arXiv:2602.22265v1 Announce Type: new Abstract: Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or stochastic diffusions (SDEs). Despite strong empirical performance, standard flow-matching objectives do not directly control the information geometry of the trajectory, allowing low-entropy bottlenecks that can transiently deplete semantic modes. We propose Entropy-Controlled Flow Matching (ECFM): a constrained variational principle over continuity-equation paths enforcing a global entropy-rate budget d/dt H(mu_t) >= -lambda. ECFM is a convex […]

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arXiv:2602.04761v1 Announce Type: cross Abstract: Gradient-variation online learning has drawn increasing attention due to its deep connections to game theory, optimization, etc. It has been studied extensively in the full-information setting, but is underexplored with bandit feedback. In this work, we focus on gradient variation in Bandit Convex Optimization (BCO) with two-point feedback. By proposing a refined analysis on the non-consecutive gradient variation, a fundamental quantity in gradient variation with bandits, we improve the dimension dependence for both […]

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It’s another month in AI research, and it’s hard to pick favorites. This month, I am going over a paper that discusses strategies for the continued pretraining of LLMs, followed by a discussion of reward modeling used in reinforcement learning with human feedback (a popular LLM alignment method), along with a new benchmark. Continued pretraining for LLMs is an important topic because it allows us to update existing LLMs, for instance, ensuring that these models remain up-to-date with […]

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arXiv:2601.14485v1 Announce Type: new Abstract: The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities and their corresponding execution modes. Genetic programming has been widely applied as a hyper-heuristic to evolve priority rules that guide the selection of activity-mode pairs from the current eligible set. Recently, an activity group selection strategy has been proposed to select a subset of activities rather than a single activity […]

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arXiv:2601.05466v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, however, they remain critically vulnerable to jailbreak attacks that elicit harmful responses violating human values and safety guidelines. Despite extensive research on defense mechanisms, existing safeguards prove insufficient against sophisticated adversarial strategies. In this work, we propose iMIST (underline{i}nteractive underline{M}ulti-step underline{P}rogreunderline{s}sive underline{T}ool-disguised Jailbreak Attack), a novel adaptive jailbreak method that synergistically exploits vulnerabilities in current defense mechanisms. iMIST disguises malicious queries as […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The Hidden Cost of AI: Why It’s Making Workers Smarter, but Organisations Dumber By @yuliiaharkusha [ 8 Min read ] AI boosts individual performance but weakens organisational thinking. Why smarter workers and faster tools can leave companies less intelligent than before. Read More. Why […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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I’ve been spending time with Apache ADBC (Arrow Database Connectivity) and DuckLake (lakehouse architecture using DuckDB) to read columnar data. I realized XGBoost took Arrow tables as a data input and I was able to pass arrow tables with little memory overhead to train. I also wanted to try to not use scikit-learn so I built a train and test split function with PyArrow instead. ADBC also allows you to stream larger than memory data and train a […]

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