Knowledge catalogue
arXiv:2604.13882v1 Announce Type: new Abstract: The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread avail
arXiv:2604.13232v1 Announce Type: new Abstract: This discussion paper re-examines SemEval-2020 Task 1, the most influential shared benchmark for lexical semantic change detection, through a three-part
arXiv:2604.02709v2 Announce Type: replace Abstract: The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack syste
arXiv:2604.13071v1 Announce Type: new Abstract: We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Int
arXiv:2601.06536v2 Announce Type: replace Abstract: We present Exposia, the first public dataset that connects writing and feedback in higher education, enabling research on educationally grounded com
arXiv:2601.08605v2 Announce Type: replace Abstract: Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insi
arXiv:2601.11329v3 Announce Type: replace Abstract: Spoken conversational systems require more than accurate speech generation to have human-like conversations: to feel natural and engaging, they must
arXiv:2509.18847v3 Announce Type: replace-cross Abstract: Tool-augmented large language models (LLMs) are usually trained with supervised imitation or coarse-grained reinforcement learning that optimi
arXiv:2405.20836v3 Announce Type: replace-cross Abstract: Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Info
arXiv:2505.19662v3 Announce Type: replace-cross Abstract: This paper introduces FieldWorkArena, a benchmark for agentic AI targeting real-world field work. With the recent increase in demand for agent
arXiv:2509.16445v2 Announce Type: replace Abstract: Enabling robotic assistants to navigate complex environments and locate objects described in free-form language is a critical capability for real-wo
Anthropic is working on improvements to help users maximize the utility of Claude Code functionality, with additional details and updates to be announced later in the same week. This announcement come
arXiv:2602.23636v3 Announce Type: replace Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed
arXiv:2505.16051v4 Announce Type: replace-cross Abstract: Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinica
arXiv:2511.21760v3 Announce Type: replace Abstract: Recent advances in multimodal large language models (LLMs) have enabled unified reasoning across images, audio, and video, but extending such capabi
This is an announcement for the official Claude Developers Twitter/X account that serves as a direct communication channel from Anthropic's team to developers using Claude's API. The account provides
Claude Code users should ensure they are using Opus 4.7 with the latest updates to experience performance improvements or feature enhancements. The post suggests that users not observing expected incr
arXiv:2604.14048v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models are efficient but rigid: once trained, they perform inference in a zero-shot manner and cannot adapt to the test s
arXiv:2604.14137v1 Announce Type: new Abstract: Evaluating LLMs is challenging, as benchmark scores often fail to capture models' real-world usefulness. Instead, users often rely on ``vibe-testing'':
arXiv:2603.19790v3 Announce Type: replace Abstract: Modern vision-language models (VLMs) can act as generative OCR engines, yet open-ended decoding can expose rare but consequential failures. We ident
arXiv:2604.14090v1 Announce Type: new Abstract: Post-training adaptation of language models is commonly achieved through parameter updates or input-based methods such as fine-tuning, parameter-efficie
arXiv:2604.13466v1 Announce Type: cross Abstract: The Claude Mythos Preview system card deploys emotion vectors, sparse autoencoder (SAE) features, and activation verbalisers to study model internals
arXiv:2604.13803v1 Announce Type: new Abstract: Vision-language models are increasingly deployed in high-stakes settings, yet their susceptibility to sycophantic manipulation remains poorly understood
Gemini now uses user interests and Google Photos to create personalized AI images without requiring long descriptions, allowing users to simply ask for pictures of themselves or family members. This f
Google's Personal Intelligence feature, which lets Gemini pull data from apps like Google Photos to offer responses tailored to you, can now use that data and its Nano Banana 2 image model to create i
arXiv:2512.02697v3 Announce Type: replace Abstract: Cross-view geo-localization infers a location by retrieving geo-tagged reference images that visually correspond to a query image. However, the trad
GLM-5.1 Tool Calling Issue Fix & Chat Template Update If you are running GLM-5.1 with vLLM/SGLang and using tool calling, please update your chat template. http://huggingface.co/zai-org/GLM-5.1/blob/m
For many data professionals, the most daunting part of a new project isn't the complexity of the data or the sophistication of the model, it’s the 'blank slate.' Staring at an empty notebook while cre
Google DeepMind released MedGemma, a collection of medical vision-language foundation models based on Gemma 3 in 4B and 27B parameter sizes, demonstrating advanced medical understanding and reasoning
Google LLC’s DeepMind artificial intelligence unit today rolled out a new text-to-speech model called Gemini 3.1 Flash TTS. Unlike its earlier, robotic predecessors, it enables users to direct the voc
Had a great time at PyCon & PyData DE. Highly recommend it. Great open-source, community-focused conference with lots of builders in the Python AI, LLM and agent space. Taking a short family break, my
Happy coding! Opus 4.7 is a significant step up. To get the most out of it, take the time to adjust your workflow to take advantage of Claude running for longer & being more agentic. It feels like a n
This post compares the performance of Qwen 3.6-35B-A3B and Claude Opus 4.7 models on a creative task of generating SVG code for a flamingo riding a unicycle, likely demonstrating differences in their
arXiv:2604.13258v1 Announce Type: new Abstract: Attribution methods seek to explain language model predictions by quantifying the contribution of input tokens to generated outputs. However, most exist
Hi! I'm here with *another launch*, it just happens to be extremely niche, nerdy, and probably only for a handful of people. In the desktop app, Claude Cowork and Code now have a little Bluetooth API
arXiv:2604.14032v1 Announce Type: cross Abstract: Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion management. However, its de
arXiv:2604.13954v1 Announce Type: new Abstract: Existing agent-safety evaluation has focused mainly on externally induced risks. Yet agents may still enter unsafe trajectories under benign conditions.
