Search: “gpt”

Aleph 2.0 Introducing Runway Aleph 2.0. Edit videos using AI, keeping camera and motion all consistent. Select specific frames in your video to reprompt with GPT Images 2.0 or Nano Banana 2, then appl

Command A+ sets a new high for Cohere's machine translation capabilities. Opening a clear gap over open source peers Mistral Medium 3.5, DeepSeek, & OpenAI's gpt-oss, as well as Claude Opus 4.6. A+ al

arXiv:2605.29001v1 Announce Type: cross Abstract: A paraphrase-quality audit of MathCheck (ICLR 2025) detected 4 semantically incorrect paraphrases in 129 groups (3.1%); removing them drops GPT-4o fro

arXiv:2605.30096v1 Announce Type: cross Abstract: Large language models (LLMs) can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials

arXiv:2605.28876v1 Announce Type: cross Abstract: CI failure logs are large (median 5k lines, max 200k in this corpus) and noisy. Coding agents that try to debug them depend on an upstream tool to red

OpenAI launches Rosalind Biodefense, expanding trusted access to GPT-Rosalind for vetted developers and U.S. government partners advancing biodefense, public health, and pandemic preparedness through

arXiv:2605.29744v1 Announce Type: new Abstract: The impressive performance of generalist large language models (LLMs) such as GPT and Claude in healthcare raises a critical question: will domain-speci

Worked on some code this morning using Opus 4.8 and so far I'm really liking it. Much more cooperative than 4.7 and less 'over agentic'. Stops and asks for my input when needed in places 4.7 (and GPT

I had Opus 4.8 in Claude Code write a sophisticated, if minor, academic paper from a archive of hundreds of de-identified research files from years ago I had to use GPT-5.5 Pro as a reviewer, it spott

Opus 4.8 formulated the hypotheses in advance, conducting data cleaning, did research on references, conducted analyses, did robustness checks, and put out the whole paper in LaTEX style. GPT-5.5 foun

this is so funny, training opus 4.7 on business skills makes it misaligned and dishonest 😭 Learnings from testing Claude Opus 4.8: > Much worse than Opus 4.7 and GPT 5.5 on Vending Bench > More aligne

arXiv:2605.27072v1 Announce Type: cross Abstract: We present E3, an automated review assistant that augments reviewers and engineering teams by identifying decision-relevant technical concerns in rese

Anthropic are strongly rumored to be about to have their first profitable quarter. Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staf

arXiv:2605.26394v1 Announce Type: new Abstract: Multi-turn Text-to-SQL is central to enterprise analytics yet remains predominantly evaluated in single-turn settings. We introduce EnterpriseMem-Bench,

arXiv:2605.26560v1 Announce Type: cross Abstract: Objective. Outpatient notes carry follow-up instructions pairing actions with future times ('MRI brain in two weeks'). Extracting (action, date) pairs

arXiv:2605.24229v1 Announce Type: new Abstract: Frontier AI developers now train models against long written behavioral specifications, such as Anthropic's constitution (Anthropic, 2025a) and OpenAI's

Human in the loop 🤖 Made in @ComfyUI with a laundry list of tools: @ltx_model 2.3 + @thesystms FLW LoRA, @Alibaba_Wan 2.2 I2V + T2V, Florence2, @Meta Sapiens2, @OpenAI GPT Image 2.0, @suno , @AdobeAE

arXiv:2605.24902v1 Announce Type: cross Abstract: Reasoning-enabled LLMs perform strongly on medical reasoning benchmarks, but it remains unclear whether these gains transfer to structured clinical do

interesting to listen to this years later. what held up? what changed? I had a podcast where I was fortunate to have amazing guests. This was just as GPT 3 came out. Despite LLMs exceeding so many exp

Added a DeepSeek Sparse Attention (DSA) from-scratch implementation to my LLMs-from-scratch repo thanks to an awesome new reader contrib. With motivation, overview, and GPT-style model reference imple

