Knowledge catalogue
arXiv:2604.23356v1 Announce Type: new Abstract: Large language models (LLMs) show promise in medical diagnosis, but real-world deployment remains challenging due to high-stakes clinical decisions and
'AI should elevate your thinking, not replace it.' I don't disagree, but the issue is that current LLMs are not really trained to support that out of the box. I've solved this by building my own agent
arXiv:2507.03014v2 Announce Type: replace-cross Abstract: Large language models (LLMs) face significant copyright and intellectual property challenges as the cost of training increases and model reuse
arXiv:2602.17681v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent s
Gary Marcus critiques common misconceptions about how large language models (LLMs) function, suggesting that most users have an incorrect mental model of what LLMs actually do when generating response
arXiv:2604.21251v1 Announce Type: cross Abstract: Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearni
Good agent memory paper. And great insights on the benefits of structured memory for long-horizon behavior in LLMs. Why it matters: It treats memory less like search and more like a system that will n
arXiv:2507.03933v3 Announce Type: replace Abstract: Multilingual Large Language Models considerably changed how technologies influence language. While previous technologies could mediate or assist hum
arXiv:2604.21700v1 Announce Type: cross Abstract: The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studie
arXiv:2604.21549v1 Announce Type: new Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is
arXiv:2604.20389v1 Announce Type: cross Abstract: The rapid evolution and use of Large Language Models (LLMs) in professional workflows require an evaluation of their domain-specific knowledge against
Don't burn tokens on search. 📉🔥 Will Templeton, CTO and co-founder at Allspice, explains why they don't let LLMs search their 10,000+ ingredient database: ❌ Searching 10k items in a prompt = waste of
arXiv:2501.03624v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel on many NLP benchmarks, but their behavior on real-world, semi-structured prediction remains underexplored.
arXiv:2604.19262v1 Announce Type: cross Abstract: Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities.
Cohere researchers Conway Zhu, Ali Edalati, and Zewen Shen published research on vLLM integration and quality recommendations. The blog post, shared via Cohere's official X account, likely discusses o
Excited to share our work on production-ready W4A8 inference, now integrated in vLLM! By combining 4-bit weights (low memory) with 8-bit activations (high compute), we hit the sweet spot for both deco
arXiv:2505.22811v5 Announce Type: replace-cross Abstract: Weight binarization has emerged as a promising strategy to reduce the complexity of large language models (LLMs). Existing approaches fall int
arXiv:2604.18729v1 Announce Type: new Abstract: Humor holds up a mirror to social perception: what we find funny often reflects who we are and how we judge others. When language models engage with hum
arXiv:2604.19292v1 Announce Type: cross Abstract: Multilingual large language models (LLMs) have minimized the fluency gap between languages. This advancement, however, exposes models to the risk of b
arXiv:2512.06380v3 Announce Type: replace-cross Abstract: Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby by
arXiv:2604.19070v1 Announce Type: new Abstract: Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure wi
arXiv:2510.19028v3 Announce Type: replace Abstract: As LLMs are increasingly deployed in real-world interactions, their social reasoning in interpersonal communication becomes critical. To explore the
arXiv:2508.16846v5 Announce Type: replace-cross Abstract: Sycophancy (overly agreeable or flattering behavior) poses a fundamental challenge for human-AI collaboration, particularly in high-stakes dec
arXiv:2603.23987v2 Announce Type: replace Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we st
arXiv:2502.20295v2 Announce Type: replace-cross Abstract: Handwriting text recognition (HTR) remains a challenging task. Existing approaches require fine-tuning on labeled data, which is impractical t
arXiv:2604.18360v1 Announce Type: cross Abstract: Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, the
arXiv:2506.05606v5 Announce Type: replace Abstract: Can large language models (LLMs) accurately simulate the next web action of a specific user? While LLMs have shown promising capabilities in generat
arXiv:2604.16358v1 Announce Type: cross Abstract: MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploi
arXiv:2602.11528v2 Announce Type: replace-cross Abstract: Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user-generate
arXiv:2507.01936v3 Announce Type: replace Abstract: Large language models (LLMs) are excellent at maintaining high-level, convincing dialogue, but it remains unclear whether their persuasive success r
arXiv:2508.15815v3 Announce Type: replace Abstract: Modern large language models (LLMs) are typically trained and deployed using structured role tags (e.g. system, user, assistant, tool) that explicit
arXiv:2604.16217v1 Announce Type: cross Abstract: Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilitie
arXiv:2510.09033v3 Announce Type: replace Abstract: Recent work suggests that LLMs 'know what they don't know', positing that hallucinated and factually correct outputs arise from distinct internal pr
arXiv:2604.15841v1 Announce Type: new Abstract: While large language models (LLMs) have achieved remarkable success in general language tasks, their performance on Chouxiang Language, a representative
arXiv:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T
arXiv:2604.15411v1 Announce Type: cross Abstract: The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current s
arXiv:2511.02626v3 Announce Type: replace Abstract: Prior works have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect out
arXiv:2604.13243v1 Announce Type: cross Abstract: Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require spe
arXiv:2602.13529v2 Announce Type: replace-cross Abstract: Federated learning (FL) enables collaborative training across organizational silos without sharing raw data, making it attractive for privacy-
arXiv:2604.13074v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual pr
arXiv:2505.18232v3 Announce Type: replace-cross Abstract: The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a pr
arXiv:2604.02830v2 Announce Type: replace Abstract: Detecting whether a model's internal knowledge is sufficient to correctly answer a given question is a fundamental challenge in deploying responsibl
arXiv:2604.12573v1 Announce Type: new Abstract: Large Language Models are increasingly deployed for decision-making, yet their adoption in high-stakes domains remains limited by miscalibrated probabil
arXiv:2512.14732v2 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established
arXiv:2604.12049v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) for reliable, enterprise-grade analytics such as text categorization is often hindered by the stochastic natur
arXiv:2604.12459v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in politically sensitive environments, where memorisation of personal data or confidential conten
arXiv:2604.12770v1 Announce Type: new Abstract: Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a d
arXiv:2604.03401v2 Announce Type: replace-cross Abstract: Understanding student engagement usually requires time-consuming manual observation or invasive recording that raises privacy concerns. We pre
arXiv:2604.10690v1 Announce Type: new Abstract: Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and
arXiv:2604.10535v1 Announce Type: cross Abstract: Incorporating large language models (LLMs) in medical question answering demands more than high average accuracy: a model that returns substantively d
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:2604.09855v1 Announce Type: new Abstract: The recent advancement of Large Language Models (LLMs) has established their potential as autonomous interactive agents. However, they often struggle in
arXiv:2502.14644v5 Announce Type: replace Abstract: Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine
arXiv:2604.10557v1 Announce Type: cross Abstract: This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in
arXiv:2604.10603v1 Announce Type: cross Abstract: Large language models are transforming all areas of academia and industry, attracting the attention of researchers, professionals, and the general pub
arXiv:2604.09940v1 Announce Type: new Abstract: Fine-tuning Large Language Models (LLMs) typically involves either full fine-tuning, which updates all model parameters, or Parameter-Efficient Fine-Tun
arXiv:2604.10480v1 Announce Type: new Abstract: Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifact
Benchmarked @DJLougen ’s Ornstein-27B-v2 Q6_K on my RTX 3090 using hermes-bench, my new open-source benchmarking UI for local LLMs and Hermes agents. Ornstein is a Qwen 3.5 27B fine-tune trained on re
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:2508.08992v3 Announce Type: replace Abstract: Prospect Theory (PT) models human decision-making behaviour under uncertainty, among which linguistic uncertainty is commonly adopted in real-world