Self-Compacting Language Model Agents

Johns Hopkins University researchers Tianjian Li, Jingyu Zhang, William Jurayj, Xi Wang, Chuanyang Jin, Mehrdad Farajtabar, Eric Nalisnick and Daniel Khashabi published SELFCOMPACT in October 2026, a scaffold that lets a language model decide when and how to compact its own accumulated context instead of relying on fixed-interval token-threshold triggers. Across six benchmarks (competitive math and agentic search) and seven models, SELFCOMPACT matched or exceeded fixed-interval summarization at a fraction of the token cost, improving over a no-summarization baseline by up to 18.1 points on math and 5-9 points on agentic search at 30-70% lower per-question cost. The authors report that the compaction tool alone is unevenly used across open-weight models while the rubric alone cannot act, and that together they elicit adaptive compaction without fine-tuning or external supervision.

content type paper https://machinelearning.apple.com/research/ published October 2026 Self-Compacting Language Model Agents AuthorsTianjian Li†, Jingyu Zhang†, William Jurayj†, Xi Wang†, Chuanyang Jin†, Mehrdad Farajtabar, Eric Nalisnick†, Daniel Khashabi† Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window. Existing scaffolds mitigate it with fixed-interval compaction triggered at a token threshold. Such triggers pay no heed to trajectory structure, risking discard of partial results mid-derivation or mid-search. We propose SELFCOMPACT, a scaffold that allows the model itself to decide when and how to compact. Specifically, it pairs two inference-time elements: i a compaction tool the model invokes to summarize the accumulated context, and ii a lightweight rubric specifying when to fire a sub-task has resolved, or the trajectory is converging and when to suppress mid-derivation, or when stuck . Both are needed. The tool alone is unevenly used across open-weight models, often invoked at unhelpful moments or not at all; the rubric alone cannot act. Together, they elicit effective adaptive compaction without any fine-tuning or external supervision. We present empirical results on six benchmarks competitive math and agentic search and seven models. Our results show that SELFCOMPACT matches or exceeds fixed-interval summarization at a fraction of the token cost, improving over a no-summarization baseline by up to 18.1 points on math and 5–9 points on agentic search at 30–70% lower per-question cost. Our results expose a meta-cognitive gap: although unprompted models cannot reliably tell when their own context is rotting, a lightweight rubric closes this gap, reframing when to compact as a capability that scaffolds can supply without training. RISED: Rubrics for Agentic Multi-Environment Selection and Self-Distillation October 6, 2026 research area Data Science and Annotation https://machinelearning.apple.com/research/?domain=Data%20Science%20and%20Annotation , research area Speech and Natural Language Processing https://machinelearning.apple.com/research/?domain=Speech%20and%20Natural%20Language%20Processing 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… From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers August 27, 2026 research area Methods and Algorithms https://machinelearning.apple.com/research/?domain=Methods%20and%20Algorithms , research area Speech and Natural Language Processing https://machinelearning.apple.com/research/?domain=Speech%20and%20Natural%20Language%20Processing Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during…