January 2026

Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation […]

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2 AM. The screen burned my retinas. Coffee was a memory. The tax-portal.io bug bounty program was a bust. Nothing. Just another dead end in a long line of dead ends. I was ready to quit. Close the laptop. Sleep. One last look at the proxy logs. A flicker in the traffic history. A standard GET request to fetch a user’s documents. My own, from my test account. The URL was clean, but the parameter caught my eye. user_id=1054. An Insecure […]

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TheoryFest 2026 will hold workshops during the STOC 2026 conference week, June 22–26, 2026, in Salt Lake City, Utah, USA. We invite groups of interested researchers to submit workshop proposals!  See here for more details: https://acm-stoc.org/stoc2026/callforworkshops.html Submission deadline: March 6, 2026

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Recent advances, such as RegretNet, ALGnet, RegretFormer and CITransNet, use deep learning to approximate optimal multi item auctions by relaxing incentive compatibility (IC) and measuring its violation via ex post regret. However, the true accuracy of these regret estimates remains unclear. Computing exact regret is computationally intractable, and current models rely on gradient based optimizers whose outcomes depend heavily on hyperparameter choices. Through extensive experiments, we reveal that existing methods systematically underestimate actual regret (In some models, the […]

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jordanhubbard/nanolang Plenty of people have mused about what a new programming language specifically designed to be used by LLMs might look like. Jordan Hubbard (co-founder of FreeBSD, with serious stints at Apple and NVIDIA) just released exactly that. A minimal, LLM-friendly programming language with mandatory testing and unambiguous syntax. NanoLang transpiles to C for native performance while providing a clean, modern syntax optimized for both human readability and AI code generation. The syntax strikes me as an interesting […]

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Table of links Abstract 1 Introduction 2 Background and Related Work Software Security Coding Weaknesses Security Shift-Left Modern Code Review Code Review for Software Security Security Concern Handling Process in Code Review 3 Motivating Examples 4 Case Study Design Research Questions Studied Projects Data Collection Coding Weakness Taxonomy Study Overview Security Concern Identification Approach (RQ1) Alignment Analysis of Known Vulnerabilities (RQ2) Handling Process Identification (RQ3) 5 Preliminary Analysis PA1: Prevalence of Coding Weakness Comments PA2: Preliminary Evaluation of […]

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The increasing reliance on human preference feedback to judge AI-generated pseudo labels has created a pressing need for principled, budget-conscious data acquisition strategies. We address the crucial question of how to optimally allocate a fixed annotation budget between ground-truth labels and pairwise preferences in AI. Our solution, grounded in semi-parametric inference, casts the budget allocation problem as a monotone missing data framework. Building on this formulation, we introduce Preference-Calibrated Active Learning (PCAL), a novel method that learns the […]

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Federated Learning struggles under temporal concept drift where client data distributions shift over time. We demonstrate that standard FedAvg suffers catastrophic forgetting under seasonal drift on Fashion-MNIST, with accuracy dropping from 74% to 28%. We propose client-side experience replay, where each client maintains a small buffer of past samples mixed with current data during local training. This simple approach requires no changes to server aggregation. Experiments show that a 50-sample-per-class buffer restores performance to 78-82%, effectively preventing forgetting. […]

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