As the founder of LLM Watch, my goal is to curate the most relevant Large Language Model (LLM) research and make it accessible to a broad audience. 

I distill complex technical topics into summaries that respect your time. 

Before embarking on this journey, I led Deep Learning R&D at one of Europe's largest telecom companies. There, I spearheaded numerous AI projects with a focus on Natural Language Processing (NLP) and Automatic Speech Recognition (ASR). 

With more than 6 years of experience in AI, I was there before the hype. 

I try to offer a healthy balance between shiny new things and what really matters.

Pascal Biese

Data scientists spend up to 80% of their time manually crafting features. What if an agent could do it for you?

FAMOSE is a new framework that uses the ReAct paradigm to automatically discover, generate, and select features for tabular data.

Think of it like giving an ML engineer a reasoning loop: the agent observes the data, thinks about what features might help, acts by generating them, then evaluates whether they actually improved performance.

The key insight: instead of brute-forcing through an exponentially large feature space, FAMOSE explores strategically. It generates candidate features, tests them against model performance, and iteratively refines its approach based on what works.

This matters because feature engineering has always required deep domain expertise. You needed to know that "time since last purchase" matters for churn prediction, or that "ratio of income to debt" predicts loan defaults.
FAMOSE shifts that burden from human intuition to autonomous exploration.

For practitioners working with tabular data, this could dramatically reduce the manual iteration cycle. 

The question worth asking: how well does automated feature discovery generalize across domains where human intuition currently dominates?
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FAMOSE: A ReAct Approach to Automated Feature Discovery

19.5 FPS video generation on a single H100.

That's real-time. From a 14B parameter model.

Conventional wisdom said you needed KV-cache, sparse attention, or quantization to hit these speeds. You also needed anti-drifting heuristics like self-forcing or keyframe sampling to prevent long videos from degrading into visual noise. 

Helios throws out both assumptions. The team built a 14B autoregressive diffusion model that generates minute-scale videos without the standard acceleration toolkit. Instead of bolting on complexity, they went the opposite direction: heavy compression of historical context, fewer sampling steps, and infrastructure-level optimizations that cut memory so aggressively you can fit four 14B models in 80 GB of VRAM.

For the drifting problem, they characterized exactly how videos fall apart over time and built training strategies that simulate drift explicitly. The model learns to recover from its own mistakes rather than relying on runtime heuristics to mask them.

The computational cost ends up comparable to 1.3B models - roughly 10x smaller architectures.

On both short and long video benchmarks, Helios outperforms prior methods while running at speeds that actually matter for production. Code, base model, and distilled model are all coming.

Real-time, minute-scale video generation without the usual engineering overhead. The tradeoffs just shifted.
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 Helios: Real Real-Time Long Video Generation Model

Simple factual questions don't need chain-of-thought reasoning.

That's been the assumption. Why would a model need to "think step by step" to recall a single fact?

New research indicates that this assumption is wrong.

Enabling reasoning on simple, single-hop factual questions substantially expands what a model can recall from its own parameters. Facts that are effectively unreachable through direct answering become accessible when the model reasons first.

The researchers identified two mechanisms driving this:

First, a computational buffer effect. The model uses generated reasoning tokens to perform latent computation - regardless of what those tokens actually say. The extra token generation acts as processing room, giving the model more compute to locate the right answer.

Second, factual priming. When the model generates topically related facts during reasoning, those facts act as a semantic bridge to the correct answer. It's self-retrieval: the model primes itself by producing nearby knowledge, then follows that trail to what it actually needs.

The big problem? When the model hallucinates intermediate facts during reasoning, the final answer becomes more likely to hallucinate too. The same priming mechanism that helps retrieval can poison it.

Filtering reasoning trajectories for hallucination-free intermediate statements directly improved accuracy. If you're using reasoning-enabled models for knowledge-intensive tasks, the quality of the chain-of-thought matters - not just its presence.
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