3,698 items tracked: 33 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2607.16004v2 Announce Type: replace-cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids.
arXiv:2607.22766v2 Announce Type: replace-cross Abstract: Post-training alignment of Large Language Models is increasingly bottlenecked by data quality: preference and instruction-tuning corpora accumulate label noise, s…
arXiv:2608.06202v2 Announce Type: replace-cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.
arXiv:2608.30386v2 Announce Type: replace-cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substantially reduc…
arXiv:2609.07199v2 Announce Type: replace-cross Abstract: Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan.
arXiv:2609.07937v2 Announce Type: replace-cross Abstract: Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, which current Vision Language Models (VLMs) often lack.
arXiv:2609.08642v2 Announce Type: replace-cross Abstract: Exploration in reinforcement learning (RL) remains a fundamental challenge.
arXiv:2609.10002v2 Announce Type: replace-cross Abstract: Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred…
Introducion Can decision models like Jev plan ahead?
Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it.
Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window.
The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints.
Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation.
Reinforcement Learning (RL) has emerged as a key driver for LLM reasoning.
On-policy self-distillation (OPSD) trains a large language model using supervision from a teacher instantiated from the same model as the student.
Multilingual translation requires a model to preserve meaning while producing text in a requested language.
Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while scaling to more players remains challenging.
Finding the right video often requires distinguishing similar scenes in which different events occur.
Chest X-ray (CXR) interpretation often requires integrating visual findings with task-specific evidence rather than relying on direct visual recognition alone.
Generating images beyond the training resolution of Diffusion Transformers (DiTs) remains challenging, as the number of tokens grows rapidly with resolution, full attention becomes expensive, and outputs often suffer fro…
Hour-long videos, streaming feeds, and movie-length content are pushing vision-language models (VLMs) past their breaking point -- the standard recipe of encoding each frame into visual tokens yields sequences that grow…
Reinforcement learning with verifiable rewards (RLVR) is reshaping how large language models (LLMs) acquire reasoning capabilities, yet the optimizers used remain largely inherited from teacher-forcing training.
Reconstructing reference videos as editable dynamic 3D scenes is an important path toward controllable video generation for filmmaking: these scenes provide modifiable, renderable visual proxies that enable explicit cont…
Proximal Policy Optimization (PPO) is widely used in reinforcement learning with verifiable rewards for language-model reasoning.
Note: details have been obfuscated and changed to protect – not the innocent, who are invariably vindicated by the end of act three – but the guilty, for whom the narrative provides no such guarantees.
Nvidia Corp.
So I've been tasked by my neural network professor at university to replicate the following research: Intelligent Breast Cancer Diagnosis Using Hybrid GA-ANN .
I'm diving into agentic coding loops to get my organization to automerge more.
The feature is explained below: The user enters a search query: E.g.
There’s no question that 2026 is the year of the artificial intelligence agent.
Independent research; early stage.
When President Donald Trump announced his plans to rebrand artificial intelligence as "super intelligence," you'd be forgiven for being skeptical that it would catch on.
Epistemic status: Personal history anchored to dated records (chat exports, git log, daily notes).
On Equity, we debate the impact of data center moratoriums and whether the AI industry has a communication problem.
AI is helping companies interpret biological data for drug discovery and more personalized care.
Teams of AI agents barely outperform solo agents but cost up to 5.1x more, according to Vals AI.
Actor and functional application matter Originally published at AGI SurveilLance .
Voice AI often misses important points for its context layer, and causes the whole pipeline to break.
Epoch AI and Anthropic independently found the same thing: current AI models like GPT-5.6 Sol and Claude Fable 5 can run experiments but lack scientific self-criticism and genuine creative thinking.
Most traditional enterprise systems were built around three assumptions: Jobs finish quickly, retrying one is free and the same input always produces the same output.
Meanwhile, AI agents move robotic arms, 'make OT device operator screens lie'
‘Even if AI supercharged the economy, its impact on the government’s finances would be muted’ Back through political history to the age of Ronald Reagan , Republicans have repeatedly promised the US public that tax cuts…
A long day’s haul in the video game slop mines.
As with most AI tools, Grok's free tier is pretty limited.
Data from a16z shows a Jevons paradox in the AI market: token prices keep falling, but H100 GPU rental prices hold steady or climb.