Collaborative filtering methods based on implicit feedback (e.g., purchase records and browsing history) are widely used in recommender systems. Compared to explicit feedback (e.g., 1-5 star ratings), implicit feedback is more abundant and accessible in real-world applications. However, the missing data of implicit feedback also brings two challenges.

New Year is a time of reinvention. In that spirit, I would like to shake up this preview of 2026's best sci-fi and science-related TV with a radical act: including a series that started last year. That may seem strange, but the second season of Fallout (Amazon Prime Video) aired in only mid-December, so, for my money, it counts. Set in a retrofuturistic US, generations of humans have lived inside radiation-proof bunkers sold to them by the shadowy Vault-Tec corporation. Last season, former vault-dweller Lucy (Ella Purnell) went surface-side to find her missing father, encountering cowboys and cannibals along the way.

As large language models (LLMs) are increasingly used in human-AI interactions, their social reasoning capabilities in interpersonal contexts are critical. We introduce SCRIPTS, a 1k-dialogue dataset in English and Korean, sourced from movie scripts. The task involves evaluating models' social reasoning capability to infer the interpersonal relationships (e.g., friends, sisters, lovers) between speakers in each dialogue. Each dialogue is annotated with probabilistic relational labels (Highly Likely, Less Likely, Unlikely) by native (or equivalent) Korean and English speakers from Korea and the U.S. Evaluating nine models on our task, current proprietary LLMs achieve around 75-80% on the English dataset, whereas their performance on Korean drops to 58-69%. More strikingly, models select Unlikely relationships in 10-25% of their responses. Furthermore, we find that thinking models and chain-of-thought prompting, effective for general reasoning, provide minimal benefits for social reasoning and occasionally amplify social biases. Our findings reveal significant limitations in current LLMs' social reasoning capabilities, highlighting the need for efforts to develop socially-aware language models.

While language models (LMs) offer great potential for conversational recommender systems (CRSs), the paucity of public CRS data makes fine-tuning LMs for CRSs challenging. In response, LMs as user simulators qua data generators can be used to train LM-based CRSs, but often lack behavioral consistency, generating utterance sequences inconsistent with those of any real user. To address this, we develop a methodology for generating natural dialogues that are consistent with a user's underlying state using behavior simulators together with LM-prompting. We illustrate our approach by generating a large, open-source CRS data set with both preference elicitation and example critiquing. Rater evaluation on some of these dialogues shows them to exhibit considerable consistency, factuality and naturalness.

One of Chantal Akerman's Best Films Is in Legal Limbo The Belgian-born director's 1994 coming-of-age masterwork, about a precocious teen-ager's romantic audacity, can't be reissued because of its needle drops. Much of direction is production: the material conditions under which a movie is made plays a major role in the creative process. Movie lovers tend to think of producers as dictators of formulas, oppressors of originality, the enemies of art, but that just reflects the unfortunate history of studio filmmaking in Hollywood and elsewhere. In fact, producing a movie can be a kind of art in itself, a practical imagining of possibilities for filmmakers that they wouldn't themselves have come up with. The complete retrospective of Chantal Akerman's work that runs at from September 11th to October 16th includes a superb instance of this phenomenon--of visionary production fostering directorial artistry--in her "Portrait of a Young Girl at the End of the 60s in Brussels," an hour-long movie from 1994.

I am 85 hours into Death Stranding 2, an apocalyptic nightmare about Earth becoming infected with death monsters, and I've realised that I'm playing it as a cosy game. For hours at a time, I trundle along the photorealistic landscapes in my pick-up truck, delivering parcels to isolated communities and building new roads. The only reason I complete the main story missions is to open new areas of the map so that I can meet new people and build more roads. I find it blissfully enjoyable. Of course, I am far from alone in playing video games this way.

Multi-agent systems (MAS) have demonstrated significant effectiveness in addressing complex problems through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, characterized by frequent changes, uncertainty, and variability. Despite this, most existing MAS frameworks rely on static architectures with fixed agent capabilities and rigid task allocation strategies, which greatly limits their adaptability to evolving conditions. This inflexibility poses substantial challenges for sustaining robust and efficient multi-agent cooperation in dynamic and unpredictable scenarios. To address these limitations, we propose DRAMA: a Dynamic and Robust Allocation-based Multi-Agent System designed to facilitate resilient collaboration in rapidly changing environments. DRAMA features a modular architecture with a clear separation between the control plane and the worker plane. Both agents and tasks are abstracted as resource objects with well-defined lifecycles, while task allocation is achieved via an affinity-based, loosely coupled mechanism. The control plane enables real-time monitoring and centralized planning, allowing flexible and efficient task reassignment as agents join, depart, or become unavailable, thereby ensuring continuous and robust task execution. The worker plane comprises a cluster of autonomous agents, each with local reasoning, task execution, the ability to collaborate, and the capability to take over unfinished tasks from other agents when needed.

Adam Roberts' Lake of Darkness opens as two space ships investigate a black hole The starting point for this novel was that I wanted to write utopian fiction. I hadn't done this before: all my previous novels have been straight science fiction. But utopia, the genre that imagines a better, or a perfect, world, is older than science fiction: the first utopian novel, the work that coined the term, was written by Thomas More all the way back in 1516. I was interested in what happened to the mode: More's Utopia generated lots of imitators. Through the 17th and 18th centuries, a great many utopian books, novels, tracts and treatises were written.

Despite recent progress in vision-language models (VLMs), holistic understanding of long-form video content remains a significant challenge, partly due to limitations in current benchmarks. Many focus on peripheral, ``needle-in-a-haystack'' details, encouraging context-insensitive retrieval over deep comprehension. Others rely on large-scale, semi-automatically generated questions (often produced by language models themselves) that are easier for models to answer but fail to reflect genuine understanding. In this paper, we introduce MF$^2$, a new benchmark for evaluating whether models can comprehend, consolidate, and recall key narrative information from full-length movies (50-170 minutes long). MF$^2$ includes over 50 full-length, open-licensed movies, each paired with manually constructed sets of claim pairs -- one true (fact) and one plausible but false (fib), totalling over 850 pairs. These claims target core narrative elements such as character motivations and emotions, causal chains, and event order, and refer to memorable moments that humans can recall without rewatching the movie. Instead of multiple-choice formats, we adopt a binary claim evaluation protocol: for each pair, models must correctly identify both the true and false claims. This reduces biases like answer ordering and enables a more precise assessment of reasoning. Our experiments demonstrate that both open-weight and closed state-of-the-art models fall well short of human performance, underscoring the relative ease of the task for humans and their superior ability to retain and reason over critical narrative information -- an ability current VLMs lack.

As the Switch 2's release date crawls closer, the internet is not thrilled with Nintendo's choices. Between reports of modding bans that could brick your console, unreliable Joy-Con performance in demos, and digital games tied so tightly to online access that you basically lease them, many gamers are looking to buy other handheld gaming consoles. One great option is the Retropian Color, a modern version of the Game Boy Color. Not only can you ditch the poor screen quality and AA batteries, but you can also play multiple generations of console titles in one with open-source emulators already built-in (including Nintendo). The price tag is also far more friendly than the Switch 2 at 79.99 with free shipping (reg.