores in hardware, yet the vendor throttles FP16 matrix multiplication at the driver level, leaving usable tensor throughput well below the hardware peak.
arXiv Computer Science ARRead article →arXiv:2611.05415v1 Announce Type: new Abstract: The brain's hippocampus enables episodic memory - the ability to encode events within their context, organize them into sequences, and generate predictive representations to guide adaptive behavior. Modern computing architectures, however, are optimized for large-scale arithmetic and struggle with such context-dependent sequential prediction or "cognitive" tasks.
arXiv Computer Science ARRead article →arXiv:2611.05828v1 Announce Type: new Abstract: Intelligent production requires systems to make decisions and organize work according to production goals and operating conditions. It also requires continuous adjustment through feedback. This paper analyzes intelligence in human production organizations from a complex adaptive systems (CAS) perspective.
arXiv AIRead article →arXiv:2611.05798v1 Announce Type: new Abstract: Recognizing visual content from neural-network weights requires separating functional information from arbitrary parameter coordinates. We audit this separation with hidden-neuron permutations and positive ReLU rescalings that preserve the represented function.
arXiv AIRead article →arXiv:2611.05791v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory stores to retain information across conversations, typically via dense vector embeddings searched with approximate nearest-neighbor methods.
arXiv AIRead article →arXiv:2611.05759v1 Announce Type: new Abstract: Before an AI agent takes on a paid job, someone has to judge whether it is likely to succeed. We test whether frontier language models can make this call before any attempt. We propose PAYWORTHY which contains 1{,}494 real freelance job postings, sampled from 18.8M postings made between 2020 and 2026 on a freelancing platform.
arXiv AIRead article →arXiv:2611.05753v1 Announce Type: new Abstract: Organizations accumulate evidence in conversations, documents, decisions and relationships, but they act on interpretations: judgements assembled from several observations, resting on assumptions, and revised as circumstances change. Existing retrieval, knowledge graphs and agent memory keep the records and lose the interpretation.
arXiv AIRead article →arXiv:2611.05739v1 Announce Type: new Abstract: Coding agents must deliver correct software while respecting users' expertise, preferences, and working conditions. Existing benchmarks increasingly incorporate interaction, yet personalized requirement fulfillment remains underexplored.
arXiv AIRead article →arXiv:2611.05711v1 Announce Type: new Abstract: Facial Expression Recognition (FER) on optomyographic (OMG) smart glasses such as OCOsense enables privacy-preserving, camera-free affect sensing for health and well-being. Most FER systems are fully supervised, i.e., they rely either on hand-crafted expert features with classical machine learning, or on end-to-end deep models trained from labelled data.
arXiv AIRead article →arXiv:2611.05709v1 Announce Type: new Abstract: LLM agent distillation transfers agentic capabilities to student models by training on complete teacher trajectories that typically interleave thoughts and actions. We question how critical thought supervision is for agents' ultimate action performance.
arXiv AIRead article →arXiv:2611.05703v1 Announce Type: new Abstract: Structural Causal Models and Bayesian Networks are the dominant framework for representing causal relations. They are fundamentally type-level formalisms since their estimands, Average Treatment Effects and Conditional Average Treatment Effects, are properties of distributions over populations, not of individual units.
arXiv AIRead article →arXiv:2611.05671v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated significant advances in reasoning with visual data, which is particularly important given that much of human knowledge is encoded visually in scientific articles and other sources.
arXiv AIRead article →arXiv:2611.05664v1 Announce Type: new Abstract: A neuro-symbolic system should combine only relevant answers from expert agents or other knowledge sources. Using only relevant experts for a given input query becomes ever more important for accuracy and scalability as the knowledge network grows and connects ever more knowledge sources.
arXiv AIRead article →arXiv:2611.05662v1 Announce Type: new Abstract: Evaluating open-ended agent tasks increasingly falls to an Agent-as-a-Judge: an evaluator handed tools and the agent's full trajectory so it can verify a result against the environment. The premise is that a judge that gathers its own evidence should be grounded by it.
arXiv AIRead article →arXiv:2611.05652v1 Announce Type: new Abstract: Electroencephalography (EEG) measures brain activity through recording electrical potentials of the scalp. EEG recordings capture two distinct sets of characteristics in the signal: (1) \emph{Traits} -- information that remains relatively constant over the course of a recording session, such as subject identity, age, and sex; (2) \emph{States} -- signatures that encode…
arXiv AIRead article →arXiv:2611.05650v1 Announce Type: new Abstract: Published explanations of why long-horizon agents fail are commonly taxonomies labeled over completed trajectories: a judge reads a transcript and names the failure. We argue that a claim attributing an agent's behavior to its history should carry intervention-verified evidence, per instance or via a validated estimator: freeze an executable mid-trajectory state, alter…
arXiv AIRead article →arXiv:2611.05636v1 Announce Type: new Abstract: The memory of production agents is increasingly becoming a bottleneck and is built almost exclusively from text: key points are extracted from out-of-limit context and retrieved into context at query time. Therefore, every new query incurs an additional cost that grows with the accumulated history.
arXiv AIRead article →arXiv:2611.05632v1 Announce Type: new Abstract: Autonomous software agents can act through tools, delegate work, and change their models or execution environments while earlier authorizations remain in flight. We present Assurance-Conditioned Continuous Authorization (ACCA), an approach designed to be harness-independent, for maintaining operational authority according to currently justified assurance.
arXiv AIRead article →