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arXiv:2511.00602v2 Announce Type: replace Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models’ ability to surpass human-level performance. While self-play offers a promising alternative, existing approaches depend on external verifiers or cannot learn open-endedly. We present Open-Ended Self-Improving Reasoner (OpenSIR), a self-play framework where an LLM learns to generate and solve novel problems by alternating teacher and student roles without external supervision. To generate novel problems, OpenSIR optimises for both difficulty and diversity, rewarding problems that challenge appropriately while exploring distinct concepts, enabling open-ended mathematical discovery. Starting from a single trivial seed problem, OpenSIR substantially improves instruction models: Llama-3.2-3B-Instruct advances from 73.9 to 78.3 on GSM8K, and from 28.8 to 34.4 on College Math, while Gemma-2-2B-Instruct rises from 38.5 to 58.7 on GSM8K. Our analyses reveal that OpenSIR achieves open-ended learning through co-evolving teacher-student roles that adaptively calibrate difficulty and drive diverse exploration, progressing autonomously from basic to advanced mathematics.

arXiv:2512.23328v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robust spatial mental model. We identify three core cognitive challenges hindering this transition: spatial reasoning, long-horizon state tracking via mental simulation, and active exploration under partial observation. To isolate and evaluate these faculties, we introduce CubeBench, a novel generative benchmark centered on the Rubik’s Cube. CubeBench uses a three-tiered diagnostic framework that progressively assesses agent capabilities, from foundational state tracking with full symbolic information to active exploration with only partial visual data. Our experiments on leading LLMs reveal critical limitations, including a uniform 0.00% pass rate on all long-horizon tasks, exposing a fundamental failure in long-term planning. We also propose a diagnostic framework to isolate these cognitive bottlenecks by providing external solver tools. By analyzing the failure modes, we provide key insights to guide the development of more physically-grounded intelligent agents.

arXiv:2512.24181v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have demonstrated significant promise in clinical diagnosis. However, current models struggle to emulate the iterative, diagnostic hypothesis-driven reasoning of real clinical scenarios. Specifically, current LLMs suffer from three critical limitations: (1) generating hallucinated medical content due to weak grounding in verified knowledge, (2) asking redundant or inefficient questions rather than discriminative ones that hinder diagnostic progress, and (3) losing coherence over multi-turn dialogues, leading to contradictory or inconsistent conclusions. To address these challenges, we propose MedKGI, a diagnostic framework grounded in clinical practices. MedKGI integrates a medical knowledge graph (KG) to constrain reasoning to validated medical ontologies, selects questions based on information gain to maximize diagnostic efficiency, and adopts an OSCE-format structured state to maintain consistent evidence tracking across turns. Experiments on clinical benchmarks show that MedKGI outperforms strong LLM baselines in both diagnostic accuracy and inquiry efficiency, improving dialogue efficiency by 30% on average while maintaining state-of-the-art accuracy.

Have you ever felt “velvetmist”?  It’s a “complex and subtle emotion that elicits feelings of comfort, serenity, and a gentle sense of floating.” It’s peaceful, but more ephemeral and intangible than contentment. It might be evoked by the sight of a sunset or a moody, low-key album.   If you haven’t ever felt this sensation—or even heard of it—that’s not surprising. A Reddit user named noahjeadie generated it with ChatGPT, along with advice on how to evoke the feeling. With the right essential oils and soundtrack, apparently, you too can feel like “a soft fuzzy draping ghost floating through a lavender suburb.” Don’t scoff: Researchers say more and more terms for these “neo-­emotions” are showing up online, describing new dimensions and aspects of feeling. Velvetmist was a key example in a journal article about the phenomenon published in July 2025. But most neo-emotions aren’t the inventions of emo artificial intelligences. Humans come up with them, and they’re part of a big change in the way researchers are thinking about feelings, one that emphasizes how people continuously spin out new ones in response to a changing world.  Velvetmist might’ve been a chatbot one-off, but it’s not unique. The sociologist Marci Cottingham—whose 2024 paper got this vein of neo-emotion research started—cites many more new terms in circulation. There’s “Black joy” (Black people celebrating embodied pleasure as a form of political resistance), “trans euphoria” (the joy of having one’s gender identity affirmed and celebrated), “eco-anxiety” (the hovering fear of climate disaster), “hypernormalization” (the surreal pressure to continue performing mundane life and labor under capitalism during a global pandemic or fascist takeover), and the sense of “doom” found in “doomer” (one who is relentlessly pessimistic) or “doomscrolling” (being glued to an endless feed of bad news in an immobilized state combining apathy and dread).  Of course, emotional vocabulary is always evolving. During the Civil War, doctors used the centuries-old term “nostalgia,” combining the Greek words for “returning home”and “pain,” to describe a sometimes fatal set of symptoms suffered by soldiers—a condition we’d probably describe today as post-traumatic stress disorder. Now nostalgia’s meaning has mellowed and faded to a gentle affection for an old cultural product or vanished way of life. And people constantly import emotion words from other cultures when they’re convenient or evocative—like hygge (the Danish word for friendly coziness) or kvell (a Yiddish term for brimming over with happy pride).  Cottingham believes that neo-­emotions are proliferating as people spend more of their lives online. These coinages help us relate to one another and make sense of our experiences, and they get a lot of engagement on social media. So even when a neo-emotion is just a subtle variation on, or combination of, existing feelings, getting super-specific about those feelings helps us reflect and connect with other people. “These are potentially signals that tell us about our place in the world,” she says.  These neo-emotions are part of a paradigm shift in emotion science. For decades, researchers argued that humans all share a set of a half-dozen or so basic emotions. But over the last decade, Lisa Feldman Barrett, a clinical psychologist at Northeastern University, has become one of the most cited scientists in the world for work demonstrating otherwise. By using tools like advanced brain imaging and studying babies and people from relatively isolated cultures, she has concluded there’s no such thing as a basic emotional palette. The way we experience and talk about our feelings is culturally determined. “How do you know what anger and sadness and fear are? Because somebody taught you,” Barrett says.  If there are no true “basic” biological emotions, this puts more emphasis on social and cultural variations in how we interpret our experiences. And these interpretations can change over time. “As a sociologist, we think of all emotions as created,” Cottingham says. Just like any other tool humans make and use, “emotions are a practical resource people are using as they navigate the world.”  Some neo-emotions, like velvetmist, might be mere novelties. Barrett playfully suggests “chiplessness” to describe the combined hunger, frustration, and relief of getting to the bottom of the bag. But others, like eco-anxiety and Black joy, can take on a life of their own and help galvanize social movements.   Both reading about and crafting your own neo-emotions, with or without chatbot assistance, could be surprisingly helpful. Lots of research supports the benefits of emotional granularity. Basically, the more detailed and specific words you can use to describe your emotions, both positive and negative, the better.  Researchers analogize this “emodiversity” to biodiversity or cultural diversity, arguing that a more diverse world is more enriched. It turns out that people who exhibit higher emotional granularity go to the doctor less frequently, spend fewer days hospitalized for illness, and are less likely to drink when stressed, drive recklessly, or smoke cigarettes. And many studies show emodiversity is a skill that, with training, people can develop at any age. Just imagine cruising into this sweet, comforting future. Is the idea giving you a certain dreamy thrill? Are you sure you’ve never felt velvetmist? Anya Kamenetz is a freelance education reporter who writes the Substack newsletter The Golden Hour.

arXiv:2512.23280v1 Announce Type: new Abstract: E-commerce live streaming in China, particularly on platforms like Douyin, has become a major sales channel, but hosts often use morphs to evade scrutiny and engage in false advertising. This study introduces the Live Auditory Morph Resolution (LiveAMR) task to detect such violations. Unlike previous morph research focused on text-based evasion in social media and underground industries, LiveAMR targets pronunciation-based evasion in health and medical live streams. We constructed the first LiveAMR dataset with 86,790 samples and developed a method to transform the task into a text-to-text generation problem. By leveraging large language models (LLMs) to generate additional training data, we improved performance and demonstrated that morph resolution significantly enhances live streaming regulation.

