Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle Polynomial Relaxation (PPR), a moment-based nonconvex continuous reformulation possessing an advantageous optimization landscape. We warm-start PPR with Combinatorial Mult...
M. Harper Langston, Pierre-David Letourneau, Dalton Jones et al.· 0 citations
Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve into structured linguistic capabilities. However, most existing studies focus primarily on fully trained models, leaving the developmental trajectory across intermediate tr...
Welfare-Aware Preference Acquisition (WAPA) uses auxiliary reference models to estimate query gains and individual utilities, then selects queries by estimated marginal welfare gain while updating recipient utilities, and it is proved exact optimality of the acquisition algorithm for the estimated welfare objective und...
Ruo-Meng Ding, Tian-Wei Gao, Lian-Rui Geng et al.· 0 citations
Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs, without explicitly requiring their representations to preserve how the underlying signals evolve. We introduce forecast grounding, a pretraining principle that requires language-aligned time-ser...
Van Dai Do, H. Nguyen, M. Nguyen et al.· 0 citations
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Text watermarking has emerged as a crucial technique for identifying AI-generated content. However, current watermarking schemes entail a trade-off between text quality and watermark detectability, which is further intensified by the inherent low-entropy nature of code. To mitigate this dilemma, we propose Grammar-Driv...
In large-scale enterprise settings, centralized multi-agent systems (MAS) are increasingly adopted, in which a coordinator delegates user requests to lightweight, domain-specialized sub-agents. While this architecture improves modularity, scalability, and cost efficiency, its reliability depends not only on accurate ro...
When language models answer open-ended questions, they implicitly make decisions that shape their answers. However, the models do not show these decisions to the user, so users cannot contextualize answers: they cannot see the assumptions an answer rests on or the alternatives to it. Drawing on multiverse analysis from...
Andre Ye, Jenny Y. Huang, Alicia Guo et al.· 0 citations
Multimodal adaptation can weaken temporal reasoning (TR) in video-language models (VLMs) even when the visual evidence needed for an answer is correctly perceived. This weakened competence, however, remains partly recoverable from the model's paired pre-adaptation language checkpoint. We introduce MERIT, a gradient-fre...
Zihang Fu, Haonan Wang, Jian Kang et al.· 0 citations
We present frequency-ordered tokenization (FOT), a simple preprocessing technique that improves lossless text compression by exploiting the power-law frequency distribution of natural language (Zipf's law). FOT segments text into the vocabulary of a pretrained Byte Pair Encoding (BPE) tokenizer, renumbers tokens by fre...
AI systems increasingly shape how individuals access and evaluate information, intensifying concerns about misinformation. Computational simulation offers a controlled complement to direct empirical study, with fine-tuned LLMs enabling powerful agent-based simulations. However, fine-tuning often induces broad behaviora...
Victor De Lima, Jiqun Liu, Grace Hui Yang· 0 citations
As text-to-image (T2I) models advance and gain widespread adoption, their associated safety concerns are becoming increasingly critical. Malicious users exploit these models to generate Not-Safe-for-Work (NSFW) images using harmful or adversarial prompts, underscoring the need for effective safeguards to ensure the int...
Yiming Wang, Jiahao Chen, Qingming Li et al.· 0 citations
This paper examines the diffusion of media slant. We document the influence of Fox News Channel (FNC) on the partisan slant of local newspapers in the U.S. over the years 1995-2008. We measure the political slant of local newspapers by scaling the news article texts to Republicans’ and Democrats’ speeches in Congress...
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
