Knowledge catalogue
arXiv:2604.18106v1 Announce Type: new Abstract: Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although
arXiv:2604.16610v1 Announce Type: cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability. Conventional fairness
arXiv:2604.17105v1 Announce Type: new Abstract: Tokenization is the first step in every language model (LM), yet it never takes the sounds of words into account. We investigate how tokenization influe
Kimi 2.6 Thinking seems very good for an open weights model, but many rough edges compared to closed SoTA. The Lem Test resulted in a 74 page thinking trace... and an okay-ish answer. It did an okay T
Kimi K2.6, a language model developed by Moonshot AI, has achieved the top ranking on the open-weight category of the Vals Index benchmark, while placing 7th overall across all model categories. The V
arXiv:2604.16862v1 Announce Type: new Abstract: Recent deployments of large language models (LLMs) as autonomous trading agents raise questions about whether financial decision-making competence gener
arXiv:2506.00955v2 Announce Type: replace Abstract: Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, exi
arXiv:2604.17770v1 Announce Type: new Abstract: Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting
arXiv:2509.11983v2 Announce Type: replace Abstract: Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlo
This DigitalOcean guide covers strategies and best practices for deploying and optimizing very large language models (600 billion+ parameters) on cloud infrastructure, focusing on inference performanc
arXiv:2604.16929v1 Announce Type: new Abstract: The accurate extraction of scientific measurements from literature is a critical yet challenging task in AI4Science, enabling large-scale analysis and i
arXiv:2604.17730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored as scalable tools for mental health counseling, yet evaluating their safety remains challenging d
arXiv:2604.18069v1 Announce Type: new Abstract: Humans often hold different perspectives on the same issues. In many NLP tasks, annotation disagreement can reflect valid subjective perspectives. Model
arXiv:2604.17972v1 Announce Type: new Abstract: Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work
arXiv:2604.16967v1 Announce Type: new Abstract: Path planning is usually solved by addressing either the (high-level) route planning problem (waypoint sequencing to achieve the final goal) or the (low
arXiv:2604.18002v1 Announce Type: new Abstract: Chain-of-thought reasoning has driven striking advances in language model capability, yet every reasoning step grows the KV cache, creating a bottleneck
arXiv:2502.08531v3 Announce Type: replace Abstract: Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variable
arXiv:2502.19499v3 Announce Type: replace Abstract: Diffusion models have achieved remarkable progress in various domains with an intriguing ability to produce new data that do not exist in the traini
arXiv:2604.17338v1 Announce Type: cross Abstract: Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often regenerate correct but o
arXiv:2604.17415v1 Announce Type: cross Abstract: Reward-based fine-tuning aims to steer a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the
arXiv:2604.16606v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, yet a unified treatment of their overlapping safety challenges remains
arXiv:2604.16424v1 Announce Type: cross Abstract: State-Space Models (SSMs) -- structured SSMs (S4, S4D, DSS, S5), selective SSMs (Mamba, Mamba-2), and hybrid architectures (Jamba) -- are deployed in
arXiv:2506.18141v3 Announce Type: replace Abstract: We identify semantically coherent, context-consistent network components in large language models (LLMs) using coactivation of sparse autoencoder (S
arXiv:2604.16774v1 Announce Type: new Abstract: Long-horizon language model systems increasingly rely on persistent memory, yet many current designs still treat memory primarily as a static store: wri
arXiv:2603.18361v2 Announce Type: replace Abstract: Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering m
OpenAI has been using multiple informal names internally and externally for its new image generation model, including 'ChatGPT Images 2.0,' 'Image gen 2,' and 'gpt-image-2.' The post highlights incons
arXiv:2604.17248v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing sp
arXiv:2604.16841v1 Announce Type: new Abstract: Land surface temperature (LST) super-resolution is important for environmental monitoring. However, it remains challenging as coarse thermal observation
arXiv:2603.22368v2 Announce Type: replace Abstract: Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. Whi
arXiv:2604.18484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex
arXiv:2604.15789v1 Announce Type: new Abstract: As Large Language Models (LLMs) receive increasing attention and are being deployed across various domains, their potential risks, including generating