LLMRouter is an open source routing library from the U Lab at the University of Illinois Urbana Champaign that treats model selection as a first class system problem. It sits between applications and a pool of LLMs and chooses a model for each query based on task complexity, quality targets, and cost, all exposed through a unified Python API and CLI. The project ships with more than 16 routing models, a data generation pipeline over 11 benchmarks, and a plugin system for custom routers. Router families and supported models LLMRouter organizes routing algorithms into four families, Single-Round Routers, Multi-Round Routers, Personalized Routers, and Agentic Routers. Single round routers include knnrouter, svmrouter, mlprouter, mfrouter, elorouter, routerdc, automix, hybrid_llm, graphrouter, causallm_router, and the baselines smallest_llm and largest_llm. These models implement strategies such as k nearest neighbors, support vector machines, multilayer perceptrons, matrix factorization, Elo rating, dual contrastive learning, automatic model mixing, and graph based routing. Multi round routing is exposed through router_r1, a pre trained instance of Router R1 integrated into LLMRouter. Router R1 formulates multi LLM routing and aggregation as a sequential decision process where the router itself is an LLM that alternates between internal reasoning steps and external model calls. It is trained with reinforcement learning using a rule based reward that balances format, outcome, and cost. In LLMRouter, router_r1 is available as an extra installation target with pinned dependencies tested on vllm==0.6.3 and torch==2.4.0. Personalized routing is handled by gmtrouter, described as a graph based personalized router with user preference learning. GMTRouter represents multi turn user LLM interactions as a heterogeneous graph over users, queries, responses, and models. It runs a message passing architecture over this graph to infer user specific routing preferences from few shot interaction data, and experiments show accuracy and AUC gains over non personalized baselines. Agentic routers in LLMRouter extend routing to multi step reasoning workflows. knnmultiroundrouter uses k nearest neighbor reasoning over multi turn traces and is intended for complex tasks. llmmultiroundrouter exposes an LLM based agentic router that performs multi step routing without its own training loop. These agentic routers share the same configuration and data formats as the other router families and can be swapped through a single CLI flag. Data generation pipeline for routing datasets LLMRouter ships with a full data generation pipeline that turns standard benchmarks and LLM outputs into routing datasets. The pipeline supports 11 benchmarks, Natural QA, Trivia QA, MMLU, GPQA, MBPP, HumanEval, GSM8K, CommonsenseQA, MATH, OpenBookQA, and ARC Challenge. It runs in three explicit stages. First, data_generation.py extracts queries and ground truth labels and creates train and test JSONL splits. Second, generate_llm_embeddings.py builds embeddings for candidate LLMs from metadata. Third, api_calling_evaluation.py calls LLM APIs, evaluates responses, and fuses scores with embeddings into routing records. (GitHub) The pipeline outputs query files, LLM embedding JSON, query embedding tensors, and routing data JSONL files. A routing entry includes fields such as task_name, query, ground_truth, metric, model_name, response, performance, embedding_id, and token_num. Configuration is handled entirely through YAML, so engineers point the scripts to new datasets and candidate model lists without modifying code. Chat interface and plugin system For interactive use, llmrouter chat launches a Gradio based chat frontend over any router and configuration. The server can bind to a custom host and port and can expose a public sharing link. Query modes control how routing sees context. current_only uses only the latest user message, full_context concatenates the dialogue history, and retrieval augments the query with the top k similar historical queries. The UI visualizes model choices in real time and is driven by the same router configuration used for batch inference. LLMRouter also provides a plugin system for custom routers. New routers live under custom_routers, subclass MetaRouter, and implement route_single and route_batch. Configuration files under that directory define data paths, hyperparameters, and optional default API endpoints. Plugin discovery scans the project custom_routers folder, a ~/.llmrouter/plugins directory, and any extra paths in the LLMROUTER_PLUGINS environment variable. Example custom routers include randomrouter, which selects a model at random, and thresholdrouter, which is a trainable router that estimates query difficulty. Key Takeaways Routing as a first class abstraction: LLMRouter is an open source routing layer from UIUC that sits between applications and heterogeneous LLM pools and centralizes model selection as a cost and quality aware prediction task rather than ad hoc scripts. Four router families covering 16 plus algorithms: The library standardizes more than 16 routers into four families, single round, multi round, personalized, and agentic, including knnrouter, graphrouter, routerdc, router_r1, and gmtrouter, all exposed through a unified config and CLI. Multi round RL routing via Router R1: router_r1 integrates the Router R1 framework, where an LLM router interleaves internal “think” steps with external “route” calls and is trained with a rule based reward that combines format, outcome, and cost to optimize performance cost trade offs. Graph based personalization with GMTRouter: gmtrouter models users, queries, responses and LLMs as nodes in a heterogeneous graph and uses message passing to learn user specific routing preferences from few shot histories, achieving up to around 21% accuracy gains and substantial AUC improvements over strong baselines. End to end pipeline and extensibility: LLMRouter provides a benchmark driven data pipeline, CLI for training and inference, a Gradio chat UI, centralized API key handling, and a plugin system based on MetaRouter that allows teams to register custom routers while reusing the same routing datasets and infrastructure. Check out the GitHub Repo and Technical details. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Meet LLMRouter: An Intelligent Routing System designed to Optimize LLM Inference by Dynamically Selecting the most Suitable Model for Each Query appeared first on MarkTechPost.