HOLY 🤯 The one and only @elder_plinius just dropped an unlocked Gemma 4 E4B, and the specs are INSANE. Look at the performance shifts: → Refusal rate: 98.8% down to 2.1% (!!) → Compliance: 1.2% up to
Editor’s note: Today we hear from Perry Nightingale, SVP of Creative AI at WPP about the workflow that cuts training time for humanoid robots from days to minutes — plus access to the open-source code
arXiv:2604.13728v1 Announce Type: cross Abstract: We present a hybrid retrieval system for COVID-19 scientific literature, evaluated on the TREC-COVID benchmark (171,332 papers, 50 expert queries). Th
I edited the intro because I realized I buried the lede originally- The 1M context window is a double-edged sword. It allows Claude to do more complex tasks but it can also leads to more context pollu
I think the adaptive thinking requirement in Claude Opus 4.7 is bad in the ways that all AI effort routers are bad, but magnified by the fact that there is no manual override like in ChatGPT. It regul
arXiv:2604.14114v1 Announce Type: cross Abstract: Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract
In Claude Code the default effort is now xhigh, a new level between high and max giving finer control over the reasoning/latency tradeoff. 4.7 thinks more, so token use runs higher than 4.6. Manage it
in the grand narrative of Meta x AI, we saw the flop (Llama 4 hurhurhur), and now we’re seeing the turn: - *more* hiring since the soup wars of 2025 - Zuck literally moved in with Alexandr and Nat and
arXiv:2604.13686v1 Announce Type: new Abstract: While Large Language Models (LLMs) have significantly advanced Text-to-SQL performance, existing benchmarks predominantly focus on Western contexts and
arXiv:2604.13201v1 Announce Type: new Abstract: Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging. Benchmarks
Introducing Claude Opus 4.7, our most capable Opus model yet. It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. Yo
GPT-Rosalind is an AI model developed by OpenAI specifically designed to assist with life sciences research tasks. The model is trained to help researchers with applications such as analyzing biologic
It is not well-explained, but with the adaptive switch off, I get no thinking. I can set thinking levels in Claude Code, but not in Claude Cowork. AI companies keep seeming to assume that coding/techn
arXiv:2604.13484v1 Announce Type: cross Abstract: Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been ch
arXiv:2604.13058v1 Announce Type: new Abstract: We introduce KMMMU, a native Korean benchmark for evaluating multimodal understanding in Korean cultural and institutional settings. KMMMU contains 3,46
arXiv:2604.13226v1 Announce Type: new Abstract: Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: re
arXiv:2604.13285v1 Announce Type: new Abstract: Clinical text classification requires choosing between specialized fine-tuned models (BERT variants) and general-purpose large language models (LLMs), y
arXiv:2510.13849v3 Announce Type: replace Abstract: Multilingual Large Language Models (LLMs) often exhibit hallucinations such as unintended code-switching, reducing reliability in downstream tasks.
arXiv:2511.11334v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian
arXiv:2604.13713v1 Announce Type: new Abstract: Metaphor detection models achieve strong benchmark performance, yet it remains unclear whether this reflects transferable generalization or lexical memo
arXiv:2604.13979v1 Announce Type: new Abstract: Open-world Question Answering (OW-QA) over knowledge graphs (KGs) aims to answer questions over incomplete or evolving KGs. Traditional KGQA assumes a c
LiteParse hit 4.3K+ GitHub stars in a few weeks. Today it officially joins the LlamaIndex ecosystem, with its own page at http://www.llamaindex.ai/liteparse?utm_medium=socials&utm_source=twitter&utm_c
LiteParse should be the default document parser you use with any AI agent (Claude Code, Claude Cowork, OpenClaw, Codex, and more) The core is extremely fast text and accurate parsing from any document