I called both missteps and that’s part of why OpenAI and their shills hate me. 🤷‍♂️ @GaryMarcus It all went wrong with GPT-5 and Sora 2 IMO. Until then OpenAI could do no wrong in the eyes of most peo

arXiv:2605.22005v1 Announce Type: cross Abstract: We show that singular value decomposition of the lm_head} weight matrix of a transformer-based large language model -- requiring only five lines of Py

ha ha, so much for step change? maybe this problem was just easier than some? The standard GPT-5.5 reproduced the proof ~ 👇 https://chatgpt.com/share/6a0e9e04-8cb0-8332-a4f1-ec68acd2e03e You don't nee

oops! wild update, strongly supports @emollick’s overall take: I have to eat crow on this, in light of further information. whatever OpenAI spent on Erdos using a new model, apparently you can get GPT

arXiv:2605.21333v1 Announce Type: new Abstract: Natively trained spiking language models struggle to combine Transformer-like language quality, stable multi-domain pre-training, and high activation sp

Did we ever learn what model won gold at the IMO from OpenAI? It was a year ago and it was called an unreleased internal general purpose model back then. Has GPT-5.5 Pro Extended caught up with whatev

arXiv:2410.18856v4 Announce Type: replace Abstract: Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools c

arXiv:2601.14506v3 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in STEM education for personalized instruction and feedback across institutions in high- and l

Gemini Gemini 3.5 Flash ARC-AGI (Verified) ARC-AGI-2: - High: 72.1%, 0.85 - Minimal: 8.9%, 0.11 ARC-AGI-1: - High: 92.5%, 0.42 - Minimal: 48.8%, 0.06 Gemini 3.5 Flash is on par with GPT-5.5 (Medium) o

arXiv:2605.17106v1 Announce Type: new Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences. Existing routers make binary st

arXiv:2605.17062v1 Announce Type: cross Abstract: Spracklen et al. (USENIX Security '25) showed that code-generating large language models hallucinate package names that do not exist on PyPI or npm at

arXiv:2505.03205v3 Announce Type: replace Abstract: Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Em

arXiv:2511.09378v2 Announce Type: replace Abstract: A series of influential studies established that large language models cannot reliably solve even simple planning tasks. We show that the latest gen

almost all LLM “thought” is derivative. below just sounds like hype to me 📁 Jensen Huang, CEO of NVIDIA, says GPT was never just about generating images or text but about generating thought itself. Th

arXiv:2605.12746v1 Announce Type: cross Abstract: Monitoring the chain-of-thought (CoT) of reasoning models is a promising approach for detecting covert misbehavior (i.e., hidden objectives) in code g

Release: datasette-ip-rate-limit 0.1a0 The datasette.io site was being hammered by poorly-behaved crawlers, so I had Codex (GPT-5.5 xhigh) build a configurable rate limiting plugin to block IPs that w

arXiv:2605.12850v1 Announce Type: cross Abstract: Fine-tuning large language models on narrow data with harmful content produces broadly misaligned behavior on unrelated prompts, a phenomenon known as

arXiv:2605.12501v1 Announce Type: new Abstract: Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions i

arXiv:2605.12199v1 Announce Type: new Abstract: Emergent misalignment (EM), where fine-tuning on a narrow task (like insecure code) causes broad misalignment across unrelated domains, was first demons

Vox-style match-cut montage. This workflow splits your input word by word, renders each one as a bold editorial headline on aged newsprint via gpt-image-1, then assembles the frames into a stop-motion

Deep learning hit a wall. Neurosymbolic AI rescued it. 🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 symbolic tools, 5

arXiv:2605.08462v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge in Large Language Models (LLMs), particularly in context-grounded settings such as RAG and agentic AI sys

arXiv:2508.20325v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly integral to various domains, their potential to generate harmful responses has prompted si

arXiv:2605.10504v1 Announce Type: new Abstract: A causal-decoder block is hierarchical: lower layers build the residual basis that upper layers attend over. We identify a failure mode in GPT pretraini

arXiv:2510.09887v2 Announce Type: replace Abstract: Vision and language models frequently ignore semantically critical input edits, defaulting to pretraining priors. For example, models will confident

Really amazing dissection. LLMs hitting rarely. Tools list is amazing. 🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 s