News without the noise
cs.AI, cs.LG, stat.ML
18 articles
Suleymanzade et al. propose looped flows, a method that trains recurrent neural networks for many-step inference using local denoising losses. This overcomes the difficulty of training early updates to support later ones when backpropagation is truncated. The authors report state-of-the-art accuracy among looped models on six reasoning benchmarks, including 58.8% on ARC-AGI-1 and 12.2% on ARC-AGI-2.
13 Sept 2026The authors propose a guided super-resolution approach for digital surface models (DSMs) using denoising diffusion, improving coarse 5 m DSMs to 0.5 m resolution by leveraging high-resolution optical imagery. Experiments on several Central European cities show the method produces DSMs with crisper building outlines and more detailed roof structures than conventional interpolation or filtering.
13 Sept 2026Jha et al. trained dense Transformers and mixture-of-experts models under varying data-repetition rates, domain mixtures, expert counts, and expert granularities. They find that MoE models lose performance more rapidly than dense models as examples are reused, with susceptibility tracking total parameter count rather than the number of parameters activated per token. Strong masking-based regularization preserves an MoE advantage beyond 64 repetitions, but does not match training on unique data.
13 Sept 2026The authors operationalize the importance of a chain-of-thought reasoning step as its advantage—the change in expected reward from including that step—estimated via Monte Carlo rollouts. They find that LLM judges asked to identify high-importance steps outperform a prevalence baseline but fall well short of a noise ceiling, and that fine-tuning a step-level critic improves performance for incorrect responses but remains far from ceiling for correct ones.
7 Sept 2026Sahoo et al. introduce Uno, a model class that retains standard next-token training and its resulting autoregressive distribution but uses discrete diffusion to generate several tokens concurrently. Their Ψ-Spec samplers provide lossless acceleration without a separate draft model, outperforming tested speculative-decoding systems across evaluated batch sizes.
5 Sept 2026The authors introduce the Graph Machine (GM), an architecture that maintains an O(n)-sized state and accesses it via sparse, dynamic routing using edges—pointer-like objects updated by a referral mechanism. They replace 75% of the dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrain from scratch on 15.7B tokens. With only 2 of 4,096 tokens retrieved per KV head per sparse layer, loss degrades only slightly; with 4 tokens, the best model marginally improves loss over the dense baseline.
4 Sept 2026Lopetegui Gonzalez, Pacaud, and Schmid adapt a pretrained video diffusion model into a 3D-aware robot policy by training it to predict actions, future images, and depth together. The authors report state-of-the-art performance on RoboCasa and LIBERO-Plus, along with improved results on a real UR5 arm, particularly when environments are randomized.
4 Sept 2026Rashid and Kellis tested structural retrieval under controlled surface-form changes using 500 mathematics queries and 118 embodied-agent queries. At the hardest mathematics tier, two production embedding models scored 0% strict Hit@1 despite placing the correct item in the top 10 nearly every time; trajectory retrieval fell to chance or below when matching required different object names. Reranking with an LLM improved results, but some mathematics gains appeared attributable to competition-problem memorization.
3 Sept 2026Baek tests whether large language models can solve individual operations-research problems and design algorithms for entire problem classes. With one untuned prompt and access to a compute-limited Python sandbox, the strongest tested model matched or exceeded established methods on almost all evaluated instances.
31 Aug 2026The authors trained a 0–10 sepsis severity index from 43 routinely charted variables collected over 72 hours, using treatment-level mortality to rank trajectories rather than assigning mortality labels to every hour. In held-out testing, the score provided hourly prognostic information, correlated with changes in lactate and other physiological measures, and showed partial transfer between hospital systems.
30 Aug 2026Liu and Shorinwa introduce CLAP, an action-conditioned video model designed to learn across different robot morphologies and human demonstrations. The authors report that a curriculum combining unlabeled-video pretraining with robot-action grounding enables zero-shot deployment and performance competitive with specialized, single-embodiment models.
29 Aug 2026The authors present VISA, an agentic framework that reformulates multimodal instruction synthesis as a self-evolving loop. VISA iteratively generates diverse and challenging instruction-following samples, using verifier signals and target-model failure profiles to guide subsequent rounds. The method consistently improves multimodal instruction following on MM-IFEval while maintaining general multimodal capability across seven benchmarks.
28 Aug 2026Liu et al. study effective learning rate (ELR), defined as the learning rate relative to parameter norm, as a common coordinate for language model pretraining. They report that runs with matched ELR follow closely aligned loss curves across optimizers, architectures, datasets, and model scales, with typical discrepancies smaller than measured seed-to-seed variation.
27 Aug 2026Kimi Team introduces Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104B activated parameters, native vision, and a 1M-token context window. The authors demonstrate that innovations in attention (Kimi Delta Attention, Attention Residuals) and routing (Stable LatentMoE) yield roughly 2.5× scaling efficiency improvement over Kimi K2, enabling frontier-level performance across long-horizon coding, agentic, reasoning, and vision tasks.
28 July 2026The authors introduce Skill Self-Play (Skill-SP), a framework where LLMs generate and solve challenging tasks by dynamically routing through learned skills. In a reinforcement learning loop, the system co-evolves a proposer, solver, and skill controller, showing consistent improvements on tool-use and reasoning benchmarks, even turning around initially misaligned models.
25 July 2026The authors introduce STRACE, a framework that constructs high-signal optimization contexts for long-horizon agents by removing redundant and non-causal steps from execution traces. On a formal verification benchmark, STRACE boosted the success rate of human-expert designed agents from 42.5% to 58.5%, a 1.4× improvement over standard context-filtering baselines.
9 July 2026Genewein et al. present a report examining how AI might develop beyond human-level AGI to artificial superintelligence (ASI). They characterize ASI as systems more cognitively capable than large organizations of humans, outline four possible pathways, and conclude that a single step-change event may be less likely than a series of transformative, AI-driven breakthroughs.
11 June 2026The authors introduce Claw-SWE-Bench, a benchmark and adapter protocol for evaluating OpenClaw-style agent harnesses (claws) on coding tasks. They show that adapter design dramatically affects performance: OpenClaw with a minimal adapter scores only 19.1% Pass@1, while a full adapter achieves 73.4% with the same backbone model. The benchmark also reveals that model choice and harness choice each account for roughly 30 percentage points of variation in performance.
11 June 2026