AI Shorts

SwiReasoning is a decoding-time framework that lets a reasoning LLM resolve when to suppose in latent house and when to write specific chain-of-thought, utilizing block-wise confidence estimated from entropy developments in next-token distributions. The methodology is training-free, model-agnostic, and targets Pareto-superior accuracy/effectivity trade-offs on arithmetic and STEM benchmarks. Reported outcomes present +1.5%–2.8% common accuracy enhancements…

A important improvement is about to remodel AI in healthcare. Researchers at Stanford University, in collaboration with ETH Zurich and tech leaders together with Google Research and Amazon, have launched OpenTSLM, a novel household of Time-Series Language Models (TSLMs). This breakthrough addresses a crucial limitation in present LLMs by enabling them to interpret and cause…

TL;DR: Skala is a deep-learning change–correlation practical for Kohn–Sham Density Functional Theory (DFT) that targets hybrid-level accuracy at semi-local value, reporting MAE ≈ 1.06 kcal/mol on W4-17 (0.85 on the single-reference subset) and WTMAD-2 ≈ 3.89 kcal/mol on GMTKN55; evaluations use a mounted D3(BJ) dispersion correction. It is positioned for main-group molecular chemistry at the…

Can an iterative draft–revise solver that repeatedly updates a latent scratchpad outperform far bigger autoregressive LLMs on ARC-AGI? Samsung SAIT (Montreal) has launched Tiny Recursive Model (TRM)—a two-layer, ~7M-parameter recursive reasoner that studies 44.6–45% check accuracy on ARC-AGI-1 and 7.8–8% on ARC-AGI-2, surpassing outcomes reported for considerably bigger language fashions reminiscent of DeepSeek-R1, o3-mini-high, and…

TL;DR: A brand new analysis from Apple, formalizes what “mid-training” ought to do earlier than reinforcement studying RL post-training and introduces RA3 (Reasoning as Action Abstractions)—an EM-style process that learns temporally constant latent actions from knowledgeable traces, then fine-tunes on these bootstrapped traces. It exhibits mid-training ought to (1) prune to a compact near-optimal motion…

How a lot compression ratio and throughput would you recuperate by coaching a format-aware graph compressor and delivery solely a self-describing graph to a common decoder? Meta AI launched OpenZL, an open-source framework that builds specialised, format-aware compressors from high-level knowledge descriptions and emits a self-describing wire format that a common decoder can learn—decoupling compressor…

Salesforce AI Research launched CoDA-1.7B, a diffusion-based language mannequin for code that generates by denoising entire sequences with bidirectional context, updating a number of tokens in parallel moderately than left-to-right next-token prediction. The analysis group revealed each Base and Instruct checkpoints and an end-to-end coaching/analysis/serving stack. Understanding the structure and coaching CoDA adapts a 1.7B-parameter…