Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work, we demonstrate that: (1) prefetching and off-chip prediction often provide complementary performance benefits, yet (2) naively combining them often fails to realize their full performance potential, and (3) existing prefetcher control policies leave significant room for performance improvement behind. Our goal is to design a holistic framework that can autonomously learn to coordinate an off-chip predictor with multiple […]

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arXiv:2601.15300v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit catastrophic performance degradation when processing contexts approaching certain critical thresholds, even when information remains relevant. This intelligence degradation-defined as over 30% drop in task performance-severely limits long-context applications. This degradation shows a common pattern: models maintain strong performance up to a critical threshold, then collapse catastrophically. We term this shallow long-context adaptation-models adapt for short to medium contexts but fail beyond critical thresholds. This paper presents three contributions: […]

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Learning Path Recommendation (LPR) aims to generate personalized sequences of learning items that maximize long-term learning effect while respecting pedagogical principles and operational constraints. Although large language models (LLMs) offer rich semantic understanding for free-form recommendation, applying them to long-horizon LPR is challenging due to (i) misalignment with pedagogical objectives such as the Zone of Proximal Development (ZPD) under sparse, delayed feedback, (ii) scarce and costly expert demonstrations, and (iii) multi-objective interactions among learning effect, difficulty scheduling, length […]

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In the manuscript [1] that led to his epoch-making paper “On the Number of Primes Less Than a Given Magnitude” [2], Bernhard Riemann had already revealed the Reciprocal Sum Formula for all of the nontrivial zeros of the Riemann zeta function. We leverage this formula to prove the proposition that is our paper’s title.

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arXiv:2601.06262v1 Announce Type: cross Abstract: Community detection is a fundamental task in data analysis. Block models form a standard approach to partition nodes according to a graph model, facilitating the analysis and interpretation of the network structure. By grouping nodes with similar connection patterns, they enable the identification of a wide variety of underlying structures. The degree-corrected block model (DCBM) is an established model that accounts for the heterogeneity of node degrees. However, existing inference methods for the […]

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arXiv:2601.05386v1 Announce Type: new Abstract: Cheating in chess, by using advice from powerful software, has become a major problem, reaching the highest levels. As opposed to the large majority of previous work, which concerned {em detection} of cheating, here we try to evaluate the possible gain in performance, obtained by cheating a limited number of times during a game. Algorithms are developed and tested on a commonly used chess engine (i.e software).footnote{Needless to say, the goal of this […]

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Hey folks Announcing Call for Papers for GRAIL-V Workshop (Grounded Retrieval and Agentic Intelligence for Vision-Language) at CVPR 2026, happening June 3–4 in Denver. If you’re working at the intersection of Computer Vision, NLP, and Information Retrieval, this workshop is squarely aimed at you. The goal is to bring together researchers thinking about retrieval-augmented, agentic, and grounded multimodal systems—especially as they scale to real-world deployment. ❓️Why submit to GRAIL-V? Strong keynote lineup Keynotes from Kristen Grauman (UT Austin), […]

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Key Highlights: Over the past few years, the recruitment industry has been increasingly using AI to screen candidates and even carry out interviews. With AI’s influence already becoming more significant, more and more platforms are incorporating AI to help candidates find their best jobs. Speaking of which, LinkedIn, which is one of the prominent job search and social platforms, has integrated AI for better job recommendations and a seamless application process. OpenAI’s upcoming AI agent, ChatGPT Jobs, is […]

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The Hopfield model, originally inspired by spin-glass physics, occupies a central place at the intersection of statistical mechanics, neural networks, and modern artificial intelligence. Despite its conceptual simplicity and broad applicability — from associative memory to near-optimal solutions of combinatorial optimization problems — it is rarely integrated into standard undergraduate physics curricula. In this paper, we present the Hopfield model as a pedagogically rich framework that naturally unifies core topics from undergraduate statistical physics, dynamical systems, linear algebra, […]

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