It is an open question whether the search and decision versions of promise CSPs are equivalent. Most known algorithms for PCSPs solve only their \emph{decision} variant, and it is unknown whether they can be adapted to solve \emph{search} as well. The main approaches, called BLP, AIP and BLP+AIP, handle a PCSP by finding a solution to a relaxation of some integer program. We prove that rounding those solutions to a proper search certificate can be as hard as any problem in the class TFNP. In other words, these algorithms are ineffective for search. Building on the algebraic approach to PCSPs, we find sufficient conditions that imply ineffectiveness for search. Our tools are tailored to algorithms that are characterized by minions in a suitable way, and can also be used to prove undecidability results for meta-problems. This way, we show that the families of templates solvable via BLP, AIP, and BLP+AIP are undecidable. Using the same techniques we also analyze several algebraic conditions that are known to guarantee the tractability of finite-template CSPs. We prove that several meta-problems related to cyclic polymorphims and WNUs are undecidable for PCSPs. In particular, there is no algorithm deciding whether a finite PCSP template (1) admits cyclic a polymorphism, (2) admits a WNU.

T eleportation System is an exceptional strategy. The Skill I can send teammates back to the spring, and Skill II can teleport teammates to Da Qiao's vicinity. We use the teleport ratio to evaluate the regional intention's Skill I and Skill II's teleport rates increase by 0.76 and 0.92, respectively. Baseline and MGG agents also each play 30 games against human players.Method Experience Money Damage Kill/D eath/A ssist Player 14573.92 Due to confidentiality agreements, we can't reveal any more The core of the system is that teammates give more resources to the marksman in the early stage to quickly open the money gap with opponents.

It's been a busy month for Midjourney. This week, the generative AI startup released its sophisticated new video tool, V1, which lets users make short animated clips from images they generate or upload. The current version of Midjourney's AI video tool requires an image as a starting point; generating videos using text-only prompts is not supported. Midjourney did not immediately respond to requests for comment. Disney and Universal reiterated statements made by its executives about the lawsuit, including Disney's legal head Horacio Gutierrez alleging that Midjourney's output amounts to "piracy."

Large language models (LLMs) are increasingly used for tasks that require complex reasoning. Most benchmarks focus on final outcomes but overlook the intermediate reasoning steps - such as planning, revision, and decision making under resource constraints. We argue that measuring these internal processes is essential for understanding model behavior and improving reliability. We propose using strategic games as a natural evaluation environment: closed, rule-based systems with clear states, limited resources, and automatic feedback. We introduce a framework that evaluates LLMs along three core dimensions: planning, revision, and resource-constrained decision making. To operationalize this, we define metrics beyond win rate, including overcorrection risk rate, correction success rate, improvement slope, and over-budget ratio. In 4320 adversarial rounds across 12 leading models, ChatGPT-o3-mini achieves the top composite score, with a win rate of 74.7 percent, a correction success rate of 78.6 percent, and an improvement slope of 0.041. By contrast, Qwen-Plus, despite an overcorrection risk rate of 81.6 percent, wins only 25.6 percent of its matches - primarily due to excessive resource use. We also observe a negative correlation between overcorrection risk rate and correction success rate (Pearson r = -0.51, p = 0.093), suggesting that more frequent edits do not always improve outcomes. Our findings highlight the value of assessing not only what LLMs decide but how they arrive at those decisions

Midjourney's tool, which creates images from text prompts, has 20 million users on its Discord server, where users type their inputs. In the lawsuit, the two movie-making giants share examples in which Midjourney is able to create images that uncannily resemble characters each company owns the rights to, such as the Minions, controlled by Universal, or the Lion King, owned by Disney. They also say Midjourney "ignored" their attempts to remediate the issue prior to taking legal action. Midjourney did not immediately respond to New Scientist's request for comment. The lawsuit has been welcomed by Ed Newton-Rex at Fairly Trained, a non-profit organisation that promotes fairer training practices for AI companies.

We investigate an emerging setup in which a small, on-device language model (LM) with access to local data communicates with a frontier, cloud-hosted LM to solve real-world tasks involving financial, medical, and scientific reasoning over long documents. Can a local-remote collaboration reduce cloud inference costs while preserving quality? First, we consider a naive collaboration protocol where the local and remote models simply chat back and forth. Because only the local model reads the full context, this protocol achieves a 30.4x reduction in remote costs, but recovers only 87% of the performance of the frontier model. We identify two key limitations of this protocol: the local model struggles to (1) follow the remote model's multi-step instructions and (2) reason over long contexts. Motivated by these observations, we study an extension of this protocol, coined MinionS, in which the remote model decomposes the task into easier subtasks over shorter chunks of the document, that are executed locally in parallel. MinionS reduces costs by 5.7x on average while recovering 97.9% of the performance of the remote model alone. Our analysis reveals several key design choices that influence the trade-off between cost and performance in local-remote systems.

AI companions based on large language models can role-play and converse very naturally. When value conflicts arise between the AI companion and the user, it may offend or upset the user. Yet, little research has examined such conflicts. We first conducted a formative study that analyzed 151 user complaints about conflicts with AI companions, providing design implications for our study. Based on these, we created Minion, a technology probe to help users resolve human-AI value conflicts. Minion applies a user-empowerment intervention method that provides suggestions by combining expert-driven and user-driven conflict resolution strategies. We conducted a technology probe study, creating 40 value conflict scenarios on Character.AI and Talkie. 22 participants completed 274 tasks and successfully resolved conflicts 94.16% of the time. We summarize user responses, preferences, and needs in resolving value conflicts, and propose design implications to reduce conflicts and empower users to resolve them more effectively.

This paper presents a way to create adversarial examples based on a task loss (e.g. The approach is tested on a few different domains (pose estimation, semantic segmentation, speech recognition). Overall the approach is nice and the results are impressive. My main issues with the paper (prompting my "marginal accept" decision) are: - The math and notation is confusing and contradictory in places, e.g. It needs to be cleaned up.