Yuan Li

Weiqi Wang, Peisen Yao, Hanrui Zuo et al.

Abstract interpretation provides a principled foundation for constructing sound static analyses through systematic abstraction. A central challenge is synthesizing the best abstract transformers that achieve optimal precision within a given abstract domain. This paper addresses this problem for low-level code modeled with fixed-size bit-vectors. Recent approaches formulate the synthesis task as a multi-objective Optimization Modulo Theories (OMT) problem, but suffer from limited scalability. We introduce Spear, a parallel synthesis framework that exploits a key structural insight: while the bits within each objective must be processed sequentially, the objectives themselves are independent. Spear leverages the independence of inter-objective bits to better parallelize the synthesis. Experimental results on benchmarks across two binary analysis domains show that Spear consistently outperforms state-of-the-art OMT solvers, solving more instances and achieving significantly improved runtimes. To our knowledge, this is the first approach to apply parallelism to accelerate the synthesis of optimal abstract transformers.

Yuanhao Zou, Arthad Kulkarni, Lucas Tonanez et al.

Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing every event that satisfies compositional temporal and spatial conditions. Existing benchmarks fall short: they localize only a single moment per query, count without temporal conditions, or treat grounding and counting as disjoint tasks. We introduce CoMET-Bench for Conditional Multi-Event Temporal Grounding in long-form video, comprising 2789 queries over 600 videos averaging 33.8 minutes across five real-world domains, with each query composed from 4 temporal conditions, 3 spatial conditions, and a dedicated negative-query subset. We further propose a unified evaluation protocol jointly measuring counting, grounding, and negative-query recognition, including a new Rejection-F1 metric that prevents trivial gaming by lazy "always-empty" models. Benchmarking a broad suite of MLLMs, agent-based, and grounding-specialized methods reveals that existing approaches remain far from solving this task. Building on these findings, we propose CoMET-Agent, a training-free agentic framework that reformulates the task as structured search-and-aggregate, improving F1@0.5 by 6.1% over GPT-5 purely through structural reasoning. Failure analysis further surfaces three open directions: fine-grained entity tracking, position-uniform retrieval, and causal event pairing.