Hi everyone, I’m Jacob, the creator of the NotebookLM-MCP that I shared here a while back. Today I’m excited to reveal my next project: NotebookLM-CLI 🚀 What is it? A full-featured command-line interface for NotebookLM. Same HTTP/RPC approach as the MCP (no browser automation, except for login process and cookie/tokens extraction), but packaged as a standalone CLI you can run directly from your terminal. Installation and example commands: # Using pip pip install notebooklm-cli # Using pipx (recommended […]

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Ashforth and Anand show how bad acts become business as usual—first embedded in routines, then justified by clever self-deception, and finally taught to newcomers as “how we do things.” The villain isn’t a monster in the mirror, but a system that slowly teaches good people to forget what good means.

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arXiv:2602.09131v1 Announce Type: new Abstract: While the CHERI instruction-set architecture extensions for capabilities enable strong spatial memory safety, CHERI lacks built-in temporal safety, particularly for heap allocations. Prior attempts to augment CHERI with temporal safety fall short in terms of scalability, memory overhead, and incomplete security guarantees due to periodical sweeps of the system’s memory to individually revoke stale capabilities. We address these limitations by introducing colored capabilities that add a controlled form of indirection to CHERI’s capability […]

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Machine unlearning (MU) for large language models has become critical for AI safety, yet existing methods fail to generalize to Mixture-of-Experts (MoE) architectures. We identify that traditional unlearning methods exploit MoE’s architectural vulnerability: they manipulate routers to redirect queries away from knowledgeable experts rather than erasing knowledge, causing a loss of model utility and superficial forgetting. We propose Geometric Routing Invariance Preservation (GRIP), an algorithm-agnostic framework for unlearning for MoE. Our core contribution is a geometric constraint, implemented […]

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Accurately upscaling terrestrial carbon fluxes is central to estimating the global carbon budget, yet remains challenging due to the sparse and regionally biased distribution of ground measurements. Existing data-driven upscaling products often fail to generalize beyond observed domains, leading to systematic regional biases and high predictive uncertainty. We introduce Task-Aware Modulation with Representation Learning (TAM-RL), a framework that couples spatio-temporal representation learning with knowledge-guided encoder-decoder architecture and loss function derived from the carbon balance equation. Across 150+ flux […]

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This paper advances the theoretical understanding of active learning label complexity for decision trees as binary classifiers. We make two main contributions. First, we provide the first analysis of the disagreement coefficient for decision trees-a key parameter governing active learning label complexity. Our analysis holds under two natural assumptions required for achieving polylogarithmic label complexity, (i) each root-to-leaf path queries distinct feature dimensions, and (ii) the input data has a regular, grid-like structure. We show these assumptions are […]

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arXiv:2601.02433v1 Announce Type: new Abstract: Digital AI systems spanning large language models, vision models, and generative architectures that operate primarily in symbolic, linguistic, or pixel domains. They have achieved striking progress, but almost all of this progress lives in virtual spaces. These systems transform embeddings and tokens, yet do not themselves touch the world and rarely admit a physical interpretation. In this work we propose a physical transformer that couples modern transformer style computation with geometric representation and […]

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arXiv:2602.07632v2 Announce Type: replace Abstract: In this work, we investigate the large-scale mean-field variational inference (MFVI) problem from a mini-batch primal-dual perspective. By reformulating MFVI as a constrained finite-sum problem, we develop a novel primal-dual algorithm based on an augmented Lagrangian formulation, termed primal-dual variational inference (PD-VI). PD-VI jointly updates global and local variational parameters in the evidence lower bound in a scalable manner. To further account for heterogeneous loss geometry across different variational parameter blocks, we introduce […]

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arXiv:2603.08739v1 Announce Type: new Abstract: The memory-for-computation paradigm of KV caching is essential for accelerating large language model (LLM) inference service, but limited GPU high-bandwidth memory (HBM) capacity motivates offloading the KV cache to cheaper external storage tiers. While this expands capacity, it introduces the challenge of dynamically managing heterogeneous storage resources to balance cost, throughput, and latency under varying workloads. We formulate this as a multi-objective optimization problem: identifying the Pareto frontier across these metrics within the […]

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