arXiv:2604.15609v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) for black-box models accessible only via APIs remains a largely unexplored challenge. Existing approaches such as post-hoc
Anthropic announced its Mythos AI model on April 7 and refused to release it publicly, citing its unprecedented ability to find and exploit software vulnerabilities . The company is instead limiting a
arXiv:2604.16258v1 Announce Type: new Abstract: Competency Questions (CQs) are a cornerstone of requirement elicitation in ontology engineering. CQs represent requirements as a set of natural language
arXiv:2602.01766v2 Announce Type: replace-cross Abstract: The quadratic complexity and indefinitely growing key-value (KV) cache of standard Transformers pose a major barrier to long-context processin
arXiv:2604.15866v1 Announce Type: cross Abstract: Large language models (LLMs) have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER), yet their ge
arXiv:2509.14977v2 Announce Type: replace Abstract: Ultrasound imaging has become the preferred imaging modality for early cancer screening due to its advantages of non-ionizing radiation, low cost, a
Kimi has been the most popular model on Fireworks, both out of the box and as a fine-tuning base (including Composer 2) Now Kimi K2.6 is live with huge jumps (10+%) in coding, long-running agents and
arXiv:2604.15760v1 Announce Type: new Abstract: We introduce the first version of KWBench (Knowledge Work Bench), a benchmark for unprompted problem recognition in large language models: can an LLM id
arXiv:2412.19363v3 Announce Type: replace Abstract: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to ge
arXiv:2601.02996v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit
literally my basic model since 1998. crazy that some people still haven’t figured this out. My basic model of capabilities: LLMs are good at problems similar to those that appear in their training dat
arXiv:2604.15314v1 Announce Type: cross Abstract: This research aimed to develop an intelligent system to evaluate performance and extract behavioral models for children with ASD and neurotypical (TD)
Nice paper combining the strength of Skills and RAG. Most RAG systems retrieve on every query, whether the model needs help or not. This is wasteful when the model already knows the answer, and often
arXiv:2604.03444v3 Announce Type: replace-cross Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid m
arXiv:2412.04497v5 Announce Type: replace-cross Abstract: Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite the
opus 4.7 dropped last week with two new features: xhigh effort: more compute when you need it task budgets: the model knows its limit and wraps up gracefully, this is a great example of giving the mod
Our researchers are heading to ICLR with new work: model efficiency, long-context reasoning, next-gen attention and decoding, and more. Check out what we've been building 👇🏼 #TogetherResearch #AINativ
arXiv:2512.06987v2 Announce Type: replace Abstract: Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in c
arXiv:2604.15483v1 Announce Type: new Abstract: We present a new robotic foundation model, called {pi}_{0.7}, that can enable strong out-of-the-box performance in a wide range of scenarios. {pi}_{0.7}
arXiv:2604.15670v1 Announce Type: new Abstract: Reasoning segmentation has recently expanded from ground-level scenes to remote-sensing imagery, yet UAV data poses distinct challenges, including obliq
arXiv:2604.15780v1 Announce Type: cross Abstract: Machine learning models are increasingly deployed in real-world applications, but even aligned models such as Mistral and LLaVA still exhibit unsafe b
arXiv:2604.15577v1 Announce Type: cross Abstract: Consider an auto-regressive model that produces outputs x (e.g., answers to questions, molecules) each of which can be summarized by an attribute vect
arXiv:2602.07006v2 Announce Type: replace Abstract: Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine d
arXiv:2602.05638v3 Announce Type: replace Abstract: While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that w
arXiv:2512.05722v2 Announce Type: replace Abstract: Chemical reaction mechanisms are the foundation of how chemists evaluate reactivity and feasibility, yet current Computer-Assisted Synthesis Plannin
arXiv:2509.13397v3 Announce Type: replace-cross Abstract: Social scientists are now using large language models to create 'silicon samples': synthetic datasets intended to stand in for human responden
arXiv:2601.05547v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable progress in multimodal tasks, but remain susceptible to hallucinations, where gener
A major lesson to take away from Opus 4.7 is that, while there is a lot of arguments about implementation choices and personality, models keep improving measurably on economically important tasks with
arXiv:2604.13812v1 Announce Type: new Abstract: Quantum circuit optimization is a central task in Quantum Computing, as current Noisy Intermediate Scale Quantum devices suffer from error propagation t