arXiv:2603.12403v1 Announce Type: new Abstract: We study a setting where a set of agents engage in pairwise exchanges of freely replicable goods (e.g., digital goods such as data), where two agents grant each other a copy of a good they possess in exchange for a good they lack. Such exchanges introduce a fundamental tension: while agents benefit from acquiring additional goods, they incur negative externalities when others do the same. This dynamic typically arises in real-world scenarios where […]

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arXiv:2603.03464v1 Announce Type: new Abstract: We introduce Graph Hopfield Networks, whose energy function couples associative memory retrieval with graph Laplacian smoothing for node classification. Gradient descent on this joint energy yields an iterative update interleaving Hopfield retrieval with Laplacian propagation. Memory retrieval provides regime-dependent benefits: up to 2.0~pp on sparse citation networks and up to 5 pp additional robustness under feature masking; the iterative energy-descent architecture itself is a strong inductive bias, with all variants (including the memory-disabled […]

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A conversation with Yale’s Jeffrey Sonnenfeld about the current relationship between the state and private enterprise.

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2025 is the year AI stopped feeling like a “tool you try” and started being treated as something engineering teams have to operate. In January, most engineering teams experienced AI through copilots and chat assistants. They were useful, sometimes impressive, but still easy to keep at arm’s length: a tab in your IDE, a prompt window on the side, a helper that sped up the parts of the job you already understood. By December, the center of gravity […]

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arXiv:2601.09727v1 Announce Type: new Abstract: Cross-domain scientific synthesis requires connecting mechanistic explanations across fragmented literature, a capability that remains challenging for both retrieval-based systems and unconstrained language models. While recent work has applied large language models to scientific summarization and question answering, these approaches provide limited control over reasoning depth and structural grounding. We frame mechanistic synthesis as a graph-constrained multi-hop reasoning problem over literature-derived concept graphs. Given a scientific query and a compact, query-local corpus, SciNets constructs […]

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Trade tension between the US and China has been at an all-time peak now for multiple reasons. Unfortunately, NVIDIA has found itself in the midst of everything that has been happening. The drama around NVIDIA H200 AI chips doesn’t seem to be stopping anytime soon. China allows DeepSeek to buy NVIDIA’s cutting-edge H200 AI chips, with undisclosed conditions According to an exclusive report by Reuters, Chinese authorities have now cleared their top AI startup DeepSeek to purchase NVIDIA’s […]

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We develop a reinforcement learning algorithm to study the holographic entropy cone. Given a target entropy vector, our algorithm searches for a graph realization whose min-cut entropies match the target vector. If the target vector does not admit such a graph realization, it must lie outside the cone, in which case the algorithm finds a graph whose corresponding entropy vector most nearly approximates the target and allows us to probe the location of the facets. For the $sf […]

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arXiv:2603.03388v2 Announce Type: new Abstract: Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-world scenarios. A core challenge is encoding the relational features in asymmetric distance matrices of VRPs. Early attempts directly encoded these matrices but often failed to produce compact embeddings and generalized poorly at scale. In this paper, we propose RADAR, a scalable neural framework that augments existing neural VRP solvers with […]

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Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations-generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation […]

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