https://machinelearning.apple.com Apple machine learning teams are engaged in state of the art research in machine learning and artificial intelligence. Learn about the latest advancements. en Thu, 08 Oct 2026 00:00:00 GMT normalizing-trajectory-models https://machinelearning.apple.com/research/normalizing-trajectory-models Diffusion-based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood framework in the process. We introduce Normalizing Trajectory Models (NTM), which models each reverse step as an expressive conditional normalizing flow with exact likelihood training. Architecturally, NTM combines shallow invertible blocks within each step with a deep parallel… Thu, 08 Oct 2026 00:00:00 GMT rised-multi-environment-selection https://machinelearning.apple.com/research/rised-multi-environment-selection Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data-selection strategies often allocate training at the environment level or prioritize local reward-based signals, without explicitly considering relationships between current rollouts across environments for prompt-group selection. Meanwhile, as environments are learned at different rates, all-failure and all-success rollout groups can coexist within a batch, leaving those data without group-relative reward signals. Both… Tue, 06 Oct 2026 00:00:00 GMT ontological-boundary-negotiation https://machinelearning.apple.com/research/ontological-boundary-negotiation Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not they are usable, useful, or technically feasible, leaving questions of ontological boundary negotiation, unexamined. We design two open-ended probes that utilize a Wizard of Oz technique to enable the experience of training a personalized machine learning… Mon, 05 Oct 2026 00:00:00 GMT language-discrimination-multilingual-learning https://machinelearning.apple.com/research/language-discrimination-multilingual-learning Multilingual self-supervised speech models can benefit from sharing information across languages, but under a matched total pretraining data budget they still fall short of monolingual models. We show that strengthening the model’s ability to discriminate languages during pretraining reduces and, on some measures, closes this multilingual gap on continuous phonetic and higher-level linguistic measures, while preserving substantial cross-language sharing. Using a controlled English/French HuBERT setting, we test two interventions which strengthen language discrimination: an auxiliary language… Fri, 02 Oct 2026 00:00:00 GMT limits-confidence-diffusion https://machinelearning.apple.com/research/limits-confidence-diffusion Discrete diffusion, including remasking and uniform-state samplers, generate a sequence by writing multiple token positions per step, drawing each from a per-position distribution and choosing which positions to write from those same distributions. For domains of general interest (pixels, phonemes, or words) there are inherent dependencies between tokens. We show that a step matches the training distribution only when the positions it writes are conditionally independent given the tokens already fixed, that no product of per-position distributions can match a dependent group, and that… Fri, 02 Oct 2026 00:00:00 GMT harness-autonomous-ml-engineering https://machinelearning.apple.com/research/harness-autonomous-ml-engineering Recent autonomous machine learning engineering (MLE) agents have made significant progress on public leaderboards. Often motivated by progress stagnation over long-horizon cycles and limited Large Language Model (LLM) primitives, modern MLE agents are deployed on top of increasingly elaborate machinery: multi-agent orchestrators, dedicated retrieval subagents, and more. While such harnesses expand, the use of more primitive but improved coding agents—where LLMs have direct access to the execution environment through read, write, and bash primitives—has received little attention in the field… Thu, 01 Oct 2026 00:00:00 GMT rltl-dr-self-improvement https://machinelearning.apple.com/research/rltl-dr-self-improvement The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones. This becomes problematic in the realms of self-improvement, where tasks are so difficult that the agent has a low or even no chance of success, and where there are no teacher models or example solutions to distill from. In this paper, we introduce RLTL;DR. After each failed attempt, we show the policy the verifier outputs and let it write its own feedback, in the form of a single TL;DR insight. The next rollout is conditioned… Thu, 01 Oct 2026 00:00:00 GMT effectiveness-fluency-llm-conditioning https://machinelearning.apple.com/research/effectiveness-fluency-llm-conditioning Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved trade-offs remains elusive. Current approaches to conditioning are often evaluated with a narrow focus on their effectiveness at injecting or removing a target concept, neglecting generation quality. We systematically investigate a range of conditioning methods in both injection and removal scenarios. We find that efficient steering methods frequently achieve conditioning at a steep cost to fluency. Furthermore, we identify a critical yet… Wed, 30 Sep 2026 00:00:00 GMT sclate-agent-training-evaluation https://machinelearning.apple.com/research/sclate-agent-training-evaluation Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requires interleaving tasks with agent-side events such as session stop and start, crons, and memory consolidation. Yet existing benchmarks and training frameworks schedule only the benchmark’s own events, leaving each benchmark and agent pair to build a custom scheduling loop. We present SCLATE, an execution substrate where benchmarks and unmodified agents each add their events to one open event scheduler through an adapter. A hybrid simulated clock… Wed, 30 Sep 2026 00:00:00 GMT communication-bottleneck-serialization https://machinelearning.apple.com/research/communication-bottleneck-serialization When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How much tree-structured compositional content survives this bottleneck? We propose a round-trip protocol that answers this question empirically for tree-structured expressions. A generator converts a procedurally generated arithmetic expression into a word problem, a separate extractor recovers the expression from the word problem alone, and symbolic equivalence provides an exact oracle. Evaluating all pairwise combinations of sixteen models yields… Tue, 29 Sep 2026 00:00:00 GMT