Knowledge catalogue
arXiv:2505.18931v4 Announce Type: replace Abstract: Understanding and inferring causal relationships from texts is a core aspect of human cognition and is essential for advancing large language models
arXiv:2601.07516v2 Announce Type: replace-cross Abstract: Vision-language models are increasingly employed as multimodal conversational agents (MCAs) for diverse conversational tasks. Recently, reinfo
arXiv:2603.23916v2 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-sta
arXiv:2604.09644v1 Announce Type: cross Abstract: Corporate AI-washing-the strategic misrepresentation of AI capabilities via exaggerated or fabricated cross-channel disclosures-has emerged as a syste
arXiv:2604.11005v1 Announce Type: new Abstract: While diffusion Multimodal Large Language Models (dMLLMs) have recently achieved remarkable strides in multimodal generation, the development of interpr
arXiv:2604.11322v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to to
arXiv:2601.03926v2 Announce Type: replace Abstract: The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined poli
arXiv:2603.18806v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them wi
arXiv:2602.04849v2 Announce Type: replace-cross Abstract: We present El Agente Estructural, a multimodal, natural-language-driven geometry-generation and manipulation agent for autonomous chemistry an
arXiv:2604.10015v1 Announce Type: new Abstract: Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon fin
arXiv:2601.04932v2 Announce Type: replace Abstract: Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as
arXiv:2511.11653v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasonin
arXiv:2604.11430v1 Announce Type: cross Abstract: AI agents that pay for resources via the x402 protocol embed payment metadata - resource URLs, descriptions, and reason strings - in every HTTP paymen
Hermes Trismegistus is likely a language model released by Nous Research, continuing their Hermes series of fine-tuned models known for instruction-following, reasoning, and agentic capabilities. The
arXiv:2604.09674v1 Announce Type: new Abstract: Do large language models (LLMs) think? Daniel Stoljar and Zhihe Vincent Zhang have recently developed an argument from rationality for the claim that LL
arXiv:2604.09670v1 Announce Type: cross Abstract: Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments and changing goals. This capacity,
I'm excited about voice as a UI layer for existing visual applications — where speech and screen update together. This goes well beyond voice-only use cases like call center automation. The barrier ha
In order to make sure ARC-AGI-3 games were solvable by humans we tested over 450 people If a game was too hard we either revised it or tossed it completely For maximum transparency we just open source
arXiv:2509.22887v2 Announce Type: replace Abstract: Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based socia
Is there somewhere a collection of the best agent/coding harnesses for each models, especially open-source and local ones? In my opinion, the biggest reason why people are struggling with open/local m
arXiv:2508.20567v2 Announce Type: replace Abstract: Multi-hop question answering faces substantial challenges due to data sparsity, which increases the likelihood of language models learning spurious
arXiv:2507.14800v2 Announce Type: replace-cross Abstract: With the advanced reasoning, contextual understanding, and information synthesis capabilities of large language models (LLMs), a novel paradig
arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit
arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m
arXiv:2603.23516v2 Announce Type: replace-cross Abstract: Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in
arXiv:2603.02123v3 Announce Type: replace Abstract: The development of affective multimodal language models (MLMs) has long been constrained by a gap between low-level perception and high-level intera
arXiv:2604.11415v1 Announce Type: new Abstract: Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of
arXiv:2604.11120v1 Announce Type: new Abstract: Personality imbuing customizes LLM behavior, but safety evaluations almost always study prompt-based personas alone. We show this is incomplete: prompti
arXiv:2604.09791v1 Announce Type: new Abstract: Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them
arXiv:2502.19731v2 Announce Type: replace Abstract: Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between p
arXiv:2604.11070v1 Announce Type: new Abstract: Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms. This paper argues that red lines
arXiv:2604.11554v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has proven effective at unlocking reasoning, self-reflection, and tool-use capabilities in large language mode
arXiv:2604.11407v1 Announce Type: cross Abstract: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external
arXiv:2604.10695v1 Announce Type: new Abstract: Recent Audio-Visual Question Answering (AVQA) methods have advanced significantly. However, most AVQA methods lack effective mechanisms for handling mis
arXiv:2604.09624v1 Announce Type: new Abstract: Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existin
arXiv:2604.10132v1 Announce Type: cross Abstract: Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and genera
arXiv:2604.11225v1 Announce Type: cross Abstract: Recent Vision Language Models (VLMs) have demonstrated strong performance across a wide range of multimodal reasoning tasks. This raises the question
arXiv:2512.18682v2 Announce Type: replace Abstract: In the high-cost simulation-driven design domain, translating ambiguous design requirements into a mathematical optimization formulation is a bottle
arXiv:2505.17012v3 Announce Type: replace-cross Abstract: Existing evaluations of multimodal large language models (MLLMs) on spatial intelligence are typically fragmented and limited in scope. In thi
arXiv:2604.09780v1 Announce Type: new Abstract: Mixture of Experts (MoEs) are now ubiquitous in large language models, yet the mechanisms behind their 'expert specialization' remain poorly understood.
arXiv:2512.21648v2 Announce Type: replace-cross Abstract: Monte Carlo Tree Search (MCTS) has profoundly influenced reinforcement learning (RL) by integrating planning and learning in tasks requiring l
arXiv:2603.08715v2 Announce Type: replace-cross Abstract: Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between mod
arXiv:2603.22003v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models typically map visual observations and linguistic instructions directly to robotic control signals. This 'black-b
We don’t have the budget for that. We don’t have the time to do something like that. It’s too hard and will take too long. You have to be reasonable. The client doesn’t have budget for an idea like th
A Reddit post on r/ChatGPT in which a user describes canceling their Claude Pro subscription within 48 hours, citing frustration with Claude's usage rate limits as the primary reason. The post reflect
arXiv:2512.17012v3 Announce Type: replace Abstract: Despite advances in Multimodal LLMs (MLLMs), their ability to reason over 3D structures and temporal dynamics remains limited, constrained by weak 4
arXiv:2604.08355v2 Announce Type: replace Abstract: Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered.
arXiv:2603.06665v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision
arXiv:2604.08974v1 Announce Type: new Abstract: Uncertainty quantification is a set of techniques that measure confidence in language models. They can be used, for example, to detect hallucinations or
arXiv:2604.09174v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) aims to reduce hallucination by grounding answers in retrieved evidence, yet hallucinated answers remain common eve
Grok continues to lead global benchmarks: • #1 in AA Omniscience (lowest hallucination rate) • #1 in IFBench performance • #1 on BridgeBench Reasoning • #1 on BridgeBench Speed • #1 on BridgeBench Low
arXiv:2604.08970v1 Announce Type: cross Abstract: We study predictive multilingual evaluation: estimating how well a model will perform on a task in a target language when direct benchmark results are
arXiv:2604.09360v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Model (LLM) providers--each exposing proprietary API formats--has created a fragmented ecosystem where appli
arXiv:2604.08719v1 Announce Type: cross Abstract: Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck
arXiv:2604.09352v1 Announce Type: new Abstract: Monocular Depth Estimation (MDE) is crucial for autonomous lunar rover navigation using electro-optical cameras. However, deploying terrestrial MDE netw
arXiv:2604.08782v1 Announce Type: new Abstract: Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational
arXiv:2604.09430v1 Announce Type: cross Abstract: Text embeddings are central to modern information retrieval and Retrieval-Augmented Generation (RAG). While dense models derived from Large Language M
arXiv:2604.09162v1 Announce Type: cross Abstract: Most affective computing research treats emotion as a static property of text, focusing on the writer's sentiment while overlooking the reader's persp
arXiv:2604.08987v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained
arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio