Reinforcement learning has advanced video reasoning in large multi-modal models, yet dominant pipelines either rely on on-policy self-exploration, which plateau...

Reinforcement learning with verifiable rewards (RLVR) typically optimizes for outcome rewards without imposing constraints on intermediate reasoning. This leave...

Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language models handle s...

Time-to-Collision (TTC) forecasting is a critical task in collision prevention, requiring precise temporal prediction and comprehending both local and global pa...

In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimizing a loca...

Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts e...

This paper presents a systematic benchmark of state-of-the-art multilingual large language models (LLMs) adapted via token pruning - a compression technique tha...

Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is inefficien...

In my last articlehttps://towardsdatascience.com/beyond-code-generation-ai-for-the-full-data-science-workflow/, I shared how to use MCP to integrate LLMs into y...

Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilities, entropy...

Back to Articleshttps://huggingface.co/blog !https://huggingface.co/avatars/a514f0d2b2f9937dd6fd97560f8319a8.svghttps://huggingface.co/emelryan Training a high...

Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank upd...

Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both contribute...

!https://9to5mac.com/wp-content/uploads/sites/6/2026/04/claude-design.webp?w=1600 Claude Design is Anthropic’s latest research preview Powered by Opus 4.7, Clau...

Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned) model. T...

Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the f...

We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels of vale...

Introduction usually comes with an implicit assumption: you need a lot of labeled data. At the same time, many models are capable of discovering structure in d...

Probabilistic Synchronous Parallel (PSP) is a technique in distributed learning systems to reduce synchronization bottlenecks by sampling a subset of participat...

Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, but they pro...

MIT Associate Professor Jacob Andreashttps://www.eecs.mit.edu/people/jacob-andreas/ of the Department of Electrical Engineering and Computer Science EECS and MI...

The rapid proliferation of Large Language Models (LLMs) in software development has made distinguishing AI-generated code from human-written code a critical cha...

Code localization is a cornerstone of autonomous software engineering. Recent advancements have achieved impressive performance on real-world issue benchmarks. ...

Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. I...

Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compares three tr...

Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules acr...

Key Takeaways - Anthropic's prompt cache has a 5‑minute TTL. - Orchestrator loops running faster than 270 seconds pay ~10 % of full input token costs. What Cha...

!Cover image for Designing ChatGPT Prompts & Workflows Like a Developerhttps://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto,format=a...

!Cover image for Profling Claude Converstaionshttps://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-...

Designing optimizers that remain effective under tight evaluation budgets is critical in expensive black-box settings such as cardiac digital twinning. We propo...

Influence maximization (IM) is a fundamental problem in complex network analysis, with a wide range of real-world applications. To date, existing approaches to ...

Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates sp...

Artificial intelligence is already proving it can accelerate drug development and improve our understanding of disease. But to turn AI into novel treatments we...

Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG), improving...

I tracked every Manus AI task for 30 days. Here’s what I found about credit usage and optimization. Task Categorization | Category | % of Tasks | Avg Credits |...

I’ve been building AI agents at work and kept running into the same problem: every framework lets agents call any registered tool with zero safety checks. An ag...

Categories: Literature, Technology | Date: April 16 th, 2026 | 3 Commentshttps://www.openculture.com/2026/04/how-george-orwell-predicted-the-rise-of-ai-slop.htm...

Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information through sup...

Model Tuning and Skepticism To address LLMs’ tendencies toward sycophancy and over‑enthusiasm, OpenAI says it has tuned the model to be more skeptical, making...

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Generative AI vs Agentic AI From Content Creation to Autonomous Action As we move beyond AWS DeepRacer and the “AWS AI League,” the shift from model‑ML design...

Devlog: Kiwi-chan's Great Oak Adventure – Or, How My LLM Became a Lumberjack Again! Hey tech enthusiasts and fellow pixel pioneers! It's another glorious day i...

OpenAI’s Codex Revamp Targets Anthropic’s Claude Code There is currently a low‑grade war between OpenAI and Anthropic over who can release the most convenient...

The Journey from Lab Hypothesis to Pharmacy Shelf The journey from a laboratory hypothesis to a pharmacy shelf is one of the most grueling marathons in modern...

I was unsure if my parents would notice that the voice on the other end wasn't mine—or that it was mine, sort of, but it wasn't me. The voice said hello, asked...

OpenAI Codex Desktop Update – 3 Million Weekly Developers OpenAI announced a massive update to its Codex developer environment Mac & Windows desktop apps as it...

Claude Opus 4.7 is Anthropic's most intelligent model available to the general public. In a press release, Anthropic noted that Opus 4.7 is not as powerful as C...

!https://www.androidauthority.com/wp-content/uploads/2025/09/google-maps-my-maps-custom-map-example-3.jpg TL;DR - Google Maps users have a history of submitting...