arXiv:2602.03975v1 Announce Type: new Abstract: Test-time computation has become a primary driver of progress in large language model (LLM) reasoning, but it is increasingly bottlenecked by expensive verification. In many reasoning systems, a large fraction of verifier calls are spent on redundant or unpromising intermediate hypotheses. We study reasoning under a emph{verification-cost-limited} setting and ask how verification effort should be allocated across intermediate states. We propose a state-level selective verification framework that combines (i) deterministic feasibility gating over […]

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The Black Box Problem Most developers treat a SQL database like a black box: you put a query in, and a result comes out. But when you’re working with petabytes of data in a distributed Lakehouse like Snowflake or Databricks, that black box mindset is exactly what leads to five-figure cloud bills and constant timeouts. In my experience, the secret to high-performance data engineering isn’t writing clever SQL—it’s writing SQL that is easy for the Query Optimizer to […]

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Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems. A popular alternative is reward learning, where reward functions are inferred from human feedback rather than manually specified. Recent work has proposed learning reward functions from human feedback in the form of ratings, rather than traditional binary preferences, enabling richer and potentially less cognitively demanding supervision. Building on this paradigm, we introduce a new rating-based RL method, Ranked Return Regression for RL (R4). At […]

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arXiv:2603.06625v1 Announce Type: new Abstract: Pavement condition data is important in providing information regarding the current state of the road network and in determining the needs of maintenance and rehabilitation treatments. However, the condition data is often incomplete due to various reasons such as sensor errors and non-periodic inspection schedules. Missing data, especially data missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing methods in dealing with missing data usually discard entire data […]

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Student engagement is crucial for improving learning outcomes in group activities. Highly engaged students perform better both individually and contribute to overall group success. However, most existing automated engagement recognition methods are designed for online classrooms or estimate engagement at the individual level. Addressing this gap, we propose DualEngage, a novel two-stream framework for group-level engagement recognition from in-classroom videos. It models engagement as a joint function of both individual and group-level behaviors. The primary stream models person-level […]

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:::info Astounding Stories of Super-Science May 2001, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. A ROOM WITH A VIEW – Chapter X – Cecil as a Humourist Astounding Stories of Super-Science May 2001: A ROOM WITH A VIEW – Chapter X – Cecil as a Humourist By E. M. Forster ::: The society out of which Cecil proposed to rescue Lucy was perhaps no very […]

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How Blitz scaled their game coaching app with lower latency and leaner operations Blitz is a fast-growing startup that provides personalized coaching for games such as League of Legends, Valorant, and Fortnite. They aim to help gamers become League of Legends legends through real-time insights and post-match analysis. While players play, the app does quite a lot of work. It captures live match data, analyzes it quickly, and uses it for real-time game screen overlays plus personalized post-game coaching. […]

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